UWWi - Primary_Secondary_Needs_EDA_&_Viz.ipynb

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    "### Summary: \n",
    "- Task #1: [Visualize primary vs secondary needs](#primary_vs_secondary) \n",
    "  - Only used the 2022 interactions dataset since previous years don't have secondary needs data. \n",
    "  - Used headmap to visualize the number of times a secondary need is addressed on the same call as a given primary need. \n",
    "  - Primary needs are ordered based on the sum of the secondary needs count. Adjacent bar chart shows an 'impact' ranking, hopefully helpful to understand which primary need is associated with more secondary needs.   \n",
    "  - The visualization is produced from loading the raw interactions dataset. I did not use a dataset already cleaned by other volunteers to make the code self contained. \n",
    "- Task #2: [Visualize primary vs taxonomy](#taxonomy_vs_primary) \n",
    "  - Used 2020, 2021 and 2022 interactions data\n",
    "  - Also used a headmap for this visualization, but limited the taxonomies to the top ten. The ranking was based on the sum of primary needs related to a given taxonomy name. \n",
    "  - There are 300+ taxonomy names, displaying them all made the heatmap not very useful. Most taxonomy names have a score (total count of associated primary needs) of less than 50, while the top 3 taxonomies were above 1000 counts of associated primary needs. This also made scaling difficult. \n",
    "  - The visualization is produced from loading the raw interactions datasets. I did not use a dataset already cleaned by other volunteers to make the code self contained. \n",
    "  \n",
    "### Next steps:\n",
    "- Add visualizations to PowerBi or WebApp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df957790-258e-402c-8f89-fe7ba17ff9af",
   "metadata": {},
   "source": [
    "### ETL task #1: Visualization of Primary vs Secondary needs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "879ea21d-efcf-4dd1-a748-29804675f849",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from ast import literal_eval #dataset has list and dictionaries stored in columns as string,\n",
    "                             #literal_eval converts them to python list/dict\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6861931-a3ca-4e91-be77-4f6b489bae4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only using 2022 data since previous years don't have secondary needs data. \n",
    "inter22 = pd.read_csv('./uwwi_datasets/uwwi_dataset_interactions/uwwi_dataset_interactions_2022.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "799f597d-db5c-49ef-bc9c-172ec50c1db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Shorten the name of the Primary and Secondary needs columns\n",
    "inter22 = inter22.rename(columns={'InteractionOption_SwfPrimaryNeeds': 'Primary',\n",
    "                        'InteractionOption_SwfSecondaryNeeds': 'Secondary'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94f6ecc8-b90b-426d-a9c3-19a2c829e561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]                                                                   7100\n",
       "['Caller declined']                                                   270\n",
       "['Warm Transferred']                                                    9\n",
       "['Housing Payment Assistance']                                          5\n",
       "['Food']                                                                4\n",
       "['Other']                                                               3\n",
       "['Mental Health', 'Other Financial Assistance']                         1\n",
       "['Shelter']                                                             1\n",
       "['Food', 'Other Financial Assistance']                                  1\n",
       "['Shelter', 'Transportation']                                           1\n",
       "['Housing Payment Assistance', 'Legal Assistance', 'Other']             1\n",
       "['Utility/Energy', 'Other Financial Assistance']                        1\n",
       "['Food', 'Transportation']                                              1\n",
       "['Food', 'Utility/Energy', 'Housing Payment Assistance', 'Other']       1\n",
       "Name: Secondary, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Most Secondary needs rows are empty or are caller declined. Hopefully overtime there will be more Secondary need data to visualize. \n",
    "inter22['Secondary'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1273b86-6a0e-417c-a0c0-738bf39b9ea8",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Secondary</th>\n",
       "      <th>Food</th>\n",
       "      <th>Housing Payment Assistance</th>\n",
       "      <th>Legal Assistance</th>\n",
       "      <th>Mental Health</th>\n",
       "      <th>Other</th>\n",
       "      <th>Other Financial Assistance</th>\n",
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       "      <th>Transportation</th>\n",
       "      <th>Utility/Energy</th>\n",
