Pre_Final_Data_Cleaning_CRNY.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e693c226",
   "metadata": {
    "id": "e693c226"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19839746",
   "metadata": {
    "id": "19839746"
   },
   "outputs": [],
   "source": [
    "data = pd.read_excel('GI and POA Survey Data.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6a3da83",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>g14_city</th>\n",
       "      <th>g15_zipcode</th>\n",
       "      <th>g16_county</th>\n",
       "      <th>g17_region</th>\n",
       "      <th>g18_community</th>\n",
       "      <th>g20_raceethnicity1</th>\n",
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       "      <th>g20_raceethnicity3</th>\n",
       "      <th>g20_raceethnicity4</th>\n",
       "      <th>...</th>\n",
       "      <th>p_agerange</th>\n",
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       "      <th>p41_gender1</th>\n",
       "      <th>p41_gender2</th>\n",
       "      <th>p41_gender3</th>\n",
       "      <th>p41_gender4</th>\n",
       "      <th>p41_gender5</th>\n",
       "      <th>p46_caregiving1</th>\n",
       "      <th>p46_caregiving2</th>\n",
       "      <th>p46_caregiving3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N3R8BXQE4K</td>\n",
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       "      <td>Erie County</td>\n",
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       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>White</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E555HATF05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Withdrawn</td>\n",
       "      <td>gender_oth</td>\n",
       "      <td>twospirit</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0LTH6QJQH8</td>\n",
       "      <td>New York City</td>\n",
       "      <td>10040.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R3S4V21D40</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>10031.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BZEFVJAYBT</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>Arab or Middle Eastern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 180 columns</p>\n",
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      ],
      "text/plain": [
       "           id       g14_city  g15_zipcode                      g16_county  \\\n",
       "0  N3R8BXQE4K        BUFFALO      14201.0                  Erie County      \n",
       "1  E555HATF05            NaN          NaN                             NaN   \n",
       "2  0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)      \n",
       "3  R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)      \n",
       "4  BZEFVJAYBT       Brooklyn      11238.0       Kings County (Brooklyn)     \n",
       "\n",
       "         g17_region g18_community          g20_raceethnicity1  \\\n",
       "0  Western New York       Urban    Asian or Pacific Islander    \n",
       "1               NaN           NaN                         NaN   \n",
       "2     New York City       Urban                       White     \n",
       "3     New York City       Urban    Black or African American    \n",
       "4     New York City       Urban    Asian or Pacific Islander    \n",
       "\n",
       "         g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                   White                  NaN                NaN  ...   \n",
       "1                       NaN                NaN                NaN  ...   \n",
       "2                       NaN                NaN                NaN  ...   \n",
       "3                       NaN                NaN                NaN  ...   \n",
       "4   Arab or Middle Eastern                 NaN                NaN  ...   \n",
       "\n",
       "  p_agerange   p_status p41_gender1 p41_gender2 p41_gender3 p41_gender4  \\\n",
       "0        NaN        NaN         NaN         NaN         NaN         NaN   \n",
       "1      25-34  Withdrawn  gender_oth   twospirit       woman         NaN   \n",
       "2        NaN        NaN         NaN         NaN         NaN         NaN   \n",
       "3        NaN        NaN         NaN         NaN         NaN         NaN   \n",
       "4        NaN        NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "  p41_gender5 p46_caregiving1 p46_caregiving2 p46_caregiving3  \n",
       "0         NaN             NaN             NaN             NaN  \n",
       "1         NaN   no_caregiving             NaN             NaN  \n",
       "2         NaN             NaN             NaN             NaN  \n",
       "3         NaN             NaN             NaN             NaN  \n",
       "4         NaN             NaN             NaN             NaN  \n",
       "\n",
       "[5 rows x 180 columns]"
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   ],
   "source": [
    "data.head()"
   ]
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  {
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   "execution_count": 4,
   "id": "e41eee51",
   "metadata": {
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    },
    "id": "e41eee51",
    "outputId": "523fd6a4-4ece-48c2-ab5f-fb125ac4ba86"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23797 entries, 0 to 23796\n",
      "Columns: 180 entries, id to p46_caregiving3\n",
      "dtypes: float64(2), object(178)\n",
      "memory usage: 32.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3deab19e",
   "metadata": {
    "colab": {
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     "height": 300
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    "id": "3deab19e",
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    "scrolled": true
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>g15_zipcode</th>\n",
       "      <th>p_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21921.000000</td>\n",
       "      <td>13066.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11190.702386</td>\n",
       "      <td>36.244528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1090.929157</td>\n",
       "      <td>12.998512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10314.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11216.000000</td>\n",
       "      <td>33.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11361.000000</td>\n",
       "      <td>42.000000</td>\n",
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       "    <tr>\n",
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       "      <td>121.000000</td>\n",
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      "text/plain": [
       "        g15_zipcode         p_age\n",
       "count  21921.000000  13066.000000\n",
       "mean   11190.702386     36.244528\n",
       "std     1090.929157     12.998512\n",
       "min    10001.000000      0.000000\n",
       "25%    10314.000000     27.000000\n",
       "50%    11216.000000     33.000000\n",
       "75%    11361.000000     42.000000\n",
       "max    14905.000000    121.000000"
      ]
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     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e88fe02b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e88fe02b",
    "outputId": "e54a8212-3bbc-42af-d260-3d42bc103f85"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23797, 180)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6fe34690",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6fe34690",
    "outputId": "d0264493-c8e3-46f1-bbe0-9456e9e86bb2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23797"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data['id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac6d97c3",
   "metadata": {
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     "height": 359
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    "id": "ac6d97c3",
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       "      <td>N3R8BXQE4K</td>\n",
       "      <td>BUFFALO</td>\n",
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       "      <td>Erie County</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>0LTH6QJQH8</td>\n",
       "      <td>New York City</td>\n",
       "      <td>10040.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R3S4V21D40</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>10031.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>25-34</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BZEFVJAYBT</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>Arab or Middle Eastern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Earned Income Tax Credit (EITC)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       g14_city  g15_zipcode                      g16_county  \\\n",
       "0  N3R8BXQE4K        BUFFALO      14201.0                  Erie County      \n",
       "1  E555HATF05            NaN          NaN                             NaN   \n",
       "2  0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)      \n",
       "3  R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)      \n",
       "4  BZEFVJAYBT       Brooklyn      11238.0       Kings County (Brooklyn)     \n",
       "\n",
       "         g17_region g18_community          g20_raceethnicity1  \\\n",
       "0  Western New York       Urban    Asian or Pacific Islander    \n",
       "1               NaN           NaN                         NaN   \n",
       "2     New York City       Urban                       White     \n",
       "3     New York City       Urban    Black or African American    \n",
       "4     New York City       Urban    Asian or Pacific Islander    \n",
       "\n",
       "         g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                   White                  NaN                NaN  ...   \n",
       "1                       NaN                NaN                NaN  ...   \n",
       "2                       NaN                NaN                NaN  ...   \n",
       "3                       NaN                NaN                NaN  ...   \n",
       "4   Arab or Middle Eastern                 NaN                NaN  ...   \n",
       "\n",
       "                 g32b_enrolled_name2 g32b_enrolled_name3 g32b_enrolled_name4  \\\n",
       "0                                NaN                 NaN                 NaN   \n",
       "1                                NaN                 NaN                 NaN   \n",
       "2                                NaN                 NaN                 NaN   \n",
       "3                                NaN                 NaN                 NaN   \n",
       "4   Earned Income Tax Credit (EITC)                  NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name5 g32b_enrolled_name6 g32b_enrolled_name7  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "1                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name8 g32b_enrolled_name9 g_agerange g_enrollmentstatus  \n",
       "0                 NaN                 NaN      25-34                NaN  \n",
       "1                 NaN                 NaN        NaN                NaN  \n",
       "2                 NaN                 NaN      25-34                NaN  \n",
       "3                 NaN                 NaN      25-34                NaN  \n",
       "4                 NaN                 NaN      25-34                NaN  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data = data.iloc[:, 0:57]\n",
    "g_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16e2aa5b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "id": "16e2aa5b",
    "outputId": "fcd96891-7253-4c4b-c271-0c0438a8a8fb"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p1_discipline1</th>\n",
       "      <th>p1_discipline2</th>\n",
       "      <th>p1_discipline3</th>\n",
       "      <th>p2_approachtopractice1</th>\n",
       "      <th>p2_approachtopractice2</th>\n",
       "      <th>p2_approachtopractice3</th>\n",
       "      <th>p2_approachtopractice4</th>\n",
       "      <th>p2_approachtopractice5</th>\n",
       "      <th>p2_approachtopractice6</th>\n",
       "      <th>p3_impacts</th>\n",
       "      <th>...</th>\n",
       "      <th>p_agerange</th>\n",
       "      <th>p_status</th>\n",
       "      <th>p41_gender1</th>\n",
       "      <th>p41_gender2</th>\n",
       "      <th>p41_gender3</th>\n",
       "      <th>p41_gender4</th>\n",
       "      <th>p41_gender5</th>\n",
       "      <th>p46_caregiving1</th>\n",
       "      <th>p46_caregiving2</th>\n",
       "      <th>p46_caregiving3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Interdisciplinary Arts</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>Media Arts</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Withdrawn</td>\n",
       "      <td>gender_oth</td>\n",
       "      <td>twospirit</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           p1_discipline1 p1_discipline2 p1_discipline3  \\\n",
       "0                     NaN            NaN            NaN   \n",
       "1  Interdisciplinary Arts    Visual Arts     Media Arts   \n",
       "2                     NaN            NaN            NaN   \n",
       "3                     NaN            NaN            NaN   \n",
       "4                     NaN            NaN            NaN   \n",
       "\n",
       "  p2_approachtopractice1 p2_approachtopractice2 p2_approachtopractice3  \\\n",
       "0                    NaN                    NaN                    NaN   \n",
       "1         collab_artists      collab_nonartists            solo_artist   \n",
       "2                    NaN                    NaN                    NaN   \n",
       "3                    NaN                    NaN                    NaN   \n",
       "4                    NaN                    NaN                    NaN   \n",
       "\n",
       "  p2_approachtopractice4   p2_approachtopractice5 p2_approachtopractice6  \\\n",
       "0                    NaN                      NaN                    NaN   \n",
       "1     public_involvement  perform_present_exhibit        teaching_others   \n",
       "2                    NaN                      NaN                    NaN   \n",
       "3                    NaN                      NaN                    NaN   \n",
       "4                    NaN                      NaN                    NaN   \n",
       "\n",
       "  p3_impacts  ... p_agerange   p_status p41_gender1 p41_gender2 p41_gender3  \\\n",
       "0        NaN  ...        NaN        NaN         NaN         NaN         NaN   \n",
       "1        Yes  ...      25-34  Withdrawn  gender_oth   twospirit       woman   \n",
       "2        NaN  ...        NaN        NaN         NaN         NaN         NaN   \n",
       "3        NaN  ...        NaN        NaN         NaN         NaN         NaN   \n",
       "4        NaN  ...        NaN        NaN         NaN         NaN         NaN   \n",
       "\n",
       "  p41_gender4 p41_gender5 p46_caregiving1 p46_caregiving2 p46_caregiving3  \n",
       "0         NaN         NaN             NaN             NaN             NaN  \n",
       "1         NaN         NaN   no_caregiving             NaN             NaN  \n",
       "2         NaN         NaN             NaN             NaN             NaN  \n",
       "3         NaN         NaN             NaN             NaN             NaN  \n",
       "4         NaN         NaN             NaN             NaN             NaN  \n",
       "\n",
       "[5 rows x 123 columns]"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_data = data.iloc[:, 57:]\n",
    "p_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "73e57d4d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "id": "73e57d4d",
    "outputId": "16cc28e6-f8dd-4b55-f884-3f871934b2e1"
   },
   "outputs": [
    {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>p46_caregiving3</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N3R8BXQE4K</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Interdisciplinary Arts</td>\n",
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       "      <td>Media Arts</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Withdrawn</td>\n",
       "      <td>gender_oth</td>\n",
       "      <td>twospirit</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E555HATF05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0LTH6QJQH8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R3S4V21D40</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BZEFVJAYBT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           p1_discipline1 p1_discipline2 p1_discipline3  \\\n",
       "0                     NaN            NaN            NaN   \n",
       "1  Interdisciplinary Arts    Visual Arts     Media Arts   \n",
       "2                     NaN            NaN            NaN   \n",
       "3                     NaN            NaN            NaN   \n",
       "4                     NaN            NaN            NaN   \n",
       "\n",
       "  p2_approachtopractice1 p2_approachtopractice2 p2_approachtopractice3  \\\n",
       "0                    NaN                    NaN                    NaN   \n",
       "1         collab_artists      collab_nonartists            solo_artist   \n",
       "2                    NaN                    NaN                    NaN   \n",
       "3                    NaN                    NaN                    NaN   \n",
       "4                    NaN                    NaN                    NaN   \n",
       "\n",
       "  p2_approachtopractice4   p2_approachtopractice5 p2_approachtopractice6  \\\n",
       "0                    NaN                      NaN                    NaN   \n",
       "1     public_involvement  perform_present_exhibit        teaching_others   \n",
       "2                    NaN                      NaN                    NaN   \n",
       "3                    NaN                      NaN                    NaN   \n",
       "4                    NaN                      NaN                    NaN   \n",
       "\n",
       "  p3_impacts  ...   p_status p41_gender1 p41_gender2 p41_gender3 p41_gender4  \\\n",
       "0        NaN  ...        NaN         NaN         NaN         NaN         NaN   \n",
       "1        Yes  ...  Withdrawn  gender_oth   twospirit       woman         NaN   \n",
       "2        NaN  ...        NaN         NaN         NaN         NaN         NaN   \n",
       "3        NaN  ...        NaN         NaN         NaN         NaN         NaN   \n",
       "4        NaN  ...        NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "  p41_gender5 p46_caregiving1 p46_caregiving2 p46_caregiving3          id  \n",
       "0         NaN             NaN             NaN             NaN  N3R8BXQE4K  \n",
       "1         NaN   no_caregiving             NaN             NaN  E555HATF05  \n",
       "2         NaN             NaN             NaN             NaN  0LTH6QJQH8  \n",
       "3         NaN             NaN             NaN             NaN  R3S4V21D40  \n",
       "4         NaN             NaN             NaN             NaN  BZEFVJAYBT  \n",
       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_data['id'] = data['id']\n",
    "p_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8b7f982",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b8b7f982",
    "outputId": "eca8c407-cfab-435a-b33a-77898df52b3b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'g14_city', 'g15_zipcode', 'g16_county', 'g17_region',\n",
       "       'g18_community', 'g20_raceethnicity1', 'g20_raceethnicity2',\n",
       "       'g20_raceethnicity3', 'g20_raceethnicity4', 'g20_raceethnicity5',\n",
       "       'g20_raceethnicity6', 'g20_raceethnicity7', 'g20_raceethnicity8',\n",
       "       'g21_immigrant', 'g22_language', 'g23_gender1', 'g23_gender2',\n",
       "       'g23_gender3', 'g23_gender4', 'g23_gender5', 'g24_transgender',\n",
       "       'g25_lgbtqiap', 'g26_deafdisabled', 'g27_providecare1',\n",
       "       'g27_providecare2', 'g27_providecare3', 'g27_providecare4',\n",
       "       'g27_providecare5', 'g28_criminallegalsystem',\n",
       "       'g28b_criminal_findemploy', 'g29_financialsafetynet1',\n",
       "       'g29_financialsafetynet2', 'g29_financialsafetynet3',\n",
       "       'g29_financialsafetynet4', 'g29_financialsafetynet5', 'g30_discipline1',\n",
       "       'g30_discipline2', 'g30_discipline3', 'g31_approach1', 'g31_approach2',\n",
       "       'g31_approach3', 'g31_approach4', 'g31_approach5', 'g31_approach6',\n",
       "       'g32_enrolled', 'g32b_enrolled_name1', 'g32b_enrolled_name2',\n",
       "       'g32b_enrolled_name3', 'g32b_enrolled_name4', 'g32b_enrolled_name5',\n",
       "       'g32b_enrolled_name6', 'g32b_enrolled_name7', 'g32b_enrolled_name8',\n",
       "       'g32b_enrolled_name9', 'g_agerange', 'g_enrollmentstatus'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping null values from g_Data\n",
    "\n",
    "g_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "860d826a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "id": "860d826a",
    "outputId": "00bddf23-750a-4350-c522-5ced2dcf7882"
   },
   "outputs": [
    {
     "data": {
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       "variable_name": "new_gdata"
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>g14_city</th>\n",
       "      <th>g15_zipcode</th>\n",
       "      <th>g16_county</th>\n",
       "      <th>g17_region</th>\n",
       "      <th>g18_community</th>\n",
       "      <th>g20_raceethnicity1</th>\n",
       "      <th>g20_raceethnicity2</th>\n",
       "      <th>g20_raceethnicity3</th>\n",
       "      <th>g20_raceethnicity4</th>\n",
       "      <th>...</th>\n",
       "      <th>g32b_enrolled_name2</th>\n",
       "      <th>g32b_enrolled_name3</th>\n",
       "      <th>g32b_enrolled_name4</th>\n",
       "      <th>g32b_enrolled_name5</th>\n",
       "      <th>g32b_enrolled_name6</th>\n",
       "      <th>g32b_enrolled_name7</th>\n",
       "      <th>g32b_enrolled_name8</th>\n",
       "      <th>g32b_enrolled_name9</th>\n",
       "      <th>g_agerange</th>\n",
       "      <th>g_enrollmentstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N3R8BXQE4K</td>\n",
       "      <td>BUFFALO</td>\n",
       "      <td>14201.0</td>\n",
       "      <td>Erie County</td>\n",
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       "      <td>White</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0LTH6QJQH8</td>\n",
       "      <td>New York City</td>\n",
       "      <td>10040.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R3S4V21D40</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>10031.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BZEFVJAYBT</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>Arab or Middle Eastern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Earned Income Tax Credit (EITC)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4HF20W8D84</td>\n",
       "      <td>New York</td>\n",
       "      <td>11237.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
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       "\n",
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       "      fill: #D2E3FC;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
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       "        document.querySelector('#df-5d56e652-fb22-4f31-8d3d-45d4a9658ede button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-5d56e652-fb22-4f31-8d3d-45d4a9658ede');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
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       "\n",
       "\n",
       "<div id=\"df-a0f1afc7-36c0-4de4-b1e4-4f04048c2df3\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a0f1afc7-36c0-4de4-b1e4-4f04048c2df3')\"\n",
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       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-a0f1afc7-36c0-4de4-b1e4-4f04048c2df3 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "           id       g14_city  g15_zipcode                      g16_county  \\\n",
       "0  N3R8BXQE4K        BUFFALO      14201.0                  Erie County      \n",
       "2  0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)      \n",
       "3  R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)      \n",
       "4  BZEFVJAYBT       Brooklyn      11238.0       Kings County (Brooklyn)     \n",
       "5  4HF20W8D84       New York      11237.0       Kings County (Brooklyn)     \n",
       "\n",
       "         g17_region g18_community          g20_raceethnicity1  \\\n",
       "0  Western New York       Urban    Asian or Pacific Islander    \n",
       "2     New York City       Urban                       White     \n",
       "3     New York City       Urban    Black or African American    \n",
       "4     New York City       Urban    Asian or Pacific Islander    \n",
       "5     New York City       Urban                       White     \n",
       "\n",
       "         g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                   White                  NaN                NaN  ...   \n",
       "2                       NaN                NaN                NaN  ...   \n",
       "3                       NaN                NaN                NaN  ...   \n",
       "4   Arab or Middle Eastern                 NaN                NaN  ...   \n",
       "5                       NaN                NaN                NaN  ...   \n",
       "\n",
       "                 g32b_enrolled_name2 g32b_enrolled_name3 g32b_enrolled_name4  \\\n",
       "0                                NaN                 NaN                 NaN   \n",
       "2                                NaN                 NaN                 NaN   \n",
       "3                                NaN                 NaN                 NaN   \n",
       "4   Earned Income Tax Credit (EITC)                  NaN                 NaN   \n",
       "5                                NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name5 g32b_enrolled_name6 g32b_enrolled_name7  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "5                 NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name8 g32b_enrolled_name9 g_agerange g_enrollmentstatus  \n",
       "0                 NaN                 NaN      25-34                NaN  \n",
       "2                 NaN                 NaN      25-34                NaN  \n",
       "3                 NaN                 NaN      25-34                NaN  \n",
       "4                 NaN                 NaN      25-34                NaN  \n",
       "5                 NaN                 NaN      35-44                NaN  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata = g_data.dropna(subset=['g14_city', 'g15_zipcode', 'g16_county', 'g17_region',\n",
    "       'g18_community', 'g20_raceethnicity1', 'g20_raceethnicity2',\n",
    "       'g20_raceethnicity3', 'g20_raceethnicity4', 'g20_raceethnicity5',\n",
    "       'g20_raceethnicity6', 'g20_raceethnicity7', 'g20_raceethnicity8',\n",
    "       'g21_immigrant', 'g22_language', 'g23_gender1', 'g23_gender2',\n",
    "       'g23_gender3', 'g23_gender4', 'g23_gender5', 'g24_transgender',\n",
    "       'g25_lgbtqiap', 'g26_deafdisabled', 'g27_providecare1',\n",
    "       'g27_providecare2', 'g27_providecare3', 'g27_providecare4',\n",
    "       'g27_providecare5', 'g28_criminallegalsystem',\n",
    "       'g28b_criminal_findemploy', 'g29_financialsafetynet1',\n",
    "       'g29_financialsafetynet2', 'g29_financialsafetynet3',\n",
    "       'g29_financialsafetynet4', 'g29_financialsafetynet5', 'g30_discipline1',\n",
    "       'g30_discipline2', 'g30_discipline3', 'g31_approach1', 'g31_approach2',\n",
    "       'g31_approach3', 'g31_approach4', 'g31_approach5', 'g31_approach6',\n",
    "       'g32_enrolled', 'g32b_enrolled_name1', 'g32b_enrolled_name2',\n",
    "       'g32b_enrolled_name3', 'g32b_enrolled_name4', 'g32b_enrolled_name5',\n",
    "       'g32b_enrolled_name6', 'g32b_enrolled_name7', 'g32b_enrolled_name8',\n",
    "       'g32b_enrolled_name9', 'g_agerange', 'g_enrollmentstatus'], how='all')\n",
    "\n",
    "new_gdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "79be3f60",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "79be3f60",
    "outputId": "b19fa101-06e9-40ce-d1d5-32992f7e5869"
   },
   "outputs": [
    {
     "data": {
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       "g14_city                        1\n",
       "g15_zipcode                     0\n",
       "g16_county                      0\n",
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       "g29_financialsafetynet4     19576\n",
       "g29_financialsafetynet5     21916\n",
       "g30_discipline1                 0\n",
       "g30_discipline2                 0\n",
       "g30_discipline3                 0\n",
       "g31_approach1                   0\n",
       "g31_approach2                3681\n",
       "g31_approach3                7130\n",
       "g31_approach4               12016\n",
       "g31_approach5               16343\n",
       "g31_approach6               19368\n",
       "g32_enrolled                    0\n",
       "g32b_enrolled_name1             0\n",
       "g32b_enrolled_name2         18900\n",
       "g32b_enrolled_name3         20748\n",
       "g32b_enrolled_name4         21537\n",
       "g32b_enrolled_name5         21811\n",
       "g32b_enrolled_name6         21891\n",
       "g32b_enrolled_name7         21911\n",
       "g32b_enrolled_name8         21919\n",
       "g32b_enrolled_name9         21920\n",
       "g_agerange                      0\n",
       "g_enrollmentstatus          19520\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fc6e1446",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fc6e1446",
    "outputId": "2af74f51-0d44-4704-b134-d1d99ec74af1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23797, 180)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
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   "execution_count": 15,
   "id": "365669bf",
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     "height": 377
    },
    "id": "365669bf",
    "outputId": "a2824d95-970a-41fb-9ce9-e0fafe59d632"
   },
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       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-right-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "      border-bottom-color: var(--fill-color);\n",
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       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
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       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "           id       g14_city  g15_zipcode                      g16_county  \\\n",
       "0  N3R8BXQE4K        BUFFALO      14201.0                  Erie County      \n",
       "2  0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)      \n",
       "3  R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)      \n",
       "4  BZEFVJAYBT       Brooklyn      11238.0       Kings County (Brooklyn)     \n",
       "5  4HF20W8D84       New York      11237.0       Kings County (Brooklyn)     \n",
       "\n",
       "         g17_region g18_community          g20_raceethnicity1  \\\n",
       "0  Western New York       Urban    Asian or Pacific Islander    \n",
       "2     New York City       Urban                       White     \n",
       "3     New York City       Urban    Black or African American    \n",
       "4     New York City       Urban    Asian or Pacific Islander    \n",
       "5     New York City       Urban                       White     \n",
       "\n",
       "         g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                   White                  NaN                NaN  ...   \n",
       "2                       NaN                NaN                NaN  ...   \n",
       "3                       NaN                NaN                NaN  ...   \n",
       "4   Arab or Middle Eastern                 NaN                NaN  ...   \n",
       "5                       NaN                NaN                NaN  ...   \n",
       "\n",
       "                 g32b_enrolled_name2 g32b_enrolled_name3 g32b_enrolled_name4  \\\n",
       "0                                NaN                 NaN                 NaN   \n",
       "2                                NaN                 NaN                 NaN   \n",
       "3                                NaN                 NaN                 NaN   \n",
       "4   Earned Income Tax Credit (EITC)                  NaN                 NaN   \n",
       "5                                NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name5 g32b_enrolled_name6 g32b_enrolled_name7  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "5                 NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name8 g32b_enrolled_name9 g_agerange g_enrollmentstatus  \n",
       "0                 NaN                 NaN      25-34                NaN  \n",
       "2                 NaN                 NaN      25-34                NaN  \n",
       "3                 NaN                 NaN      25-34                NaN  \n",
       "4                 NaN                 NaN      25-34                NaN  \n",
       "5                 NaN                 NaN      35-44                NaN  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9bee8aca",
   "metadata": {
    "id": "9bee8aca"
   },
   "outputs": [],
   "source": [
    "#new_gdata.to_csv('applicant.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5fea4587",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 689
    },
    "id": "5fea4587",
    "outputId": "29efbae6-6da5-496f-c7e8-6a2c6f051c89"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-552b5f8cb60b>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata[columns_to_fill] = new_gdata[columns_to_fill].fillna('')\n",
      "<ipython-input-17-552b5f8cb60b>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata['race'] = new_gdata[columns_to_fill].apply(lambda row: ''.join(row), axis=1)\n"
     ]
    },
    {
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       "<p>5 rows × 58 columns</p>\n",
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       "\n",
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       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
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       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-right-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
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       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "\n",
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      ],
      "text/plain": [
       "           id       g14_city  g15_zipcode                      g16_county  \\\n",
       "0  N3R8BXQE4K        BUFFALO      14201.0                  Erie County      \n",
       "2  0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)      \n",
       "3  R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)      \n",
       "4  BZEFVJAYBT       Brooklyn      11238.0       Kings County (Brooklyn)     \n",
       "5  4HF20W8D84       New York      11237.0       Kings County (Brooklyn)     \n",
       "\n",
       "         g17_region g18_community          g20_raceethnicity1  \\\n",
       "0  Western New York       Urban    Asian or Pacific Islander    \n",
       "2     New York City       Urban                       White     \n",
       "3     New York City       Urban    Black or African American    \n",
       "4     New York City       Urban    Asian or Pacific Islander    \n",
       "5     New York City       Urban                       White     \n",
       "\n",
       "         g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                   White                                          ...   \n",
       "2                                                                  ...   \n",
       "3                                                                  ...   \n",
       "4   Arab or Middle Eastern                                         ...   \n",
       "5                                                                  ...   \n",
       "\n",
       "  g32b_enrolled_name3 g32b_enrolled_name4 g32b_enrolled_name5  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "5                 NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name6 g32b_enrolled_name7 g32b_enrolled_name8  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "5                 NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name9 g_agerange g_enrollmentstatus  \\\n",
       "0                 NaN      25-34                NaN   \n",
       "2                 NaN      25-34                NaN   \n",
       "3                 NaN      25-34                NaN   \n",
       "4                 NaN      25-34                NaN   \n",
       "5                 NaN      35-44                NaN   \n",
       "\n",
       "                                                race  \n",
       "0                 Asian or Pacific Islander  White    \n",
       "2                                            White    \n",
       "3                         Black or African American   \n",
       "4  Asian or Pacific Islander  Arab or Middle East...  \n",
       "5                                            White    \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new_gdata['race'] = new_gdata[['g20_raceethnicity1', 'g20_raceethnicity2', 'g20_raceethnicity3', 'g20_raceethnicity4', 'g20_raceethnicity5', 'g20_raceethnicity6', 'g20_raceethnicity7', 'g20_raceethnicity8']].astype(str).apply(', '.join, axis=1)\n",
    "\n",
    "# Fill NaN values with empty strings only in the specified columns\n",
    "columns_to_fill = ['g20_raceethnicity1', 'g20_raceethnicity2', 'g20_raceethnicity3',\n",
    "                   'g20_raceethnicity4', 'g20_raceethnicity5', 'g20_raceethnicity6',\n",
    "                   'g20_raceethnicity7', 'g20_raceethnicity8']\n",
    "new_gdata[columns_to_fill] = new_gdata[columns_to_fill].fillna('')\n",
    "\n",
    "# Concatenate values of all columns along the rows and separate them with a comma\n",
    "new_gdata['race'] = new_gdata[columns_to_fill].apply(lambda row: ''.join(row), axis=1)\n",
    "\n",
    "# Display the DataFrame with the new merged column\n",
    "new_gdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "11faf88b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 759
    },
    "id": "11faf88b",
    "outputId": "c8545fd3-a951-47b1-8f37-1b3bcc032b20"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
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       "variable_name": "new_gdata"
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       "      <td>New York City</td>\n",
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       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>New York</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>T8RAL818WX</td>\n",
       "      <td>New York</td>\n",
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       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7LMUEX56QJ</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11206.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Enrolled</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CK5Q7211S1</td>\n",
       "      <td>Binghamton</td>\n",
       "      <td>13905.0</td>\n",
       "      <td>Broome County</td>\n",
       "      <td>Southern Tier</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 58 columns</p>\n",
       "</div>\n",
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       "\n",
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       "\n",
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       "\n",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "          + ' to learn more about interactive tables.';\n",
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       "\n",
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       "      spin 1s steps(1) infinite;\n",
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       "  @keyframes spin {\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
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       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "            id       g14_city  g15_zipcode                      g16_county  \\\n",
       "0   N3R8BXQE4K        BUFFALO      14201.0                  Erie County      \n",
       "2   0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)      \n",
       "3   R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)      \n",
       "4   BZEFVJAYBT       Brooklyn      11238.0       Kings County (Brooklyn)     \n",
       "5   4HF20W8D84       New York      11237.0       Kings County (Brooklyn)     \n",
       "6   DBLJ7W1AJV       New York      10003.0  New York County (Manhattan)      \n",
       "7   T8RAL818WX       New York      10030.0  New York County (Manhattan)      \n",
       "8   VJSNADKNIA      Ridgewood      11385.0                Queens County      \n",
       "9   7LMUEX56QJ       Brooklyn      11206.0       Kings County (Brooklyn)     \n",
       "10  CK5Q7211S1     Binghamton      13905.0                Broome County      \n",
       "\n",
       "          g17_region g18_community          g20_raceethnicity1  \\\n",
       "0   Western New York       Urban    Asian or Pacific Islander    \n",
       "2      New York City       Urban                       White     \n",
       "3      New York City       Urban    Black or African American    \n",
       "4      New York City       Urban    Asian or Pacific Islander    \n",
       "5      New York City       Urban                       White     \n",
       "6      New York City       Urban           Hispanic or Latinx    \n",
       "7      New York City       Urban    Black or African American    \n",
       "8      New York City       Urban                       White     \n",
       "9      New York City       Urban                       White     \n",
       "10     Southern Tier       Urban                       White     \n",
       "\n",
       "          g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                    White                                          ...   \n",
       "2                                                                   ...   \n",
       "3                                                                   ...   \n",
       "4    Arab or Middle Eastern                                         ...   \n",
       "5                                                                   ...   \n",
       "6                                                                   ...   \n",
       "7                                                                   ...   \n",
       "8                                                                   ...   \n",
       "9                                                                   ...   \n",
       "10                                                                  ...   \n",
       "\n",
       "          g32b_enrolled_name3 g32b_enrolled_name4 g32b_enrolled_name5  \\\n",
       "0                         NaN                 NaN                 NaN   \n",
       "2                         NaN                 NaN                 NaN   \n",
       "3                         NaN                 NaN                 NaN   \n",
       "4                         NaN                 NaN                 NaN   \n",
       "5                         NaN                 NaN                 NaN   \n",
       "6                         NaN                 NaN                 NaN   \n",
       "7    Other (please describe)                  NaN                 NaN   \n",
       "8                         NaN                 NaN                 NaN   \n",
       "9                         NaN                 NaN                 NaN   \n",
       "10                        NaN                 NaN                 NaN   \n",
       "\n",
       "   g32b_enrolled_name6 g32b_enrolled_name7 g32b_enrolled_name8  \\\n",
       "0                  NaN                 NaN                 NaN   \n",
       "2                  NaN                 NaN                 NaN   \n",
       "3                  NaN                 NaN                 NaN   \n",
       "4                  NaN                 NaN                 NaN   \n",
       "5                  NaN                 NaN                 NaN   \n",
       "6                  NaN                 NaN                 NaN   \n",
       "7                  NaN                 NaN                 NaN   \n",
       "8                  NaN                 NaN                 NaN   \n",
       "9                  NaN                 NaN                 NaN   \n",
       "10                 NaN                 NaN                 NaN   \n",
       "\n",
       "   g32b_enrolled_name9 g_agerange g_enrollmentstatus  \\\n",
       "0                  NaN      25-34                NaN   \n",
       "2                  NaN      25-34                NaN   \n",
       "3                  NaN      25-34                NaN   \n",