       "      <th>Warm Transferred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Crisis</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Housing Payment Assistance</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legal Assistance</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mental Health</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other Financial Assistance</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shelter</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supportive Listening</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>Transportation</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "Secondary                   Food  Housing Payment Assistance  \\\n",
       "Primary                                                        \n",
       "Crisis                       1.0                         1.0   \n",
       "Food                         1.0                         3.0   \n",
       "Housing Payment Assistance   0.0                         1.0   \n",
       "Legal Assistance             0.0                         0.0   \n",
       "Mental Health                1.0                         2.0   \n",
       "Other                        0.0                         0.0   \n",
       "Other Financial Assistance   1.0                         0.0   \n",
       "Shelter                      1.0                         1.0   \n",
       "Supportive Listening         3.0                         0.0   \n",
       "Transportation               0.0                         0.0   \n",
       "\n",
       "Secondary                   Legal Assistance  Mental Health  Other  \\\n",
       "Primary                                                              \n",
       "Crisis                                   0.0            0.0    0.0   \n",
       "Food                                     0.0            1.0    0.0   \n",
       "Housing Payment Assistance               0.0            0.0    0.0   \n",
       "Legal Assistance                         0.0            0.0    1.0   \n",
       "Mental Health                            1.0            0.0    2.0   \n",
       "Other                                    0.0            0.0    0.0   \n",
       "Other Financial Assistance               0.0            0.0    0.0   \n",
       "Shelter                                  0.0            0.0    1.0   \n",
       "Supportive Listening                     0.0            0.0    1.0   \n",
       "Transportation                           0.0            0.0    1.0   \n",
       "\n",
       "Secondary                   Other Financial Assistance  Shelter  \\\n",
       "Primary                                                           \n",
       "Crisis                                             0.0      0.0   \n",
       "Food                                               2.0      0.0   \n",
       "Housing Payment Assistance                         0.0      0.0   \n",
       "Legal Assistance                                   0.0      0.0   \n",
       "Mental Health                                      1.0      0.0   \n",
       "Other                                              0.0      1.0   \n",
       "Other Financial Assistance                         1.0      0.0   \n",
       "Shelter                                            0.0      2.0   \n",
       "Supportive Listening                               0.0      0.0   \n",
       "Transportation                                     0.0      0.0   \n",
       "\n",
       "Secondary                   Transportation  Utility/Energy  Warm Transferred  \n",
       "Primary                                                                       \n",
       "Crisis                                 0.0             0.0               3.0  \n",
       "Food                                   0.0             0.0               0.0  \n",
       "Housing Payment Assistance             0.0             0.0               0.0  \n",
       "Legal Assistance                       0.0             0.0               1.0  \n",
       "Mental Health                          0.0             2.0               4.0  \n",
       "Other                                  1.0             0.0               3.0  \n",
       "Other Financial Assistance             0.0             0.0               0.0  \n",
       "Shelter                                1.0             0.0               0.0  \n",
       "Supportive Listening                   1.0             0.0               0.0  \n",
       "Transportation                         0.0             0.0               0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = (inter22\n",
    "    .assign(Primary=inter22.Primary.apply(lambda x: literal_eval(x)), #Convert string literal to list. \n",
    "            Secondary=inter22.Secondary.apply(lambda x: literal_eval(x)) #Convert string literal to list. \n",
    "           )\n",
    "    .explode('Primary') #Place each list item in a row. Some primary entries where list of len > 1 - e.g. ['Food', 'Crisis'] \n",
    "    .explode('Secondary') #Same as with primary, place each list item on a separate row. \n",
    "    [['Primary', 'Secondary']]\n",
    "    .query('Secondary!=\"Caller declined\"') #Remove rows with caller declined entries\n",
    "    .dropna() \n",
    "    .groupby(['Primary','Secondary'])['Secondary'] \n",