       "4                  NaN      25-34                NaN   \n",
       "5                  NaN      35-44                NaN   \n",
       "6                  NaN      18-24                NaN   \n",
       "7                  NaN        65+                NaN   \n",
       "8                  NaN      35-44                NaN   \n",
       "9                  NaN      35-44           Enrolled   \n",
       "10                 NaN      25-34                NaN   \n",
       "\n",
       "                                                 race  \n",
       "0                  Asian or Pacific Islander  White    \n",
       "2                                             White    \n",
       "3                          Black or African American   \n",
       "4   Asian or Pacific Islander  Arab or Middle East...  \n",
       "5                                             White    \n",
       "6                                 Hispanic or Latinx   \n",
       "7                          Black or African American   \n",
       "8                                             White    \n",
       "9                                             White    \n",
       "10                                            White    \n",
       "\n",
       "[10 rows x 58 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4987ec2a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4987ec2a",
    "outputId": "2a8a4e7b-8866-4c45-88e8-9922d151e544",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-b3ca3dfb7c8b>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata.drop(columns=['race'], inplace=True)\n"
     ]
    }
   ],
   "source": [
    "new_gdata.drop(columns=['race'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "78f24fc0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 481
    },
    "id": "78f24fc0",
    "outputId": "a4eac5f4-1eb2-4e58-ee02-bf2d4ecca835"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-9300eeb16eee>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata['g_enrollmentstatus'] = new_gdata['g_enrollmentstatus'].fillna('Not Enrolled')\n"
     ]
    },
    {
     "data": {
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       "<p>5 rows × 57 columns</p>\n",
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       "\n",
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       "\n",
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       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-52376733-a42f-4ee4-887c-9f019aabd00e button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "           id       g14_city  g15_zipcode                      g16_county  \\\n",
       "0  N3R8BXQE4K        BUFFALO      14201.0                  Erie County      \n",
       "2  0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)      \n",
       "3  R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)      \n",
       "4  BZEFVJAYBT       Brooklyn      11238.0       Kings County (Brooklyn)     \n",
       "5  4HF20W8D84       New York      11237.0       Kings County (Brooklyn)     \n",
       "\n",
       "         g17_region g18_community          g20_raceethnicity1  \\\n",
       "0  Western New York       Urban    Asian or Pacific Islander    \n",
       "2     New York City       Urban                       White     \n",
       "3     New York City       Urban    Black or African American    \n",
       "4     New York City       Urban    Asian or Pacific Islander    \n",
       "5     New York City       Urban                       White     \n",
       "\n",
       "         g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                   White                                          ...   \n",
       "2                                                                  ...   \n",
       "3                                                                  ...   \n",
       "4   Arab or Middle Eastern                                         ...   \n",
       "5                                                                  ...   \n",
       "\n",
       "                 g32b_enrolled_name2 g32b_enrolled_name3 g32b_enrolled_name4  \\\n",
       "0                                NaN                 NaN                 NaN   \n",
       "2                                NaN                 NaN                 NaN   \n",
       "3                                NaN                 NaN                 NaN   \n",
       "4   Earned Income Tax Credit (EITC)                  NaN                 NaN   \n",
       "5                                NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name5 g32b_enrolled_name6 g32b_enrolled_name7  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "5                 NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name8 g32b_enrolled_name9 g_agerange g_enrollmentstatus  \n",
       "0                 NaN                 NaN      25-34       Not Enrolled  \n",
       "2                 NaN                 NaN      25-34       Not Enrolled  \n",
       "3                 NaN                 NaN      25-34       Not Enrolled  \n",
       "4                 NaN                 NaN      25-34       Not Enrolled  \n",
       "5                 NaN                 NaN      35-44       Not Enrolled  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata['g_enrollmentstatus'].unique()\n",
    "new_gdata['g_enrollmentstatus'] = new_gdata['g_enrollmentstatus'].fillna('Not Enrolled')\n",
    "new_gdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dc8571da",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dc8571da",
    "outputId": "e42e211a-bbf2-42d9-9ae8-03a6172a941e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2400"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(g_data[g_data['g_enrollmentstatus'] == 'Enrolled'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9e8f578",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e9e8f578",
    "outputId": "b13d4f4d-8725-4e08-8a51-177b8d50ceff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Asian or Pacific Islander ', nan, 'White  ',\n",
       "       'Black or African American ', 'Hispanic or Latinx ',\n",
       "       'Arab or Middle Eastern ',\n",
       "       'Indigenous American, First Nation, or Alaska Native ',\n",
       "       'Other (please describe)  ', 'I prefer not to answer ',\n",
       "       'Pacific Islander or Native Hawaiian '], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g20_raceethnicity1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "11d965e4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "11d965e4",
    "outputId": "653ca31f-cbb1-436f-ad34-3eff96c8b34d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' White  ', nan, ' Arab or Middle Eastern ',\n",
       "       ' Hispanic or Latinx ', ' Black or African American ',\n",
       "       ' Other (please describe)  ', ' Asian or Pacific Islander ',\n",
       "       ' Indigenous American, First Nation, or Alaska Native ',\n",
       "       ' I prefer not to answer ',\n",
       "       ' Pacific Islander or Native Hawaiian '], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g20_raceethnicity2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8beeee3a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8beeee3a",
    "outputId": "c14e2a56-d5d8-4a29-fbf7-6b139bffc5ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, ' Indigenous American, First Nation, or Alaska Native ',\n",
       "       ' Black or African American ', ' White  ',\n",
       "       ' Other (please describe)  ', ' Hispanic or Latinx ',\n",
       "       ' Arab or Middle Eastern ', ' Asian or Pacific Islander ',\n",
       "       ' Pacific Islander or Native Hawaiian ',\n",
       "       ' I prefer not to answer '], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g20_raceethnicity3'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ffa08847",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ffa08847",
    "outputId": "8c58000a-51c5-4b54-eb3d-43152e73439f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, ' White  ', ' Other (please describe)  ',\n",
       "       ' Indigenous American, First Nation, or Alaska Native ',\n",
       "       ' Arab or Middle Eastern ', ' I prefer not to answer ',\n",
       "       ' Asian or Pacific Islander ', ' Hispanic or Latinx ',\n",
       "       ' Black or African American ',\n",
       "       ' Pacific Islander or Native Hawaiian '], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g20_raceethnicity4'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "419ff922",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "419ff922",
    "outputId": "a94fff4f-4d8c-471d-a7d2-1a5cb3d8d2d1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, ' Asian or Pacific Islander [20.2', ' White  [20.7',\n",
       "       ' Other (please describe)  [20.8',\n",
       "       ' Indigenous American, First Nation, or Alaska Native [20.5',\n",
       "       ' Black or African American [20.3'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g20_raceethnicity5'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eee9acdf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eee9acdf",
    "outputId": "b0e80be9-861b-4410-ca24-55ea8dc42614"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, ' Hispanic or Latinx [20.4', ' Arab or Middle Eastern [20.1',\n",
       "       ' Pacific Islander or Native Hawaiian [20.6'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g20_raceethnicity6'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "be948a6c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "be948a6c",
    "outputId": "aea4bf67-46eb-4439-863d-f0c480e8b1d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, ' White  '], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g20_raceethnicity7'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f92cd91e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f92cd91e",
    "outputId": "8fd9f795-5921-454d-982f-800acbc2bbbd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, ' Other (please describe)  '], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g20_raceethnicity8'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b17046d1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "id": "b17046d1",
    "outputId": "03687262-a24c-4cc5-d2f0-42a5d7754b4b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "repr_error": "'str' object has no attribute 'empty'",
       "type": "dataframe"
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       "\n",
       "  <div id=\"df-ad0f9cc7-6342-454c-9faf-a53d737e11b2\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th>g20_raceethnicity1</th>\n",
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       "      <th>g20_raceethnicity3</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad0f9cc7-6342-454c-9faf-a53d737e11b2')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "  <style>\n",
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       "      display:flex;\n",
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       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-ad0f9cc7-6342-454c-9faf-a53d737e11b2 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-ad0f9cc7-6342-454c-9faf-a53d737e11b2');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
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       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
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       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-3bee9364-6cfd-42b9-8e8b-4b7537b416f7 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "           g20_raceethnicity1        g20_raceethnicity2 g20_raceethnicity3  \\\n",
       "0  Asian or Pacific Islander                    White                  NaN   \n",
       "1                         NaN                       NaN                NaN   \n",
       "2                     White                         NaN                NaN   \n",
       "3  Black or African American                        NaN                NaN   \n",
       "4  Asian or Pacific Islander    Arab or Middle Eastern                 NaN   \n",
       "5                     White                         NaN                NaN   \n",
       "6         Hispanic or Latinx                        NaN                NaN   \n",
       "7  Black or African American                        NaN                NaN   \n",
       "8                     White                         NaN                NaN   \n",
       "9                     White                         NaN                NaN   \n",
       "\n",
       "  g20_raceethnicity4 g20_raceethnicity5 g20_raceethnicity6 g20_raceethnicity7  \\\n",
       "0                NaN                NaN                NaN                NaN   \n",
       "1                NaN                NaN                NaN                NaN   \n",
       "2                NaN                NaN                NaN                NaN   \n",
       "3                NaN                NaN                NaN                NaN   \n",
       "4                NaN                NaN                NaN                NaN   \n",
       "5                NaN                NaN                NaN                NaN   \n",
       "6                NaN                NaN                NaN                NaN   \n",
       "7                NaN                NaN                NaN                NaN   \n",
       "8                NaN                NaN                NaN                NaN   \n",
       "9                NaN                NaN                NaN                NaN   \n",
       "\n",
       "  g20_raceethnicity8  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "5                NaN  \n",
       "6                NaN  \n",
       "7                NaN  \n",
       "8                NaN  \n",
       "9                NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data[['g20_raceethnicity1', 'g20_raceethnicity2', 'g20_raceethnicity3',\n",
    "                   'g20_raceethnicity4', 'g20_raceethnicity5', 'g20_raceethnicity6',\n",
    "                   'g20_raceethnicity7', 'g20_raceethnicity8']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7e224143",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7e224143",
    "outputId": "6c8f7eb4-9719-4885-906e-03bd6bffcd86"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Woman ', nan, 'Man ', 'Non-binary ', 'Two-spirit ',\n",
       "       'I prefer not to answer ', 'Other (please describe) '],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g23_gender1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1840770a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1840770a",
    "outputId": "6a466e0f-e20f-401a-bf0c-2270e107ea2d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, ' Woman ', ' Non-binary ', ' Man ',\n",
       "       ' Other (please describe) ', ' I prefer not to answer ',\n",
       "       ' Two-spirit '], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g23_gender2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9e448d28",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9e448d28",
    "outputId": "a9faebfe-0216-4d0d-a361-fe3ed0604a11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, ' Two-spirit ', ' Man ', ' Other (please describe) ',\n",
       "       ' Woman ', ' Non-binary ', ' I prefer not to answer '],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g23_gender3'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0b3dd8e8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0b3dd8e8",
    "outputId": "b7522a9d-318e-4012-cbf2-6643ee2563cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, ' Non-binary ', ' Other (please describe) ', ' Man ',\n",
       "       ' Woman ', ' Two-spirit '], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data['g23_gender4'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d810b756",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "id": "d810b756",
    "outputId": "61743b6e-c943-4dbb-e46d-644b09ff8c3a"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
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       "\n",
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       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th>g14_city</th>\n",
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       "      <th>g18_community</th>\n",
       "      <th>g20_raceethnicity1</th>\n",
       "      <th>g20_raceethnicity2</th>\n",
       "      <th>g20_raceethnicity3</th>\n",
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       "      <th>g_agerange</th>\n",
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       "      <td>10040.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>New York City</td>\n",
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       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4503e986-48ad-4b2c-86d2-d6aee3d1a4f6')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-4503e986-48ad-4b2c-86d2-d6aee3d1a4f6 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-4503e986-48ad-4b2c-86d2-d6aee3d1a4f6');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
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       "\n",
       "\n",
       "<div id=\"df-27f943c9-59c9-4bac-913e-fc972e60ea88\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-27f943c9-59c9-4bac-913e-fc972e60ea88')\"\n",
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       "            style=\"display:none;\">\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-27f943c9-59c9-4bac-913e-fc972e60ea88 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "           id       g14_city  g15_zipcode                      g16_county  \\\n",
       "0  N3R8BXQE4K        BUFFALO      14201.0                  Erie County      \n",
       "1  E555HATF05            NaN          NaN                             NaN   \n",
       "2  0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)      \n",
       "3  R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)      \n",
       "4  BZEFVJAYBT       Brooklyn      11238.0       Kings County (Brooklyn)     \n",
       "\n",
       "         g17_region g18_community          g20_raceethnicity1  \\\n",
       "0  Western New York       Urban    Asian or Pacific Islander    \n",
       "1               NaN           NaN                         NaN   \n",
       "2     New York City       Urban                       White     \n",
       "3     New York City       Urban    Black or African American    \n",
       "4     New York City       Urban    Asian or Pacific Islander    \n",
       "\n",
       "         g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                   White                  NaN                NaN  ...   \n",
       "1                       NaN                NaN                NaN  ...   \n",
       "2                       NaN                NaN                NaN  ...   \n",
       "3                       NaN                NaN                NaN  ...   \n",
       "4   Arab or Middle Eastern                 NaN                NaN  ...   \n",
       "\n",
       "                 g32b_enrolled_name2 g32b_enrolled_name3 g32b_enrolled_name4  \\\n",
       "0                                NaN                 NaN                 NaN   \n",
       "1                                NaN                 NaN                 NaN   \n",
       "2                                NaN                 NaN                 NaN   \n",
       "3                                NaN                 NaN                 NaN   \n",
       "4   Earned Income Tax Credit (EITC)                  NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name5 g32b_enrolled_name6 g32b_enrolled_name7  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "1                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name8 g32b_enrolled_name9 g_agerange g_enrollmentstatus  \n",
       "0                 NaN                 NaN      25-34                NaN  \n",
       "1                 NaN                 NaN        NaN                NaN  \n",
       "2                 NaN                 NaN      25-34                NaN  \n",
       "3                 NaN                 NaN      25-34                NaN  \n",
       "4                 NaN                 NaN      25-34                NaN  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "281ddd2d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "id": "281ddd2d",
    "outputId": "3454a617-7ea1-4935-a1d0-c62cf2d72fdb"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "variable_name": "new_gdata"
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       "\n",
       "  <div id=\"df-fa09b60a-ca51-47dd-93b4-037cfda888d7\" class=\"colab-df-container\">\n",
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>g14_city</th>\n",
       "      <th>g15_zipcode</th>\n",
       "      <th>g16_county</th>\n",
       "      <th>g17_region</th>\n",
       "      <th>g18_community</th>\n",
       "      <th>g20_raceethnicity1</th>\n",
       "      <th>g20_raceethnicity2</th>\n",
       "      <th>g20_raceethnicity3</th>\n",
       "      <th>g20_raceethnicity4</th>\n",
       "      <th>...</th>\n",
       "      <th>g32b_enrolled_name2</th>\n",
       "      <th>g32b_enrolled_name3</th>\n",
       "      <th>g32b_enrolled_name4</th>\n",
       "      <th>g32b_enrolled_name5</th>\n",
       "      <th>g32b_enrolled_name6</th>\n",
       "      <th>g32b_enrolled_name7</th>\n",
       "      <th>g32b_enrolled_name8</th>\n",
       "      <th>g32b_enrolled_name9</th>\n",
       "      <th>g_agerange</th>\n",
       "      <th>g_enrollmentstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N3R8BXQE4K</td>\n",
       "      <td>BUFFALO</td>\n",
       "      <td>14201.0</td>\n",
       "      <td>Erie County</td>\n",
       "      <td>Western New York</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0LTH6QJQH8</td>\n",
       "      <td>New York City</td>\n",
       "      <td>10040.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R3S4V21D40</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>10031.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BZEFVJAYBT</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>Arab or Middle Eastern</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>Earned Income Tax Credit (EITC)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4HF20W8D84</td>\n",
       "      <td>New York</td>\n",
       "      <td>11237.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Not Enrolled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa09b60a-ca51-47dd-93b4-037cfda888d7')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-fa09b60a-ca51-47dd-93b4-037cfda888d7 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-fa09b60a-ca51-47dd-93b4-037cfda888d7');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
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       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-de151510-f27e-465e-b662-515b11742d4f\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de151510-f27e-465e-b662-515b11742d4f')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-de151510-f27e-465e-b662-515b11742d4f button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "           id       g14_city  g15_zipcode                      g16_county  \\\n",
       "0  N3R8BXQE4K        BUFFALO      14201.0                  Erie County      \n",
       "2  0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)      \n",
       "3  R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)      \n",
       "4  BZEFVJAYBT       Brooklyn      11238.0       Kings County (Brooklyn)     \n",
       "5  4HF20W8D84       New York      11237.0       Kings County (Brooklyn)     \n",
       "\n",
       "         g17_region g18_community          g20_raceethnicity1  \\\n",
       "0  Western New York       Urban    Asian or Pacific Islander    \n",
       "2     New York City       Urban                       White     \n",
       "3     New York City       Urban    Black or African American    \n",
       "4     New York City       Urban    Asian or Pacific Islander    \n",
       "5     New York City       Urban                       White     \n",
       "\n",
       "         g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                   White                                          ...   \n",
       "2                                                                  ...   \n",
       "3                                                                  ...   \n",
       "4   Arab or Middle Eastern                                         ...   \n",
       "5                                                                  ...   \n",
       "\n",
       "                 g32b_enrolled_name2 g32b_enrolled_name3 g32b_enrolled_name4  \\\n",
       "0                                NaN                 NaN                 NaN   \n",
       "2                                NaN                 NaN                 NaN   \n",
       "3                                NaN                 NaN                 NaN   \n",
       "4   Earned Income Tax Credit (EITC)                  NaN                 NaN   \n",
       "5                                NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name5 g32b_enrolled_name6 g32b_enrolled_name7  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "5                 NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name8 g32b_enrolled_name9 g_agerange g_enrollmentstatus  \n",
       "0                 NaN                 NaN      25-34       Not Enrolled  \n",
       "2                 NaN                 NaN      25-34       Not Enrolled  \n",
       "3                 NaN                 NaN      25-34       Not Enrolled  \n",
       "4                 NaN                 NaN      25-34       Not Enrolled  \n",
       "5                 NaN                 NaN      35-44       Not Enrolled  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dac3dc84",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dac3dc84",
    "outputId": "a12b9d5f-ee6c-4f8d-c328-f293fb727277",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'g14_city', 'g15_zipcode', 'g16_county', 'g17_region',\n",
       "       'g18_community', 'g20_raceethnicity1', 'g20_raceethnicity2',\n",
       "       'g20_raceethnicity3', 'g20_raceethnicity4', 'g20_raceethnicity5',\n",
       "       'g20_raceethnicity6', 'g20_raceethnicity7', 'g20_raceethnicity8',\n",
       "       'g21_immigrant', 'g22_language', 'g23_gender1', 'g23_gender2',\n",
       "       'g23_gender3', 'g23_gender4', 'g23_gender5', 'g24_transgender',\n",
       "       'g25_lgbtqiap', 'g26_deafdisabled', 'g27_providecare1',\n",
       "       'g27_providecare2', 'g27_providecare3', 'g27_providecare4',\n",
       "       'g27_providecare5', 'g28_criminallegalsystem',\n",
       "       'g28b_criminal_findemploy', 'g29_financialsafetynet1',\n",
       "       'g29_financialsafetynet2', 'g29_financialsafetynet3',\n",
       "       'g29_financialsafetynet4', 'g29_financialsafetynet5', 'g30_discipline1',\n",
       "       'g30_discipline2', 'g30_discipline3', 'g31_approach1', 'g31_approach2',\n",
       "       'g31_approach3', 'g31_approach4', 'g31_approach5', 'g31_approach6',\n",
       "       'g32_enrolled', 'g32b_enrolled_name1', 'g32b_enrolled_name2',\n",
       "       'g32b_enrolled_name3', 'g32b_enrolled_name4', 'g32b_enrolled_name5',\n",
       "       'g32b_enrolled_name6', 'g32b_enrolled_name7', 'g32b_enrolled_name8',\n",
       "       'g32b_enrolled_name9', 'g_agerange', 'g_enrollmentstatus'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c7710e",
   "metadata": {
    "id": "d9c7710e"
   },
   "source": [
    "## Preprocessing g_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8f0b335e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8f0b335e",
    "outputId": "c4e8ede1-8b9f-4a99-8996-c6ca7909447e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-38-bdae8c918c9d>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata['g14_city'] = new_gdata['g14_city'].astype(str).apply(lambda x: x.title())\n"
     ]
    }
   ],
   "source": [
    "# Convert city column to camel case\n",
    "\n",
    "new_gdata['g14_city'] = new_gdata['g14_city'].astype(str).apply(lambda x: x.title())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "96d5b94d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "96d5b94d",
    "outputId": "6d1ccf39-8741-416e-ac64-fc1dc51c4c3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Uniondale' 'Uniondale ' 'Uniondale Ny']\n"
     ]
    }
   ],
   "source": [
    "values = new_gdata[new_gdata['g14_city'].str.contains('Union', case=False, na=False)]['g14_city'].unique()\n",
    "print(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9ef9bb83",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 147
    },
    "id": "9ef9bb83",
    "outputId": "23a96ed9-06b8-4cd7-e861-835a6acca46d"
   },
   "outputs": [
    {
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       "\n",
       "  <div id=\"df-5942f2b3-e790-43e8-a28b-1da1f890599a\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>g14_city</th>\n",
       "      <th>g15_zipcode</th>\n",
       "      <th>g16_county</th>\n",
       "      <th>g17_region</th>\n",
       "      <th>g18_community</th>\n",
       "      <th>g20_raceethnicity1</th>\n",
       "      <th>g20_raceethnicity2</th>\n",
       "      <th>g20_raceethnicity3</th>\n",
       "      <th>g20_raceethnicity4</th>\n",
       "      <th>...</th>\n",
       "      <th>g32b_enrolled_name2</th>\n",
       "      <th>g32b_enrolled_name3</th>\n",
       "      <th>g32b_enrolled_name4</th>\n",
       "      <th>g32b_enrolled_name5</th>\n",
       "      <th>g32b_enrolled_name6</th>\n",
       "      <th>g32b_enrolled_name7</th>\n",
       "      <th>g32b_enrolled_name8</th>\n",
       "      <th>g32b_enrolled_name9</th>\n",
       "      <th>g_agerange</th>\n",
       "      <th>g_enrollmentstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20343</th>\n",
       "      <td>7VHXGGQ7IP</td>\n",
       "      <td>Woodstock, Ny</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Other (please describe)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Not Enrolled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 57 columns</p>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5942f2b3-e790-43e8-a28b-1da1f890599a')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "        document.querySelector('#df-5942f2b3-e790-43e8-a28b-1da1f890599a button.colab-df-convert');\n",
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       "        const element = document.querySelector('#df-5942f2b3-e790-43e8-a28b-1da1f890599a');\n",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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      ],
      "text/plain": [
       "               id       g14_city  g15_zipcode        g16_county  g17_region  \\\n",
       "20343  7VHXGGQ7IP  Woodstock, Ny      12498.0  Ulster County     Mid-Hudson   \n",
       "\n",
       "      g18_community         g20_raceethnicity1 g20_raceethnicity2  \\\n",
       "20343       Rural    Other (please describe)                        \n",
       "\n",
       "      g20_raceethnicity3 g20_raceethnicity4  ... g32b_enrolled_name2  \\\n",
       "20343                                        ...                 NaN   \n",
       "\n",
       "      g32b_enrolled_name3 g32b_enrolled_name4 g32b_enrolled_name5  \\\n",
       "20343                 NaN                 NaN                 NaN   \n",
       "\n",
       "      g32b_enrolled_name6 g32b_enrolled_name7 g32b_enrolled_name8  \\\n",
       "20343                 NaN                 NaN                 NaN   \n",
       "\n",
       "      g32b_enrolled_name9 g_agerange g_enrollmentstatus  \n",
       "20343                 NaN      35-44       Not Enrolled  \n",
       "\n",
       "[1 rows x 57 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata[new_gdata['id']=='7VHXGGQ7IP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "591f8be3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "591f8be3",
    "outputId": "c8da59b3-1b81-4612-bbf4-25b71b37866e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            id   g14_city  g15_zipcode        g16_county     g17_region  \\\n",
      "67  LUC0FP3US5  Sunnyside      11104.0  Queens County     New York City   \n",
      "\n",
      "   g18_community g20_raceethnicity1 g20_raceethnicity2 g20_raceethnicity3  \\\n",
      "67       Urban              White                                           \n",
      "\n",
      "   g20_raceethnicity4  ... g32b_enrolled_name2 g32b_enrolled_name3  \\\n",
      "67                     ...                 NaN                 NaN   \n",
      "\n",
      "   g32b_enrolled_name4 g32b_enrolled_name5 g32b_enrolled_name6  \\\n",
      "67                 NaN                 NaN                 NaN   \n",
      "\n",
      "   g32b_enrolled_name7 g32b_enrolled_name8 g32b_enrolled_name9 g_agerange  \\\n",
      "67                 NaN                 NaN                 NaN      25-34   \n",
      "\n",
      "   g_enrollmentstatus  \n",
      "67       Not Enrolled  \n",
      "\n",
      "[1 rows x 57 columns]\n"
     ]
    }
   ],
   "source": [
    "ids = ['LUC0FP3US5', 'ILYISCUXTP', 'HQ1OTBXC2N', '3HYCIGU1XU', 'L0JGRP9LTB', '9B6OZ48FMJ', 'WN82SWVQI2', '8UOBNA4560', 'EB3QKV6MW9', 'YUAZLXS57X', '53SPZPP9QD', '53HWPHWHWY', 'B99Q6CG52I', 'LF6V7AX5JS', 'S7HRFHLOYX', 'Y4NJ0J7LW9', '3P3VPCYVWG', 'NWREXG692K', '83BQ5ICJWC', 'YIDNLRLZEH', 'QWHK7NQZR6', '5LLRDOJDZ7', 'GWLITC84MV', 'ESL43IIMT2', 'LTH4SB0VQ1', 'PR3YEP11ZX', 'Z5S5X42ZQC', '7GFMFVTROP', '5ZUCGD6LV2', '4WEZ1J9CT9', 'CKW6HOLWEU', '4U8SV84FZA', 'GQN8VHWQQZ', 'UTNVHIEWT8', '5Z17VYIDYG', '0OC4QPHBBF', 'DIMWFXA0SL', 'IO3W61XULE', 'RFCAJIGI4B', 'VK09FDO8NA', 'PQTWJMK8QO', 'UY0TLAC94J', 'LPB1RN049P', 'KY55I6KMXP', '6IUAJ5VZGG', 'PWOJI34D48', '2C8TVXBYGI', 'SKFM4ZWMMZ', 'H2W7YA2T7U', 'RMNJ914GM8', 'NIIRLFSEIS', 'YPY2T2B6F9', 'R0Q9VC52N7', 'C3THEWVWLW', 'SBU5C2RYYA', '1AZDN9JP6G', 'JKMF6PTUVM', '915SGAZDL7', '8EXTKZ4HMH', 'U95MC7V0D1', '62X7WGAKTQ', '4VXQ44HXYO']\n",
    "\n",
    "# Replace 'g14_city' column value with 'Sunnyside' for the specified IDs\n",
    "for id in ids:\n",
    "    new_gdata.loc[new_gdata['id'] == id, 'g14_city'] = 'Sunnyside'\n",
    "\n",
    "# Check the updated rows for one of the IDs to confirm the change\n",
    "print(new_gdata[new_gdata['id'] == 'LUC0FP3US5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "560513d4",
   "metadata": {
    "id": "560513d4"
   },
   "outputs": [],
   "source": [
    "h_ids = ['24EOJV2IH9', '9YWXSWI5O4', 'N319RZXTE2', '525BTRF47H']\n",
    "\n",
    "for h_id in h_ids:\n",
    "    new_gdata.loc[new_gdata['id'] == h_id, 'g14_city'] = 'West Henrietta'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8c3a4c72",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 245
    },
    "id": "8c3a4c72",
    "outputId": "3a838e8e-354e-45ed-e2ea-cebd2372da7a"
   },
   "outputs": [
    {
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       "\n",
       "  <div id=\"df-eeb489a3-fdb2-458e-b1bb-16538225cc6d\" class=\"colab-df-container\">\n",
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>g14_city</th>\n",
       "      <th>g15_zipcode</th>\n",
       "      <th>g16_county</th>\n",
       "      <th>g17_region</th>\n",
       "      <th>g18_community</th>\n",
       "      <th>g20_raceethnicity1</th>\n",
       "      <th>g20_raceethnicity2</th>\n",
       "      <th>g20_raceethnicity3</th>\n",
       "      <th>g20_raceethnicity4</th>\n",
       "      <th>...</th>\n",
       "      <th>g32b_enrolled_name2</th>\n",
       "      <th>g32b_enrolled_name3</th>\n",
       "      <th>g32b_enrolled_name4</th>\n",
       "      <th>g32b_enrolled_name5</th>\n",
       "      <th>g32b_enrolled_name6</th>\n",
       "      <th>g32b_enrolled_name7</th>\n",
       "      <th>g32b_enrolled_name8</th>\n",
       "      <th>g32b_enrolled_name9</th>\n",
       "      <th>g_agerange</th>\n",
       "      <th>g_enrollmentstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7592</th>\n",
       "      <td>TW57M7UGAU</td>\n",
       "      <td>Baldwinsville</td>\n",
       "      <td>13027.0</td>\n",
       "      <td>Onondaga County</td>\n",
       "      <td>Central New York</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>I prefer not to answer</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>Section 8 Housing Voucher</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Not Enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13025</th>\n",
       "      <td>DVD6KEZH95</td>\n",
       "      <td>Baldwinsville</td>\n",
       "      <td>13027.0</td>\n",
       "      <td>Onondaga County</td>\n",
       "      <td>Central New York</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16534</th>\n",
       "      <td>5LU1OHXI7Y</td>\n",
       "      <td>Baldwinsville</td>\n",
       "      <td>13027.0</td>\n",
       "      <td>Onondaga County</td>\n",
       "      <td>Central New York</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Not Enrolled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 57 columns</p>\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "                                                    [key], {});\n",
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       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "\n",
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       "      --disabled-fill-color: #AAA;\n",
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       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
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       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
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       "    border-radius: 50%;\n",
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       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
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       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
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       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
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       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
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       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "               id       g14_city  g15_zipcode          g16_county  \\\n",
       "7592   TW57M7UGAU  Baldwinsville      13027.0  Onondaga County      \n",
       "13025  DVD6KEZH95  Baldwinsville      13027.0  Onondaga County      \n",
       "16534  5LU1OHXI7Y  Baldwinsville      13027.0  Onondaga County      \n",
       "\n",
       "             g17_region g18_community g20_raceethnicity1  \\\n",
       "7592   Central New York       Urban              White     \n",
       "13025  Central New York    Suburban              White     \n",
       "16534  Central New York    Suburban              White     \n",
       "\n",
       "             g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "7592    I prefer not to answer                                         ...   \n",
       "13025                                                                  ...   \n",
       "16534                                                                  ...   \n",
       "\n",
       "               g32b_enrolled_name2 g32b_enrolled_name3 g32b_enrolled_name4  \\\n",
       "7592    Section 8 Housing Voucher                  NaN                 NaN   \n",
       "13025                          NaN                 NaN                 NaN   \n",
       "16534                          NaN                 NaN                 NaN   \n",
       "\n",
       "      g32b_enrolled_name5 g32b_enrolled_name6 g32b_enrolled_name7  \\\n",
       "7592                  NaN                 NaN                 NaN   \n",
       "13025                 NaN                 NaN                 NaN   \n",
       "16534                 NaN                 NaN                 NaN   \n",
       "\n",
       "      g32b_enrolled_name8 g32b_enrolled_name9 g_agerange g_enrollmentstatus  \n",
       "7592                  NaN                 NaN      35-44       Not Enrolled  \n",
       "13025                 NaN                 NaN      25-34       Not Enrolled  \n",
       "16534                 NaN                 NaN      18-24       Not Enrolled  \n",
       "\n",
       "[3 rows x 57 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a list of IDs for which 'Baldwin' should be replaced with 'Baldwinsville'\n",
    "specific_ids = ['TW57M7UGAU', 'DVD6KEZH95', '5LU1OHXI7Y']\n",
    "\n",
    "# Update the 'g14_city' column for the specific IDs\n",
    "new_gdata.loc[new_gdata['id'].isin(specific_ids), 'g14_city'] = 'Baldwinsville'\n",
    "new_gdata.loc[new_gdata['id'] == 'N06KK164Z5', 'g14_city'] = 'Shirley'\n",
    "new_gdata[new_gdata['g14_city']=='Baldwinsville'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bfcbd193",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "id": "bfcbd193",
    "outputId": "8aecde60-6820-48d1-c6b4-c3bc5619e1e6"
   },
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <td>Brooklyn</td>\n",
       "      <td>11238.0</td>\n",
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       "      <td>New York City</td>\n",
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       "      <td>11237.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
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      ],
      "text/plain": [
       "           id       g14_city  g15_zipcode                      g16_county  \\\n",
       "0  N3R8BXQE4K        BUFFALO      14201.0                  Erie County      \n",
       "2  0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)      \n",
       "3  R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)      \n",
       "4  BZEFVJAYBT       Brooklyn      11238.0       Kings County (Brooklyn)     \n",
       "5  4HF20W8D84       New York      11237.0       Kings County (Brooklyn)     \n",
       "\n",
       "         g17_region g18_community          g20_raceethnicity1  \\\n",
       "0  Western New York       Urban    Asian or Pacific Islander    \n",
       "2     New York City       Urban                       White     \n",
       "3     New York City       Urban    Black or African American    \n",
       "4     New York City       Urban    Asian or Pacific Islander    \n",
       "5     New York City       Urban                       White     \n",
       "\n",
       "         g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                   White                  NaN                NaN  ...   \n",
       "2                       NaN                NaN                NaN  ...   \n",
       "3                       NaN                NaN                NaN  ...   \n",
       "4   Arab or Middle Eastern                 NaN                NaN  ...   \n",
       "5                       NaN                NaN                NaN  ...   \n",
       "\n",
       "                 g32b_enrolled_name2 g32b_enrolled_name3 g32b_enrolled_name4  \\\n",
       "0                                NaN                 NaN                 NaN   \n",
       "2                                NaN                 NaN                 NaN   \n",
       "3                                NaN                 NaN                 NaN   \n",
       "4   Earned Income Tax Credit (EITC)                  NaN                 NaN   \n",
       "5                                NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name5 g32b_enrolled_name6 g32b_enrolled_name7  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "5                 NaN                 NaN                 NaN   \n",
       "\n",
       "  g32b_enrolled_name8 g32b_enrolled_name9 g_agerange g_enrollmentstatus  \n",
       "0                 NaN                 NaN      25-34                NaN  \n",
       "2                 NaN                 NaN      25-34                NaN  \n",
       "3                 NaN                 NaN      25-34                NaN  \n",
       "4                 NaN                 NaN      25-34                NaN  \n",
       "5                 NaN                 NaN      35-44                NaN  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#New gdata running function on it\n",
    "\n",
    "new_gdata1 = g_data.dropna(subset=['g14_city', 'g15_zipcode', 'g16_county', 'g17_region',\n",
    "       'g18_community', 'g20_raceethnicity1', 'g20_raceethnicity2',\n",
    "       'g20_raceethnicity3', 'g20_raceethnicity4', 'g20_raceethnicity5',\n",
    "       'g20_raceethnicity6', 'g20_raceethnicity7', 'g20_raceethnicity8',\n",
    "       'g21_immigrant', 'g22_language', 'g23_gender1', 'g23_gender2',\n",
    "       'g23_gender3', 'g23_gender4', 'g23_gender5', 'g24_transgender',\n",
    "       'g25_lgbtqiap', 'g26_deafdisabled', 'g27_providecare1',\n",
    "       'g27_providecare2', 'g27_providecare3', 'g27_providecare4',\n",
    "       'g27_providecare5', 'g28_criminallegalsystem',\n",
    "       'g28b_criminal_findemploy', 'g29_financialsafetynet1',\n",
    "       'g29_financialsafetynet2', 'g29_financialsafetynet3',\n",