    "    .count() #Count the number of times a primary needs triggers a given secondary need\n",
    "    .reset_index(name='ct') #add count to new column name 'ct'\n",
    "    .pivot_table(index='Primary', columns='Secondary', values='ct') #create pivot table to easily produce heatmap from it\n",
    "    .fillna(0)\n",
    "    )\n",
    "df"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "id": "9f4a7923-6143-4cce-ace7-3123b14dbe6a",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Secondary</th>\n",
       "      <th>Food</th>\n",
       "      <th>Housing Payment Assistance</th>\n",
       "      <th>Legal Assistance</th>\n",
       "      <th>Mental Health</th>\n",
       "      <th>Other</th>\n",
       "      <th>Other Financial Assistance</th>\n",
       "      <th>Shelter</th>\n",
       "      <th>Transportation</th>\n",
       "      <th>Utility/Energy</th>\n",
       "      <th>Warm Transferred</th>\n",
       "      <th>impact_on_secondary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mental Health</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shelter</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crisis</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Secondary      Food  Housing Payment Assistance  Legal Assistance  \\\n",
       "Primary                                                             \n",
       "Mental Health   1.0                         2.0               1.0   \n",
       "Food            1.0                         3.0               0.0   \n",
       "Shelter         1.0                         1.0               0.0   \n",
       "Crisis          1.0                         1.0               0.0   \n",
       "Other           0.0                         0.0               0.0   \n",
       "\n",
       "Secondary      Mental Health  Other  Other Financial Assistance  Shelter  \\\n",
       "Primary                                                                    \n",
       "Mental Health            0.0    2.0                         1.0      0.0   \n",
       "Food                     1.0    0.0                         2.0      0.0   \n",
       "Shelter                  0.0    1.0                         0.0      2.0   \n",
       "Crisis                   0.0    0.0                         0.0      0.0   \n",
       "Other                    0.0    0.0                         0.0      1.0   \n",
       "\n",
       "Secondary      Transportation  Utility/Energy  Warm Transferred  \\\n",
       "Primary                                                           \n",
       "Mental Health             0.0             2.0               4.0   \n",
       "Food                      0.0             0.0               0.0   \n",
       "Shelter                   1.0             0.0               0.0   \n",
       "Crisis                    0.0             0.0               3.0   \n",
       "Other                     1.0             0.0               3.0   \n",
       "\n",
       "Secondary      impact_on_secondary  \n",
       "Primary                             \n",
       "Mental Health                 13.0  \n",
       "Food                           7.0  \n",
       "Shelter                        6.0  \n",
       "Crisis                         5.0  \n",
       "Other                          5.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We thought it would be good to sort primary needs based on the total number of secondary needs it triggers\n",
    "#Create a score column named 'impact_on_secondary' to use as sorting criteria\n",
    "df = (df\n",
    "     .assign(impact_on_secondary=df.sum(axis=1)) \n",
    "     .sort_values(by='impact_on_secondary', ascending=False)\n",
    "     )\n",
    "df.head()"
   ]
  },
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   "metadata": {},
   "source": [
    "### Output task #1: Visualization of Primary vs Secondary needs\n",
    "<a id='primary_vs_secondary'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eee03f81-d232-47c7-a95e-1cfe6219c2ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(11, 7), gridspec_kw={'width_ratios': [2, 1]})\n",
    "\n",
    "heat = sns.heatmap(df.drop('impact_on_secondary', axis=1), \n",
    "                   annot=True, \n",
    "                   cmap=\"YlGnBu\", \n",
    "                   #cbar=False, \n",
    "                   ax=axes[0])\n",
    "_ = heat.set(xlabel='Secondary Needs')\n",
    "_ = heat.set(ylabel='Primary Needs')\n",
    "\n",
    "bar = sns.barplot(x=df.impact_on_secondary, \n",
    "            y=df.index, \n",
    "            color='gray',\n",
    "            ax=axes[1])\n",
    "_ = bar.set(yticklabels=[])\n",
    "_ = bar.set(ylabel=None)\n",
    "_ = bar.set(xlabel='No. Secondary needs impacted')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e32bfb1-4ecb-4f90-9ec0-5ee6fe85c2fe",
   "metadata": {},
   "source": [
    "## ETL task #2: Taxonomy vs Primary needs visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "518c4137-eb3d-4c85-be2d-00746731206a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the data again in case this visualization is run independently of the previous one\n",
    "#Using 2020, 2021 and 2022 interactions datasets\n",
    "inter20 = pd.read_csv('./uwwi_datasets/uwwi_dataset_interactions/uwwi_dataset_interactions_2020.csv')\n",
    "inter21 = pd.read_csv('./uwwi_datasets/uwwi_dataset_interactions/uwwi_dataset_interactions_2021.csv')\n",
    "inter22 = pd.read_csv('./uwwi_datasets/uwwi_dataset_interactions/uwwi_dataset_interactions_2022.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9cc69090-1afa-4133-8ea0-f152f2a5f63b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['InteractionOption_SwfPrimaryNeeds', \n",
    "        'InteractionReferral_ReferralsModule']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d3d6940-48b5-454f-8f09-070a7af746e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select only columns of interest for efficiency\n",
    "taxonomy = (pd.concat([inter20[cols], inter21[cols], inter22[cols]])\n",
    "        .dropna()\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ca4fbc4-9add-4df4-adad-f1bc40a50c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a dataframe with taxonomy name as index and primary needs as columns. Values are the number of times a \n",
    "#call with a given taxonomy name had a given primary need \n",
    "taxonomy = (taxonomy\n",
    "        #Convert string literal to list. Primary needs are lists saved as strings\n",
    "        .assign(Primary=taxonomy.InteractionOption_SwfPrimaryNeeds.apply(lambda x: literal_eval(x)), \n",
    "                #Parse the taxonomy name\n",
    "                taxonomy_name=(taxonomy\n",
    "                               .InteractionReferral_ReferralsModule\n",
    "                               .str.split(\"'Taxonomy_Name':\")\n",
    "                               .apply(lambda x: x[1])\n",
    "                               .str.split(',')\n",
    "                               .apply(lambda x: x[0])\n",
    "                               .str.strip().str.replace(\"'\", \"\")\n",
    "                              )\n",
    "                )\n",
    "        .explode('Primary') #Place each list item in a row. Some primary entries where list of len > 1 - e.g. ['Food', 'Crisis'] \n",
    "        [['Primary', 'taxonomy_name']]\n",
    "        .dropna()\n",
    "        .groupby(['taxonomy_name','Primary'])['Primary']\n",
    "        .count()#Count the number of times a taxonomy name is in a call with a given primary need\n",
    "        .reset_index(name='ct')\n",
    "        .pivot_table(index='taxonomy_name', columns='Primary', values='ct')#Pivot table to easily display it as a heatmap\n",
    "        .fillna(0)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ede924e9-f8db-41aa-a387-ad52fb1f83d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We thought it would be good to sort taxonomy names based on the total number of primary needs it is associated with\n",
    "#Create a score column named 'impact_on_primary' to use as sorting criteria\n",
    "taxonomy = (taxonomy\n",
    "            .assign(impact_on_primary=taxonomy.sum(axis=1))\n",
    "            .sort_values(by='impact_on_primary', ascending=False)\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "240a473d-fd78-43bf-aeb7-db9ea3246df0",
   "metadata": {},
   "source": [
    "### Visualization: Taxonomy name vs Primary needs heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13cd9e05-13ad-46d2-90ad-8792323b8eed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Talklines/Warmlines', 'Food Pantries', 'Community Shelters',\n",
       "       'Housing Related Coordinated Entry',\n",
       "       'Electric Service Payment Assistance',\n",
       "       'Low Income/Subsidized Private Rental Housing',\n",
       "       'Rent Payment Assistance', 'General Crisis Intervention Hotlines',\n",
       "       'Directory Assistance', 'Mental Health Crisis Lines',\n",
       "       ...\n",
       "       '\"Renters Tax Credit Information\"', 'Housing Down Payment Loans/Grants',\n",
       "       'In Home Attendants for People With Disabilities',\n",
       "       'Independent Living Communities/Complexes',\n",
       "       'Independent Living Skills Instruction', 'Individual Advocacy',\n",
       "       'Individual Taxpayer Identification Numbers',\n",
       "       'Indoor Air Pollution Information',\n",
       "       'Inpatient Drug Use Disorder Treatment Facilities', 'Youth Shelters'],\n",
       "      dtype='object', name='taxonomy_name', length=371)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are over 300 taxonomy names\n",
    "taxonomy.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aace94bb-5766-4f26-b61d-b36a5a8f72c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select top 10 taxonomy names only. Displaying all makes the heatmap not informative. \n",
    "taxonomy = taxonomy.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "386eea51-f4e2-489a-ba3a-91e9562347b8",
   "metadata": {},
   "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>Primary</th>\n",
       "      <th>Crisis</th>\n",
       "      <th>Directory Assistance</th>\n",
       "      <th>Food</th>\n",
       "      <th>Health Care Coverage</th>\n",
       "      <th>Housing Payment Assistance</th>\n",
       "      <th>Legal Assistance</th>\n",