    "       'g29_financialsafetynet4', 'g29_financialsafetynet5', 'g30_discipline1',\n",
    "       'g30_discipline2', 'g30_discipline3', 'g31_approach1', 'g31_approach2',\n",
    "       'g31_approach3', 'g31_approach4', 'g31_approach5', 'g31_approach6',\n",
    "       'g32_enrolled', 'g32b_enrolled_name1', 'g32b_enrolled_name2',\n",
    "       'g32b_enrolled_name3', 'g32b_enrolled_name4', 'g32b_enrolled_name5',\n",
    "       'g32b_enrolled_name6', 'g32b_enrolled_name7', 'g32b_enrolled_name8',\n",
    "       'g32b_enrolled_name9', 'g_agerange', 'g_enrollmentstatus'], how='all')\n",
    "\n",
    "new_gdata1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88c010f9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "88c010f9",
    "outputId": "8f4e1d4e-b558-4189-cd9d-43a446c606b2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1250405393.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1['g14_city'] = new_gdata1['g14_city'].astype(str).apply(lambda x: x.title())\n"
     ]
    }
   ],
   "source": [
    "# Convert city column to camel case\n",
    "\n",
    "new_gdata1['g14_city'] = new_gdata1['g14_city'].astype(str).apply(lambda x: x.title())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b1cd501d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "b1cd501d",
    "outputId": "0bf3b723-4be0-47b5-b1b8-8f8ec1cc8ce8",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\520643867.py:220: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1['g14_city'] = new_gdata1['g14_city'].apply(clean_city1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>g14_city</th>\n",
       "      <th>g15_zipcode</th>\n",
       "      <th>g16_county</th>\n",
       "      <th>g17_region</th>\n",
       "      <th>g18_community</th>\n",
       "      <th>g20_raceethnicity1</th>\n",
       "      <th>g20_raceethnicity2</th>\n",
       "      <th>g20_raceethnicity3</th>\n",
       "      <th>g20_raceethnicity4</th>\n",
       "      <th>...</th>\n",
       "      <th>g32b_enrolled_name2</th>\n",
       "      <th>g32b_enrolled_name3</th>\n",
       "      <th>g32b_enrolled_name4</th>\n",
       "      <th>g32b_enrolled_name5</th>\n",
       "      <th>g32b_enrolled_name6</th>\n",
       "      <th>g32b_enrolled_name7</th>\n",
       "      <th>g32b_enrolled_name8</th>\n",
       "      <th>g32b_enrolled_name9</th>\n",
       "      <th>g_agerange</th>\n",
       "      <th>g_enrollmentstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>RMU3JWLC6H</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Supplemental Nutrition Assistance Program (SN...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4269</th>\n",
       "      <td>TMF07D11KE</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5029</th>\n",
       "      <td>F3OIX8HPTP</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45-54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5492</th>\n",
       "      <td>CK3XA73LKU</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Medicaid for Adults and Child Health Plus</td>\n",
       "      <td>Other (please describe)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10405</th>\n",
       "      <td>FZI17VWO6J</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>I prefer not to answer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Medicaid for Adults and Child Health Plus</td>\n",
       "      <td>Child Tax Credit (CTC)</td>\n",
       "      <td>Earned Income Tax Credit (EITC)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45-54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10424</th>\n",
       "      <td>9A9AKPJD1W</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Medicaid for Adults and Child Health Plus</td>\n",
       "      <td>Child Tax Credit (CTC)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45-54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10481</th>\n",
       "      <td>OBW2PLOLI3</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>I prefer not to answer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65+</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11163</th>\n",
       "      <td>IRPTY0XXYA</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>Other (please describe)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11432</th>\n",
       "      <td>DQTTVB9W5M</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Other (please describe)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45-54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11773</th>\n",
       "      <td>T3ERUN5MIC</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55-64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14637</th>\n",
       "      <td>V65BFFT06Y</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Urban</td>\n",
       "      <td>I prefer not to answer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45-54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15577</th>\n",
       "      <td>26EYM1PV6J</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Other (please describe)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65+</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15672</th>\n",
       "      <td>4R294JUNVB</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Other (please describe)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55-64</td>\n",
       "      <td>Enrolled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16715</th>\n",
       "      <td>87NI2660YW</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65+</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17232</th>\n",
       "      <td>32GI46TNLD</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55-64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17734</th>\n",
       "      <td>EG662ON9WW</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12409.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Section 8 Housing Voucher</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45-54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19788</th>\n",
       "      <td>MDHTLRKIIX</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>Other (please describe)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20343</th>\n",
       "      <td>7VHXGGQ7IP</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Other (please describe)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20614</th>\n",
       "      <td>5OERR9PK5E</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21089</th>\n",
       "      <td>XZC2MRW4UC</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65+</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22988</th>\n",
       "      <td>DPB6DH7OV8</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23024</th>\n",
       "      <td>FACLV19YOL</td>\n",
       "      <td>Woodstock</td>\n",
       "      <td>12498.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18-24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id   g14_city  g15_zipcode        g16_county  g17_region  \\\n",
       "487    RMU3JWLC6H  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "4269   TMF07D11KE  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "5029   F3OIX8HPTP  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "5492   CK3XA73LKU  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "10405  FZI17VWO6J  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "10424  9A9AKPJD1W  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "10481  OBW2PLOLI3  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "11163  IRPTY0XXYA  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "11432  DQTTVB9W5M  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "11773  T3ERUN5MIC  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "14637  V65BFFT06Y  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "15577  26EYM1PV6J  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "15672  4R294JUNVB  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "16715  87NI2660YW  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "17232  32GI46TNLD  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "17734  EG662ON9WW  Woodstock      12409.0  Ulster County     Mid-Hudson   \n",
       "19788  MDHTLRKIIX  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "20343  7VHXGGQ7IP  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "20614  5OERR9PK5E  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "21089  XZC2MRW4UC  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "22988  DPB6DH7OV8  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "23024  FACLV19YOL  Woodstock      12498.0  Ulster County     Mid-Hudson   \n",
       "\n",
       "      g18_community          g20_raceethnicity1          g20_raceethnicity2  \\\n",
       "487         Rural                       White                           NaN   \n",
       "4269        Rural                       White                           NaN   \n",
       "5029        Rural                       White                           NaN   \n",
       "5492        Rural                       White                           NaN   \n",
       "10405       Rural       I prefer not to answer                          NaN   \n",
       "10424       Rural                       White                           NaN   \n",
       "10481       Rural       I prefer not to answer                          NaN   \n",
       "11163       Rural                       White     Other (please describe)     \n",
       "11432       Rural    Black or African American    Other (please describe)     \n",
       "11773       Rural                       White                           NaN   \n",
       "14637       Urban       I prefer not to answer                          NaN   \n",
       "15577    Suburban                       White                           NaN   \n",
       "15672       Rural                       White                           NaN   \n",
       "16715       Rural                       White                           NaN   \n",
       "17232       Rural                       White                           NaN   \n",
       "17734       Rural                       White                           NaN   \n",
       "19788       Rural                       White     Other (please describe)     \n",
       "20343       Rural     Other (please describe)                           NaN   \n",
       "20614       Rural                       White                           NaN   \n",
       "21089       Rural                       White                           NaN   \n",
       "22988       Rural           Hispanic or Latinx                          NaN   \n",
       "23024       Rural                       White                           NaN   \n",
       "\n",
       "      g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "487                  NaN                NaN  ...   \n",
       "4269                 NaN                NaN  ...   \n",
       "5029                 NaN                NaN  ...   \n",
       "5492                 NaN                NaN  ...   \n",
       "10405                NaN                NaN  ...   \n",
       "10424                NaN                NaN  ...   \n",
       "10481                NaN                NaN  ...   \n",
       "11163                NaN                NaN  ...   \n",
       "11432                NaN                NaN  ...   \n",
       "11773                NaN                NaN  ...   \n",
       "14637                NaN                NaN  ...   \n",
       "15577                NaN                NaN  ...   \n",
       "15672                NaN                NaN  ...   \n",
       "16715                NaN                NaN  ...   \n",
       "17232                NaN                NaN  ...   \n",
       "17734                NaN                NaN  ...   \n",
       "19788                NaN                NaN  ...   \n",
       "20343                NaN                NaN  ...   \n",
       "20614                NaN                NaN  ...   \n",
       "21089                NaN                NaN  ...   \n",
       "22988                NaN                NaN  ...   \n",
       "23024                NaN                NaN  ...   \n",
       "\n",
       "                                     g32b_enrolled_name2  \\\n",
       "487     Supplemental Nutrition Assistance Program (SN...   \n",
       "4269                                                 NaN   \n",
       "5029                                                 NaN   \n",
       "5492          Medicaid for Adults and Child Health Plus    \n",
       "10405         Medicaid for Adults and Child Health Plus    \n",
       "10424         Medicaid for Adults and Child Health Plus    \n",
       "10481                                                NaN   \n",
       "11163                                                NaN   \n",
       "11432                                                NaN   \n",
       "11773                                                NaN   \n",
       "14637                                                NaN   \n",
       "15577                           Other (please describe)    \n",
       "15672                           Other (please describe)    \n",
       "16715                                                NaN   \n",
       "17232                                                NaN   \n",
       "17734                         Section 8 Housing Voucher    \n",
       "19788                                                NaN   \n",
       "20343                                                NaN   \n",
       "20614                                                NaN   \n",
       "21089                                                NaN   \n",
       "22988                                                NaN   \n",
       "23024                                                NaN   \n",
       "\n",
       "             g32b_enrolled_name3                g32b_enrolled_name4  \\\n",
       "487                          NaN                                NaN   \n",
       "4269                         NaN                                NaN   \n",
       "5029                         NaN                                NaN   \n",
       "5492    Other (please describe)                                 NaN   \n",
       "10405    Child Tax Credit (CTC)    Earned Income Tax Credit (EITC)    \n",
       "10424    Child Tax Credit (CTC)                                 NaN   \n",
       "10481                        NaN                                NaN   \n",
       "11163                        NaN                                NaN   \n",
       "11432                        NaN                                NaN   \n",
       "11773                        NaN                                NaN   \n",
       "14637                        NaN                                NaN   \n",
       "15577                        NaN                                NaN   \n",
       "15672                        NaN                                NaN   \n",
       "16715                        NaN                                NaN   \n",
       "17232                        NaN                                NaN   \n",
       "17734                        NaN                                NaN   \n",
       "19788                        NaN                                NaN   \n",
       "20343                        NaN                                NaN   \n",
       "20614                        NaN                                NaN   \n",
       "21089                        NaN                                NaN   \n",
       "22988                        NaN                                NaN   \n",
       "23024                        NaN                                NaN   \n",
       "\n",
       "      g32b_enrolled_name5 g32b_enrolled_name6 g32b_enrolled_name7  \\\n",
       "487                   NaN                 NaN                 NaN   \n",
       "4269                  NaN                 NaN                 NaN   \n",
       "5029                  NaN                 NaN                 NaN   \n",
       "5492                  NaN                 NaN                 NaN   \n",
       "10405                 NaN                 NaN                 NaN   \n",
       "10424                 NaN                 NaN                 NaN   \n",
       "10481                 NaN                 NaN                 NaN   \n",
       "11163                 NaN                 NaN                 NaN   \n",
       "11432                 NaN                 NaN                 NaN   \n",
       "11773                 NaN                 NaN                 NaN   \n",
       "14637                 NaN                 NaN                 NaN   \n",
       "15577                 NaN                 NaN                 NaN   \n",
       "15672                 NaN                 NaN                 NaN   \n",
       "16715                 NaN                 NaN                 NaN   \n",
       "17232                 NaN                 NaN                 NaN   \n",
       "17734                 NaN                 NaN                 NaN   \n",
       "19788                 NaN                 NaN                 NaN   \n",
       "20343                 NaN                 NaN                 NaN   \n",
       "20614                 NaN                 NaN                 NaN   \n",
       "21089                 NaN                 NaN                 NaN   \n",
       "22988                 NaN                 NaN                 NaN   \n",
       "23024                 NaN                 NaN                 NaN   \n",
       "\n",
       "      g32b_enrolled_name8 g32b_enrolled_name9 g_agerange g_enrollmentstatus  \n",
       "487                   NaN                 NaN      35-44                NaN  \n",
       "4269                  NaN                 NaN      25-34                NaN  \n",
       "5029                  NaN                 NaN      45-54                NaN  \n",
       "5492                  NaN                 NaN      35-44           Enrolled  \n",
       "10405                 NaN                 NaN      45-54                NaN  \n",
       "10424                 NaN                 NaN      45-54                NaN  \n",
       "10481                 NaN                 NaN        65+                NaN  \n",
       "11163                 NaN                 NaN      35-44           Enrolled  \n",
       "11432                 NaN                 NaN      45-54                NaN  \n",
       "11773                 NaN                 NaN      55-64                NaN  \n",
       "14637                 NaN                 NaN      45-54                NaN  \n",
       "15577                 NaN                 NaN        65+                NaN  \n",
       "15672                 NaN                 NaN      55-64           Enrolled  \n",
       "16715                 NaN                 NaN        65+                NaN  \n",
       "17232                 NaN                 NaN      55-64                NaN  \n",
       "17734                 NaN                 NaN      45-54                NaN  \n",
       "19788                 NaN                 NaN      25-34                NaN  \n",
       "20343                 NaN                 NaN      35-44                NaN  \n",
       "20614                 NaN                 NaN      25-34                NaN  \n",
       "21089                 NaN                 NaN        65+                NaN  \n",
       "22988                 NaN                 NaN      35-44                NaN  \n",
       "23024                 NaN                 NaN      18-24                NaN  \n",
       "\n",
       "[22 rows x 57 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a function to clean city names\n",
    "def clean_city1(city):\n",
    "    city = city.strip().lower()  # Remove leading/trailing spaces and convert to lowercase\n",
    "    if 'albany' in city:\n",
    "        return 'Albany'\n",
    "    elif 'annandale' in city:\n",
    "        return 'Annandale On Hudson'\n",
    "    elif 'arverne' in city:\n",
    "        return 'Arverne'\n",
    "    elif 'astoria' in city or 'asotira' in city:\n",
    "        return 'Astoria'\n",
    "    elif 'atlantic' in city:\n",
    "        return 'Atlantic Beach'\n",
    "    elif 'baldwin' in city:\n",
    "        return 'Baldwin'\n",
    "    elif 'bay shore' in city or 'bayshore' in city:\n",
    "        return 'Bay Shore'\n",
    "    elif 'bayside' in city:\n",
    "        return 'Bayside'\n",
    "    elif 'beacon' in city:\n",
    "        return 'Beacon'\n",
    "    elif 'bed stuy' in city or 'bed-stuy' in city:\n",
    "        return 'Bed Stuy'\n",
    "    elif 'bemus' in city:\n",
    "        return 'Bemus Point'\n",
    "    elif 'bronc' in city or 'bronca' in city or 'bronx' in city:\n",
    "        return 'Bronx'\n",
    "    elif 'bufalo' in city or 'buffal' in city or 'buffalo' in city:\n",
    "        return 'Buffalo'\n",
    "    elif 'clifton' in city:\n",
    "        return 'Clifton Park'\n",
    "    elif 'cold spring' in city:\n",
    "        return 'Cold Spring'\n",
    "    elif 'copiague' in city:\n",
    "        return 'Copiague'\n",
    "    elif 'cornwall on' in city:\n",
    "        return 'Cornwall On Hudson'\n",
    "    elif 'corona' in city:\n",
    "        return 'Corona'\n",
    "    elif any(keyword in city for keyword in ['cortlandt manor', 'cortlandt mnr', 'cortland manor']):\n",
    "        return 'Cortland Manor'\n",
    "    elif any(keyword in city for keyword in ['craryville', 'crarryville']):\n",
    "        return 'Craryville'\n",
    "    elif any(keyword in city for keyword in ['croton-on-hudson', 'croton on hudson', 'croton falls']):\n",
    "        return 'Croton'\n",
    "    elif 'de kalb' in city:\n",
    "        return 'De kalb Junction'\n",
    "    elif any(keyword in city for keyword in ['east elhurst', 'east elmhurst', 'east elmhurts', 'east elmhust']):\n",
    "        return 'East Elmhurst'\n",
    "    elif 'ellenville' in city:\n",
    "        return 'Ellenville'\n",
    "    elif any(keyword in city for keyword in ['elmhruts', 'elmhurst', 'emhurst']):\n",
    "        return 'Elmhurst'\n",
    "    elif 'elmira' in city:\n",
    "        return 'Elmira'\n",
    "    elif 'far rockaway' in city or 'farrockaway' in city:\n",
    "        return 'Far Rockaway'\n",
    "    elif 'flushing' in city:\n",
    "        return 'Flushing'\n",
    "    elif any(keyword in city for keyword in ['fores hills', 'forest hill', 'forest hills']):\n",
    "        return 'Forest Hills'\n",
    "    elif any(keyword in city for keyword in ['fresh meadows', 'fresh medows']):\n",
    "        return 'Fresh Meadows'\n",
    "    elif 'garden city' in city:\n",
    "        return 'Garden City'\n",
    "    elif 'glen cove' in city:\n",
    "        return 'Glen Cove'\n",
    "    elif 'hammondsport' in city:\n",
    "        return 'Hammondsport'\n",
    "    elif 'hammond' in city:\n",
    "        return 'Hammond'\n",
    "    elif 'harlem' in city:\n",
    "        return 'Harlem'\n",
    "    elif 'highland lake' in city:\n",
    "        return 'Highland Lake'\n",
    "    elif 'highland mills' in city:\n",
    "        return 'Highland Mills'\n",
    "    elif any(keyword in city for keyword in ['high falls', 'highland']):\n",
    "        return 'Highland Falls'\n",
    "    elif 'hogansburg' in city:\n",
    "        return 'Hogansburg'\n",
    "    elif 'hopewell' in city:\n",
    "        return 'Hopewell Junction'\n",
    "    elif 'hunting' in city:\n",
    "        return 'Huntington Station'\n",
    "    elif 'ithaca' in city:\n",
    "        return 'Ithaca'\n",
    "    elif 'jackson h' in city:\n",
    "        return 'Jackson Heights'\n",
    "    elif any(keyword in city for keyword in ['jamaica', 'jamacia', 'janaica']):\n",
    "        return 'Jamaica'\n",
    "    elif 'keene' in city:\n",
    "        return 'Keene'\n",
    "    elif 'kew' in city:\n",
    "        return 'Kew Gardens'\n",
    "    elif 'kingston' in city:\n",
    "        return 'Kingston'\n",
    "    elif 'lanesville,' in city:\n",
    "        return 'Lanesville'\n",
    "        return 'Highland Lake'\n",
    "    elif 'little neck' in city:\n",
    "        return 'Little Neck'\n",
    "    elif 'liver' in city:\n",
    "        return 'Liverpool'\n",
    "    elif 'long b' in city:\n",
    "        return 'Long Beach'\n",
    "    elif any(keyword in city for keyword in ['long island', 'longisand']):\n",
    "        return 'Long Island'\n",
    "    elif 'manhat' in city:\n",
    "        return 'Manhattan'\n",
    "    elif 'maspeth' in city:\n",
    "        return 'Maspeth'\n",
    "    elif 'massapequa' in city:\n",
    "        return 'Massapequa'\n",
    "    elif 'mastic' in city:\n",
    "        return 'Mastic'\n",
    "    elif 'middletown' in city:\n",
    "        return 'Middletown'\n",
    "    elif 'monticello' in city:\n",
    "        return 'Monticello'\n",
    "    elif any(keyword in city for keyword in ['mount vernon', 'mt vernon', 'mt. vernon', 'mt.vernon','mont vernon']):\n",
    "        return 'Mount Vernon'\n",
    "    elif any(keyword in city for keyword in ['n baldwin', 'north baldwin']):\n",
    "        return 'North Baldwin'\n",
    "    elif any(keyword in city for keyword in ['n massapequa', 'north massapequa']):\n",
    "        return 'North Massapequa'\n",
    "    elif 'nassau' in city:\n",
    "        return 'Nassau'\n",
    "    elif 'new rochelle' in city:\n",
    "        return 'New Rochelle'\n",
    "    elif 'oceanside ny' in city:\n",
    "        return 'Oceanside'\n",
    "    elif 'oakland' in city or 'oaklland' in city:\n",
    "        return 'Oakland Gardens'\n",
    "    elif 'oneonta,' in city:\n",
    "        return 'Oneonta'\n",
    "    elif 'oyster' in city:\n",
    "        return 'Oyster Bay'\n",
    "    elif 'ozone' in city or 'ozene' in city:\n",
    "        return 'Ozone Park'\n",
    "    elif 'palenville' in city:\n",
    "        return 'Palenville'\n",
    "    elif 'pine plains' in city  or 'pine plains, ny' in city:\n",
    "        return 'Pine Plains'\n",
    "    elif 'stillwater' in city:\n",
    "        return 'Stillwater'\n",
    "    elif any(keyword in city for keyword in ['port jefferson']):\n",
    "        return 'Port Jefferson'\n",
    "    elif any(keyword in city for keyword in ['port jervis']):\n",
    "        return 'Port Jervis'\n",
    "    elif any(keyword in city for keyword in ['queens']):\n",
    "        return 'Queens'\n",
    "    elif any(keyword in city for keyword in ['red hook', 'redhook']):\n",
    "        return 'Red Hook'\n",
    "    elif any(keyword in city for keyword in ['rego']):\n",
    "        return 'Rego Park'\n",
    "    elif any(keyword in city for keyword in ['richmond hill', 'richmond  hill']):\n",
    "        return 'Richmond Hill'\n",
    "    elif any(keyword in city for keyword in ['rideg', 'ridgewo', 'ridgwood']):\n",
    "        return 'Ridgewood'\n",
    "    elif any(keyword in city for keyword in ['rosendale']):\n",
    "        return 'Rosendale'\n",
    "    elif any(keyword in city for keyword in ['round lake']):\n",
    "        return 'Round Lake'\n",
    "    elif any(keyword in city for keyword in ['rochester']):\n",
    "        return 'Rochester'\n",
    "    elif any(keyword in city for keyword in ['saint albans']):\n",
    "        return 'Saint Albans'\n",
    "    elif any(keyword in city for keyword in ['saranac lake']):\n",
    "        return 'Saranac Lake'\n",
    "    elif any(keyword in city for keyword in ['saratoga']):\n",
    "        return 'Saratoga Springs'\n",
    "    elif any(keyword in city for keyword in ['saugerties']):\n",
    "        return 'Saugerties'\n",
    "    elif any(keyword in city for keyword in ['seneca', 'senca']):\n",
    "        return 'Seneca Falls'\n",
    "    elif any(keyword in city for keyword in ['shirley']):\n",
    "        return 'Shirley'\n",
    "    elif any(keyword in city for keyword in ['sidney']):\n",
    "        return 'Sidney'\n",
    "    elif any(keyword in city for keyword in ['sleepy h', 'sleepyh']):\n",
    "        return 'Sleepy Hollow'\n",
    "    elif any(keyword in city for keyword in ['smihtown', 'smithtown']):\n",
    "        return 'Smithtown'\n",
    "    elif any(keyword in city for keyword in ['southamp']):\n",
    "        return 'Southampton'\n",
    "    elif any(keyword in city for keyword in ['sparrow b', 'sparrowb']):\n",
    "        return 'Sparrow Bush'\n",
    "    elif any(keyword in city for keyword in ['sracuse', 'syracuse']):\n",
    "        return 'Syracuse'\n",
    "    elif any(keyword in city for keyword in ['st albans', 'st.albans', 'st. albans']):\n",
    "        return 'St. Albans'\n",
    "    elif any(keyword in city for keyword in ['st james', 'st. james']):\n",
    "        return 'St. James'\n",
    "    elif any(keyword in city for keyword in ['statne', 'staten']):\n",
    "        return 'Staten Island'\n",
    "    elif any(keyword in city for keyword in ['uniondale']):\n",
    "        return 'Uniondale'\n",
    "    elif any(keyword in city for keyword in ['wappinger']):\n",
    "        return 'Wappingers Falls'\n",
    "    elif any(keyword in city for keyword in ['west henrietta']):\n",
    "        return 'West Henrietta'\n",
    "    elif any(keyword in city for keyword in ['williston park']):\n",
    "        return 'Williston Park'\n",
    "    elif any(keyword in city for keyword in ['woodhaven']):\n",
    "        return 'Woodhaven'\n",
    "    elif any(keyword in city for keyword in ['woodisde', 'woodside']):\n",
    "        return 'Woodside'\n",
    "    elif any(keyword in city for keyword in ['woodstock']):\n",
    "        return 'Woodstock'\n",
    "    elif any(keyword in city for keyword in ['yonkers']):\n",
    "        return 'Yonkers'\n",
    "    elif any(keyword in city for keyword in ['booklyn', 'beooklyn', 'bk', 'bklyn', 'broklyn', 'brooklym', 'brooklyb', 'brookly', 'brookln', 'brokklyn', 'brooklyn', 'brookyn', 'broolkyn', 'broolyn', 'broooklyn', 'brrooklyn']):\n",
    "        return 'Brooklyn'\n",
    "    elif any(keyword in city for keyword in ['new yorl', 'ne york', 'nee york', 'new yokr', 'new yok', 'new ork', 'new yotk', 'neww york', 'newyork', 'ney york', 'nnew york', 'nw york','nyc']):\n",
    "        return 'New York City'\n",
    "    else:\n",
    "        return city.title()  # Convert other city names to title case\n",
    "\n",
    "new_gdata1['g14_city'] = new_gdata1['g14_city'].apply(clean_city1)\n",
    "new_gdata1[new_gdata1['g14_city']=='Woodstock'].head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "07a16e2d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "07a16e2d",
    "outputId": "6dfa1f48-9519-44a5-a15d-423501ae188a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250\n"
     ]
    }
   ],
   "source": [
    "# Define the list of IDs\n",
    "nyc_ids = ['RYX3CGW7Z7', 'BBPJ23ZQ2A', 'GCAUCE6JAQ', 'OILJ47HUT1', 'WEMC74EPDM', 'UKRBVCJMNP', 'UAKUTVQMT6', 'WRHNR2WTIX',\n",
    "           '2GN1B766WM', 'HT0RCZ7DFW', '3Y1SJUSRKM', '2ELY0RD076', 'KP0PE90V93', 'L4YQNNXZDJ', 'S978JC8SVM', 'HYEEPCEZ2B',\n",
    "           'W1QCCENY16', 'OYG1V08W49', 'E3DKXDUFNJ', '8XZC6JHFLK', '5LQ1RU8TZG', 'GWW3PSP4MG', 'ZQEC1JO25M', 'Q7I2PD7JKK',\n",
    "           '5BMO8WDYO0', '5HZ1LGRKX4', 'DUC2VMM7X0', '6RRIZ0S1U5', 'INMQ6VHS98', 'CCR4P52R7H', '5M6OQWETB8', 'XUQG4P06GM',\n",
    "           'WVOOQZIBZR', 'G63RUG47BM', 'VK79DMBV0P', 'FQFT98MOPR', '2H7PMFXP5M', 'YQWVZ2NIC4', 'PJBFE67J8E', 'P8EKM19XEU',\n",
    "           'ZAMYSIK1W7', 'N5V37LP471', '50K3FOMGUV', '5C9HBAN3J3', 'L4Y49WZV3T', 'PG1STMJ6LP', 'L6X1NU2X4S', 'XJDV0DXXKP',\n",
    "           '7GO3CSZBQ4', 'P1Y91GY7BQ', '3ILUY0IR9B', 'FLTBLCLV6D', 'OUTWNUV5H5', 'J87WY32L52', '3YCREWS5AQ', '0KB2PKLXMJ',\n",
    "           'OUGJNIVWVJ', 'AO9CKTIXTE', 'UCJI90QV5P', 'GVVB3UF2VM', 'D48WPYXCMJ', 'YTMAEUTM6E', 'KA3RAGL3E4', '3AC9EZBA2K',\n",
    "           'LG29Z9D0F5', 'C7GNX58IAT', 'ETWYVWFBXL', 'NMG64ZP58P', '1WBJDX6DSS', 'S71KF6SETS', '3YRF0IP0EC', 'PZQVYX8P0E',\n",
    "           'NZ1VAGX159', 'QED77UWJ81', 'URO4360WM5', '5CDA9FNJ67', 'Z5F0CWK9A8', 'ANU422RCQW', '811SOVTN19', 'FY001U0U3A',\n",
    "           'SK37WR0ZII', 'WJ5VFB9O8L', 'BGN0Z1UR0M', '5RHHVFAZ80', '10NQRN0QPU', 'YJ9EI3EUYU', '0CXJNLOU8Q', 'IXCFEG0D51',\n",
    "           'VUC9YCT8L1', 'OZKNX9VJ5V', '1TB2A9NT5C', 'O9V44P3NM3', 'MLAYJC98RB', 'KUKCACIBML', 'QDJWMIXT5J', '61PP0CXUJC',\n",
    "           '1ASRNWTUVF', '6A0O7P0H80', 'WZQBUPBN0W', 'TGM2QPPIR1', 'XYPMF5STH0', 'U12ZA8R5UZ', 'XZ7ZKZ2K0T', '9SE3STAEAT',\n",
    "           'WWVNWJQJVH', 'TT3CUPNAJJ', 'NLS50VQ4FQ', 'KJ3YCHL2SE', 'PH4W44NLR3', 'SKHOZNF95Z', 'IDP52PGHC1', 'WG1V36Q2RO',\n",
    "           'R44GVE2YJP', 'YZUM0YT3N3', 'M8UMSQ5BDK', 'A8TAL5KLLO', 'GUMX2DYTKS', '8G1I2G3XTC', '5K9T0GP48P', 'T2KXVS88JI',\n",
    "           'KYI7PAD2LW', 'IDSXMHSDRG', 'TDLPEF7M4I', '75ASPQM3WR', 'LUBYUWT0P6', 'AE2CI4DT8I', 'A04DB13LHL', 'IVJPH21QBC',\n",
    "           '7MCNV10XHW', 'WTLC73Q1DT', 'SGG9742GL4', 'X9YV0EX7D6', '4P0X02HFIJ', 'FBXHRQIC2W', 'NESLM39781', 'DK3QGN96KV',\n",
    "           '5M3QUNB8SC', '3KKB4A11B6', '2YBX9BK63U', '40IIWL9A1K', 'SLC93B6E98', 'ARBIWGUJ78', '5Y5SY7C7NU', '8ZN61CJGS2',\n",
    "           'FY5RLXRSS2', 'PV7V318FTL', 'HKN7H7I17C', '2OH077Y6TB', 'NIDVC3JNVM', '9P9OG0S74R', 'RY8D9BA59O', 'UP8UCNH6D4',\n",
    "           '08KC24RD71', 'WP2YVMCVUR', 'YN7W4YUHZ5', 'CFNK3D7ESY', '0N3HCI00SI', 'GUW4M09PXQ', 'DKPMMU3ZUX', 'IQKTO7UMME',\n",
    "           'G2L2EQHKMS', '7ZSMTTJP6R', 'JQEQ6RGBAE', '9BC53X1PT8', 'HDAB5R3BI4', '6PUEQB8JEP', 'ALOM4BQDT2', 'FC72WN0GUA',\n",
    "           '9PO62GLZYZ', '129GEH6G5D', '2H3WWN62WH', 'JV2TD4P1IN', '83CIAEP0IE', 'JEFC63GT12', 'PQQLFALTUM', 'B2L7HWEKP7',\n",
    "           'Z2IR0W9I61', 'KICWCO2AM5', 'J9266JYN9S', 'MC7JBZ97UB', 'QTE09BJH2V', '91IAJ8OWY3', 'XDN13A2HFI', 'VLY96D2E2P',\n",
    "           '3E2P1LTJ3N', '2SEROLG5N0', '7Y62YVKTMW', 'DTQHLKH6B6', 'TXZIPUZCFS', 'ZAHUSN0CQI', 'OLHO5TFDUE', 'EV88FY5UBR',\n",
    "           '7LAIXRE2BE', 'HZ865Y257L', '6RJ0EA2RDG', '4UX09A8XO2', 'GG8GOYF40D', '66STS6QYI1', 'AQTWJVVM7Z', '19B6VCT408',\n",
    "           'KPYA7243M6', '2BRZGQDVZW', 'FGRCZQ83HY', 'ILBOVXIZ19', 'RFO56NFXTG', '7IIZ93NED9', 'D2RJ0C7TWA', '3FTJJ0HV7D',\n",
    "           '2PT24ZRJV7', 'SLF6J7W5BE', '7QLGG5RSLU', '0EQOFD5464', '3ELN1S15VR', 'MOX1814IQ2', 'JV4W0VP5D0', '3LF66E5GCP',\n",
    "           'TZJWKEWGM1', '5Q5VEZ4SL4', 'D48Y60Z3UD', 'ZOACPTEPEQ', '2C38MPNQ2B', 'B41G598C6H', 'OJHD7J9CEC', '2ER6J17HPA',\n",
    "           'JTZISS4RVB', '1VVHX8YSND', '28BHXB3W5T', 'RTHC2CM45W', 'OS5Y53DY2R', 'OSLUMMKBH5', '7ERTTTHCOE', 'VFCHDWXG8T',\n",
    "           '3YETC8A59W', 'NF950GGH4W', 'D6ZNV1B8XI', 'QDO1Q7MT7L', 'YP8SSVISIZ', 'ZKQEJ05RBI', '9KX7XBZCI5', '1XPVWACDIQ',\n",
    "           'Y9BS13U9WO', 'DJOLGA7FY5', 'KP91EJ5RPM', 'KWOX876ZAS', 'RLM7INE78K', 'X9B0NY2HYV', 'S7USYMLMNN', '5OHQLZ7Z90',\n",
    "           '6OV31G0HQH', 'IG8UG05W2Y']\n",
    "\n",
    "print(len(nyc_ids))\n",
    "# Replace the g14_city column value with \"New York City\" for the specified IDs\n",
    "for nyc_id in nyc_ids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == nyc_id, 'g14_city'] = 'New York City'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "004e1b53",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "004e1b53",
    "outputId": "01e4075d-14bb-4cb5-ca06-38e6269fd3f9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2261225434.py:37: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1.loc[new_gdata1['id'] == mid, 'g4_city'] = 'Manhattan'\n"
     ]
    }
   ],
   "source": [
    "nb_ids = ['AYPK625BZ2', 'K3RNUB35ZB', 'JPYGXKB5RN', 'AAILU4OLYL']\n",
    "nm_ids = ['HCF0C8EZOG', 'M89Y02BG4Q', 'FVLCSEW80R']\n",
    "ns_ids = ['SCOSZ2I4LP', 'MUEDUPXOJR', 'W0GO0N1LTZ', 'URURGPP6GM']\n",
    "srh_ids = ['JANHP4LA30', 'PH2A6AVLQK', 'N9II7AZALG', '1FKF3MVONF', 'SRREVVH4KX', 'AEI47UODMK', 'NUXQ09G5W8']\n",
    "sop_ids = ['HK0XQ0EZ4M', '3XX4FQHARS', '7FLM4OUQ6O', 'T7AEHYD6H9', '47YAHS27Q6', 'RFWKORC8SA', 'IIXUUOYL5V', '3GMKUO4XNH', '1I817M0EGD', 'NT161VUG9A', 'CYD8NOZUKJ', 'Y8NTQ46AYZ', 'O36PFBMY18', '3P6DQIBF61', 'MET2FVJO2I', 'IMJWU5RUE0', 'BB3JWQAFN6']\n",
    "ids = ['LUC0FP3US5', 'ILYISCUXTP', 'HQ1OTBXC2N', '3HYCIGU1XU', 'L0JGRP9LTB', '9B6OZ48FMJ', 'WN82SWVQI2', '8UOBNA4560', 'EB3QKV6MW9', 'YUAZLXS57X', '53SPZPP9QD', '53HWPHWHWY', 'B99Q6CG52I', 'LF6V7AX5JS', 'S7HRFHLOYX', 'Y4NJ0J7LW9', '3P3VPCYVWG', 'NWREXG692K', '83BQ5ICJWC', 'YIDNLRLZEH', 'QWHK7NQZR6', '5LLRDOJDZ7', 'GWLITC84MV', 'ESL43IIMT2', 'LTH4SB0VQ1', 'PR3YEP11ZX', 'Z5S5X42ZQC', '7GFMFVTROP', '5ZUCGD6LV2', '4WEZ1J9CT9', 'CKW6HOLWEU', '4U8SV84FZA', 'GQN8VHWQQZ', 'UTNVHIEWT8', '5Z17VYIDYG', '0OC4QPHBBF', 'DIMWFXA0SL', 'IO3W61XULE', 'RFCAJIGI4B', 'VK09FDO8NA', 'PQTWJMK8QO', 'UY0TLAC94J', 'LPB1RN049P', 'KY55I6KMXP', '6IUAJ5VZGG', 'PWOJI34D48', '2C8TVXBYGI', 'SKFM4ZWMMZ', 'H2W7YA2T7U', 'RMNJ914GM8', 'NIIRLFSEIS', 'YPY2T2B6F9', 'R0Q9VC52N7', 'C3THEWVWLW', 'SBU5C2RYYA', '1AZDN9JP6G', 'JKMF6PTUVM', '915SGAZDL7', '8EXTKZ4HMH', 'U95MC7V0D1', '62X7WGAKTQ', '4VXQ44HXYO']\n",
    "h_ids = ['24EOJV2IH9', '9YWXSWI5O4', 'N319RZXTE2', '525BTRF47H']\n",
    "es_ids = ['PXZL2GM44K', '0E9YQ8KE6F', 'SYIEV7699X', 'N8E45WV0JA']\n",
    "np_ids = ['0VLIKINON7', 'S6P38B3KQD']\n",
    "specific_ids = ['TW57M7UGAU', 'DVD6KEZH95', '5LU1OHXI7Y']\n",
    "rw_ids = ['GS4XV8824L', 'EMB97FLT4R', 'LA70PY89YI', 'J6NTN7ZL5Z', 'CFDB71NLC1', 'CXMNU0W5DS', 'BBEMWETMMS']\n",
    "mids = ['NKJCAXMQLZ', 'SF16P0YLLW', 'A6UYGR6O69']\n",
    "\n",
    "\n",
    "new_gdata1.loc[new_gdata1['id'].isin(specific_ids), 'g14_city'] = 'Baldwinsville'\n",
    "new_gdata1.loc[new_gdata1['id'] == '6QOMQ79SPO', 'g14_city'] = 'Williston Park'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'N06KK164Z5', 'g14_city'] = 'Shirley'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'WZ9IG8BHEY', 'g14_city'] = 'West Bronx'\n",
    "new_gdata1.loc[new_gdata1['id'] == '5PFPL50N4W', 'g14_city'] = 'East Nassau'\n",
    "new_gdata1.loc[new_gdata1['id'] == '7F2JAW7CKN', 'g14_city'] = 'Garden City South'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'ZAAKC7AFEX', 'g14_city'] = 'Lakewood'\n",
    "new_gdata1.loc[new_gdata1['id'] == '9H2WO2HWII', 'g14_city'] = 'North White Plains'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'ZCMV9HVZMF', 'g14_city'] = 'Pleasant Valley'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'YH47F68IOG', 'g14_city'] = 'Richmond Hill'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'S433SF0QE2', 'g14_city'] = 'West Seneca'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'W5MAX3UOFB', 'g14_city'] = 'Woodside'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'GAA014NYXQ', 'g14_city'] = 'Brooklyn'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'XL6Z3NIS0B', 'g14_city'] = 'Queens'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'T023XGKL0M', 'g14_city'] = 'New York City'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'MJQ8485AQI', 'g14_city'] = 'Bronx'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'KH2XJLIHFV', 'g14_city'] = 'Brooklyn'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'O92YHC8MDY', 'g14_city'] = 'Brooklyn'\n",
    "new_gdata1.loc[new_gdata1['id'] == 'F1YFUWDYXH', 'g14_city'] = 'New York City'\n",
    "\n",
    "\n",
    "for mid in mids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == mid, 'g4_city'] = 'Manhattan'\n",
    "\n",
    "\n",
    "for rw_id in rw_ids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == rw_id, 'g14_city'] = 'Ridgewood'\n",
    "\n",
    "\n",
    "for np_id in np_ids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == np_id, 'g14_city'] = 'New Paltz'\n",
    "\n",
    "for es_id in es_ids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == es_id, 'g14_city'] = 'East Syracuse'\n",
    "\n",
    "for h_id in h_ids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == h_id, 'g14_city'] = 'West Henrietta'\n",
    "\n",
    "for id in ids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == id, 'g14_city'] = 'Sunnyside'\n",
    "\n",
    "for id in sop_ids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == id, 'g14_city'] = 'South Ozone Park'\n",
    "\n",
    "for id in srh_ids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == id, 'g14_city'] = 'South Richmond Hill'\n",
    "\n",
    "for id in nb_ids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == id, 'g14_city'] = 'North Baldwin'\n",
    "\n",
    "for id in nm_ids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == id, 'g14_city'] = 'North Massapequa'\n",
    "\n",
    "for id in ns_ids:\n",
    "    new_gdata1.loc[new_gdata1['id'] == id, 'g14_city'] = 'North Syracuse'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8f8aee92",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8f8aee92",
    "outputId": "d83169da-c4ac-49df-fddb-c3e416f1b033"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['I work as a solo artist. ',\n",
       "       'Performing, presenting, or exhibiting to an audience or viewers is core to my practice. ',\n",
       "       'I collaborate regularly with other artists. ',\n",
       "       'Teaching or educating others is core to my practice. ',\n",
       "       'My practice requires public or community involvement to have meaning. ',\n",
       "       'I collaborate regularly with other non-arts practitioners. '],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the IDs with more than one approach column filled\n",
    "# multiple_approach_ids = new_gdata[(new_gdata[['g31_approach1', 'g31_approach2', 'g31_approach3', 'g31_approach4', 'g31_approach5', 'g31_approach6']].notna().sum(axis=1) > 1)]['id']\n",
    "\n",
    "# Update the value of g31_approach1 for these IDs\n",
    "# new_gdata.loc[new_gdata['id'].isin(multiple_approach_ids), 'g31_approach1'] = 'Practices Multiple Approach'\n",
    "new_gdata1['g31_approach1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f4351824",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f4351824",
    "outputId": "2c18b4b4-dee1-4ac4-a4e7-f40ae1cf15da",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I work as a solo artist.                                                                    15272\n",
      "I collaborate regularly with other artists.                                                  4577\n",
      "Performing, presenting, or exhibiting to an audience or viewers is core to my practice.       841\n",
      "Teaching or educating others is core to my practice.                                          510\n",
      "My practice requires public or community involvement to have meaning.                         488\n",
      "I collaborate regularly with other non-arts practitioners.                                    233\n",
      "Name: g31_approach1, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "approach_counts = new_gdata1['g31_approach1'].value_counts()\n",
    "\n",
    "print(approach_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e06c178",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7e06c178",
    "outputId": "c2a4b9c0-d3d4-4e99-9eb5-b0e51805c3d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1062"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_gdata1['g15_zipcode'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95a0b302",
   "metadata": {
    "id": "95a0b302"
   },
   "source": [
    "## Chnage values in approach columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d0d03237",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d0d03237",
    "outputId": "421935df-7e89-497d-bfd0-59bf75c87f11"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1023761834.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[['g31_approach1', 'g31_approach2', 'g31_approach3', 'g31_approach4', 'g31_approach5', 'g31_approach6']] = new_gdata1[['g31_approach1', 'g31_approach2', 'g31_approach3', 'g31_approach4', 'g31_approach5', 'g31_approach6']].apply(lambda x: x.str.strip())\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1023761834.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = new_gdata1[column].map(replacement_dict)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1023761834.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = new_gdata1[column].map(replacement_dict)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1023761834.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = new_gdata1[column].map(replacement_dict)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1023761834.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = new_gdata1[column].map(replacement_dict)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1023761834.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = new_gdata1[column].map(replacement_dict)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1023761834.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = new_gdata1[column].map(replacement_dict)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0                                          Solo Artist\n",
       "2    Performing, Presenting, or Exhibiting to an Au...\n",
       "3                                          Solo Artist\n",
       "4                                          Solo Artist\n",
       "5                                          Solo Artist\n",
       "Name: g31_approach1, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a dictionary mapping the values to the headings\n",
    "# Remove trailing spaces from all approach columns\n",
    "new_gdata1[['g31_approach1', 'g31_approach2', 'g31_approach3', 'g31_approach4', 'g31_approach5', 'g31_approach6']] = new_gdata1[['g31_approach1', 'g31_approach2', 'g31_approach3', 'g31_approach4', 'g31_approach5', 'g31_approach6']].apply(lambda x: x.str.strip())\n",
    "\n",
    "# Define a dictionary mapping the values to the headings\n",
    "replacement_dict = {\n",
    "    'I work as a solo artist.': 'Solo Artist',\n",
    "    'I collaborate regularly with other artists.': 'Collaboration with Other Artists',\n",
    "    'I collaborate regularly with other non-arts practitioners.': 'Collaboration with Non-arts Practitioners',\n",
    "    'My practice requires public or community involvement to have meaning.': 'Practice Requires Public or Community Involvement',\n",
    "    'Performing, presenting, or exhibiting to an audience or viewers is core to my practice.': 'Performing, Presenting, or Exhibiting to an Audience',\n",
    "    'Teaching or educating others is core to my practice.': 'Teaching or Educating Others'\n",
    "}\n",
    "\n",
    "# Loop through each approach column and replace values without using .loc\n",
    "for column in ['g31_approach1', 'g31_approach2', 'g31_approach3', 'g31_approach4', 'g31_approach5', 'g31_approach6']:\n",
    "    new_gdata1[column] = new_gdata1[column].map(replacement_dict)\n",
    "\n",
    "# Display the updated dataframe\n",
    "new_gdata1['g31_approach1'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a626c06e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a626c06e",
    "outputId": "f50b12ee-d206-48f3-c686-1354fd5d2143"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing, Presenting, or Exhibiting to an Audience    3417\n",
      "Practice Requires Public or Community Involvement       2822\n",
      "Teaching or Educating Others                            2219\n",
      "Collaboration with Non-arts Practitioners                632\n",
      "Collaboration with Other Artists                         480\n",
      "Solo Artist                                              335\n",
      "Name: g31_approach4, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "approach_counts = new_gdata1['g31_approach4'].value_counts()\n",
    "\n",
    "print(approach_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fc2673a",
   "metadata": {
    "id": "4fc2673a"
   },
   "source": [
    "## Provide Care Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "736f6ad9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "736f6ad9",
    "outputId": "466b8e85-05f2-4904-8eb1-56653d4e76d2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2709268593.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = new_gdata1[column].str.strip()\n"
     ]
    }
   ],
   "source": [
    "# Iterate through each column in the DataFrame\n",
    "for column in new_gdata1.columns:\n",
    "    # Check if the column is a string type (object)\n",
    "    if new_gdata1[column].dtype == 'object':\n",
    "        # Strip leading and trailing spaces from each value in the column\n",
    "        new_gdata1[column] = new_gdata1[column].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6b5ca4b2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6b5ca4b2",
    "outputId": "40b62455-7159-41d5-9781-61a54478e00b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               id         g14_city  g15_zipcode                   g16_county  \\\n",
      "0      N3R8BXQE4K          Buffalo      14201.0                  Erie County   \n",
      "2      0LTH6QJQH8    New York City      10040.0  New York County (Manhattan)   \n",
      "3      R3S4V21D40        Manhattan      10031.0  New York County (Manhattan)   \n",
      "4      BZEFVJAYBT         Brooklyn      11238.0      Kings County (Brooklyn)   \n",
      "5      4HF20W8D84         New York      11237.0      Kings County (Brooklyn)   \n",
      "...           ...              ...          ...                          ...   \n",
      "23786  T2L67W0132         New York      10034.0  New York County (Manhattan)   \n",
      "23787  XFH2ZTUFVB  Oakland Gardens      11364.0                Queens County   \n",
      "23788  ZRHE2YHWAJ            Bronx      10457.0                 Bronx County   \n",
      "23789  2FDTOSCAXH         Brooklyn      11207.0      Kings County (Brooklyn)   \n",
      "23790  GW843FFWXQ         New York      10033.0  New York County (Manhattan)   \n",
      "\n",
      "             g17_region g18_community         g20_raceethnicity1  \\\n",
      "0      Western New York         Urban  Asian or Pacific Islander   \n",
      "2         New York City         Urban                      White   \n",
      "3         New York City         Urban  Black or African American   \n",
      "4         New York City         Urban  Asian or Pacific Islander   \n",
      "5         New York City         Urban                      White   \n",
      "...                 ...           ...                        ...   \n",
      "23786     New York City         Urban         Hispanic or Latinx   \n",
      "23787     New York City         Urban         Hispanic or Latinx   \n",
      "23788     New York City         Urban         Hispanic or Latinx   \n",
      "23789     New York City         Urban         Hispanic or Latinx   \n",
      "23790     New York City         Urban         Hispanic or Latinx   \n",
      "\n",
      "           g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
      "0                       White                NaN                NaN  ...   \n",
      "2                         NaN                NaN                NaN  ...   \n",
      "3                         NaN                NaN                NaN  ...   \n",
      "4      Arab or Middle Eastern                NaN                NaN  ...   \n",
      "5                         NaN                NaN                NaN  ...   \n",
      "...                       ...                ...                ...  ...   \n",
      "23786                     NaN                NaN                NaN  ...   \n",
      "23787                     NaN                NaN                NaN  ...   \n",
      "23788                     NaN                NaN                NaN  ...   \n",
      "23789                     NaN                NaN                NaN  ...   \n",
      "23790                     NaN                NaN                NaN  ...   \n",
      "\n",
      "      g32b_enrolled_name4 g32b_enrolled_name5 g32b_enrolled_name6  \\\n",
      "0                     NaN                 NaN                 NaN   \n",
      "2                     NaN                 NaN                 NaN   \n",
      "3                     NaN                 NaN                 NaN   \n",
      "4                     NaN                 NaN                 NaN   \n",
      "5                     NaN                 NaN                 NaN   \n",
      "...                   ...                 ...                 ...   \n",
      "23786                 NaN                 NaN                 NaN   \n",
      "23787                 NaN                 NaN                 NaN   \n",
      "23788                 NaN                 NaN                 NaN   \n",
      "23789                 NaN                 NaN                 NaN   \n",
      "23790                 NaN                 NaN                 NaN   \n",
      "\n",
      "      g32b_enrolled_name7 g32b_enrolled_name8 g32b_enrolled_name9 g_agerange  \\\n",
      "0                     NaN                 NaN                 NaN      25-34   \n",
      "2                     NaN                 NaN                 NaN      25-34   \n",
      "3                     NaN                 NaN                 NaN      25-34   \n",
      "4                     NaN                 NaN                 NaN      25-34   \n",
      "5                     NaN                 NaN                 NaN      35-44   \n",
      "...                   ...                 ...                 ...        ...   \n",
      "23786                 NaN                 NaN                 NaN      55-64   \n",
      "23787                 NaN                 NaN                 NaN      55-64   \n",
      "23788                 NaN                 NaN                 NaN      25-34   \n",
      "23789                 NaN                 NaN                 NaN      25-34   \n",
      "23790                 NaN                 NaN                 NaN      55-64   \n",
      "\n",
      "      g_enrollmentstatus g4_city provide_care_status  \n",
      "0                    NaN     NaN                  No  \n",
      "2                    NaN     NaN                  No  \n",
      "3                    NaN     NaN                  No  \n",
      "4                    NaN     NaN                  No  \n",
      "5                    NaN     NaN                  No  \n",
      "...                  ...     ...                 ...  \n",
      "23786                NaN     NaN                  No  \n",
      "23787                NaN     NaN                 Yes  \n",
      "23788                NaN     NaN                 Yes  \n",
      "23789           Enrolled     NaN                  No  \n",
      "23790                NaN     NaN                None  \n",
      "\n",
      "[21921 rows x 59 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3861188699.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1['provide_care_status'] = new_gdata1.apply(lambda row: map_provide_care(row['g27_providecare1'])\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def map_provide_care(value):\n",
    "    if value.startswith('Yes'):\n",
    "        return 'Yes'\n",
    "    elif value == 'No':\n",
    "        return 'No'\n",
    "\n",
    "new_gdata1['provide_care_status'] = new_gdata1.apply(lambda row: map_provide_care(row['g27_providecare1'])\n",
    "                                        if not pd.isnull(row['g27_providecare1'])\n",
    "                                        else map_provide_care(row['g27_providecare2'])\n",
    "                                            if not pd.isnull(row['g27_providecare2'])\n",
    "                                            else map_provide_care(row['g27_providecare3'])\n",
    "                                                if not pd.isnull(row['g27_providecare3'])\n",
    "                                                else map_provide_care(row['g27_providecare4'])\n",
    "                                                    if not pd.isnull(row['g27_providecare4'])\n",
    "                                                    else map_provide_care(row['g27_providecare5'])\n",
    "                                                        if not pd.isnull(row['g27_providecare5'])\n",
    "                                                        else None,\n",
    "                                    axis=1)\n",
    "\n",
    "# Display the updated DataFrame\n",
    "print(new_gdata1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ab533db4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ab533db4",
    "outputId": "b51c2b05-1db9-4865-ab3e-1c90e4a05559"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', None], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata1['provide_care_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7aebe68b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7aebe68b",
    "outputId": "7c4e53a4-3fb1-4087-84a5-6e2198b74c4e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1289408327.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1['g_enrollmentstatus'] = new_gdata1['g_enrollmentstatus'].fillna('Not Enrolled')\n"
     ]
    }
   ],
   "source": [
    "new_gdata1['g_enrollmentstatus'] = new_gdata1['g_enrollmentstatus'].fillna('Not Enrolled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bf9f8090",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bf9f8090",
    "outputId": "50fa6e89-470a-48a3-edfc-64ae1c1f245a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\4283442519.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1['provide_care_status'] = new_gdata1['provide_care_status'].fillna('I prefer not to answer')\n"
     ]
    }
   ],
   "source": [
    "new_gdata1['provide_care_status'] = new_gdata1['provide_care_status'].fillna('I prefer not to answer')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1a442a",
   "metadata": {
    "id": "ae1a442a"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c2026473",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c2026473",
    "outputId": "de15ce4d-2c8c-482e-fccd-f8832e3726cb",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\740376987.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1['provide_care_count'] = new_gdata1.apply(count_yes_entries, axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "5     0\n",
       "6     0\n",
       "7     0\n",
       "8     0\n",
       "9     0\n",
       "10    0\n",
       "11    0\n",
       "12    0\n",
       "13    0\n",
       "14    0\n",
       "15    0\n",
       "17    0\n",
       "18    0\n",
       "19    0\n",
       "20    0\n",
       "22    1\n",
       "Name: provide_care_count, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def count_yes_entries(row):\n",
    "    care_columns = ['g27_providecare1', 'g27_providecare2', 'g27_providecare3', 'g27_providecare4', 'g27_providecare5']\n",
    "    care_options = ['Yes, a child or children', 'Yes, a spouse or partner who is elderly, ill, or disabled', 'Yes, an adult/adults who is/are elderly, ill, or disabled']\n",
    "\n",
    "    # Initialize count\n",
    "    count = 0\n",
    "\n",
    "    # Loop through care columns and count 'Yes' entries for applicable options\n",
    "    for column in care_columns:\n",
    "        if row[column] in care_options:\n",
    "            count += 1\n",
    "\n",
    "    return count\n",
    "\n",
    "# Apply the function to each row to create a new column\n",
    "new_gdata1['provide_care_count'] = new_gdata1.apply(count_yes_entries, axis=1)\n",
    "\n",
    "# Display the new column\n",
    "new_gdata1['provide_care_count'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9b9af121",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "9b9af121",
    "outputId": "101e1060-208a-4fdc-994d-5c209d992104"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>g14_city</th>\n",
       "      <th>g15_zipcode</th>\n",
       "      <th>g16_county</th>\n",
       "      <th>g17_region</th>\n",
       "      <th>g18_community</th>\n",
       "      <th>g20_raceethnicity1</th>\n",
       "      <th>g20_raceethnicity2</th>\n",
       "      <th>g20_raceethnicity3</th>\n",
       "      <th>g20_raceethnicity4</th>\n",
       "      <th>...</th>\n",
       "      <th>g32b_enrolled_name5</th>\n",
       "      <th>g32b_enrolled_name6</th>\n",
       "      <th>g32b_enrolled_name7</th>\n",
       "      <th>g32b_enrolled_name8</th>\n",
       "      <th>g32b_enrolled_name9</th>\n",
       "      <th>g_agerange</th>\n",
       "      <th>g_enrollmentstatus</th>\n",
       "      <th>g4_city</th>\n",
       "      <th>provide_care_status</th>\n",
       "      <th>provide_care_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N3R8BXQE4K</td>\n",
       "      <td>Buffalo</td>\n",
       "      <td>14201.0</td>\n",
       "      <td>Erie County</td>\n",
       "      <td>Western New York</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0LTH6QJQH8</td>\n",
       "      <td>New York City</td>\n",
       "      <td>10040.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R3S4V21D40</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>10031.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BZEFVJAYBT</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>Arab or Middle Eastern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4HF20W8D84</td>\n",
       "      <td>New York</td>\n",
       "      <td>11237.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DBLJ7W1AJV</td>\n",
       "      <td>New York</td>\n",
       "      <td>10003.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T8RAL818WX</td>\n",
       "      <td>New York</td>\n",
       "      <td>10030.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65+</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VJSNADKNIA</td>\n",
       "      <td>Ridgewood</td>\n",
       "      <td>11385.0</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7LMUEX56QJ</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11206.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CK5Q7211S1</td>\n",
       "      <td>Binghamton</td>\n",
       "      <td>13905.0</td>\n",
       "      <td>Broome County</td>\n",
       "      <td>Southern Tier</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KYO3UY4G8C</td>\n",
       "      <td>New York</td>\n",
       "      <td>10128.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55-64</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G3BSHLQTSB</td>\n",
       "      <td>Long Island</td>\n",
       "      <td>11103.0</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9FW9R9KMUV</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11237.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IN0O94G3O9</td>\n",
       "      <td>St. Albans</td>\n",
       "      <td>11412.0</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65+</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CIH1I40YRR</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11218.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>YV4VD3CW87</td>\n",
       "      <td>New York</td>\n",
       "      <td>11232.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HFDFZ3YNBQ</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11226.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>JMYWUBVZDQ</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11221.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>78J9XORDGO</td>\n",
       "      <td>Lake Hill</td>\n",
       "      <td>12448.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55-64</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>88MLU3K5VI</td>\n",
       "      <td>Astoria</td>\n",
       "      <td>11102.0</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Not Enrolled</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id       g14_city  g15_zipcode                   g16_county  \\\n",
       "0   N3R8BXQE4K        Buffalo      14201.0                  Erie County   \n",
       "2   0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)   \n",
       "3   R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)   \n",
       "4   BZEFVJAYBT       Brooklyn      11238.0      Kings County (Brooklyn)   \n",
       "5   4HF20W8D84       New York      11237.0      Kings County (Brooklyn)   \n",
       "6   DBLJ7W1AJV       New York      10003.0  New York County (Manhattan)   \n",
       "7   T8RAL818WX       New York      10030.0  New York County (Manhattan)   \n",
       "8   VJSNADKNIA      Ridgewood      11385.0                Queens County   \n",
       "9   7LMUEX56QJ       Brooklyn      11206.0      Kings County (Brooklyn)   \n",
       "10  CK5Q7211S1     Binghamton      13905.0                Broome County   \n",
       "11  KYO3UY4G8C       New York      10128.0  New York County (Manhattan)   \n",
       "12  G3BSHLQTSB    Long Island      11103.0                Queens County   \n",
       "13  9FW9R9KMUV       Brooklyn      11237.0      Kings County (Brooklyn)   \n",
       "14  IN0O94G3O9     St. Albans      11412.0                Queens County   \n",
       "15  CIH1I40YRR       Brooklyn      11218.0      Kings County (Brooklyn)   \n",
       "17  YV4VD3CW87       New York      11232.0      Kings County (Brooklyn)   \n",
       "18  HFDFZ3YNBQ       Brooklyn      11226.0      Kings County (Brooklyn)   \n",
       "19  JMYWUBVZDQ       Brooklyn      11221.0      Kings County (Brooklyn)   \n",
       "20  78J9XORDGO      Lake Hill      12448.0                Ulster County   \n",
       "24  88MLU3K5VI        Astoria      11102.0                Queens County   \n",
       "\n",
       "          g17_region g18_community         g20_raceethnicity1  \\\n",
       "0   Western New York         Urban  Asian or Pacific Islander   \n",
       "2      New York City         Urban                      White   \n",
       "3      New York City         Urban  Black or African American   \n",
       "4      New York City         Urban  Asian or Pacific Islander   \n",
       "5      New York City         Urban                      White   \n",
       "6      New York City         Urban         Hispanic or Latinx   \n",
       "7      New York City         Urban  Black or African American   \n",
       "8      New York City         Urban                      White   \n",
       "9      New York City         Urban                      White   \n",
       "10     Southern Tier         Urban                      White   \n",
       "11     New York City         Urban  Black or African American   \n",
       "12     New York City         Urban                      White   \n",
       "13     New York City         Urban  Black or African American   \n",
       "14     New York City         Urban  Black or African American   \n",
       "15     New York City         Urban         Hispanic or Latinx   \n",
       "17     New York City         Urban                      White   \n",
       "18     New York City         Urban                      White   \n",
       "19     New York City         Urban  Black or African American   \n",
       "20        Mid-Hudson         Rural                      White   \n",
       "24     New York City         Urban                      White   \n",
       "\n",
       "        g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                    White                NaN                NaN  ...   \n",
       "2                      NaN                NaN                NaN  ...   \n",
       "3                      NaN                NaN                NaN  ...   \n",
       "4   Arab or Middle Eastern                NaN                NaN  ...   \n",
       "5                      NaN                NaN                NaN  ...   \n",
       "6                      NaN                NaN                NaN  ...   \n",
       "7                      NaN                NaN                NaN  ...   \n",
       "8                      NaN                NaN                NaN  ...   \n",
       "9                      NaN                NaN                NaN  ...   \n",
       "10                     NaN                NaN                NaN  ...   \n",
       "11                     NaN                NaN                NaN  ...   \n",
       "12      Hispanic or Latinx                NaN                NaN  ...   \n",
       "13      Hispanic or Latinx                NaN                NaN  ...   \n",
       "14                     NaN                NaN                NaN  ...   \n",
       "15                     NaN                NaN                NaN  ...   \n",
       "17                     NaN                NaN                NaN  ...   \n",
       "18                     NaN                NaN                NaN  ...   \n",
       "19                     NaN                NaN                NaN  ...   \n",
       "20                     NaN                NaN                NaN  ...   \n",
       "24                     NaN                NaN                NaN  ...   \n",
       "\n",
       "   g32b_enrolled_name5 g32b_enrolled_name6 g32b_enrolled_name7  \\\n",
       "0                  NaN                 NaN                 NaN   \n",
       "2                  NaN                 NaN                 NaN   \n",
       "3                  NaN                 NaN                 NaN   \n",
       "4                  NaN                 NaN                 NaN   \n",
       "5                  NaN                 NaN                 NaN   \n",
       "6                  NaN                 NaN                 NaN   \n",
       "7                  NaN                 NaN                 NaN   \n",
       "8                  NaN                 NaN                 NaN   \n",
       "9                  NaN                 NaN                 NaN   \n",
       "10                 NaN                 NaN                 NaN   \n",
       "11                 NaN                 NaN                 NaN   \n",
       "12                 NaN                 NaN                 NaN   \n",
       "13                 NaN                 NaN                 NaN   \n",
       "14                 NaN                 NaN                 NaN   \n",
       "15                 NaN                 NaN                 NaN   \n",
       "17                 NaN                 NaN                 NaN   \n",
       "18                 NaN                 NaN                 NaN   \n",
       "19                 NaN                 NaN                 NaN   \n",
       "20                 NaN                 NaN                 NaN   \n",
       "24                 NaN                 NaN                 NaN   \n",
       "\n",
       "   g32b_enrolled_name8 g32b_enrolled_name9 g_agerange g_enrollmentstatus  \\\n",
       "0                  NaN                 NaN      25-34       Not Enrolled   \n",
       "2                  NaN                 NaN      25-34       Not Enrolled   \n",
       "3                  NaN                 NaN      25-34       Not Enrolled   \n",
       "4                  NaN                 NaN      25-34       Not Enrolled   \n",
       "5                  NaN                 NaN      35-44       Not Enrolled   \n",
       "6                  NaN                 NaN      18-24       Not Enrolled   \n",
       "7                  NaN                 NaN        65+       Not Enrolled   \n",
       "8                  NaN                 NaN      35-44       Not Enrolled   \n",
       "9                  NaN                 NaN      35-44           Enrolled   \n",
       "10                 NaN                 NaN      25-34       Not Enrolled   \n",
       "11                 NaN                 NaN      55-64       Not Enrolled   \n",
       "12                 NaN                 NaN      35-44           Enrolled   \n",
       "13                 NaN                 NaN      18-24       Not Enrolled   \n",
       "14                 NaN                 NaN        65+       Not Enrolled   \n",
       "15                 NaN                 NaN      25-34       Not Enrolled   \n",
       "17                 NaN                 NaN      18-24           Enrolled   \n",
       "18                 NaN                 NaN      35-44       Not Enrolled   \n",
       "19                 NaN                 NaN      25-34       Not Enrolled   \n",
       "20                 NaN                 NaN      55-64       Not Enrolled   \n",
       "24                 NaN                 NaN      25-34       Not Enrolled   \n",
       "\n",
       "   g4_city provide_care_status provide_care_count  \n",
       "0      NaN                  No                  0  \n",
       "2      NaN                  No                  0  \n",
       "3      NaN                  No                  0  \n",
       "4      NaN                  No                  0  \n",
       "5      NaN                  No                  0  \n",
       "6      NaN                  No                  0  \n",
       "7      NaN                  No                  0  \n",
       "8      NaN                  No                  0  \n",
       "9      NaN                  No                  0  \n",
       "10     NaN                  No                  0  \n",
       "11     NaN                  No                  0  \n",
       "12     NaN                  No                  0  \n",
       "13     NaN                  No                  0  \n",
       "14     NaN                  No                  0  \n",
       "15     NaN                  No                  0  \n",
       "17     NaN                  No                  0  \n",
       "18     NaN                  No                  0  \n",
       "19     NaN                  No                  0  \n",
       "20     NaN                  No                  0  \n",
       "24     NaN                  No                  0  \n",
       "\n",
       "[20 rows x 60 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata1[new_gdata1['provide_care_count'] == 0].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1fd04b5",
   "metadata": {
    "id": "f1fd04b5"
   },
   "source": [
    "## Enrolled In Public Benefits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bbf39709",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bbf39709",
    "outputId": "df8e0cb9-ce9a-41b8-e91e-945add543ea8",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['(blank)', 'Medicaid for Adults and Child Health Plus',\n",
       "       'Supplemental Nutrition Assistance Program (SNAP)',\n",
       "       'Supplemental Security Income (SSI)', 'Child Tax Credit (CTC)',\n",
       "       'Health Insurance Marketplace Subsidies',\n",
       "       'Head Start/Early Head Start', 'Childcare Subsidy (CCDF)',\n",
       "       'Other (please describe)',\n",
       "       'Social Security Disability Insurance (SSDI)',\n",
       "       'Section 8 Housing Voucher', 'Temporary Cash Assistance (TANF)',\n",
       "       'Women, Infants, and Children Nutrition Program (WIC)',\n",
       "       'Earned Income Tax Credit (EITC)',\n",
       "       'Federal Child and Dependent Care Tax Credit (CDCTC)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata1['g32b_enrolled_name1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c51876f5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c51876f5",
    "outputId": "d4661c6a-903b-4e64-aa1f-384701d66eb7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3160852576.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1['g32b_enrolled_name1'] = new_gdata1['g32b_enrolled_name1'].replace('(blank)', np.nan)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Replace '(blank)' with NaN in 'g32b_enrolled_name1' column\n",
    "new_gdata1['g32b_enrolled_name1'] = new_gdata1['g32b_enrolled_name1'].replace('(blank)', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8e51bb3a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8e51bb3a",
    "outputId": "40acf6e0-76da-4723-b85c-f225129e5259",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1988580880.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1['total_benefits_count'] = new_gdata1[['g32b_enrolled_name1', 'g32b_enrolled_name2', 'g32b_enrolled_name3',\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "2     1\n",
       "3     1\n",
       "4     2\n",
       "5     0\n",
       "6     0\n",
       "7     3\n",
       "8     2\n",
       "9     0\n",
       "10    1\n",
       "11    0\n",
       "12    0\n",
       "13    0\n",
       "14    0\n",
       "15    0\n",
       "17    0\n",
       "18    0\n",
       "19    0\n",
       "20    0\n",
       "22    0\n",
       "Name: total_benefits_count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column with the count of different benefits an artist is getting\n",
    "new_gdata1['total_benefits_count'] = new_gdata1[['g32b_enrolled_name1', 'g32b_enrolled_name2', 'g32b_enrolled_name3',\n",
    "                                               'g32b_enrolled_name4', 'g32b_enrolled_name5', 'g32b_enrolled_name6',\n",
    "                                               'g32b_enrolled_name7', 'g32b_enrolled_name8', 'g32b_enrolled_name9']].count(axis=1)\n",
    "new_gdata1['total_benefits_count'].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1033cc",
   "metadata": {
    "id": "ed1033cc"
   },
   "source": [
    "## Handling values in Approach column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f2354dd8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f2354dd8",
    "outputId": "902efbd5-d773-4a4f-8e14-6ec248d688df"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Solo Artist',\n",
       "       'Performing, Presenting, or Exhibiting to an Audience',\n",
       "       'Collaboration with Other Artists', 'Teaching or Educating Others',\n",
       "       'Practice Requires Public or Community Involvement',\n",
       "       'Collaboration with Non-arts Practitioners'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new_gdata1.drop('consolidated_approaches', axis=1, inplace=True)\n",
    "new_gdata1['g31_approach1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bd663dbb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bd663dbb",
    "outputId": "702b8b57-53b5-45a3-c153-faab0a101d82"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\649925338.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\649925338.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\649925338.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\649925338.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\649925338.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\649925338.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[column] = 0\n"
     ]
    }
   ],
   "source": [
    "# Define the columns\n",
    "columns_to_add = ['solo', 'performance',\n",
    "                  'collab_artists', 'teach_edu',\n",
    "                  'public_involvement',\n",
    "                  'collab_nonartists']\n",
    "\n",
    "# Initialize the new columns with zeros\n",
    "for column in columns_to_add:\n",
    "    new_gdata1[column] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e55c983e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e55c983e",
    "outputId": "b580e4b3-9ff2-414b-9ba6-78af4014392c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Solo Artist': 'solo', 'Performing, Presenting, or Exhibiting to an Audience': 'performance', 'Collaboration with Other Artists': 'collab_artists', 'Teaching or Educating Others': 'teach_edu', 'Practice Requires Public or Community Involvement': 'public_involvement', 'Collaboration with Non-arts Practitioners': 'collab_nonartists'}\n"
     ]
    }
   ],
   "source": [
    "# Define approach values and columns\n",
    "approach_values = [\n",
    "    'Solo Artist',\n",
    "    'Performing, Presenting, or Exhibiting to an Audience',\n",
    "    'Collaboration with Other Artists',\n",
    "    'Teaching or Educating Others',\n",
    "    'Practice Requires Public or Community Involvement',\n",
    "    'Collaboration with Non-arts Practitioners'\n",
    "]\n",
    "\n",
    "approach_cols = ['solo', 'performance', 'collab_artists', 'teach_edu', 'public_involvement', 'collab_nonartists']\n",
    "\n",
    "# Create a dictionary\n",
    "approach_dict = dict(zip(approach_values, approach_cols))\n",
    "\n",
    "print(approach_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "334471de",
   "metadata": {
    "id": "334471de"
   },
   "outputs": [],
   "source": [
    "# Define the approach columns\n",
    "approach_columns = ['g31_approach1', 'g31_approach2', 'g31_approach3', 'g31_approach4', 'g31_approach5']\n",
    "\n",
    "# Iterate over each row\n",
    "for index, row in new_gdata1.iterrows():\n",
    "    # Iterate over each approach column\n",
    "    for approach_column in approach_columns:\n",
    "        approach_value = row[approach_column]\n",
    "\n",
    "        # Check if the approach value is in the approach_dict keys\n",
    "        if approach_value in approach_dict.keys():\n",
    "            # Update the corresponding value column with 1\n",
    "            value_column = approach_dict[approach_value]\n",
    "            new_gdata1.at[index, value_column] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "853f81ed",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "853f81ed",
    "outputId": "eaf22b07-33e4-4aa0-a81b-4dffb2fc22a1",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>g14_city</th>\n",
       "      <th>g15_zipcode</th>\n",
       "      <th>g16_county</th>\n",
       "      <th>g17_region</th>\n",
       "      <th>g18_community</th>\n",
       "      <th>g20_raceethnicity1</th>\n",
       "      <th>g20_raceethnicity2</th>\n",
       "      <th>g20_raceethnicity3</th>\n",
       "      <th>g20_raceethnicity4</th>\n",
       "      <th>...</th>\n",
       "      <th>g4_city</th>\n",
       "      <th>provide_care_status</th>\n",
       "      <th>provide_care_count</th>\n",
       "      <th>total_benefits_count</th>\n",
       "      <th>solo</th>\n",
       "      <th>performance</th>\n",
       "      <th>collab_artists</th>\n",
       "      <th>teach_edu</th>\n",
       "      <th>public_involvement</th>\n",
       "      <th>collab_nonartists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N3R8BXQE4K</td>\n",
       "      <td>Buffalo</td>\n",
       "      <td>14201.0</td>\n",
       "      <td>Erie County</td>\n",
       "      <td>Western New York</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0LTH6QJQH8</td>\n",
       "      <td>New York City</td>\n",
       "      <td>10040.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R3S4V21D40</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>10031.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BZEFVJAYBT</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>Arab or Middle Eastern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4HF20W8D84</td>\n",
       "      <td>New York</td>\n",
       "      <td>11237.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DBLJ7W1AJV</td>\n",
       "      <td>New York</td>\n",
       "      <td>10003.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T8RAL818WX</td>\n",
       "      <td>New York</td>\n",
       "      <td>10030.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VJSNADKNIA</td>\n",
       "      <td>Ridgewood</td>\n",
       "      <td>11385.0</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7LMUEX56QJ</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11206.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CK5Q7211S1</td>\n",
       "      <td>Binghamton</td>\n",
       "      <td>13905.0</td>\n",
       "      <td>Broome County</td>\n",
       "      <td>Southern Tier</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KYO3UY4G8C</td>\n",
       "      <td>New York</td>\n",
       "      <td>10128.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G3BSHLQTSB</td>\n",
       "      <td>Long Island</td>\n",
       "      <td>11103.0</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9FW9R9KMUV</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11237.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IN0O94G3O9</td>\n",
       "      <td>St. Albans</td>\n",
       "      <td>11412.0</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CIH1I40YRR</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11218.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>YV4VD3CW87</td>\n",
       "      <td>New York</td>\n",
       "      <td>11232.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HFDFZ3YNBQ</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11226.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>JMYWUBVZDQ</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11221.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>78J9XORDGO</td>\n",
       "      <td>Lake Hill</td>\n",
       "      <td>12448.0</td>\n",
       "      <td>Ulster County</td>\n",
       "      <td>Mid-Hudson</td>\n",
       "      <td>Rural</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>QLFML6Y0RY</td>\n",
       "      <td>Woodside</td>\n",
       "      <td>11377.0</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id       g14_city  g15_zipcode                   g16_county  \\\n",
       "0   N3R8BXQE4K        Buffalo      14201.0                  Erie County   \n",
       "2   0LTH6QJQH8  New York City      10040.0  New York County (Manhattan)   \n",
       "3   R3S4V21D40      Manhattan      10031.0  New York County (Manhattan)   \n",
       "4   BZEFVJAYBT       Brooklyn      11238.0      Kings County (Brooklyn)   \n",
       "5   4HF20W8D84       New York      11237.0      Kings County (Brooklyn)   \n",
       "6   DBLJ7W1AJV       New York      10003.0  New York County (Manhattan)   \n",
       "7   T8RAL818WX       New York      10030.0  New York County (Manhattan)   \n",
       "8   VJSNADKNIA      Ridgewood      11385.0                Queens County   \n",
       "9   7LMUEX56QJ       Brooklyn      11206.0      Kings County (Brooklyn)   \n",
       "10  CK5Q7211S1     Binghamton      13905.0                Broome County   \n",
       "11  KYO3UY4G8C       New York      10128.0  New York County (Manhattan)   \n",
       "12  G3BSHLQTSB    Long Island      11103.0                Queens County   \n",
       "13  9FW9R9KMUV       Brooklyn      11237.0      Kings County (Brooklyn)   \n",
       "14  IN0O94G3O9     St. Albans      11412.0                Queens County   \n",
       "15  CIH1I40YRR       Brooklyn      11218.0      Kings County (Brooklyn)   \n",
       "17  YV4VD3CW87       New York      11232.0      Kings County (Brooklyn)   \n",
       "18  HFDFZ3YNBQ       Brooklyn      11226.0      Kings County (Brooklyn)   \n",
       "19  JMYWUBVZDQ       Brooklyn      11221.0      Kings County (Brooklyn)   \n",
       "20  78J9XORDGO      Lake Hill      12448.0                Ulster County   \n",
       "22  QLFML6Y0RY       Woodside      11377.0                Queens County   \n",
       "\n",
       "          g17_region g18_community         g20_raceethnicity1  \\\n",
       "0   Western New York         Urban  Asian or Pacific Islander   \n",
       "2      New York City         Urban                      White   \n",
       "3      New York City         Urban  Black or African American   \n",
       "4      New York City         Urban  Asian or Pacific Islander   \n",
       "5      New York City         Urban                      White   \n",
       "6      New York City         Urban         Hispanic or Latinx   \n",
       "7      New York City         Urban  Black or African American   \n",
       "8      New York City         Urban                      White   \n",
       "9      New York City         Urban                      White   \n",
       "10     Southern Tier         Urban                      White   \n",
       "11     New York City         Urban  Black or African American   \n",
       "12     New York City         Urban                      White   \n",
       "13     New York City         Urban  Black or African American   \n",
       "14     New York City         Urban  Black or African American   \n",
       "15     New York City         Urban         Hispanic or Latinx   \n",
       "17     New York City         Urban                      White   \n",
       "18     New York City         Urban                      White   \n",
       "19     New York City         Urban  Black or African American   \n",
       "20        Mid-Hudson         Rural                      White   \n",
       "22     New York City         Urban  Asian or Pacific Islander   \n",
       "\n",
       "        g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ... g4_city  \\\n",
       "0                    White                NaN                NaN  ...     NaN   \n",
       "2                      NaN                NaN                NaN  ...     NaN   \n",
       "3                      NaN                NaN                NaN  ...     NaN   \n",
       "4   Arab or Middle Eastern                NaN                NaN  ...     NaN   \n",
       "5                      NaN                NaN                NaN  ...     NaN   \n",
       "6                      NaN                NaN                NaN  ...     NaN   \n",
       "7                      NaN                NaN                NaN  ...     NaN   \n",
       "8                      NaN                NaN                NaN  ...     NaN   \n",
       "9                      NaN                NaN                NaN  ...     NaN   \n",
       "10                     NaN                NaN                NaN  ...     NaN   \n",
       "11                     NaN                NaN                NaN  ...     NaN   \n",
       "12      Hispanic or Latinx                NaN                NaN  ...     NaN   \n",
       "13      Hispanic or Latinx                NaN                NaN  ...     NaN   \n",
       "14                     NaN                NaN                NaN  ...     NaN   \n",
       "15                     NaN                NaN                NaN  ...     NaN   \n",
       "17                     NaN                NaN                NaN  ...     NaN   \n",
       "18                     NaN                NaN                NaN  ...     NaN   \n",
       "19                     NaN                NaN                NaN  ...     NaN   \n",
       "20                     NaN                NaN                NaN  ...     NaN   \n",
       "22                     NaN                NaN                NaN  ...     NaN   \n",
       "\n",
       "   provide_care_status provide_care_count total_benefits_count solo  \\\n",
       "0                   No                  0                    0    1   \n",
       "2                   No                  0                    1    1   \n",
       "3                   No                  0                    1    1   \n",
       "4                   No                  0                    2    1   \n",
       "5                   No                  0                    0    1   \n",
       "6                   No                  0                    0    1   \n",
       "7                   No                  0                    3    1   \n",
       "8                   No                  0                    2    1   \n",
       "9                   No                  0                    0    1   \n",
       "10                  No                  0                    1    1   \n",
       "11                  No                  0                    0    1   \n",
       "12                  No                  0                    0    1   \n",
       "13                  No                  0                    0    1   \n",
       "14                  No                  0                    0    1   \n",
       "15                  No                  0                    0    1   \n",
       "17                  No                  0                    0    1   \n",
       "18                  No                  0                    0    1   \n",
       "19                  No                  0                    0    1   \n",
       "20                  No                  0                    0    1   \n",
       "22                 Yes                  1                    0    1   \n",
       "\n",
       "   performance collab_artists teach_edu public_involvement collab_nonartists  \n",
       "0            0              1         0                  0                 0  \n",
       "2            1              1         0                  1                 0  \n",
       "3            0              1         0                  0                 0  \n",
       "4            1              1         0                  0                 0  \n",
       "5            1              1         0                  0                 0  \n",
       "6            1              0         0                  1                 0  \n",
       "7            1              0         1                  1                 0  \n",
       "8            0              1         0                  0                 1  \n",
       "9            0              0         0                  0                 0  \n",
       "10           1              0         1                  1                 0  \n",
       "11           1              1         0                  0                 1  \n",
       "12           1              1         0                  0                 0  \n",
       "13           0              1         0                  0                 1  \n",
       "14           0              0         0                  0                 0  \n",
       "15           1              1         0                  1                 1  \n",
       "17           0              0         0                  1                 0  \n",
       "18           0              1         1                  0                 0  \n",
       "19           1              1         0                  0                 0  \n",
       "20           1              1         0                  1                 1  \n",
       "22           0              1         1                  1                 1  \n",
       "\n",
       "[20 rows x 67 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata1.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed4fa8bc",
   "metadata": {
    "id": "ed4fa8bc"
   },
   "source": [
    "## Fianancial Safety Net Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "77e3d48a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "77e3d48a",
    "outputId": "acafbbb6-8cf6-4073-f69b-47699747480a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,\n",
       "       'I have no financial safety net (savings, assets, family resources)',\n",
       "       'I am vulnerable to a medical emergency',\n",
       "       'I have unmanageable debt (financial obligations not paid in full each month like credit cards, personal loans, payday loans or short-term debt, student loan debt, housing debt, automobile loan, other)',\n",
       "       'I am unsure when I will make any income again',\n",
       "       'None of the above'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata1['g29_financialsafetynet3'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "23bb1192",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "23bb1192",
    "outputId": "8ca90f7c-8590-4002-a841-4456e2f23138"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2574115541.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[col] = new_gdata1[col].map(mapping)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2574115541.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[col] = new_gdata1[col].map(mapping)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2574115541.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[col] = new_gdata1[col].map(mapping)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2574115541.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[col] = new_gdata1[col].map(mapping)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2574115541.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[col] = new_gdata1[col].map(mapping)\n"
     ]
    }
   ],
   "source": [
    "# Define the mapping of long values to short words\n",
    "mapping = {\n",
    "    'I have no financial safety net (savings, assets, family resources)': 'No Safety Net',\n",
    "    'None of the above': 'None',\n",
    "    'I am vulnerable to a medical emergency': 'Medical Emergency',\n",
    "    'I am unsure when I will make any income again': 'Unsure Income',\n",
    "    'I have unmanageable debt (financial obligations not paid in full each month like credit cards, personal loans, payday loans or short-term debt, student loan debt, housing debt, automobile loan, other)': 'Unmanageable Debt'\n",
    "}\n",
    "\n",
    "# Iterate over the columns and apply the mapping\n",
    "for col in ['g29_financialsafetynet1', 'g29_financialsafetynet2', 'g29_financialsafetynet3', 'g29_financialsafetynet4', 'g29_financialsafetynet5']:\n",
    "    new_gdata1[col] = new_gdata1[col].map(mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "552de26a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "552de26a",
    "outputId": "07100e0e-43bf-4cee-a22f-654422bc99da"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\40530593.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[col_name] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\40530593.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[col_name] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\40530593.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[col_name] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\40530593.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[col_name] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\40530593.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_gdata1[col_name] = 0\n"
     ]
    }
   ],
   "source": [
    "# Define the column names\n",
    "column_names = ['f_nosafety', 'f_medical_emerg', 'f_unmanageable', 'f_unsure_income', 'f_none']\n",
    "\n",
    "# Create new columns with the specified names and fill them with zeros\n",
    "for col_name in column_names:\n",
    "    new_gdata1[col_name] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "eeff63f8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eeff63f8",
    "outputId": "af91c429-13ee-43c7-99d8-9cfe018f7088"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'No Safety Net': 'f_nosafety', 'Medical Emergency': 'f_medical_emerg', 'Unmanageable Debt': 'f_unmanageable', 'Unsure Income': 'f_unsure_income', 'None': 'f_none'}\n"
     ]
    }
   ],
   "source": [
    "# Define approach values and columns\n",
    "fnet_values = ['No Safety Net', 'Medical Emergency', 'Unmanageable Debt','Unsure Income', 'None']\n",
    "\n",
    "fnet_cols = ['f_nosafety', 'f_medical_emerg', 'f_unmanageable', 'f_unsure_income', 'f_none']\n",
    "\n",
    "# Create a dictionary\n",
    "fnet_dict = dict(zip(fnet_values, fnet_cols))\n",
    "\n",
    "print(fnet_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "15547296",
   "metadata": {
    "id": "15547296"
   },
   "outputs": [],
   "source": [
    "# Define the approach columns\n",
    "financial_net_columns = ['g29_financialsafetynet1', 'g29_financialsafetynet2', 'g29_financialsafetynet3', 'g29_financialsafetynet4', 'g29_financialsafetynet5']\n",
    "\n",
    "# Iterate over each row\n",
    "for index, row in new_gdata1.iterrows():\n",
    "    # Iterate over each approach column\n",
    "    for fnet_column in financial_net_columns:\n",
    "        fnet_value = row[fnet_column]\n",
    "\n",
    "        # Check if the approach value is in the approach_dict keys\n",
    "        if fnet_value in fnet_dict.keys():\n",
    "            # Update the corresponding value column with 1\n",
    "            value_column = fnet_dict[fnet_value]\n",
    "            new_gdata1.at[index, value_column] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8b1a9540",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8b1a9540",
    "outputId": "106da81e-978d-4b79-d49f-f69fd2bb3a5e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Media Arts\n",
       "2        Literary Arts\n",
       "3          Visual Arts\n",
       "4              (blank)\n",
       "5      Performance Art\n",
       "6                Craft\n",
       "7              (blank)\n",
       "8           Media Arts\n",
       "9              (blank)\n",
       "10    Traditional Arts\n",
       "11               Dance\n",
       "12               Music\n",
       "13                Film\n",
       "14             (blank)\n",
       "15     Performance Art\n",
       "17         Visual Arts\n",
       "18             (blank)\n",
       "19         Visual Arts\n",
       "20     Performance Art\n",
       "22             (blank)\n",
       "23             (blank)\n",
       "24       Literary Arts\n",
       "25               Craft\n",
       "26     Performance Art\n",
       "27     Performance Art\n",
       "28         Visual Arts\n",
       "29               Craft\n",
       "30       Literary Arts\n",
       "31               Craft\n",
       "32          Media Arts\n",
       "Name: g30_discipline2, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata1['g30_discipline2'].head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1519477e",
   "metadata": {
    "id": "1519477e"
   },
   "source": [
    "## Handling Gender column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b606b312",
   "metadata": {
    "id": "b606b312"
   },
   "outputs": [],
   "source": [
    "gender_df = new_gdata1[['g23_gender1', 'g23_gender2', 'g23_gender3', 'g23_gender4', 'g23_gender5']]\n",
    "new_gdata1 = new_gdata1.apply(lambda x: x.str.strip() if x.dtype == 'O' else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b7a7bcae",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 686
    },
    "id": "b7a7bcae",
    "outputId": "77f92f49-f741-459a-a691-d78c9ab0ffb5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>g14_city</th>\n",
       "      <th>g15_zipcode</th>\n",
       "      <th>g16_county</th>\n",
       "      <th>g17_region</th>\n",
       "      <th>g18_community</th>\n",
       "      <th>g20_raceethnicity1</th>\n",
       "      <th>g20_raceethnicity2</th>\n",
       "      <th>g20_raceethnicity3</th>\n",
       "      <th>g20_raceethnicity4</th>\n",
       "      <th>...</th>\n",
       "      <th>collab_artists</th>\n",
       "      <th>teach_edu</th>\n",
       "      <th>public_involvement</th>\n",
       "      <th>collab_nonartists</th>\n",
       "      <th>f_nosafety</th>\n",
       "      <th>f_medical_emerg</th>\n",
       "      <th>f_unmanageable</th>\n",
       "      <th>f_unsure_income</th>\n",
       "      <th>f_none</th>\n",
       "      <th>updated_gender1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N3R8BXQE4K</td>\n",
       "      <td>Buffalo</td>\n",
       "      <td>14201.0</td>\n",
       "      <td>Erie County</td>\n",
       "      <td>Western New York</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0LTH6QJQH8</td>\n",
       "      <td>New York City</td>\n",
       "      <td>10040.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R3S4V21D40</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>10031.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BZEFVJAYBT</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Asian or Pacific Islander</td>\n",
       "      <td>Arab or Middle Eastern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4HF20W8D84</td>\n",
       "      <td>New York</td>\n",
       "      <td>11237.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23786</th>\n",
       "      <td>T2L67W0132</td>\n",
       "      <td>New York</td>\n",
       "      <td>10034.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23787</th>\n",
       "      <td>XFH2ZTUFVB</td>\n",
       "      <td>Oakland Gardens</td>\n",
       "      <td>11364.0</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23788</th>\n",
       "      <td>ZRHE2YHWAJ</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>10457.0</td>\n",
       "      <td>Bronx County</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23789</th>\n",
       "      <td>2FDTOSCAXH</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>11207.0</td>\n",
       "      <td>Kings County (Brooklyn)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23790</th>\n",
       "      <td>GW843FFWXQ</td>\n",
       "      <td>New York</td>\n",
       "      <td>10033.0</td>\n",
       "      <td>New York County (Manhattan)</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Hispanic or Latinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Man</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21921 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id         g14_city  g15_zipcode                   g16_county  \\\n",
       "0      N3R8BXQE4K          Buffalo      14201.0                  Erie County   \n",
       "2      0LTH6QJQH8    New York City      10040.0  New York County (Manhattan)   \n",
       "3      R3S4V21D40        Manhattan      10031.0  New York County (Manhattan)   \n",
       "4      BZEFVJAYBT         Brooklyn      11238.0      Kings County (Brooklyn)   \n",
       "5      4HF20W8D84         New York      11237.0      Kings County (Brooklyn)   \n",
       "...           ...              ...          ...                          ...   \n",
       "23786  T2L67W0132         New York      10034.0  New York County (Manhattan)   \n",
       "23787  XFH2ZTUFVB  Oakland Gardens      11364.0                Queens County   \n",
       "23788  ZRHE2YHWAJ            Bronx      10457.0                 Bronx County   \n",
       "23789  2FDTOSCAXH         Brooklyn      11207.0      Kings County (Brooklyn)   \n",
       "23790  GW843FFWXQ         New York      10033.0  New York County (Manhattan)   \n",
       "\n",
       "             g17_region g18_community         g20_raceethnicity1  \\\n",
       "0      Western New York         Urban  Asian or Pacific Islander   \n",
       "2         New York City         Urban                      White   \n",
       "3         New York City         Urban  Black or African American   \n",
       "4         New York City         Urban  Asian or Pacific Islander   \n",
       "5         New York City         Urban                      White   \n",
       "...                 ...           ...                        ...   \n",
       "23786     New York City         Urban         Hispanic or Latinx   \n",
       "23787     New York City         Urban         Hispanic or Latinx   \n",
       "23788     New York City         Urban         Hispanic or Latinx   \n",
       "23789     New York City         Urban         Hispanic or Latinx   \n",
       "23790     New York City         Urban         Hispanic or Latinx   \n",
       "\n",
       "           g20_raceethnicity2 g20_raceethnicity3 g20_raceethnicity4  ...  \\\n",
       "0                       White                NaN                NaN  ...   \n",
       "2                         NaN                NaN                NaN  ...   \n",
       "3                         NaN                NaN                NaN  ...   \n",
       "4      Arab or Middle Eastern                NaN                NaN  ...   \n",
       "5                         NaN                NaN                NaN  ...   \n",
       "...                       ...                ...                ...  ...   \n",
       "23786                     NaN                NaN                NaN  ...   \n",
       "23787                     NaN                NaN                NaN  ...   \n",
       "23788                     NaN                NaN                NaN  ...   \n",
       "23789                     NaN                NaN                NaN  ...   \n",
       "23790                     NaN                NaN                NaN  ...   \n",
       "\n",
       "      collab_artists teach_edu public_involvement collab_nonartists  \\\n",
       "0                  1         0                  0                 0   \n",
       "2                  1         0                  1                 0   \n",
       "3                  1         0                  0                 0   \n",
       "4                  1         0                  0                 0   \n",
       "5                  1         0                  0                 0   \n",
       "...              ...       ...                ...               ...   \n",
       "23786              1         0                  1                 1   \n",
       "23787              0         0                  1                 0   \n",
       "23788              1         1                  0                 0   \n",
       "23789              1         0                  0                 1   \n",
       "23790              1         0                  0                 0   \n",
       "\n",
       "      f_nosafety f_medical_emerg f_unmanageable f_unsure_income f_none  \\\n",
       "0              1               0              1               0      0   \n",
       "2              0               0              0               0      1   \n",
       "3              1               1              1               0      0   \n",
       "4              1               0              0               1      0   \n",
       "5              1               0              1               0      0   \n",
       "...          ...             ...            ...             ...    ...   \n",
       "23786          1               0              0               0      0   \n",
       "23787          1               0              1               1      0   \n",
       "23788          0               1              1               0      0   \n",
       "23789          1               0              0               0      0   \n",
       "23790          0               0              1               0      0   \n",
       "\n",
       "      updated_gender1  \n",
       "0               Woman  \n",
       "2                 Man  \n",
       "3               Woman  \n",
       "4                 Man  \n",
       "5                 Man  \n",
       "...               ...  \n",
       "23786           Woman  \n",
       "23787           Woman  \n",
       "23788           Woman  \n",
       "23789           Woman  \n",
       "23790             Man  \n",
       "\n",
       "[21921 rows x 73 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Assuming your new_gdata1 is stored in a new_gdata1Frame called 'new_gdata1'\n",
    "# Replace 'new_gdata1' with the actual name of your new_gdata1Frame\n",
    "\n",
    "# Create a new column 'updated_gender1' based on the specified conditions\n",
    "new_gdata1['updated_gender1'] = np.where(\n",
    "    ((new_gdata1['g23_gender1'] == 'Man') | (new_gdata1['g23_gender1'] == 'Woman')) &\n",
    "    (new_gdata1['g23_gender2'] == 'Non-binary') &\n",
    "    new_gdata1['g23_gender3'].isnull() &\n",
    "    new_gdata1['g23_gender4'].isnull() &\n",
    "    new_gdata1['g23_gender5'].isnull(),\n",
    "    'Non-binary',\n",
    "    new_gdata1['g23_gender1']  # Keep the original value if conditions are not met\n",
    ")\n",
    "\n",
    "# Display the updated new_gdata1Frame\n",
    "new_gdata1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "69117035",
   "metadata": {
    "id": "69117035"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Non-binary') & ((new_gdata1['g23_gender2'] == 'Man') | (new_gdata1['g23_gender2'] == 'Woman')) & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Non-binary'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fc395548",
   "metadata": {
    "id": "fc395548"
   },
   "outputs": [],
   "source": [
    "# Assuming 'gender1', 'gender2', 'gender3', 'gender4', 'gender5', 'updated_gender1' are columns in your new_gdata1Frame\n",
    "condition = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Non-binary') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Non-binary'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "650780c3",
   "metadata": {
    "id": "650780c3"
   },
   "outputs": [],
   "source": [
    "# Assuming 'gender1', 'gender2', 'gender3', 'gender4', 'gender5', 'updated_gender1' are columns in your new_gdata1Frame\n",
    "condition = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Man') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Non-binary'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "848833f8",
   "metadata": {
    "id": "848833f8"
   },
   "outputs": [],
   "source": [
    "# Assuming 'gender1', 'gender2', 'gender3', 'gender4', 'gender5', 'updated_gender1' are columns in your new_gdata1Frame\n",
    "condition = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Woman') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Non-binary'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1d892767",
   "metadata": {
    "id": "1d892767"
   },
   "outputs": [],
   "source": [
    "# Assuming 'gender1', 'gender2', 'gender3', 'gender4', 'gender5', 'updated_gender1' are columns in your new_gdata1Frame\n",
    "condition = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'Man') & (new_gdata1['g23_gender3'] == 'Non-binary') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Non-binary'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2b4851cc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2b4851cc",
    "outputId": "c8609932-b286-4abd-dc27-516f7bec2abf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2881, 3)\n"
     ]
    }
   ],
   "source": [
    "# Assuming 'g23_gender1', 'g23_gender2', 'updated_gender1' are columns in your new_gdata1Frame\n",
    "NB_selected_rows = new_gdata1[new_gdata1['updated_gender1'] == 'Non-binary'][['g23_gender1', 'g23_gender2', 'updated_gender1']]\n",
    "\n",
    "# Display the shape of the selected rows new_gdata1Frame\n",
    "print(NB_selected_rows.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7ac3bb42",
   "metadata": {
    "id": "7ac3bb42"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'Two-spirit') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Two-spirit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "43f02fc6",
   "metadata": {
    "id": "43f02fc6"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Two-spirit') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Two-spirit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6bfbc872",
   "metadata": {
    "id": "6bfbc872"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Woman') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Two-spirit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "46a89bba",
   "metadata": {
    "id": "46a89bba"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Man') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Two-spirit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f2ed329d",
   "metadata": {
    "id": "f2ed329d"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Two-spirit') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Two-spirit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5b4e951e",
   "metadata": {
    "id": "5b4e951e"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'Man') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Two-spirit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fe23c611",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fe23c611",
    "outputId": "f7d7fb17-9d61-4024-eb5e-5d9673f935ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(334, 3)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TS_selected_rows = new_gdata1[(new_gdata1['updated_gender1'] == 'Two-spirit')][['g23_gender1', 'g23_gender2', 'updated_gender1']]\n",
    "TS_selected_rows.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "664694f7",
   "metadata": {
    "id": "664694f7"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a5787bc2",
   "metadata": {
    "id": "a5787bc2"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4e935e89",
   "metadata": {
    "id": "4e935e89"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3b1713b4",
   "metadata": {
    "id": "3b1713b4"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Other (please describe)') & (new_gdata1['g23_gender2'] == 'Non-binary') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "544f01cd",
   "metadata": {
    "id": "544f01cd"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Other (please describe)') & (new_gdata1['g23_gender2'] == 'Woman') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a55741f5",
   "metadata": {
    "id": "a55741f5"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Other (please describe)') & (new_gdata1['g23_gender2'] == 'Man') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f2e61942",
   "metadata": {
    "id": "f2e61942"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & new_gdata1[['g23_gender3', 'g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "281a22d7",
   "metadata": {
    "id": "281a22d7"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Other (please describe)') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "af58f5ac",
   "metadata": {
    "id": "af58f5ac"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Other (please describe)') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bc4ab7b9",
   "metadata": {
    "id": "bc4ab7b9"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Man') & (new_gdata1['g23_gender3'] == 'Other (please describe)') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5290f998",
   "metadata": {
    "id": "5290f998"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Other (please describe)') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f86e8b24",
   "metadata": {
    "id": "f86e8b24"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & (new_gdata1['g23_gender3'] == 'Woman') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "68e97da4",
   "metadata": {
    "id": "68e97da4"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & (new_gdata1['g23_gender3'] == 'Man') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e20bca64",
   "metadata": {
    "id": "e20bca64"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Other (please describe)') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Woman') & new_gdata1[['g23_gender4', 'g23_gender5']].isnull().all(axis=1)\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "534b59be",
   "metadata": {
    "id": "534b59be"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & (new_gdata1['g23_gender3'] == 'Man') & (new_gdata1['g23_gender4'] == 'Woman') & new_gdata1['g23_gender5'].isnull()\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a9a14c26",
   "metadata": {
    "id": "a9a14c26"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & (new_gdata1['g23_gender3'] == 'Non-binary') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "39bd7dcc",
   "metadata": {
    "id": "39bd7dcc"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Other (please describe)') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Non-binary') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6339b679",
   "metadata": {
    "id": "6339b679"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Other (please describe)') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Man') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "421f7361",
   "metadata": {
    "id": "421f7361"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Other (please describe)') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Two-spirit') & (new_gdata1['g23_gender4'] == 'Non-binary') & new_gdata1['g23_gender5'].isnull()\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1126d52b",
   "metadata": {
    "id": "1126d52b"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Other (please describe)') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Man') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "126524e9",
   "metadata": {
    "id": "126524e9"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Woman') & (new_gdata1['g23_gender4'] == 'Other (please describe)') & new_gdata1['g23_gender5'].isnull()\n",
    "\n",
    "# Update 'updated_gender1' column where the condition is True\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "07c28a11",
   "metadata": {
    "id": "07c28a11"
   },
   "outputs": [],
   "source": [
    "# Condition 1\n",
    "condition = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Other (please describe)') & (new_gdata1['g23_gender4'] == 'Two-spirit') & (new_gdata1['g23_gender5'] == 'Man')\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a517fb7a",
   "metadata": {
    "id": "a517fb7a"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Two-spirit') & (new_gdata1['g23_gender3'] == 'Other (please describe)') & (new_gdata1['g23_gender4'] == 'Non-binary') & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0a565190",
   "metadata": {
    "id": "0a565190"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Man') & (new_gdata1['g23_gender4'] == 'Other (please describe)') & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "9937193a",
   "metadata": {
    "id": "9937193a"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Two-spirit') & (new_gdata1['g23_gender4'] == 'Other (please describe)') & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "633059e1",
   "metadata": {
    "id": "633059e1"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Man') & (new_gdata1['g23_gender4'] == 'Non-binary') & (new_gdata1['g23_gender5'] == 'Other (please describe)')\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "649f9a17",
   "metadata": {
    "id": "649f9a17"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & (new_gdata1['g23_gender3'] == 'Two-spirit') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "dc1ac185",
   "metadata": {
    "id": "dc1ac185"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Other (please describe)') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0643c466",
   "metadata": {
    "id": "0643c466"
   },
   "outputs": [],
   "source": [
    "condition = (new_gdata1['g23_gender1'] == 'Other (please describe)') & (new_gdata1['g23_gender2'] == 'Man') & (new_gdata1['g23_gender3'] == 'Woman') & (new_gdata1['g23_gender4'] == 'Non-binary') & (new_gdata1['g23_gender5'] == 'Two-spirit')\n",
    "new_gdata1.loc[condition, 'updated_gender1'] = 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "675ae7ed",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "675ae7ed",
    "outputId": "a6ee4ba5-d557-492a-9ae5-023a4e1daf9f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(298, 3)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "O_selected_rows = new_gdata1[(new_gdata1['updated_gender1'] == 'Others')][['g23_gender1', 'g23_gender2', 'updated_gender1']]\n",
    "O_selected_rows.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "33ca6b8d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "33ca6b8d",
    "outputId": "79779264-24ac-45ca-d08a-186901914990"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>g23_gender1</th>\n",
       "      <th>g23_gender2</th>\n",
       "      <th>g23_gender3</th>\n",
       "      <th>updated_gender1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Man</td>\n",
       "      <td>Woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1414</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1572</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1780</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3009</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3020</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3178</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3618</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Other (please describe)</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3643</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4076</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4121</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4276</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4278</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4364</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4472</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4502</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4538</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4745</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4765</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4776</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4784</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4873</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5303</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5427</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5621</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5936</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5941</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5953</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6005</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6033</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6146</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6200</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     g23_gender1 g23_gender2              g23_gender3 updated_gender1\n",
       "360          Man  Two-spirit                      NaN      Two-spirit\n",
       "425   Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "625          Man  Two-spirit                      NaN      Two-spirit\n",
       "628          Man  Two-spirit                      NaN      Two-spirit\n",
       "662          Man  Two-spirit               Non-binary   Dual Identity\n",
       "669        Woman  Two-spirit                      Man           Woman\n",
       "671          Man  Two-spirit                      NaN      Two-spirit\n",
       "709          Man  Two-spirit                      NaN      Two-spirit\n",
       "895   Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "1176       Woman  Two-spirit                      NaN      Two-spirit\n",
       "1247         Man  Two-spirit                      NaN      Two-spirit\n",
       "1358       Woman  Two-spirit                      NaN      Two-spirit\n",
       "1414         Man  Two-spirit                      NaN      Two-spirit\n",
       "1439         Man  Two-spirit                      NaN      Two-spirit\n",
       "1464         Man  Two-spirit                      NaN      Two-spirit\n",
       "1572         Man  Two-spirit                      NaN      Two-spirit\n",
       "1764  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "1779         Man  Two-spirit                      NaN      Two-spirit\n",
       "1780       Woman  Two-spirit                      NaN      Two-spirit\n",
       "1982  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "2214         Man  Two-spirit                      NaN      Two-spirit\n",
       "2875       Woman  Two-spirit                      NaN      Two-spirit\n",
       "3009       Woman  Two-spirit                      NaN      Two-spirit\n",
       "3020       Woman  Two-spirit                      NaN      Two-spirit\n",
       "3178  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "3618  Non-binary  Two-spirit  Other (please describe)   Dual Identity\n",
       "3643       Woman  Two-spirit                      NaN      Two-spirit\n",
       "4076  Non-binary  Two-spirit                    Woman   Dual Identity\n",
       "4121  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "4276  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "4278  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "4364  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "4472       Woman  Two-spirit                      NaN      Two-spirit\n",
       "4502       Woman  Two-spirit                      NaN      Two-spirit\n",
       "4538  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "4745         Man  Two-spirit               Non-binary   Dual Identity\n",
       "4765       Woman  Two-spirit               Non-binary   Dual Identity\n",
       "4776         Man  Two-spirit                      NaN      Two-spirit\n",
       "4784         Man  Two-spirit                      NaN      Two-spirit\n",
       "4873  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "5303  Non-binary  Two-spirit                    Woman   Dual Identity\n",
       "5427         Man  Two-spirit                      NaN      Two-spirit\n",
       "5621       Woman  Two-spirit                      NaN      Two-spirit\n",
       "5936  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "5941         Man  Two-spirit                      NaN      Two-spirit\n",
       "5953  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "6005       Woman  Two-spirit                      NaN      Two-spirit\n",
       "6033         Man  Two-spirit                      NaN      Two-spirit\n",
       "6146         Man  Two-spirit                      NaN      Two-spirit\n",
       "6200  Non-binary  Two-spirit                      NaN   Dual Identity"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_rows = new_gdata1[(new_gdata1['g23_gender2'] == 'Two-spirit')][['g23_gender1', 'g23_gender2', 'g23_gender3', 'updated_gender1']]\n",
    "selected_rows.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8f627d84",
   "metadata": {
    "id": "8f627d84"
   },
   "outputs": [],
   "source": [
    "condition_1 = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'I prefer not to answer') & new_gdata1['g23_gender3'].isnull() & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_1, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a8c055d4",
   "metadata": {
    "id": "a8c055d4"
   },
   "outputs": [],
   "source": [
    "condition_2 = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'I prefer not to answer') & new_gdata1['g23_gender3'].isnull() & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_2, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "340aab91",
   "metadata": {
    "id": "340aab91"
   },
   "outputs": [],
   "source": [
    "condition_3 = (new_gdata1['g23_gender1'] == 'Other (please describe)') & (new_gdata1['g23_gender2'] == 'I prefer not to answer') & new_gdata1['g23_gender3'].isnull() & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_3, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5e513582",
   "metadata": {
    "id": "5e513582"
   },
   "outputs": [],
   "source": [
    "condition_4 = (new_gdata1['g23_gender1'] == 'I prefer not to answer') & (new_gdata1['g23_gender2'] == 'Woman') & new_gdata1['g23_gender3'].isnull() & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_4, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "5a3f3387",
   "metadata": {
    "id": "5a3f3387"
   },
   "outputs": [],
   "source": [
    "condition_5 = (new_gdata1['g23_gender1'] == 'I prefer not to answer') & (new_gdata1['g23_gender2'] == 'Non-binary') & new_gdata1['g23_gender3'].isnull() & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_5, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e940a3d0",
   "metadata": {
    "id": "e940a3d0"
   },
   "outputs": [],
   "source": [
    "condition_6 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & (new_gdata1['g23_gender3'] == 'I prefer not to answer') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_6, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "9cea7dd2",
   "metadata": {
    "id": "9cea7dd2"
   },
   "outputs": [],
   "source": [
    "condition_7 = (new_gdata1['g23_gender1'] == 'I prefer not to answer') & (new_gdata1['g23_gender2'] == 'Man') & new_gdata1['g23_gender3'].isnull() & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_7, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b2dc6e26",
   "metadata": {
    "id": "b2dc6e26"
   },
   "outputs": [],
   "source": [
    "condition_8 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'I prefer not to answer') & new_gdata1['g23_gender3'].isnull() & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_8, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d47010de",
   "metadata": {
    "id": "d47010de"
   },
   "outputs": [],
   "source": [
    "condition_9 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'I prefer not to answer') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_9, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "0b6b396c",
   "metadata": {
    "id": "0b6b396c"
   },
   "outputs": [],
   "source": [
    "condition_10 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'I prefer not to answer') & (new_gdata1['g23_gender3'] == 'Man') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_10, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "5d51dcd9",
   "metadata": {
    "id": "5d51dcd9"
   },
   "outputs": [],
   "source": [
    "condition_11 = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'I prefer not to answer') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_11, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1f0d255d",
   "metadata": {
    "id": "1f0d255d"
   },
   "outputs": [],
   "source": [
    "condition_12 = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'I prefer not to answer') & (new_gdata1['g23_gender4'] == 'Other (please describe)') & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_12, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "29a8c5ac",
   "metadata": {
    "id": "29a8c5ac"
   },
   "outputs": [],
   "source": [
    "condition_13 = (new_gdata1['g23_gender1'] == 'I prefer not to answer') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & new_gdata1['g23_gender3'].isnull() & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_13, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7be5adc0",
   "metadata": {
    "id": "7be5adc0"
   },
   "outputs": [],
   "source": [
    "condition_14 = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'I prefer not to answer') & (new_gdata1['g23_gender3'] == 'Non-binary') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_14, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "154157ba",
   "metadata": {
    "id": "154157ba"
   },
   "outputs": [],
   "source": [
    "condition_15 = (new_gdata1['g23_gender1'] == 'I prefer not to answer') & (new_gdata1['g23_gender2'] == 'Man') & (new_gdata1['g23_gender3'] == 'Woman') & (new_gdata1['g23_gender4'] == 'Non-binary') & (new_gdata1['g23_gender5'] == 'Two-spirit')\n",
    "new_gdata1.loc[condition_15, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "dee7ce80",
   "metadata": {
    "id": "dee7ce80"
   },
   "outputs": [],
   "source": [
    "condition_16 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Man') & (new_gdata1['g23_gender3'] == 'I prefer not to answer') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_16, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "235dc3a4",
   "metadata": {
    "id": "235dc3a4"
   },
   "outputs": [],
   "source": [
    "condition_17 = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'I prefer not to answer') & new_gdata1['g23_gender4'].isnull() & new_gdata1['g23_gender5'].isnull()\n",
    "new_gdata1.loc[condition_17, 'updated_gender1'] = 'I prefer not to answer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7560ea4f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7560ea4f",
    "outputId": "a234ddff-5da7-47d7-bd22-df66def207f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(555, 3)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_rows = new_gdata1[(new_gdata1['updated_gender1'] == 'I prefer not to answer')][['g23_gender1', 'g23_gender2', 'updated_gender1']]\n",
    "selected_rows.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96ccc80f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## NB TS, TS NB, W NB TS, NB W TS, NB TS W, M TS NB, W TS NB, M NB TS, NB TS Other, \n",
    "## TS NB W, TS NB M, NB TS M, M W NB TS\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "ba25ba6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "condition_1 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Two-spirit') & (new_gdata1['g23_gender3'].isnull()) & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_2 = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'].isnull()) & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_3 = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Two-spirit') & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_4 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Two-spirit') & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_5 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Two-spirit') & (new_gdata1['g23_gender3'] == 'Woman') & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_6 = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Two-spirit') & (new_gdata1['g23_gender3'] == 'Non-binary') & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_7 = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'Two-spirit') & (new_gdata1['g23_gender3'] == 'Non-binary') & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_8 = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Two-spirit') & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_9 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Two-spirit') & (new_gdata1['g23_gender3'] == 'Other (please describe)') & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_10 = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Woman') & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_11 = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Man') & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_12 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Two-spirit') & (new_gdata1['g23_gender3'] == 'Man') & (new_gdata1['g23_gender4'].isnull()) & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_13 = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Non-binary') & (new_gdata1['g23_gender4'] == 'Two-spirit') & (new_gdata1['g23_gender5'].isnull())\n",
    "# Define other conditions for different combinations of genders as needed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "b0a504fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_gdata1.loc[condition_1, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_2, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_3, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_4, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_5, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_6, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_7, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_8, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_9, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_10, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_11, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_12, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_13, 'updated_gender1'] = 'Dual Identity'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "dc5cc014",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>g23_gender1</th>\n",
       "      <th>g23_gender2</th>\n",
       "      <th>g23_gender3</th>\n",
       "      <th>updated_gender1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Man</td>\n",
       "      <td>Woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1414</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1572</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1780</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3009</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3020</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3178</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3618</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Other (please describe)</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3643</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4076</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4121</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4276</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4278</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4364</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4472</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4502</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4538</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4745</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4765</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4776</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4784</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4873</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5303</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5427</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5621</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5936</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5941</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5953</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6005</th>\n",
       "      <td>Woman</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6033</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6146</th>\n",
       "      <td>Man</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two-spirit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6200</th>\n",
       "      <td>Non-binary</td>\n",
       "      <td>Two-spirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dual Identity</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     g23_gender1 g23_gender2              g23_gender3 updated_gender1\n",
       "360          Man  Two-spirit                      NaN      Two-spirit\n",
       "425   Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "625          Man  Two-spirit                      NaN      Two-spirit\n",
       "628          Man  Two-spirit                      NaN      Two-spirit\n",
       "662          Man  Two-spirit               Non-binary   Dual Identity\n",
       "669        Woman  Two-spirit                      Man           Woman\n",
       "671          Man  Two-spirit                      NaN      Two-spirit\n",
       "709          Man  Two-spirit                      NaN      Two-spirit\n",
       "895   Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "1176       Woman  Two-spirit                      NaN      Two-spirit\n",
       "1247         Man  Two-spirit                      NaN      Two-spirit\n",
       "1358       Woman  Two-spirit                      NaN      Two-spirit\n",
       "1414         Man  Two-spirit                      NaN      Two-spirit\n",
       "1439         Man  Two-spirit                      NaN      Two-spirit\n",
       "1464         Man  Two-spirit                      NaN      Two-spirit\n",
       "1572         Man  Two-spirit                      NaN      Two-spirit\n",
       "1764  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "1779         Man  Two-spirit                      NaN      Two-spirit\n",
       "1780       Woman  Two-spirit                      NaN      Two-spirit\n",
       "1982  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "2214         Man  Two-spirit                      NaN      Two-spirit\n",
       "2875       Woman  Two-spirit                      NaN      Two-spirit\n",
       "3009       Woman  Two-spirit                      NaN      Two-spirit\n",
       "3020       Woman  Two-spirit                      NaN      Two-spirit\n",
       "3178  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "3618  Non-binary  Two-spirit  Other (please describe)   Dual Identity\n",
       "3643       Woman  Two-spirit                      NaN      Two-spirit\n",
       "4076  Non-binary  Two-spirit                    Woman   Dual Identity\n",
       "4121  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "4276  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "4278  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "4364  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "4472       Woman  Two-spirit                      NaN      Two-spirit\n",
       "4502       Woman  Two-spirit                      NaN      Two-spirit\n",
       "4538  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "4745         Man  Two-spirit               Non-binary   Dual Identity\n",
       "4765       Woman  Two-spirit               Non-binary   Dual Identity\n",
       "4776         Man  Two-spirit                      NaN      Two-spirit\n",
       "4784         Man  Two-spirit                      NaN      Two-spirit\n",
       "4873  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "5303  Non-binary  Two-spirit                    Woman   Dual Identity\n",
       "5427         Man  Two-spirit                      NaN      Two-spirit\n",
       "5621       Woman  Two-spirit                      NaN      Two-spirit\n",
       "5936  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "5941         Man  Two-spirit                      NaN      Two-spirit\n",
       "5953  Non-binary  Two-spirit                      NaN   Dual Identity\n",
       "6005       Woman  Two-spirit                      NaN      Two-spirit\n",
       "6033         Man  Two-spirit                      NaN      Two-spirit\n",
       "6146         Man  Two-spirit                      NaN      Two-spirit\n",
       "6200  Non-binary  Two-spirit                      NaN   Dual Identity"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_rows.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "485f6a11",
   "metadata": {},
   "outputs": [],
   "source": [
    "condition_14 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Other (please describe)') & (new_gdata1['g23_gender3'] == 'Two-spirit') & (new_gdata1['g23_gender4'] == 'Woman') & (new_gdata1['g23_gender5'] == 'Man')\n",
    "condition_15 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Man') & (new_gdata1['g23_gender3'] == 'Woman') & (new_gdata1['g23_gender4'] == 'Two-spirit') & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_16 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Two-spirit') & (new_gdata1['g23_gender3'] == 'Man') & (new_gdata1['g23_gender4'] == 'Woman') & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_17 = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Man') & (new_gdata1['g23_gender4'] == 'Non-binary') & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_18 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Man') & (new_gdata1['g23_gender3'] == 'Two-spirit') & (new_gdata1['g23_gender4'] == 'Other (please describe)') & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_19 = (new_gdata1['g23_gender1'] == 'Woman') & (new_gdata1['g23_gender2'] == 'Two-spirit') & (new_gdata1['g23_gender3'] == 'Man') & (new_gdata1['g23_gender4'] == 'Non-binary') & (new_gdata1['g23_gender5'] == 'Other (please describe)')\n",
    "condition_20 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Man') & (new_gdata1['g23_gender3'] == 'Two-spirit') & (new_gdata1['g23_gender4'] == 'Woman') & (new_gdata1['g23_gender5'] == 'Other (please describe)')\n",
    "condition_21 = (new_gdata1['g23_gender1'] == 'Two-spirit') & (new_gdata1['g23_gender2'] == 'Non-binary') & (new_gdata1['g23_gender3'] == 'Woman') & (new_gdata1['g23_gender4'] == 'Man') & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_22 = (new_gdata1['g23_gender1'] == 'Man') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Two-spirit') & (new_gdata1['g23_gender4'] == 'Other (please describe)') & (new_gdata1['g23_gender5'].isnull())\n",
    "condition_23 = (new_gdata1['g23_gender1'] == 'Non-binary') & (new_gdata1['g23_gender2'] == 'Woman') & (new_gdata1['g23_gender3'] == 'Two-spirit') & (new_gdata1['g23_gender4'] == 'Other (please describe)') & (new_gdata1['g23_gender5'] == 'Man')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f727b590",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_gdata1.loc[condition_14, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_15, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_16, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_17, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_18, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_19, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_20, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_21, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_22, 'updated_gender1'] = 'Dual Identity'\n",
    "new_gdata1.loc[condition_23, 'updated_gender1'] = 'Dual Identity'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "fad371d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Woman', 'Man', 'Non-binary', 'Two-spirit',\n",
       "       'I prefer not to answer', 'Other (please describe)', 'Others',\n",
       "       'Dual Identity'], dtype=object)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata1['updated_gender1'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "110853e8",
   "metadata": {
    "id": "110853e8"
   },
   "source": [
    "## Handling Race Ethnicity Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e535452d",
   "metadata": {
    "id": "e535452d"
   },
   "outputs": [],
   "source": [
    "columns_to_check = [\n",
    "    \"g20_raceethnicity2\", \"g20_raceethnicity3\", \"g20_raceethnicity4\",\n",
    "    \"g20_raceethnicity5\", \"g20_raceethnicity6\", \"g20_raceethnicity7\",\n",
    "    \"g20_raceethnicity8\"\n",
    "]\n",
    "multiracial_df = new_gdata1[new_gdata1[columns_to_check].notna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "ca2d5c8f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ca2d5c8f",
    "outputId": "d9fab9aa-d38b-42b8-be2d-dc46f5a5ccf0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Asian or Pacific Islander', 'White', 'Black or African American',\n",
       "       'Hispanic or Latinx', 'Arab or Middle Eastern',\n",
       "       'Indigenous American, First Nation, or Alaska Native',\n",
       "       'Other (please describe)', 'I prefer not to answer',\n",
       "       'Pacific Islander or Native Hawaiian'], dtype=object)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata1['g20_raceethnicity1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "1e163889",
   "metadata": {
    "id": "1e163889"
   },
   "outputs": [],
   "source": [
    "new_gdata1.loc[multiracial_df.index, 'g20_raceethnicity1'] = \"Multiracial\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "067a98c6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "067a98c6",
    "outputId": "88516503-17ea-4b6b-9894-519d320b3f7f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Multiracial', 'White', 'Black or African American',\n",
       "       'Hispanic or Latinx', 'Asian or Pacific Islander',\n",
       "       'Indigenous American, First Nation, or Alaska Native',\n",
       "       'Other (please describe)', 'I prefer not to answer',\n",
       "       'Arab or Middle Eastern', 'Pacific Islander or Native Hawaiian'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata1['g20_raceethnicity1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "43cfe54b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "43cfe54b",
    "outputId": "53240583-10ab-4640-b0aa-caace2163164"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiracial\n",
      "White\n",
      "Black or African American\n",
      "Hispanic or Latinx\n",
      "Asian or Pacific Islander\n",
      "Indigenous American, First Nation, or Alaska Native\n",
      "Other (please describe)\n",
      "I prefer not to answer\n",
      "Arab or Middle Eastern\n",
      "Pacific Islander or Native Hawaiian\n"
     ]
    }
   ],
   "source": [
    "distinct_race = new_gdata1[\"g20_raceethnicity1\"].unique()\n",
    "for i in distinct_race:\n",
    "  print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "918c6803",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 109
    },
    "id": "918c6803",
    "outputId": "0f344443-25b7-447f-d7e5-c940795b8cc9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>g14_city</th>\n",
       "      <th>g15_zipcode</th>\n",
       "      <th>g16_county</th>\n",
       "      <th>g17_region</th>\n",
       "      <th>g18_community</th>\n",
       "      <th>g20_raceethnicity1</th>\n",
       "      <th>g20_raceethnicity2</th>\n",
       "      <th>g20_raceethnicity3</th>\n",
       "      <th>g20_raceethnicity4</th>\n",
       "      <th>...</th>\n",
       "      <th>collab_artists</th>\n",
       "      <th>teach_edu</th>\n",
       "      <th>public_involvement</th>\n",
       "      <th>collab_nonartists</th>\n",
       "      <th>f_nosafety</th>\n",
       "      <th>f_medical_emerg</th>\n",
       "      <th>f_unmanageable</th>\n",
       "      <th>f_unsure_income</th>\n",
       "      <th>f_none</th>\n",
       "      <th>updated_gender1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, g14_city, g15_zipcode, g16_county, g17_region, g18_community, g20_raceethnicity1, g20_raceethnicity2, g20_raceethnicity3, g20_raceethnicity4, g20_raceethnicity5, g20_raceethnicity6, g20_raceethnicity7, g20_raceethnicity8, g21_immigrant, g22_language, g23_gender1, g23_gender2, g23_gender3, g23_gender4, g23_gender5, g24_transgender, g25_lgbtqiap, g26_deafdisabled, g27_providecare1, g27_providecare2, g27_providecare3, g27_providecare4, g27_providecare5, g28_criminallegalsystem, g28b_criminal_findemploy, g29_financialsafetynet1, g29_financialsafetynet2, g29_financialsafetynet3, g29_financialsafetynet4, g29_financialsafetynet5, g30_discipline1, g30_discipline2, g30_discipline3, g31_approach1, g31_approach2, g31_approach3, g31_approach4, g31_approach5, g31_approach6, g32_enrolled, g32b_enrolled_name1, g32b_enrolled_name2, g32b_enrolled_name3, g32b_enrolled_name4, g32b_enrolled_name5, g32b_enrolled_name6, g32b_enrolled_name7, g32b_enrolled_name8, g32b_enrolled_name9, g_agerange, g_enrollmentstatus, g4_city, provide_care_status, provide_care_count, total_benefits_count, solo, performance, collab_artists, teach_edu, public_involvement, collab_nonartists, f_nosafety, f_medical_emerg, f_unmanageable, f_unsure_income, f_none, updated_gender1]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 73 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_raceethnicity_all = new_gdata1[new_gdata1[['g20_raceethnicity1', 'g20_raceethnicity2', 'g20_raceethnicity3', 'g20_raceethnicity4', 'g20_raceethnicity5', 'g20_raceethnicity6', 'g20_raceethnicity7', 'g20_raceethnicity8']].isnull().all(axis=1)]\n",
    "null_raceethnicity_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "64a793e4",
   "metadata": {
    "id": "64a793e4"
   },
   "outputs": [],
   "source": [
    "# List of columns to drop\n",
    "columns_to_drop = ['g20_raceethnicity2', 'g20_raceethnicity3', 'g20_raceethnicity4', 'g20_raceethnicity5', 'g20_raceethnicity6', 'g20_raceethnicity7', 'g20_raceethnicity8']\n",
    "\n",
    "# Drop unwanted columns\n",
    "new_gdata1.drop(columns=columns_to_drop, inplace=True)\n",
    "\n",
    "# Rename g20_raceethnicity1 to g20_raceethnicity\n",
    "new_gdata1.rename(columns={'g20_raceethnicity1': 'g20_raceethnicity'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "34edd38a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "34edd38a",
    "outputId": "da771417-2128-4397-eaf1-312a0b2bebd6",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      Multiracial\n",
       "2                            White\n",
       "3        Black or African American\n",
       "4                      Multiracial\n",
       "5                            White\n",
       "                   ...            \n",
       "23786           Hispanic or Latinx\n",
       "23787           Hispanic or Latinx\n",
       "23788           Hispanic or Latinx\n",
       "23789           Hispanic or Latinx\n",
       "23790           Hispanic or Latinx\n",
       "Name: g20_raceethnicity, Length: 21921, dtype: object"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gdata1['g20_raceethnicity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "69c63f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace 'Pacific Islander or Native Hawaiian' with 'Asian or Pacific Islander' in the 'g20_raceethnicity' column\n",
    "new_gdata1['g20_raceethnicity'] = new_gdata1['g20_raceethnicity'].replace('Pacific Islander or Native Hawaiian', 'Asian or Pacific Islander')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d92f4799",
   "metadata": {
    "id": "d92f4799"
   },
   "source": [
    "## Discipline column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "8cbd9ff7",
   "metadata": {
    "id": "8cbd9ff7"
   },
   "outputs": [],
   "source": [
    "# Define the column names\n",
    "columns_to_replace = ['g30_discipline1', 'g30_discipline2', 'g30_discipline3']\n",
    "\n",
    "# Replace '(blank)' with NaN in the specified columns\n",
    "new_gdata1[columns_to_replace] = new_gdata1[columns_to_replace].replace('(blank)', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "18e9d6b0",
   "metadata": {
    "id": "18e9d6b0"
   },
   "outputs": [],
   "source": [
    "new_gdata1.to_csv('applicant6.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b6930e",
   "metadata": {
    "id": "07b6930e"
   },
   "source": [
    "## Handling p_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "bcb482c8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "id": "bcb482c8",
    "outputId": "4fe6f600-3231-44cd-cf57-f2bdfce237d4",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p1_discipline1</th>\n",
       "      <th>p1_discipline2</th>\n",
       "      <th>p1_discipline3</th>\n",
       "      <th>p2_approachtopractice1</th>\n",
       "      <th>p2_approachtopractice2</th>\n",
       "      <th>p2_approachtopractice3</th>\n",
       "      <th>p2_approachtopractice4</th>\n",
       "      <th>p2_approachtopractice5</th>\n",
       "      <th>p2_approachtopractice6</th>\n",
       "      <th>p3_impacts</th>\n",
       "      <th>...</th>\n",
       "      <th>p_status</th>\n",
       "      <th>p41_gender1</th>\n",
       "      <th>p41_gender2</th>\n",
       "      <th>p41_gender3</th>\n",
       "      <th>p41_gender4</th>\n",
       "      <th>p41_gender5</th>\n",
       "      <th>p46_caregiving1</th>\n",
       "      <th>p46_caregiving2</th>\n",
       "      <th>p46_caregiving3</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N3R8BXQE4K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Interdisciplinary Arts</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>Media Arts</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Withdrawn</td>\n",
       "      <td>gender_oth</td>\n",
       "      <td>twospirit</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E555HATF05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0LTH6QJQH8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R3S4V21D40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BZEFVJAYBT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           p1_discipline1 p1_discipline2 p1_discipline3  \\\n",
       "0                     NaN            NaN            NaN   \n",
       "1  Interdisciplinary Arts    Visual Arts     Media Arts   \n",
       "2                     NaN            NaN            NaN   \n",
       "3                     NaN            NaN            NaN   \n",
       "4                     NaN            NaN            NaN   \n",
       "\n",
       "  p2_approachtopractice1 p2_approachtopractice2 p2_approachtopractice3  \\\n",
       "0                    NaN                    NaN                    NaN   \n",
       "1         collab_artists      collab_nonartists            solo_artist   \n",
       "2                    NaN                    NaN                    NaN   \n",
       "3                    NaN                    NaN                    NaN   \n",
       "4                    NaN                    NaN                    NaN   \n",
       "\n",
       "  p2_approachtopractice4   p2_approachtopractice5 p2_approachtopractice6  \\\n",
       "0                    NaN                      NaN                    NaN   \n",
       "1     public_involvement  perform_present_exhibit        teaching_others   \n",
       "2                    NaN                      NaN                    NaN   \n",
       "3                    NaN                      NaN                    NaN   \n",
       "4                    NaN                      NaN                    NaN   \n",
       "\n",
       "  p3_impacts  ...   p_status p41_gender1 p41_gender2 p41_gender3 p41_gender4  \\\n",
       "0        NaN  ...        NaN         NaN         NaN         NaN         NaN   \n",
       "1        Yes  ...  Withdrawn  gender_oth   twospirit       woman         NaN   \n",
       "2        NaN  ...        NaN         NaN         NaN         NaN         NaN   \n",
       "3        NaN  ...        NaN         NaN         NaN         NaN         NaN   \n",
       "4        NaN  ...        NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "  p41_gender5 p46_caregiving1 p46_caregiving2 p46_caregiving3          id  \n",
       "0         NaN             NaN             NaN             NaN  N3R8BXQE4K  \n",
       "1         NaN   no_caregiving             NaN             NaN  E555HATF05  \n",
       "2         NaN             NaN             NaN             NaN  0LTH6QJQH8  \n",
       "3         NaN             NaN             NaN             NaN  R3S4V21D40  \n",
       "4         NaN             NaN             NaN             NaN  BZEFVJAYBT  \n",
       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "2fd466ea",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2fd466ea",
    "outputId": "877231ca-4ceb-41f0-d553-0edcfd302362"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['p1_discipline1', 'p1_discipline2', 'p1_discipline3',\n",
       "       'p2_approachtopractice1', 'p2_approachtopractice2',\n",
       "       'p2_approachtopractice3', 'p2_approachtopractice4',\n",
       "       'p2_approachtopractice5', 'p2_approachtopractice6', 'p3_impacts',\n",
       "       ...\n",
       "       'p_status', 'p41_gender1', 'p41_gender2', 'p41_gender3', 'p41_gender4',\n",
       "       'p41_gender5', 'p46_caregiving1', 'p46_caregiving2', 'p46_caregiving3',\n",
       "       'id'],\n",
       "      dtype='object', length=124)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "b59dab3a",
   "metadata": {
    "id": "b59dab3a"
   },
   "outputs": [],
   "source": [
    "# Assuming your DataFrame is called df\n",
    "# Extract the 'id' column\n",
    "id_column = p_data.pop('id')\n",
    "\n",
    "# Insert the 'id' column at the start of the DataFrame\n",
    "p_data.insert(0, 'id', id_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "0af92b33",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 830
    },
    "id": "0af92b33",
    "outputId": "27c9e5fb-57bf-4809-af78-7d2c7afec9d8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>p1_discipline1</th>\n",
       "      <th>p1_discipline2</th>\n",
       "      <th>p1_discipline3</th>\n",
       "      <th>p2_approachtopractice1</th>\n",
       "      <th>p2_approachtopractice2</th>\n",
       "      <th>p2_approachtopractice3</th>\n",
       "      <th>p2_approachtopractice4</th>\n",
       "      <th>p2_approachtopractice5</th>\n",
       "      <th>p2_approachtopractice6</th>\n",
       "      <th>...</th>\n",
       "      <th>p_agerange</th>\n",
       "      <th>p_status</th>\n",
       "      <th>p41_gender1</th>\n",
       "      <th>p41_gender2</th>\n",
       "      <th>p41_gender3</th>\n",
       "      <th>p41_gender4</th>\n",
       "      <th>p41_gender5</th>\n",
       "      <th>p46_caregiving1</th>\n",
       "      <th>p46_caregiving2</th>\n",
       "      <th>p46_caregiving3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E555HATF05</td>\n",
       "      <td>Interdisciplinary Arts</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>Media Arts</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>...</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Withdrawn</td>\n",
       "      <td>gender_oth</td>\n",
       "      <td>twospirit</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T8RAL818WX</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>65-74</td>\n",
       "      <td>Completed</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VJSNADKNIA</td>\n",
       "      <td>Media Arts</td>\n",
       "      <td>Media Arts</td>\n",
       "      <td>Media Arts</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Completed</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CK5Q7211S1</td>\n",
       "      <td>Craft</td>\n",
       "      <td>Traditional Arts</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Completed</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KYO3UY4G8C</td>\n",
       "      <td>Interdisciplinary Arts</td>\n",
       "      <td>Dance</td>\n",
       "      <td>Performance Art</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>55-64</td>\n",
       "      <td>Completed</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G3BSHLQTSB</td>\n",
       "      <td>Film</td>\n",
       "      <td>Music</td>\n",
       "      <td>NaN</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Funded</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IN0O94G3O9</td>\n",
       "      <td>Literary Arts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>65-74</td>\n",
       "      <td>Completed</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ZY16NLNF2S</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>Interdisciplinary Arts</td>\n",
       "      <td>Film</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>45-54</td>\n",
       "      <td>Completed</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>YV4VD3CW87</td>\n",
       "      <td>Film</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>Media Arts</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Funded</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HFDFZ3YNBQ</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Completed</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>JMYWUBVZDQ</td>\n",
       "      <td>Music</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25-34</td>\n",
       "      <td>In-Progress</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>78J9XORDGO</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>Performance Art</td>\n",
       "      <td>Literary Arts</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>...</td>\n",
       "      <td>55-64</td>\n",
       "      <td>Completed</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>V5MKNE7AYS</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>Film</td>\n",
       "      <td>Media Arts</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>...</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Withdrawn</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>QLFML6Y0RY</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>45-54</td>\n",
       "      <td>In-Progress</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>88MLU3K5VI</td>\n",
       "      <td>Film</td>\n",
       "      <td>Literary Arts</td>\n",
       "      <td>Performance Art</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Completed</td>\n",
       "      <td>nonbinary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>WEHS93JQSQ</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>Craft</td>\n",
       "      <td>Performance Art</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Completed</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>279L656A6F</td>\n",
       "      <td>Music</td>\n",
       "      <td>Performance Art</td>\n",
       "      <td>Interdisciplinary Arts</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>...</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Completed</td>\n",
       "      <td>twospirit</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>providecare_noans</td>\n",
       "      <td>adult</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>N2K8RW5G2C</td>\n",
       "      <td>Design</td>\n",
       "      <td>Craft</td>\n",
       "      <td>NaN</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Completed</td>\n",
       "      <td>man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>J4FJ66BZEP</td>\n",
       "      <td>Film</td>\n",
       "      <td>Literary Arts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Completed</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>providecare_noans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>HSVMQ0Z094</td>\n",
       "      <td>Visual Arts</td>\n",
       "      <td>Craft</td>\n",
       "      <td>Traditional Arts</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Completed</td>\n",
       "      <td>nonbinary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id          p1_discipline1          p1_discipline2  \\\n",
       "1   E555HATF05  Interdisciplinary Arts             Visual Arts   \n",
       "7   T8RAL818WX             Visual Arts                     NaN   \n",
       "8   VJSNADKNIA              Media Arts              Media Arts   \n",
       "10  CK5Q7211S1                   Craft        Traditional Arts   \n",
       "11  KYO3UY4G8C  Interdisciplinary Arts                   Dance   \n",
       "12  G3BSHLQTSB                    Film                   Music   \n",
       "14  IN0O94G3O9           Literary Arts                     NaN   \n",
       "16  ZY16NLNF2S             Visual Arts  Interdisciplinary Arts   \n",
       "17  YV4VD3CW87                    Film             Visual Arts   \n",
       "18  HFDFZ3YNBQ             Visual Arts                     NaN   \n",
       "19  JMYWUBVZDQ                   Music             Visual Arts   \n",
       "20  78J9XORDGO             Visual Arts         Performance Art   \n",
       "21  V5MKNE7AYS             Visual Arts                    Film   \n",
       "22  QLFML6Y0RY             Visual Arts                     NaN   \n",
       "24  88MLU3K5VI                    Film           Literary Arts   \n",
       "25  WEHS93JQSQ             Visual Arts                   Craft   \n",
       "27  279L656A6F                   Music         Performance Art   \n",
       "29  N2K8RW5G2C                  Design                   Craft   \n",
       "30  J4FJ66BZEP                    Film           Literary Arts   \n",
       "31  HSVMQ0Z094             Visual Arts                   Craft   \n",
       "\n",
       "            p1_discipline3 p2_approachtopractice1   p2_approachtopractice2  \\\n",
       "1               Media Arts         collab_artists        collab_nonartists   \n",
       "7                      NaN            solo_artist       public_involvement   \n",
       "8               Media Arts         collab_artists        collab_nonartists   \n",
       "10             Visual Arts            solo_artist       public_involvement   \n",
       "11         Performance Art         collab_artists        collab_nonartists   \n",
       "12                     NaN         collab_artists              solo_artist   \n",
       "14                     NaN            solo_artist                      NaN   \n",
       "16                    Film         collab_artists        collab_nonartists   \n",
       "17              Media Arts            solo_artist       public_involvement   \n",
       "18                     NaN         collab_artists              solo_artist   \n",
       "19                     NaN         collab_artists              solo_artist   \n",
       "20           Literary Arts         collab_artists        collab_nonartists   \n",
       "21              Media Arts         collab_artists        collab_nonartists   \n",
       "22                     NaN         collab_artists        collab_nonartists   \n",
       "24         Performance Art         collab_artists  perform_present_exhibit   \n",
       "25         Performance Art            solo_artist          teaching_others   \n",
       "27  Interdisciplinary Arts         collab_artists        collab_nonartists   \n",
       "29                     NaN         collab_artists              solo_artist   \n",
       "30                     NaN         collab_artists              solo_artist   \n",
       "31        Traditional Arts      collab_nonartists              solo_artist   \n",
       "\n",
       "     p2_approachtopractice3   p2_approachtopractice4   p2_approachtopractice5  \\\n",
       "1               solo_artist       public_involvement  perform_present_exhibit   \n",
       "7   perform_present_exhibit          teaching_others                      NaN   \n",
       "8               solo_artist                      NaN                      NaN   \n",
       "10  perform_present_exhibit          teaching_others                      NaN   \n",
       "11              solo_artist  perform_present_exhibit                      NaN   \n",
       "12  perform_present_exhibit                      NaN                      NaN   \n",
       "14                      NaN                      NaN                      NaN   \n",
       "16              solo_artist  perform_present_exhibit          teaching_others   \n",
       "17                      NaN                      NaN                      NaN   \n",
       "18          teaching_others                      NaN                      NaN   \n",
       "19  perform_present_exhibit                      NaN                      NaN   \n",
       "20              solo_artist       public_involvement  perform_present_exhibit   \n",
       "21              solo_artist       public_involvement  perform_present_exhibit   \n",
       "22              solo_artist       public_involvement          teaching_others   \n",
       "24                      NaN                      NaN                      NaN   \n",
       "25                      NaN                      NaN                      NaN   \n",
       "27              solo_artist       public_involvement  perform_present_exhibit   \n",
       "29                      NaN                      NaN                      NaN   \n",
       "30  perform_present_exhibit                      NaN                      NaN   \n",
       "31  perform_present_exhibit          teaching_others                      NaN   \n",
       "\n",
       "   p2_approachtopractice6  ... p_agerange     p_status p41_gender1  \\\n",
       "1         teaching_others  ...      25-34    Withdrawn  gender_oth   \n",
       "7                     NaN  ...      65-74    Completed       woman   \n",
       "8                     NaN  ...      35-44    Completed       woman   \n",
       "10                    NaN  ...      25-34    Completed       woman   \n",
       "11                    NaN  ...      55-64    Completed         man   \n",
       "12                    NaN  ...      35-44       Funded         man   \n",
       "14                    NaN  ...      65-74    Completed       woman   \n",
       "16                    NaN  ...      45-54    Completed         man   \n",
       "17                    NaN  ...      18-24       Funded         man   \n",
       "18                    NaN  ...      35-44    Completed       woman   \n",
       "19                    NaN  ...      25-34  In-Progress       woman   \n",
       "20        teaching_others  ...      55-64    Completed         man   \n",
       "21        teaching_others  ...      35-44    Withdrawn         man   \n",
       "22                    NaN  ...      45-54  In-Progress         man   \n",
       "24                    NaN  ...      25-34    Completed   nonbinary   \n",
       "25                    NaN  ...      18-24    Completed       woman   \n",
       "27        teaching_others  ...      18-24    Completed   twospirit   \n",
       "29                    NaN  ...      35-44    Completed         man   \n",
       "30                    NaN  ...      25-34    Completed       woman   \n",
       "31                    NaN  ...      18-24    Completed   nonbinary   \n",
       "\n",
       "   p41_gender2 p41_gender3 p41_gender4 p41_gender5    p46_caregiving1  \\\n",
       "1    twospirit       woman         NaN         NaN      no_caregiving   \n",
       "7          NaN         NaN         NaN         NaN      no_caregiving   \n",
       "8          NaN         NaN         NaN         NaN      no_caregiving   \n",
       "10         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "11         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "12         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "14         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "16         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "17         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "18         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "19         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "20         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "21         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "22         NaN         NaN         NaN         NaN              child   \n",
       "24         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "25         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "27       woman         NaN         NaN         NaN  providecare_noans   \n",
       "29         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "30         NaN         NaN         NaN         NaN  providecare_noans   \n",
       "31         NaN         NaN         NaN         NaN      no_caregiving   \n",
       "\n",
       "   p46_caregiving2 p46_caregiving3  \n",
       "1              NaN             NaN  \n",
       "7              NaN             NaN  \n",
       "8              NaN             NaN  \n",
       "10             NaN             NaN  \n",
       "11             NaN             NaN  \n",
       "12             NaN             NaN  \n",
       "14             NaN             NaN  \n",
       "16             NaN             NaN  \n",
       "17             NaN             NaN  \n",
       "18             NaN             NaN  \n",
       "19             NaN             NaN  \n",
       "20             NaN             NaN  \n",
       "21             NaN             NaN  \n",
       "22             NaN             NaN  \n",
       "24             NaN             NaN  \n",
       "25             NaN             NaN  \n",
       "27           adult             NaN  \n",
       "29             NaN             NaN  \n",
       "30             NaN             NaN  \n",
       "31             NaN             NaN  \n",
       "\n",
       "[20 rows x 124 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata = p_data.dropna(how='all', subset=p_data.columns.difference(['id']))\n",
    "new_pdata.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "de5ca9a2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "de5ca9a2",
    "outputId": "67498fb6-aa85-4d84-f5a2-939500d1ebf0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13119"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_pdata.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "0d3df90d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0d3df90d",
    "outputId": "74da03dc-9110-4ace-e8b1-6b3d79a810a3",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 – very little time', '5 – more than enough time',\n",
       "       '4 – sufficient time', '3 – my time fluctuates',\n",
       "       '1 – no time at all', nan], dtype=object)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p6_amountoftime'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "45c6b8f4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "45c6b8f4",
    "outputId": "65eb0f3b-3e88-4e1c-e986-569816de375f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3077370410.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = new_pdata[column].str.strip()\n"
     ]
    }
   ],
   "source": [
    "# Iterate through each column in the DataFrame\n",
    "\n",
    "for column in new_pdata.columns:\n",
    "    if new_pdata[column].dtype == 'object':\n",
    "        new_pdata[column] = new_pdata[column].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "10b07a45",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "10b07a45",
    "outputId": "b4bde662-2727-4d7f-8b36-1d1c4ffcce27"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2356642363.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p5_amountofenergy'] = new_pdata['p5_amountofenergy'].str.split('–').str[1].str.strip()\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2356642363.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p6_amountoftime'] = new_pdata['p6_amountoftime'].str.split('–').str[1].str.strip()\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2356642363.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p7_financialcapacity'] = new_pdata['p7_financialcapacity'].str.split('–').str[1].str.strip()\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2356642363.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p8_financialstability'] = new_pdata['p8_financialstability'].str.split('–').str[1].str.strip()\n"
     ]
    }
   ],
   "source": [
    "# Assuming new_pdata is your DataFrame\n",
    "new_pdata['p5_amountofenergy'] = new_pdata['p5_amountofenergy'].str.split('–').str[1].str.strip()\n",
    "new_pdata['p6_amountoftime'] = new_pdata['p6_amountoftime'].str.split('–').str[1].str.strip()\n",
    "new_pdata['p7_financialcapacity'] = new_pdata['p7_financialcapacity'].str.split('–').str[1].str.strip()\n",
    "new_pdata['p8_financialstability'] = new_pdata['p8_financialstability'].str.split('–').str[1].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "71660d82",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "71660d82",
    "outputId": "5cbf2e85-ea46-4b61-b1cd-f6e7107940f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no energy', 'my energy fluctuates', 'very low energy',\n",
       "       'more than enough energy', 'sufficient energy', nan], dtype=object)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p5_amountofenergy'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "3fca4642",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3fca4642",
    "outputId": "7ae3248b-3b2b-4b8f-f18e-dbc23bdd1cc2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\90656082.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p5_amountofenergy'] = new_pdata['p5_amountofenergy'].map(energy_mapping)\n"
     ]
    }
   ],
   "source": [
    "# Define a dictionary mapping long-form values to short forms\n",
    "energy_mapping = {\n",
    "    'no energy': 'no',\n",
    "    'my energy fluctuates': 'fluctuates',\n",
    "    'very low energy': 'very low',\n",
    "    'more than enough energy': 'more than enough',\n",
    "    'sufficient energy': 'sufficient'\n",
    "}\n",
    "\n",
    "# Map the values in the 'p5_amountofenergy' column using the defined dictionary\n",
    "new_pdata['p5_amountofenergy'] = new_pdata['p5_amountofenergy'].map(energy_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "0e313685",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0e313685",
    "outputId": "99709dd7-8ec3-4868-a6e5-2025813bed08"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['very little time', 'more than enough time', 'sufficient time',\n",
       "       'my time fluctuates', 'no time at all', nan], dtype=object)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p6_amountoftime'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "c8235778",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c8235778",
    "outputId": "61dc7f8a-e8f3-4799-f0c3-36b20d5bfeaa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\27316675.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p6_amountoftime'] = new_pdata['p6_amountoftime'].map(time_mapping)\n"
     ]
    }
   ],
   "source": [
    "# Define a dictionary mapping long-form values to short forms\n",
    "time_mapping = {\n",
    "    'very little time': 'very little',\n",
    "    'more than enough time': 'more than enough',\n",
    "    'sufficient time': 'sufficient',\n",
    "    'my time fluctuates': 'fluctuates',\n",
    "    'no time at all': 'no time'\n",
    "}\n",
    "\n",
    "# Map the values in the 'p5_amountofenergy' column using the defined dictionary\n",
    "new_pdata['p6_amountoftime'] = new_pdata['p6_amountoftime'].map(time_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "bee3529e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bee3529e",
    "outputId": "103ef7da-22b8-4c9c-990b-7d01703b50f1",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['I can afford only some of these items', 'my time fluctuates',\n",
       "       'I cannot afford these items', 'I can afford these items', nan,\n",
       "       'I have more than enough to afford materials'], dtype=object)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p7_financialcapacity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "33b0745b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "33b0745b",
    "outputId": "c8f1a1b4-8c0f-493a-c8ba-1d300bbaf31f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1254336679.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p7_financialcapacity'] = new_pdata['p7_financialcapacity'].replace('my time fluctuates', 'my financial capacity to afford these items fluctuates')\n"
     ]
    }
   ],
   "source": [
    "# Replace 'my time fluctuates' with '– my financial capacity to afford these items fluctuates'\n",
    "new_pdata['p7_financialcapacity'] = new_pdata['p7_financialcapacity'].replace('my time fluctuates', 'my financial capacity to afford these items fluctuates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "87faccf8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "87faccf8",
    "outputId": "befe2aee-95cf-41cb-bceb-0bff17af9b64"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\384211807.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p7_financialcapacity'] = new_pdata['p7_financialcapacity'].replace(replacement_dict)\n"
     ]
    }
   ],
   "source": [
    "# Define a dictionary mapping the values to their corresponding short forms\n",
    "replacement_dict = {\n",
    "    'I can afford only some of these items': 'Afford Some',\n",
    "    'my financial capacity to afford these items fluctuates': 'Capacity Fluctuates',\n",
    "    'I cannot afford these items': 'Cannot Afford',\n",
    "    'I can afford these items': 'Can Afford',\n",
    "    'I have more than enough to afford materials': 'Enough to Afford',\n",
    "}\n",
    "\n",
    "# Replace values in the p7_financialcapacity column using the dictionary\n",
    "new_pdata['p7_financialcapacity'] = new_pdata['p7_financialcapacity'].replace(replacement_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "MCvIbz5qXiEs",
   "metadata": {
    "id": "MCvIbz5qXiEs"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "3ba54953",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3ba54953",
    "outputId": "4aaf3689-a34b-412c-fe78-d2375271c053"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2342854922.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p8_financialstability'] = new_pdata['p8_financialstability'].replace('my financial stability fluctuates', 'stability fluctuates')\n"
     ]
    }
   ],
   "source": [
    "# Replace 'my financial stability fluctuates' with 'stability fluctuates' in the p8_financialstability column\n",
    "new_pdata['p8_financialstability'] = new_pdata['p8_financialstability'].replace('my financial stability fluctuates', 'stability fluctuates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "7e99628a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7e99628a",
    "outputId": "a9a93e40-87cf-451c-839d-3f008cd3b4d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['extremely unstable', 'unstable', 'stability fluctuates', 'stable',\n",
       "       nan, 'extremely stable'], dtype=object)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p8_financialstability'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "b9950af4",
   "metadata": {
    "id": "b9950af4"
   },
   "outputs": [],
   "source": [
    "# Check the value of p14_carryingdebt column and fill corresponding p14b_debtmanageable values\n",
    "new_pdata.loc[new_pdata['p14_carryingdebt'] == 'No', 'p14b_debtmanageable'] = 'No Debt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "ce00fc95",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ce00fc95",
    "outputId": "ebc46a91-0db4-4b86-88be-fcd1516cb2c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Unmanageable', 'Manageable', 'No Debt'], dtype=object)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p14b_debtmanageable'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "2c285689",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2c285689",
    "outputId": "c6809964-d9e8-4546-dc12-c447933343cf",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1 – Strongly disagree', '4 – Agree',\n",
       "       '3 – Neither agree nor disagree / Not sure', '5 – Strongly agree',\n",
       "       '2 – Disagree', nan], dtype=object)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p15_physicalhealth'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "2013d380",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2013d380",
    "outputId": "f52a9502-ef1b-4039-cda0-aab950317b03"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\264490288.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = new_pdata[column].astype(str)\n"
     ]
    }
   ],
   "source": [
    "# Iterate over columns\n",
    "for column in new_pdata.columns:\n",
    "    # Check if the column dtype is object (string)\n",
    "    if new_pdata[column].dtype == 'object':\n",
    "        # Convert the column to string type\n",
    "        new_pdata[column] = new_pdata[column].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "5c95fc41",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5c95fc41",
    "outputId": "5eeef227-5462-4eeb-d38f-a90c8aa5805e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\252520500.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p15_physicalhealth'] = new_pdata['p15_physicalhealth'].map(value_map)\n"
     ]
    }
   ],
   "source": [
    "# Define a dictionary mapping old values to new values\n",
    "value_map = {\n",
    "    '1 – Strongly disagree': 'Strongly disagree',\n",
    "    '2 – Disagree': 'Disagree',\n",
    "    '3 – Neither agree nor disagree / Not sure': 'Not Sure',\n",
    "    '4 – Agree': 'Agree',\n",
    "    '5 – Strongly agree': 'Strongly agree'\n",
    "}\n",
    "\n",
    "# Replace values in 'p15_physicalhealth' column\n",
    "new_pdata['p15_physicalhealth'] = new_pdata['p15_physicalhealth'].map(value_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "0a5f2866",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0a5f2866",
    "outputId": "3ac15913-9953-4e5f-a8f5-af215111b3d3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3329239412.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[col] = new_pdata[col].map(value_map)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3329239412.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[col] = new_pdata[col].map(value_map)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3329239412.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[col] = new_pdata[col].map(value_map)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3329239412.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[col] = new_pdata[col].map(value_map)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3329239412.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[col] = new_pdata[col].map(value_map)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3329239412.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[col] = new_pdata[col].map(value_map)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3329239412.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[col] = new_pdata[col].map(value_map)\n"
     ]
    }
   ],
   "source": [
    "col_lst = ['p16_mentalhealth','p17_stablehousing','p18_feedmyself', 'p19_socialrelationships', 'p20_purposefullife','p21_agency', 'p22_optimistic']\n",
    "for col in col_lst:\n",
    "    new_pdata[col] = new_pdata[col].map(value_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "b3b918a6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b3b918a6",
    "outputId": "94bf8c0c-67f7-46d3-8063-8fe0201fa57b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'I’m not sure', 'Yes', 'nan'], dtype=object)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p23_publicvalueswork'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "402964a5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "402964a5",
    "outputId": "0b292d1a-3e89-4819-872d-4efac7e0802f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\721911171.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p24_articulating'] = new_pdata['p24_articulating'].replace('0', 'I’m not sure')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', 'I’m not sure'], dtype=object)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p24_articulating'] = new_pdata['p24_articulating'].replace('0', 'I’m not sure')\n",
    "new_pdata['p24_articulating'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8058f7a6",
   "metadata": {
    "id": "8058f7a6"
   },
   "source": [
    "## p38_race column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "1fTJq4xyLfLQ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1fTJq4xyLfLQ",
    "outputId": "168e6442-2eae-4820-e8d8-5a1d19f8727f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\4143043241.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  multiracial_df.replace('nan', np.nan, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "multiracial_df = new_pdata\n",
    "multiracial_df.replace('nan', np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "73ae3039",
   "metadata": {
    "id": "73ae3039"
   },
   "outputs": [],
   "source": [
    "columns_to_check = [\n",
    "    \"p38_race2\", \"p38_race3\", \"p38_race4\", \"p38_race5\", \"p38_race6\", \"p38_race7\", \"p38_race8\"\n",
    "]\n",
    "\n",
    "multiracial_df = new_pdata[new_pdata[columns_to_check].notna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "XMmssPJLM3_s",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XMmssPJLM3_s",
    "outputId": "c11ec9d0-321e-4ce6-fa9b-56af08f54611"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['hispanlatinx', 'white', 'raceeth_oth', 'aianfn', 'blackaa',\n",
       "       'pinh', 'asian', 'raceeth_noans'], dtype=object)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiracial_df['p38_race2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "88f6ac0a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "88f6ac0a",
    "outputId": "3982abb3-4517-4d5a-9ab5-74d385a6908b",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     blackaa\n",
       "7     blackaa\n",
       "8       white\n",
       "10      white\n",
       "11    blackaa\n",
       "Name: p38_race1, dtype: object"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p38_race1'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "aa79a9aa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aa79a9aa",
    "outputId": "ed4d30ce-a595-48bc-a4a6-67b1445f1945"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Multiracial', 'blackaa', 'white', 'hispanlatinx', 'asian',\n",
       "       'aianfn', 'raceeth_oth', 'raceeth_noans', 'araborme', nan, 'pinh'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata.loc[multiracial_df.index, 'p38_race1'] = \"Multiracial\"\n",
    "# new_pdata.loc[multiracial_df.index, 'p38_race1'] = \"Multiracial\"\n",
    "new_pdata['p38_race1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "41556aca",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "41556aca",
    "outputId": "fbfcb0a2-39f2-47d7-d623-0600b5c9eeb1"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p38_race1</th>\n",
       "      <th>p38_race2</th>\n",
       "      <th>p38_race3</th>\n",
       "      <th>p38_race4</th>\n",
       "      <th>p38_race5</th>\n",
       "      <th>p38_race6</th>\n",
       "      <th>p38_race7</th>\n",
       "      <th>p38_race8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Multiracial</td>\n",
       "      <td>hispanlatinx</td>\n",
       "      <td>aianfn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>blackaa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>white</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>white</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>blackaa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23792</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23793</th>\n",
       "      <td>white</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23794</th>\n",
       "      <td>Multiracial</td>\n",
       "      <td>hispanlatinx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23795</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23796</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13119 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         p38_race1     p38_race2 p38_race3 p38_race4 p38_race5 p38_race6  \\\n",
       "1      Multiracial  hispanlatinx    aianfn       NaN       NaN       NaN   \n",
       "7          blackaa           NaN       NaN       NaN       NaN       NaN   \n",
       "8            white           NaN       NaN       NaN       NaN       NaN   \n",
       "10           white           NaN       NaN       NaN       NaN       NaN   \n",
       "11         blackaa           NaN       NaN       NaN       NaN       NaN   \n",
       "...            ...           ...       ...       ...       ...       ...   \n",
       "23792          NaN           NaN       NaN       NaN       NaN       NaN   \n",
       "23793        white           NaN       NaN       NaN       NaN       NaN   \n",
       "23794  Multiracial  hispanlatinx       NaN       NaN       NaN       NaN   \n",
       "23795          NaN           NaN       NaN       NaN       NaN       NaN   \n",
       "23796          NaN           NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "      p38_race7 p38_race8  \n",
       "1           NaN       NaN  \n",
       "7           NaN       NaN  \n",
       "8           NaN       NaN  \n",
       "10          NaN       NaN  \n",
       "11          NaN       NaN  \n",
       "...         ...       ...  \n",
       "23792       NaN       NaN  \n",
       "23793       NaN       NaN  \n",
       "23794       NaN       NaN  \n",
       "23795       NaN       NaN  \n",
       "23796       NaN       NaN  \n",
       "\n",
       "[13119 rows x 8 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata[[\"p38_race1\",\"p38_race2\", \"p38_race3\", \"p38_race4\", \"p38_race5\", \"p38_race6\", \"p38_race7\", \"p38_race8\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "95595bfd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "95595bfd",
    "outputId": "ef6891c2-f24e-4bb0-9a57-41940a218cd1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3975850386.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata.drop(columns=columns_to_drop, inplace=True)\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3975850386.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata.rename(columns={'p38_race1': 'p38_race'}, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# List of columns to drop\n",
    "columns_to_drop = [\"p38_race2\", \"p38_race3\", \"p38_race4\", \"p38_race5\", \"p38_race6\", \"p38_race7\", \"p38_race8\"]\n",
    "\n",
    "# Drop unwanted columns\n",
    "new_pdata.drop(columns=columns_to_drop, inplace=True)\n",
    "\n",
    "# Rename g20_raceethnicity1 to g20_raceethnicity\n",
    "new_pdata.rename(columns={'p38_race1': 'p38_race'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "076febf4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "076febf4",
    "outputId": "c17d88d5-c097-461f-c98a-3acd9d6648d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Multiracial', 'blackaa', 'white', 'hispanlatinx', 'asian',\n",
       "       'aianfn', 'raceeth_oth', 'raceeth_noans', 'araborme', nan, 'pinh'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p38_race'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69f085e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4bc84fc9",
   "metadata": {
    "id": "4bc84fc9"
   },
   "source": [
    "## ------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78f8e268",
   "metadata": {
    "id": "78f8e268"
   },
   "source": [
    "## Emergency assistance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "c3c6298a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "id": "c3c6298a",
    "outputId": "4bb21473-bf9a-4552-b6db-6b4a12bb08a1"
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>p1_discipline1</th>\n",
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       "      <th>p_agerange</th>\n",
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       "      <th>13980</th>\n",
       "      <td>OID1P8GAUV</td>\n",
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       "      <td>collab_artists</td>\n",
       "      <td>collab_nonartists</td>\n",
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       "      <td>35-44</td>\n",
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       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
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       "      <th>18258</th>\n",
       "      <td>E46UBCD7OR</td>\n",
       "      <td>Crafts</td>\n",
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       "      <td>solo_artist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>35-44</td>\n",
       "      <td>Completed</td>\n",
       "      <td>twospirit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
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       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
       "      <td>...</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Completed</td>\n",
       "      <td>nonbinary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18812</th>\n",
       "      <td>W6JY15QIHO</td>\n",
       "      <td>Interdisciplinary Arts</td>\n",
       "      <td>Crafts</td>\n",
       "      <td>Film</td>\n",
       "      <td>collab_nonartists</td>\n",
       "      <td>solo_artist</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>teaching_others</td>\n",
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       "      <td>...</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Completed</td>\n",
       "      <td>nonbinary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>23119</th>\n",
       "      <td>DDYTRLUHXY</td>\n",
       "      <td>Music</td>\n",
       "      <td>Oral Traditions</td>\n",
       "      <td>Social Practice</td>\n",
       "      <td>collab_artists</td>\n",
       "      <td>public_involvement</td>\n",
       "      <td>perform_present_exhibit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Funded</td>\n",
       "      <td>woman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 117 columns</p>\n",
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      ],
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       "               id          p1_discipline1   p1_discipline2   p1_discipline3  \\\n",
       "13980  OID1P8GAUV                  Design           Crafts            Dance   \n",
       "18258  E46UBCD7OR                  Crafts            Dance              NaN   \n",
       "18458  BA7W733BMV  Interdisciplinary Arts             Film           Design   \n",
       "18812  W6JY15QIHO  Interdisciplinary Arts           Crafts             Film   \n",
       "23119  DDYTRLUHXY                   Music  Oral Traditions  Social Practice   \n",
       "\n",
       "      p2_approachtopractice1 p2_approachtopractice2   p2_approachtopractice3  \\\n",
       "13980         collab_artists      collab_nonartists              solo_artist   \n",
       "18258            solo_artist                    NaN                      NaN   \n",
       "18458         collab_artists      collab_nonartists              solo_artist   \n",
       "18812      collab_nonartists            solo_artist       public_involvement   \n",
       "23119         collab_artists     public_involvement  perform_present_exhibit   \n",
       "\n",
       "        p2_approachtopractice4   p2_approachtopractice5  \\\n",
       "13980       public_involvement          teaching_others   \n",
       "18258                      NaN                      NaN   \n",
       "18458       public_involvement  perform_present_exhibit   \n",
       "18812  perform_present_exhibit          teaching_others   \n",
       "23119                      NaN                      NaN   \n",
       "\n",
       "      p2_approachtopractice6  ... p_agerange   p_status p41_gender1  \\\n",
       "13980                    NaN  ...      35-44  Completed       woman   \n",
       "18258                    NaN  ...      35-44  Completed   twospirit   \n",
       "18458        teaching_others  ...      25-34  Completed   nonbinary   \n",
       "18812                    NaN  ...      25-34  Completed   nonbinary   \n",
       "23119                    NaN  ...      25-34     Funded       woman   \n",
       "\n",
       "      p41_gender2 p41_gender3 p41_gender4 p41_gender5 p46_caregiving1  \\\n",
       "13980         NaN         NaN         NaN         NaN   no_caregiving   \n",
       "18258         NaN         NaN         NaN         NaN   no_caregiving   \n",
       "18458         NaN         NaN         NaN         NaN   no_caregiving   \n",
       "18812         NaN         NaN         NaN         NaN   no_caregiving   \n",
       "23119         NaN         NaN         NaN         NaN   no_caregiving   \n",
       "\n",
       "      p46_caregiving2 p46_caregiving3  \n",
       "13980             NaN             NaN  \n",
       "18258             NaN             NaN  \n",
       "18458             NaN             NaN  \n",
       "18812             NaN             NaN  \n",
       "23119             NaN             NaN  \n",
       "\n",
       "[5 rows x 117 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data = new_pdata.loc[(new_pdata['p29_emergencyassist'] == 'No') & (new_pdata['p29b_typesofassistance1'].notnull())]\n",
    "filtered_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "e2ba8be2",
   "metadata": {
    "id": "e2ba8be2"
   },
   "outputs": [],
   "source": [
    "new_pdata.loc[(new_pdata['p29_emergencyassist'] == 'No') & (new_pdata['p29b_typesofassistance1'].notnull()), 'p29b_typesofassistance1'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "90C83PRZY8ra",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "90C83PRZY8ra",
    "outputId": "587e9a25-2910-42f8-de48-1c2a31d30c6d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'emergencygrant', 'federalrelief', 'unemploymentbenefits',\n",
       "       'otherassistance', 'familypersonalassist', 'mutualaid'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p29b_typesofassistance1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "1NlkVabCY9C7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1NlkVabCY9C7",
    "outputId": "75984472-536e-4dd4-86d1-ad93a919ed48"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3899269383.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3899269383.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3899269383.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3899269383.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3899269383.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3899269383.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n"
     ]
    }
   ],
   "source": [
    "# Define the columns\n",
    "columns_to_add = ['Federal_relief', 'Unemployment_benefits',\n",
    "                  'Emergency_grant', 'Mutual_aid',\n",
    "                  'Familypersonal_assist',\n",
    "                  'Other_assistance']\n",
    "\n",
    "# Initialize the new columns with zeros\n",
    "for column in columns_to_add:\n",
    "    new_pdata[column] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "4oc14tDsY9Xp",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4oc14tDsY9Xp",
    "outputId": "29464f70-a09f-40f1-9574-962bdc5278c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'federalrelief': 'Federal_relief', 'unemploymentbenefits': 'Unemployment_benefits', 'emergencygrant': 'Emergency_grant', 'mutualaid': 'Mutual_aid', 'familypersonalassist': 'Familypersonal_assist', 'otherassistance': 'Other_assistance'}\n"
     ]
    }
   ],
   "source": [
    "# Define approach values and columns\n",
    "assistance_values = ['federalrelief', 'unemploymentbenefits',\n",
    "                     'emergencygrant', 'mutualaid',\n",
    "                     'familypersonalassist',\n",
    "                     'otherassistance']\n",
    "\n",
    "assistance_cols =  ['Federal_relief', 'Unemployment_benefits',\n",
    "                  'Emergency_grant', 'Mutual_aid',\n",
    "                  'Familypersonal_assist',\n",
    "                  'Other_assistance']\n",
    "\n",
    "# Create a dictionary\n",
    "assistance_dict = dict(zip(assistance_values, assistance_cols))\n",
    "\n",
    "print(assistance_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "WTOyncdrbIo1",
   "metadata": {
    "id": "WTOyncdrbIo1"
   },
   "outputs": [],
   "source": [
    "# Define the approach columns\n",
    "assistance_columns = ['p29b_typesofassistance1', 'p29b_typesofassistance2', 'p29b_typesofassistance3',\n",
    "                      'p29b_typesofassistance4', 'p29b_typesofassistance5','p29b_typesofassistance6']\n",
    "\n",
    "# Iterate over each row\n",
    "for index, row in new_pdata.iterrows():\n",
    "    # Iterate over each approach column\n",
    "    for assist_column in assistance_columns:\n",
    "        assistance_values = row[assist_column]\n",
    "\n",
    "        # Check if the approach value is in the approach_dict keys\n",
    "        if assistance_values in assistance_dict.keys():\n",
    "            # Update the corresponding value column with 1\n",
    "            value_column = assistance_dict[assistance_values]\n",
    "            new_pdata.at[index, value_column] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "rIxQXKBVd0Qt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "rIxQXKBVd0Qt",
    "outputId": "a4980556-df0d-4269-f3d8-d1de6ba2215b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Federal_relief</th>\n",
       "      <th>Unemployment_benefits</th>\n",
       "      <th>Emergency_grant</th>\n",
       "      <th>Mutual_aid</th>\n",
       "      <th>Familypersonal_assist</th>\n",
       "      <th>Other_assistance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23792</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23793</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23794</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23795</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23796</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13119 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Federal_relief  Unemployment_benefits  Emergency_grant  Mutual_aid  \\\n",
       "1                   0                      0                0           0   \n",
       "7                   0                      1                1           0   \n",
       "8                   1                      1                0           0   \n",
       "10                  0                      0                0           0   \n",
       "11                  1                      1                0           0   \n",
       "...               ...                    ...              ...         ...   \n",
       "23792               1                      1                0           0   \n",
       "23793               0                      0                0           0   \n",
       "23794               0                      1                0           0   \n",
       "23795               0                      0                0           0   \n",
       "23796               0                      1                0           0   \n",
       "\n",
       "       Familypersonal_assist  Other_assistance  \n",
       "1                          0                 0  \n",
       "7                          0                 0  \n",
       "8                          0                 0  \n",
       "10                         0                 0  \n",
       "11                         0                 0  \n",
       "...                      ...               ...  \n",
       "23792                      1                 0  \n",
       "23793                      0                 0  \n",
       "23794                      0                 0  \n",
       "23795                      0                 0  \n",
       "23796                      0                 0  \n",
       "\n",
       "[13119 rows x 6 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata[columns_to_add]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9fH8OeBiIGt",
   "metadata": {
    "id": "e9fH8OeBiIGt"
   },
   "source": [
    "### dropping the original assistance columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "yshXI01piPpS",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yshXI01piPpS",
    "outputId": "921f5db4-8c95-4993-d3b9-819c327978c4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2003482138.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata.drop(columns=assistance_columns, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "new_pdata.drop(columns=assistance_columns, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sD5l_mWTNdwF",
   "metadata": {
    "id": "sD5l_mWTNdwF"
   },
   "source": [
    "### Employement impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "Kz5KC1E2iSkI",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Kz5KC1E2iSkI",
    "outputId": "3e12dad0-0253-4207-e1ba-8d486ff14420"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['laidofforfired', 'industryshutdown', 'furloughed',\n",
       "       'newfreelancework', 'freelanceworkcanceled', 'newjob',\n",
       "       'other_empimpact', 'empnotimpacted', 'leftjob', nan], dtype=object)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p30_employimpact1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "UXA96rKIjL94",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UXA96rKIjL94",
    "outputId": "bf7eced0-3396-473a-c005-7f460a147878"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['freelanceworkcanceled', 'industryshutdown', nan, 'furloughed',\n",
       "       'newjob', 'laidofforfired', 'newfreelancework', 'other_empimpact',\n",
       "       'empnotimpacted'], dtype=object)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p30_employimpact2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "9oPwSB2vjVBr",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9oPwSB2vjVBr",
    "outputId": "b4ee8350-eb50-4309-d0ec-e43b78e54bae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'freelanceworkcanceled', 'laidofforfired', 'furloughed',\n",
       "       'industryshutdown', 'other_empimpact', 'newjob', 'empnotimpacted'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p30_employimpact3'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "nE9Gt3pQqGKV",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nE9Gt3pQqGKV",
    "outputId": "369f0cfd-5e9f-4b88-95e3-d4f93068f4cd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3898251617.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3898251617.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3898251617.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3898251617.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3898251617.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3898251617.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3898251617.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3898251617.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3898251617.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n"
     ]
    }
   ],
   "source": [
    "# Define the columns\n",
    "columns_to_add = ['LaidofforFired', 'IndustryShutdown', 'Furloughed',\n",
    "       'NewFreelanceWork', 'FreelanceWorkCanceled', 'NewJob',\n",
    "       'Other_EmpImpact', 'EmpnotImpacted', 'LeftJob']\n",
    "\n",
    "# Initialize the new columns with zeros\n",
    "for column in columns_to_add:\n",
    "    new_pdata[column] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "A7PnHS5rxjdF",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A7PnHS5rxjdF",
    "outputId": "079c5a40-797c-4629-de09-a013e0b6bcd0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'laidofforfired': 'LaidofforFired', 'industryshutdown': 'IndustryShutdown', 'furloughed': 'Furloughed', 'newfreelancework': 'NewFreelanceWork', 'freelanceworkcanceled': 'FreelanceWorkCanceled', 'newjob': 'NewJob', 'other_empimpact': 'Other_EmpImpact', 'empnotimpacted': 'EmpnotImpacted', 'leftjob': 'LeftJob'}\n"
     ]
    }
   ],
   "source": [
    "# Define approach values and columns\n",
    "empimpact_values = ['laidofforfired', 'industryshutdown', 'furloughed',\n",
    "       'newfreelancework', 'freelanceworkcanceled', 'newjob',\n",
    "       'other_empimpact', 'empnotimpacted', 'leftjob']\n",
    "\n",
    "empimpact_cols =  ['LaidofforFired', 'IndustryShutdown', 'Furloughed',\n",
    "       'NewFreelanceWork', 'FreelanceWorkCanceled', 'NewJob',\n",
    "       'Other_EmpImpact', 'EmpnotImpacted', 'LeftJob']\n",
    "\n",
    "# Create a dictionary\n",
    "empimpact_dict = dict(zip(empimpact_values, empimpact_cols))\n",
    "\n",
    "print(empimpact_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "_G2q2-RE0pKM",
   "metadata": {
    "id": "_G2q2-RE0pKM"
   },
   "outputs": [],
   "source": [
    "# Define the approach columns\n",
    "empimp_columns = ['p30_employimpact1','p30_employimpact2','p30_employimpact3',\n",
    "                  'p30_employimpact4','p30_employimpact5','p30_employimpact6',\n",
    "                  'p30_employimpact7','p30_employimpact8']\n",
    "\n",
    "# Iterate over each row\n",
    "for index, row in new_pdata.iterrows():\n",
    "    # Iterate over each approach column\n",
    "    for empimp_column in empimp_columns:\n",
    "        empimpact_values = row[empimp_column]\n",
    "        # Check if the approach value is in the approach_dict keys\n",
    "        if empimpact_values in empimpact_dict.keys():\n",
    "            # Update the corresponding value column with 1\n",
    "            value_column = empimpact_dict[empimpact_values]\n",
    "            new_pdata.at[index, value_column] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "PjOuMpNI3CHv",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "PjOuMpNI3CHv",
    "outputId": "f0194daa-e363-476e-b0bc-a52c47a4e1cd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LaidofforFired</th>\n",
       "      <th>IndustryShutdown</th>\n",
       "      <th>Furloughed</th>\n",
       "      <th>NewFreelanceWork</th>\n",
       "      <th>FreelanceWorkCanceled</th>\n",
       "      <th>NewJob</th>\n",
       "      <th>Other_EmpImpact</th>\n",
       "      <th>EmpnotImpacted</th>\n",
       "      <th>LeftJob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>23792</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23793</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23794</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>23795</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13119 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LaidofforFired  IndustryShutdown  Furloughed  NewFreelanceWork  \\\n",
       "1                   1                 0           0                 0   \n",
       "7                   1                 0           0                 0   \n",
       "8                   1                 1           0                 0   \n",
       "10                  0                 1           0                 0   \n",
       "11                  0                 1           1                 0   \n",
       "...               ...               ...         ...               ...   \n",
       "23792               0                 0           0                 0   \n",
       "23793               0                 0           0                 0   \n",
       "23794               0                 1           0                 0   \n",
       "23795               0                 0           0                 1   \n",
       "23796               0                 0           1                 1   \n",
       "\n",
       "       FreelanceWorkCanceled  NewJob  Other_EmpImpact  EmpnotImpacted  LeftJob  \n",
       "1                          1       0                0               0        0  \n",
       "7                          1       0                0               0        0  \n",
       "8                          1       0                0               0        0  \n",
       "10                         0       0                0               0        0  \n",
       "11                         1       0                0               0        0  \n",
       "...                      ...     ...              ...             ...      ...  \n",
       "23792                      1       1                0               0        0  \n",
       "23793                      1       0                0               0        0  \n",
       "23794                      1       0                0               0        1  \n",
       "23795                      0       0                0               0        0  \n",
       "23796                      1       0                0               0        1  \n",
       "\n",
       "[13119 rows x 9 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata[empimpact_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yPISbaRl3gu6",
   "metadata": {
    "id": "yPISbaRl3gu6"
   },
   "source": [
    "### dropping the original employ impact columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "q_8Qqcq03Om8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q_8Qqcq03Om8",
    "outputId": "c95ad52f-9f05-4c89-e10d-72dd87e53d75"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\413820956.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata.drop(columns=empimp_columns, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "new_pdata.drop(columns=empimp_columns, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "v2mHo6P-M1kN",
   "metadata": {
    "id": "v2mHo6P-M1kN"
   },
   "source": [
    "### Artistic Practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "5o08s_Q84pom",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5o08s_Q84pom",
    "outputId": "100c3154-5729-4fc7-d2e6-6d1908042e3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['canceledtravelprevented', 'newseriesofwork', 'nolongercollab',\n",
       "       'affordstudio', 'workonline', 'showscanceled', 'lessmotivated',\n",
       "       'addresscommunityneeds', 'newcollab', 'moremotivated', nan,\n",
       "       'artspractice_noimpact', 'artpracticeimpact_oth', 'newexhibitions',\n",
       "       'studioclosed'], dtype=object)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p31_artpracticeimpact1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "da2VH3-zNof0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "da2VH3-zNof0",
    "outputId": "cde677ff-3066-482b-bb3c-5606608b21c2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\352462723.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n"
     ]
    }
   ],
   "source": [
    "# Define the columns\n",
    "columns_to_add =  ['ShowsCanceled', 'CanceledTravelPrevented', 'StudioClosed',\n",
    "                   'AffordStudio','NoLongerCollab', 'LessMotivated', 'MoreMotivated',\n",
    "                   'NewCollab', 'AddressCommunityNeeds', 'WorkOnline', 'NewSeriesofWork',\n",
    "                   'NewExhibitions','OtherArtPracticeImpact','ArtsPractice_NoImpact']\n",
    "\n",
    "# Initialize the new columns with zeros\n",
    "for column in columns_to_add:\n",
    "    new_pdata[column] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "4b2r-XYERKAp",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4b2r-XYERKAp",
    "outputId": "c54f37e0-9a9e-4243-caf9-3c95212b8d0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'showscanceled': 'ShowsCanceled', 'canceledtravelprevented': 'CanceledTravelPrevented', 'studioclosed': 'StudioClosed', 'affordstudio': 'AffordStudio', 'nolongercollab': 'NoLongerCollab', 'lessmotivated': 'LessMotivated', 'moremotivated': 'MoreMotivated', 'newcollab': 'NewCollab', 'addresscommunityneeds': 'AddressCommunityNeeds', 'workonline': 'WorkOnline', 'newseriesofwork': 'NewSeriesofWork', 'newexhibitions': 'NewExhibitions', 'artpracticeimpact_oth': 'OtherArtPracticeImpact', 'artspractice_noimpact': 'ArtsPractice_NoImpact'}\n"
     ]
    }
   ],
   "source": [
    "# Define approach values and columns\n",
    "artpractice_values = ['showscanceled', 'canceledtravelprevented', 'studioclosed',\n",
    "                   'affordstudio','nolongercollab', 'lessmotivated', 'moremotivated',\n",
    "                   'newcollab', 'addresscommunityneeds', 'workonline', 'newseriesofwork',\n",
    "                   'newexhibitions','artpracticeimpact_oth','artspractice_noimpact']\n",
    "\n",
    "artpractice_cols =  ['ShowsCanceled', 'CanceledTravelPrevented', 'StudioClosed',\n",
    "                   'AffordStudio','NoLongerCollab', 'LessMotivated', 'MoreMotivated',\n",
    "                   'NewCollab', 'AddressCommunityNeeds', 'WorkOnline', 'NewSeriesofWork',\n",
    "                   'NewExhibitions','OtherArtPracticeImpact','ArtsPractice_NoImpact']\n",
    "\n",
    "# Create a dictionary\n",
    "artpractice_dict = dict(zip(artpractice_values, artpractice_cols))\n",
    "\n",
    "print(artpractice_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "u6dSljwaSWCr",
   "metadata": {
    "id": "u6dSljwaSWCr"
   },
   "outputs": [],
   "source": [
    "# Define the approach columns\n",
    "artpractice_columns = ['p31_artpracticeimpact1','p31_artpracticeimpact2','p31_artpracticeimpact3',\n",
    "                  'p31_artpracticeimpact4','p31_artpracticeimpact5','p31_artpracticeimpact6',\n",
    "                  'p31_artpracticeimpact7','p31_artpracticeimpact8','p31_artpracticeimpact9',\n",
    "                  'p31_artpracticeimpact10','p31_artpracticeimpact11','p31_artpracticeimpact12',\n",
    "                       'p31_artpracticeimpact13']\n",
    "\n",
    "# Iterate over each row\n",
    "for index, row in new_pdata.iterrows():\n",
    "    # Iterate over each approach column\n",
    "    for artpractice_column in artpractice_columns:\n",
    "        artpractice_values = row[artpractice_column]\n",
    "        # Check if the approach value is in the approach_dict keys\n",
    "        if artpractice_values in artpractice_dict.keys():\n",
    "            # Update the corresponding value column with 1\n",
    "            value_column = artpractice_dict[artpractice_values]\n",
    "            new_pdata.at[index, value_column] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "5oj4yOzTTjtr",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "5oj4yOzTTjtr",
    "outputId": "4f7f3ad9-f02c-4c84-ba71-50ada0a62399"
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>ShowsCanceled</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13119 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ShowsCanceled  CanceledTravelPrevented  StudioClosed  AffordStudio  \\\n",
       "1                  1                        1             0             1   \n",
       "7                  1                        0             1             0   \n",
       "8                  0                        0             1             0   \n",
       "10                 0                        0             0             0   \n",
       "11                 1                        0             0             0   \n",
       "...              ...                      ...           ...           ...   \n",
       "23792              0                        0             0             0   \n",
       "23793              1                        0             0             0   \n",
       "23794              0                        1             1             1   \n",
       "23795              1                        1             1             1   \n",
       "23796              1                        1             0             1   \n",
       "\n",
       "       NoLongerCollab  LessMotivated  MoreMotivated  NewCollab  \\\n",
       "1                   1              1              0          0   \n",
       "7                   0              0              1          0   \n",
       "8                   1              0              0          1   \n",
       "10                  1              1              0          1   \n",
       "11                  1              0              1          0   \n",
       "...               ...            ...            ...        ...   \n",
       "23792               0              0              0          1   \n",
       "23793               1              1              0          0   \n",
       "23794               1              0              0          0   \n",
       "23795               1              1              1          1   \n",
       "23796               1              0              1          1   \n",
       "\n",
       "       AddressCommunityNeeds  WorkOnline  NewSeriesofWork  NewExhibitions  \\\n",
       "1                          0           0                1               0   \n",
       "7                          1           1                1               1   \n",
       "8                          0           0                0               0   \n",
       "10                         0           0                1               0   \n",
       "11                         0           1                1               0   \n",
       "...                      ...         ...              ...             ...   \n",
       "23792                      1           1                1               0   \n",
       "23793                      1           1                1               0   \n",
       "23794                      0           1                0               0   \n",
       "23795                      1           1                1               1   \n",
       "23796                      1           1                1               1   \n",
       "\n",
       "       OtherArtPracticeImpact  ArtsPractice_NoImpact  \n",
       "1                           0                      0  \n",
       "7                           0                      0  \n",
       "8                           1                      0  \n",
       "10                          0                      0  \n",
       "11                          0                      0  \n",
       "...                       ...                    ...  \n",
       "23792                       0                      0  \n",
       "23793                       0                      0  \n",
       "23794                       0                      0  \n",
       "23795                       0                      0  \n",
       "23796                       0                      0  \n",
       "\n",
       "[13119 rows x 14 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata[artpractice_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9XYGAMDCUDt0",
   "metadata": {
    "id": "9XYGAMDCUDt0"
   },
   "source": [
    "#### dropping original artpractice columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "Hzg4X2DkTrbW",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Hzg4X2DkTrbW",
    "outputId": "25432db9-8b50-4fef-8854-dd0e10089219"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1755177911.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata.drop(columns=artpractice_columns, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "new_pdata.drop(columns=artpractice_columns, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "vI0ihklhUQ9b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vI0ihklhUQ9b",
    "outputId": "2905e243-8314-4223-da48-8ccbe1ab5aaf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13119, 119)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7yMSuvjBVbdC",
   "metadata": {
    "id": "7yMSuvjBVbdC"
   },
   "source": [
    "#### Well being"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "W0IqqHN4UUrW",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W0IqqHN4UUrW",
    "outputId": "ad82be22-21c6-4ea4-9254-cc04e7663574"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['accumulateddebt', 'anxiety', 'foodinsecurity', 'becamecaregiver',\n",
       "       'wellbeing_noimpact', 'physicalhealthimprove',\n",
       "       'mentalhealthimprove', nan, 'selfcareimprove', 'loneliness',\n",
       "       'gotsick', 'housinginsecurity', 'hadtomove', 'wellbeingimpact_oth'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p32_wellbeingimpact1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "lQ2WSTLBVqXj",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lQ2WSTLBVqXj",
    "outputId": "527b48d6-6c8f-4fa6-c419-bef5aa1d9556"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n",
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2059201520.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata[column] = 0\n"
     ]
    }
   ],
   "source": [
    "# Define the columns\n",
    "columns_to_add =  ['Anxiety','Loneliness','GotSick','FoodInsecurity',\n",
    "                   'HousingInsecurity','HadtoMove','AccumulatedDebt',\n",
    "                    'BecameCareGiver', 'SelfCareImprove','MentalHealthImprove',\n",
    "                   'PhysicalHealthImprove','OtherWellBeingImpact','WellBeing_NoImpact']\n",
    "# Initialize the new columns with zeros\n",
    "for column in columns_to_add:\n",
    "    new_pdata[column] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "--IOErInYSxy",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "--IOErInYSxy",
    "outputId": "0fbe61ae-8593-4a23-e774-f6503bf2f1d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'anxiety': 'Anxiety', 'loneliness': 'Loneliness', 'gotsick': 'GotSick', 'foodinsecurity': 'FoodInsecurity', 'housinginsecurity': 'HousingInsecurity', 'hadtomove': 'HadtoMove', 'accumulateddebt': 'AccumulatedDebt', 'becamecaregiver': 'BecameCareGiver', 'selfcareimprove': 'SelfCareImprove', 'mentalhealthimprove': 'MentalHealthImprove', 'physicalhealthimprove': 'PhysicalHealthImprove', 'wellbeingimpact_oth': 'OtherWellBeingImpact', 'wellbeing_noimpact': 'WellBeing_NoImpact'}\n"
     ]
    }
   ],
   "source": [
    "# Define approach values and columns\n",
    "wellbeing_values = ['anxiety','loneliness','gotsick','foodinsecurity',\n",
    "                   'housinginsecurity','hadtomove','accumulateddebt',\n",
    "                    'becamecaregiver', 'selfcareimprove','mentalhealthimprove',\n",
    "                   'physicalhealthimprove','wellbeingimpact_oth','wellbeing_noimpact']\n",
    "\n",
    "wellbeing_cols =  ['Anxiety','Loneliness','GotSick','FoodInsecurity',\n",
    "                   'HousingInsecurity','HadtoMove','AccumulatedDebt',\n",
    "                    'BecameCareGiver', 'SelfCareImprove','MentalHealthImprove',\n",
    "                   'PhysicalHealthImprove','OtherWellBeingImpact','WellBeing_NoImpact']\n",
    "\n",
    "# Create a dictionary\n",
    "wellbeing_dict = dict(zip(wellbeing_values, wellbeing_cols))\n",
    "\n",
    "print(wellbeing_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "bV0XC0ziV7f1",
   "metadata": {
    "id": "bV0XC0ziV7f1"
   },
   "outputs": [],
   "source": [
    "# Define the approach columns\n",
    "wellbeing_columns = ['p32_wellbeingimpact1','p32_wellbeingimpact2','p32_wellbeingimpact3',\n",
    "                  'p32_wellbeingimpact4','p32_wellbeingimpact5','p32_wellbeingimpact6',\n",
    "                  'p32_wellbeingimpact7','p32_wellbeingimpact8','p32_wellbeingimpact9',\n",
    "                  'p32_wellbeingimpact10','p32_wellbeingimpact11','p32_wellbeingimpact12']\n",
    "\n",
    "# Iterate over each row\n",
    "for index, row in new_pdata.iterrows():\n",
    "    # Iterate over each approach column\n",
    "    for wellbeing_column in wellbeing_columns:\n",
    "        wellbeing_values = row[wellbeing_column]\n",
    "        # Check if the approach value is in the approach_dict keys\n",
    "        if wellbeing_values in wellbeing_dict.keys():\n",
    "            # Update the corresponding value column with 1\n",
    "            value_column = wellbeing_dict[wellbeing_values]\n",
    "            new_pdata.at[index, value_column] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "BuLorzi9aZoU",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "BuLorzi9aZoU",
    "outputId": "0cdbfb4d-c4c9-478c-a270-91727eb839d1"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Anxiety</th>\n",
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       "</table>\n",
       "<p>13119 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Anxiety  Loneliness  GotSick  FoodInsecurity  HousingInsecurity  \\\n",
       "1            1           1        1               1                  1   \n",
       "7            1           1        0               0                  0   \n",
       "8            1           1        0               1                  0   \n",
       "10           1           1        1               1                  1   \n",
       "11           1           0        0               0                  0   \n",
       "...        ...         ...      ...             ...                ...   \n",
       "23792        1           1        0               0                  0   \n",
       "23793        1           1        1               0                  1   \n",
       "23794        1           0        1               1                  1   \n",
       "23795        1           1        1               0                  0   \n",
       "23796        1           1        1               0                  0   \n",
       "\n",
       "       HadtoMove  AccumulatedDebt  BecameCareGiver  SelfCareImprove  \\\n",
       "1              0                1                1                0   \n",
       "7              0                0                0                0   \n",
       "8              1                1                0                0   \n",
       "10             0                1                0                0   \n",
       "11             0                1                0                1   \n",
       "...          ...              ...              ...              ...   \n",
       "23792          0                0                0                1   \n",
       "23793          1                1                0                0   \n",
       "23794          1                1                0                1   \n",
       "23795          0                1                0                1   \n",
       "23796          0                0                0                1   \n",
       "\n",
       "       MentalHealthImprove  PhysicalHealthImprove  OtherWellBeingImpact  \\\n",
       "1                        0                      0                     0   \n",
       "7                        0                      0                     0   \n",
       "8                        0                      0                     0   \n",
       "10                       0                      0                     0   \n",
       "11                       1                      1                     0   \n",
       "...                    ...                    ...                   ...   \n",
       "23792                    1                      0                     0   \n",
       "23793                    0                      0                     0   \n",
       "23794                    0                      0                     0   \n",
       "23795                    1                      1                     0   \n",
       "23796                    0                      0                     0   \n",
       "\n",
       "       WellBeing_NoImpact  \n",
       "1                       0  \n",
       "7                       0  \n",
       "8                       0  \n",
       "10                      0  \n",
       "11                      0  \n",
       "...                   ...  \n",
       "23792                   0  \n",
       "23793                   0  \n",
       "23794                   0  \n",
       "23795                   0  \n",
       "23796                   0  \n",
       "\n",
       "[13119 rows x 13 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata[wellbeing_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "dV_VZTIiaieP",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dV_VZTIiaieP",
    "outputId": "3cc32ba8-a68b-428e-f49e-82c37835b5b1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3655629671.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata.drop(columns=wellbeing_columns, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "new_pdata.drop(columns=wellbeing_columns, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "Ez_t1B9QYMnz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ez_t1B9QYMnz",
    "outputId": "7bc9b511-2ce6-4220-faeb-f6589df5797f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13119, 120)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "J4WRGXYoYUA6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J4WRGXYoYUA6",
    "outputId": "93e1ad86-754c-4692-bdf3-66cd79af1cf2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New York City', 'Southern Tier', 'Mid-Hudson', 'Capital Region',\n",
       "       'Finger Lakes', nan, 'Long Island', 'Western New York',\n",
       "       'Central New York', 'Mohawk Valley', 'North Country',\n",
       "       'Western New Yorkâ€¯'], dtype=object)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p35_region'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "M0NWM1hmsO0n",
   "metadata": {
    "id": "M0NWM1hmsO0n"
   },
   "source": [
    "**Replacing 'Western New Yorkâ€¯' with 'Western New York'**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "OsXz1H4hlgZa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OsXz1H4hlgZa",
    "outputId": "87bdf26f-acf9-4131-f42d-ee8078342b4d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\294708315.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['p35_region'] = new_pdata['p35_region'].replace({'Western New Yorkâ€¯': 'Western New York'})\n"
     ]
    }
   ],
   "source": [
    "new_pdata['p35_region'] = new_pdata['p35_region'].replace({'Western New Yorkâ€¯': 'Western New York'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "uV66HxQFskEt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uV66HxQFskEt",
    "outputId": "3f1625f0-136f-4cfd-ac99-03fe60ed3026"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New York City', 'Southern Tier', 'Mid-Hudson', 'Capital Region',\n",
       "       'Finger Lakes', nan, 'Long Island', 'Western New York',\n",
       "       'Central New York', 'Mohawk Valley', 'North Country'], dtype=object)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p35_region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "ofrFeBpKs_Tx",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ofrFeBpKs_Tx",
    "outputId": "e9f6e3db-52c2-4cc3-8c14-7c566bd0b77e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['English', 'Spanish', 'Other (please describe)', 'Russian',\n",
       "       'Korean', 'Mandarin or Cantonese', 'Polish', 'Italian', 'Arabic',\n",
       "       nan, 'Haitian Creole', 'Prefer not to answer', 'Bengali',\n",
       "       'Other (please describe):', 'Yiddish'], dtype=object)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p40_language'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cq_Yqgu3_BFo",
   "metadata": {
    "id": "cq_Yqgu3_BFo"
   },
   "source": [
    "### p41_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "ciD8xpqDtDKI",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ciD8xpqDtDKI",
    "outputId": "28c09dd7-59f9-410a-86b8-374bdb97eb5f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\1609173116.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['new_gender'] = new_pdata['p41_gender1']\n"
     ]
    }
   ],
   "source": [
    "new_pdata['new_gender'] = new_pdata['p41_gender1']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "T1sYeMfrGdYd",
   "metadata": {
    "id": "T1sYeMfrGdYd"
   },
   "source": [
    "**Non-binary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "cgQmdeOOAtRw",
   "metadata": {
    "id": "cgQmdeOOAtRw"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Condition 1\n",
    "condition_1 = (new_pdata['p41_gender1'] == 'nonbinary') & (new_pdata['p41_gender2'] == 'woman') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_1, 'new_gender'] = 'nonbinary'\n",
    "\n",
    "# Condition 2\n",
    "condition_2 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'nonbinary') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_2, 'new_gender'] = 'nonbinary'\n",
    "\n",
    "# Condition 3\n",
    "condition_3 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'nonbinary') & (new_pdata['p41_gender3'] == 'woman') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_3, 'new_gender'] = 'nonbinary'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GWZ9J4eeIKb4",
   "metadata": {
    "id": "GWZ9J4eeIKb4"
   },
   "source": [
    "**Two spirit**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "QvUVMwehIKFL",
   "metadata": {
    "id": "QvUVMwehIKFL"
   },
   "outputs": [],
   "source": [
    "# Condition 1\n",
    "condition_1 = (new_pdata['p41_gender1'] == 'twospirit') & (new_pdata['p41_gender2'] == 'woman') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_1, 'new_gender'] = 'twospirit'\n",
    "\n",
    "# Condition 2\n",
    "condition_2 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'twospirit') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_2, 'new_gender'] = 'twospirit'\n",
    "\n",
    "# Condition 3\n",
    "condition_3 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'woman') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_3, 'new_gender'] = 'twospirit'\n",
    "\n",
    "condition_4 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'twospirit') & (new_pdata['p41_gender3'] == 'woman') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_4, 'new_gender'] = 'twospirit'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0fUJaeKMvEw",
   "metadata": {
    "id": "f0fUJaeKMvEw"
   },
   "source": [
    "**others**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "9aUNWkBcMuwA",
   "metadata": {
    "id": "9aUNWkBcMuwA"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Condition 1\n",
    "condition_1 = (new_pdata['p41_gender1'] == 'nonbinary') & (new_pdata['p41_gender2'] == 'gender_oth') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_1, 'new_gender'] = 'gender_oth'\n",
    "\n",
    "# Condition 2\n",
    "condition_2 = (new_pdata['p41_gender1'] == 'gender_oth') & (new_pdata['p41_gender2'] == 'woman') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_2, 'new_gender'] = 'gender_oth'\n",
    "\n",
    "# Condition 3\n",
    "condition_3 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'gender_oth') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_3, 'new_gender'] = 'gender_oth'\n",
    "\n",
    "# Condition 4\n",
    "condition_4 = (new_pdata['p41_gender1'] == 'nonbinary') & (new_pdata['p41_gender2'] == 'gender_oth') & (new_pdata['p41_gender3'] == 'woman') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_4, 'new_gender'] = 'gender_oth'\n",
    "\n",
    "# Condition 5\n",
    "condition_5 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'nonbinary') & (new_pdata['p41_gender3'] == 'gender_oth') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_5, 'new_gender'] = 'gender_oth'\n",
    "\n",
    "# Condition 6\n",
    "condition_6 = (new_pdata['p41_gender1'] == 'nonbinary') & (new_pdata['p41_gender2'] == 'gender_oth') & (new_pdata['p41_gender3'] == 'twospirit') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_6, 'new_gender'] = 'gender_oth'\n",
    "\n",
    "# Condition 7\n",
    "condition_7 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'nonbinary') & (new_pdata['p41_gender3'] == 'gender_oth') & (new_pdata['p41_gender4'] == 'woman') & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_7, 'new_gender'] = 'gender_oth'\n",
    "\n",
    "# Condition 8\n",
    "condition_8 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'nonbinary') & (new_pdata['p41_gender3'] == 'gender_oth') & (new_pdata['p41_gender4'] == 'twospirit') & (new_pdata['p41_gender5'] == 'woman')\n",
    "new_pdata.loc[condition_8, 'new_gender'] = 'gender_oth'\n",
    "\n",
    "# Condition 9\n",
    "condition_9 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'nonbinary') & (new_pdata['p41_gender3'] == 'gender_oth') & (new_pdata['p41_gender4'] == 'twospirit') & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_9, 'new_gender'] = 'gender_oth'\n",
    "\n",
    "# Condition 10\n",
    "condition_10 = (new_pdata['p41_gender1'] == 'gender_oth') & (new_pdata['p41_gender2'] == 'twospirit') & (new_pdata['p41_gender3'] == 'woman') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_10, 'new_gender'] = 'gender_oth'\n",
    "\n",
    "# Condition 11\n",
    "condition_11 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'gender_oth') & (new_pdata['p41_gender3'] == 'woman') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_11, 'new_gender'] = 'gender_oth'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "he2kI7BTRVYW",
   "metadata": {
    "id": "he2kI7BTRVYW"
   },
   "source": [
    "**prefer not to answer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "WpN0t_DxRVJQ",
   "metadata": {
    "id": "WpN0t_DxRVJQ"
   },
   "outputs": [],
   "source": [
    "# Condition 1\n",
    "condition_1 = (new_pdata['p41_gender1'] == 'gender_noanswer') & (new_pdata['p41_gender2'] == 'man') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_1, 'new_gender'] = 'gender_noanswer'\n",
    "\n",
    "# Condition 2\n",
    "condition_2 = (new_pdata['p41_gender1'] == 'gender_noanswer') & (new_pdata['p41_gender2'] == 'woman') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_2, 'new_gender'] = 'gender_noanswer'\n",
    "\n",
    "# Condition 3\n",
    "condition_3 = (new_pdata['p41_gender1'] == 'gender_noanswer') & (new_pdata['p41_gender2'] == 'gender_oth') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_3, 'new_gender'] = 'gender_noanswer'\n",
    "\n",
    "# Condition 4\n",
    "condition_4 = (new_pdata['p41_gender1'] == 'gender_noanswer') & (new_pdata['p41_gender2'] == 'nonbinary') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_4, 'new_gender'] = 'gender_noanswer'\n",
    "\n",
    "# Condition 5\n",
    "condition_5 = (new_pdata['p41_gender1'] == 'gender_noanswer') & (new_pdata['p41_gender2'] == 'man') & (new_pdata['p41_gender3'] == 'twospirit') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_5, 'new_gender'] = 'gender_noanswer'\n",
    "\n",
    "# Condition 6\n",
    "condition_6 = (new_pdata['p41_gender1'] == 'gender_noanswer') & (new_pdata['p41_gender2'] == 'nonbinary') & (new_pdata['p41_gender3'] == 'twospirit') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_6, 'new_gender'] = 'gender_noanswer'\n",
    "\n",
    "# Condition 7\n",
    "condition_7 = (new_pdata['p41_gender1'] == 'gender_noanswer') & (new_pdata['p41_gender2'] == 'nonbinary') & (new_pdata['p41_gender3'] == 'woman') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_7, 'new_gender'] = 'gender_noanswer'\n",
    "\n",
    "# Condition 8\n",
    "condition_8 = (new_pdata['p41_gender1'] == 'gender_noanswer') & (new_pdata['p41_gender2'] == 'man') & (new_pdata['p41_gender3'] == 'nonbinary') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_8, 'new_gender'] = 'gender_noanswer'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "0cGy4yJnHS2z",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "0cGy4yJnHS2z",
    "outputId": "287d0343-34d7-478d-9fdb-905ad3a7395e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p41_gender1</th>\n",
       "      <th>p41_gender2</th>\n",
       "      <th>new_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>gender_noanswer</td>\n",
       "      <td>man</td>\n",
       "      <td>gender_noanswer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>gender_noanswer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gender_noanswer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>gender_noanswer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gender_noanswer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>gender_noanswer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gender_noanswer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>gender_noanswer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gender_noanswer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22647</th>\n",
       "      <td>gender_noanswer</td>\n",
       "      <td>woman</td>\n",
       "      <td>gender_noanswer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23310</th>\n",
       "      <td>gender_noanswer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gender_noanswer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23422</th>\n",
       "      <td>gender_noanswer</td>\n",
       "      <td>man</td>\n",
       "      <td>gender_noanswer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23487</th>\n",
       "      <td>gender_noanswer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gender_noanswer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23553</th>\n",
       "      <td>gender_noanswer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gender_noanswer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>292 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           p41_gender1 p41_gender2       new_gender\n",
       "78     gender_noanswer         man  gender_noanswer\n",
       "92     gender_noanswer         NaN  gender_noanswer\n",
       "252    gender_noanswer         NaN  gender_noanswer\n",
       "429    gender_noanswer         NaN  gender_noanswer\n",
       "461    gender_noanswer         NaN  gender_noanswer\n",
       "...                ...         ...              ...\n",
       "22647  gender_noanswer       woman  gender_noanswer\n",
       "23310  gender_noanswer         NaN  gender_noanswer\n",
       "23422  gender_noanswer         man  gender_noanswer\n",
       "23487  gender_noanswer         NaN  gender_noanswer\n",
       "23553  gender_noanswer         NaN  gender_noanswer\n",
       "\n",
       "[292 rows x 3 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assuming 'g23_gender1', 'g23_gender2', 'updated_gender1' are columns in your DataFrame\n",
    "NB_selected_rows = new_pdata[new_pdata['new_gender'] == 'gender_noanswer'][['p41_gender1', 'p41_gender2', 'new_gender']]\n",
    "\n",
    "# Display the shape of the selected rows DataFrame\n",
    "NB_selected_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "DJ6jU_siHmPe",
   "metadata": {
    "id": "DJ6jU_siHmPe"
   },
   "outputs": [],
   "source": [
    "condition_1 = (new_pdata['p41_gender1'] == 'nonbinary') & (new_pdata['p41_gender2'] == 'twospirit') & new_pdata['p41_gender3'].isnull() & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_1, 'new_gender'] = 'dualidentity'\n",
    "\n",
    "# Condition 2\n",
    "condition_2 = (new_pdata['p41_gender1'] == 'nonbinary') & (new_pdata['p41_gender2'] == 'twospirit') & (new_pdata['p41_gender3'] == 'woman') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_2, 'new_gender'] = 'dualidentity'\n",
    "\n",
    "# Condition 3\n",
    "condition_3 = (new_pdata['p41_gender1'] == 'man') & (new_pdata['p41_gender2'] == 'nonbinary') & (new_pdata['p41_gender3'] == 'twospirit') & new_pdata['p41_gender4'].isnull() & new_pdata['p41_gender5'].isnull()\n",
    "new_pdata.loc[condition_3, 'new_gender'] = 'dualidentity'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411a1d8d",
   "metadata": {},
   "source": [
    "## Provide Care"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "512d4e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no_caregiving', 'child', 'providecare_noans', 'spouse_partner',\n",
       "       'adult', nan], dtype=object)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata['p46_caregiving1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "0cc93a2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\2509720410.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['caregiving_status'] = new_pdata['p46_caregiving1'].map(caregiving_mapping)\n"
     ]
    }
   ],
   "source": [
    "# Create a dictionary to map values\n",
    "caregiving_mapping = {\n",
    "    'no_caregiving': 'No',\n",
    "    'child': 'Yes',\n",
    "    'providecare_noans': 'Yes',\n",
    "    'spouse_partner': 'Yes',\n",
    "    'adult': 'Yes'\n",
    "}\n",
    "\n",
    "# Create a new column based on the mapping\n",
    "new_pdata['caregiving_status'] = new_pdata['p46_caregiving1'].map(caregiving_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "e9752a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p46_caregiving1</th>\n",
       "      <th>caregiving_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>child</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>providecare_noans</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>providecare_noans</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>child</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>child</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>spouse_partner</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>child</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>child</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>child</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>child</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
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       "      <th>69</th>\n",
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       "      <th>71</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>adult</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>adult</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>child</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "      p46_caregiving1 caregiving_status\n",
       "1       no_caregiving                No\n",
       "7       no_caregiving                No\n",
       "8       no_caregiving                No\n",
       "10      no_caregiving                No\n",
       "11      no_caregiving                No\n",
       "12      no_caregiving                No\n",
       "14      no_caregiving                No\n",
       "16      no_caregiving                No\n",
       "17      no_caregiving                No\n",
       "18      no_caregiving                No\n",
       "19      no_caregiving                No\n",
       "20      no_caregiving                No\n",
       "21      no_caregiving                No\n",
       "22              child               Yes\n",
       "24      no_caregiving                No\n",
       "25      no_caregiving                No\n",
       "27  providecare_noans               Yes\n",
       "29      no_caregiving                No\n",
       "30  providecare_noans               Yes\n",
       "31      no_caregiving                No\n",
       "32      no_caregiving                No\n",
       "33      no_caregiving                No\n",
       "34      no_caregiving                No\n",
       "35              child               Yes\n",
       "39      no_caregiving                No\n",
       "42      no_caregiving                No\n",
       "43      no_caregiving                No\n",
       "45      no_caregiving                No\n",
       "47              child               Yes\n",
       "48      no_caregiving                No\n",
       "49     spouse_partner               Yes\n",
       "50              child               Yes\n",
       "51              child               Yes\n",
       "53      no_caregiving                No\n",
       "56      no_caregiving                No\n",
       "58              child               Yes\n",
       "59      no_caregiving                No\n",
       "60      no_caregiving                No\n",
       "64      no_caregiving                No\n",
       "65              child               Yes\n",
       "66      no_caregiving                No\n",
       "68      no_caregiving                No\n",
       "69              adult               Yes\n",
       "71      no_caregiving                No\n",
       "75      no_caregiving                No\n",
       "76              adult               Yes\n",
       "77      no_caregiving                No\n",
       "78              adult               Yes\n",
       "79              child               Yes\n",
       "82      no_caregiving                No"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display both columns\n",
    "new_pdata.loc[:, ['p46_caregiving1', 'caregiving_status']].head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "2856e94d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      p46_caregiving1 p46_caregiving2 p46_caregiving3 caregiving_status  \\\n",
      "1       no_caregiving             NaN             NaN                No   \n",
      "7       no_caregiving             NaN             NaN                No   \n",
      "8       no_caregiving             NaN             NaN                No   \n",
      "10      no_caregiving             NaN             NaN                No   \n",
      "11      no_caregiving             NaN             NaN                No   \n",
      "...               ...             ...             ...               ...   \n",
      "23792             NaN             NaN             NaN               NaN   \n",
      "23793   no_caregiving             NaN             NaN                No   \n",
      "23794   no_caregiving             NaN             NaN                No   \n",
      "23795             NaN             NaN             NaN               NaN   \n",
      "23796             NaN             NaN             NaN               NaN   \n",
      "\n",
      "       caregiving_count  \n",
      "1                     0  \n",
      "7                     0  \n",
      "8                     0  \n",
      "10                    0  \n",
      "11                    0  \n",
      "...                 ...  \n",
      "23792                 0  \n",
      "23793                 0  \n",
      "23794                 0  \n",
      "23795                 0  \n",
      "23796                 0  \n",
      "\n",
      "[13119 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sawan\\AppData\\Local\\Temp\\ipykernel_42496\\3795274597.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_pdata['caregiving_count'] = new_pdata.apply(count_non_null, axis=1)\n"
     ]
    }
   ],
   "source": [
    "# Define a function to count non-null values in the specified columns\n",
    "def count_non_null(row):\n",
    "    caregiving_columns = ['p46_caregiving1', 'p46_caregiving2', 'p46_caregiving3']\n",
    "    if row['caregiving_status'] == 'Yes':\n",
    "        return row[caregiving_columns].notnull().sum()\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "# Apply the function row-wise to create the new column caregiving_count\n",
    "new_pdata['caregiving_count'] = new_pdata.apply(count_non_null, axis=1)\n",
    "\n",
    "# Display the DataFrame with the new column\n",
    "print(new_pdata[['p46_caregiving1', 'p46_caregiving2', 'p46_caregiving3', 'caregiving_status', 'caregiving_count' ]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "fbd759a8",
   "metadata": {},
   "outputs": [
    {
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       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
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       "      <td>no_caregiving</td>\n",
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       "      <td>No</td>\n",
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       "      <td>No</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>spouse_partner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>adult</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>adult</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>adult</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>child</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>no_caregiving</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      p46_caregiving1 p46_caregiving2 caregiving_status  caregiving_count\n",
       "1       no_caregiving             NaN                No                 0\n",
       "7       no_caregiving             NaN                No                 0\n",
       "8       no_caregiving             NaN                No                 0\n",
       "10      no_caregiving             NaN                No                 0\n",
       "11      no_caregiving             NaN                No                 0\n",
       "12      no_caregiving             NaN                No                 0\n",
       "14      no_caregiving             NaN                No                 0\n",
       "16      no_caregiving             NaN                No                 0\n",
       "17      no_caregiving             NaN                No                 0\n",
       "18      no_caregiving             NaN                No                 0\n",
       "19      no_caregiving             NaN                No                 0\n",
       "20      no_caregiving             NaN                No                 0\n",
       "21      no_caregiving             NaN                No                 0\n",
       "22              child             NaN               Yes                 1\n",
       "24      no_caregiving             NaN                No                 0\n",
       "25      no_caregiving             NaN                No                 0\n",
       "27  providecare_noans           adult               Yes                 2\n",
       "29      no_caregiving             NaN                No                 0\n",
       "30  providecare_noans             NaN               Yes                 1\n",
       "31      no_caregiving             NaN                No                 0\n",
       "32      no_caregiving             NaN                No                 0\n",
       "33      no_caregiving             NaN                No                 0\n",
       "34      no_caregiving             NaN                No                 0\n",
       "35              child             NaN               Yes                 1\n",
       "39      no_caregiving             NaN                No                 0\n",
       "42      no_caregiving             NaN                No                 0\n",
       "43      no_caregiving             NaN                No                 0\n",
       "45      no_caregiving             NaN                No                 0\n",
       "47              child             NaN               Yes                 1\n",
       "48      no_caregiving             NaN                No                 0\n",
       "49     spouse_partner             NaN               Yes                 1\n",
       "50              child             NaN               Yes                 1\n",
       "51              child             NaN               Yes                 1\n",
       "53      no_caregiving             NaN                No                 0\n",
       "56      no_caregiving             NaN                No                 0\n",
       "58              child             NaN               Yes                 1\n",
       "59      no_caregiving             NaN                No                 0\n",
       "60      no_caregiving             NaN                No                 0\n",
       "64      no_caregiving             NaN                No                 0\n",
       "65              child             NaN               Yes                 1\n",
       "66      no_caregiving             NaN                No                 0\n",
       "68      no_caregiving             NaN                No                 0\n",
       "69              adult             NaN               Yes                 1\n",
       "71      no_caregiving             NaN                No                 0\n",
       "75      no_caregiving             NaN                No                 0\n",
       "76              adult             NaN               Yes                 1\n",
       "77      no_caregiving             NaN                No                 0\n",
       "78              adult             NaN               Yes                 1\n",
       "79              child             NaN               Yes                 1\n",
       "82      no_caregiving             NaN                No                 0"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pdata.loc[:, ['p46_caregiving1', 'p46_caregiving2', 'caregiving_status', 'caregiving_count']].head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "43c6dcb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_pdata.to_csv('survey_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "24a04eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Erie County \\xa0 ', nan, 'New York County (Manhattan) \\xa0 ',\n",
       "       'Kings County (Brooklyn)  ', 'Queens County \\xa0 ',\n",
       "       'Broome County \\xa0 ', 'Ulster County \\xa0 ',\n",
       "       'Suffolk County \\xa0 ', 'Albany County \\xa0 ',\n",
       "       'Greene County \\xa0 ', 'Monroe County \\xa0 ', 'Saratoga County  ',\n",
       "       'Nassau County \\xa0 ', 'Oneida County \\xa0 ',\n",
       "       'Orange County \\xa0 ', 'Rensselaer County \\xa0 ',\n",
       "       'Columbia County \\xa0 ', 'Bronx County \\xa0 ',\n",
       "       'Herkimer County \\xa0 ', 'Westchester County \\xa0\\xa0 ',\n",
       "       'Tompkins County \\xa0 ', 'Onondaga County \\xa0 ',\n",
       "       'Dutchess County \\xa0 ', 'Putnam County \\xa0 ',\n",
       "       'Oswego County \\xa0 ', 'Warren County \\xa0 ',\n",
       "       'Otsego County \\xa0 ', 'Franklin County \\xa0 ',\n",
       "       'Rockland County \\xa0 ', 'Richmond County (Staten Island) \\xa0 ',\n",
       "       'Allegany County \\xa0 ', 'Essex County \\xa0 ',\n",
       "       'Niagara County \\xa0 ', 'Cayuga County \\xa0 ',\n",
       "       'Wyoming County \\xa0 ', 'Clinton County \\xa0 ',\n",
       "       'Chemung County \\xa0 ', 'Ontario County \\xa0 ',\n",
       "       'Washington County \\xa0\\xa0 ', 'St. Lawrence County \\xa0 ',\n",
       "       'Jefferson County \\xa0 ', 'Wayne County \\xa0 ',\n",
       "       'Seneca County \\xa0 ', 'Sullivan County \\xa0 ',\n",
       "       'Delaware County \\xa0 ', 'Chautauqua County \\xa0\\xa0 ',\n",
       "       'Genesee County \\xa0 ', 'Montgomery County \\xa0 ',\n",
       "       'Livingston County \\xa0 ', 'Schuyler County \\xa0 ',\n",
       "       'Schenectady County \\xa0 ', 'Chenango County \\xa0 ',\n",
       "       'Orleans County \\xa0 ', 'Fulton County \\xa0 ',\n",
       "       'Steuben County \\xa0 ', 'Cortland County \\xa0 ',\n",
       "       'Schoharie County \\xa0 ', 'Cattaraugus County \\xa0 ',\n",
       "       'Madison County \\xa0 ', 'Tioga County \\xa0 ', 'Lewis County \\xa0 ',\n",
       "       'Yates County \\xa0 '], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['g16_county'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b9386a4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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