       "      <th>Mental Health</th>\n",
       "      <th>Other</th>\n",
       "      <th>Other Financial Assistance</th>\n",
       "      <th>Shelter</th>\n",
       "      <th>Substance Use</th>\n",
       "      <th>Supportive Listening</th>\n",
       "      <th>Transportation</th>\n",
       "      <th>Utility/Energy</th>\n",
       "      <th>impact_on_primary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taxonomy_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Talklines/Warmlines</th>\n",
       "      <td>32.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food Pantries</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community Shelters</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Housing Related Coordinated Entry</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electric Service Payment Assistance</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Income/Subsidized Private Rental Housing</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rent Payment Assistance</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Crisis Intervention Hotlines</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Directory Assistance</th>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mental Health Crisis Lines</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Primary                                       Crisis  Directory Assistance  \\\n",
       "taxonomy_name                                                                \n",
       "Talklines/Warmlines                             32.0                   5.0   \n",
       "Food Pantries                                    0.0                   0.0   \n",
       "Community Shelters                               0.0                   1.0   \n",
       "Housing Related Coordinated Entry                0.0                   0.0   \n",
       "Electric Service Payment Assistance              1.0                   0.0   \n",
       "Low Income/Subsidized Private Rental Housing     0.0                   2.0   \n",
       "Rent Payment Assistance                          2.0                   1.0   \n",
       "General Crisis Intervention Hotlines            44.0                   1.0   \n",
       "Directory Assistance                             0.0                  84.0   \n",
       "Mental Health Crisis Lines                      23.0                   0.0   \n",
       "\n",
       "Primary                                        Food  Health Care Coverage  \\\n",
       "taxonomy_name                                                               \n",
       "Talklines/Warmlines                             1.0                   0.0   \n",
       "Food Pantries                                 437.0                   0.0   \n",
       "Community Shelters                              2.0                   0.0   \n",
       "Housing Related Coordinated Entry               1.0                   0.0   \n",
       "Electric Service Payment Assistance             1.0                   0.0   \n",
       "Low Income/Subsidized Private Rental Housing    1.0                   0.0   \n",
       "Rent Payment Assistance                         2.0                   0.0   \n",
       "General Crisis Intervention Hotlines            0.0                   0.0   \n",
       "Directory Assistance                            2.0                   0.0   \n",
       "Mental Health Crisis Lines                      0.0                   0.0   \n",
       "\n",
       "Primary                                       Housing Payment Assistance  \\\n",
       "taxonomy_name                                                              \n",
       "Talklines/Warmlines                                                  2.0   \n",
       "Food Pantries                                                        4.0   \n",
       "Community Shelters                                                   8.0   \n",
       "Housing Related Coordinated Entry                                   23.0   \n",
       "Electric Service Payment Assistance                                  1.0   \n",
       "Low Income/Subsidized Private Rental Housing                        45.0   \n",
       "Rent Payment Assistance                                             79.0   \n",
       "General Crisis Intervention Hotlines                                 0.0   \n",
       "Directory Assistance                                                 0.0   \n",
       "Mental Health Crisis Lines                                           0.0   \n",
       "\n",
       "Primary                                       Legal Assistance  Mental Health  \\\n",
       "taxonomy_name                                                                   \n",
       "Talklines/Warmlines                                        1.0          403.0   \n",
       "Food Pantries                                              0.0            0.0   \n",
       "Community Shelters                                         0.0            3.0   \n",
       "Housing Related Coordinated Entry                          1.0            2.0   \n",
       "Electric Service Payment Assistance                        0.0            0.0   \n",
       "Low Income/Subsidized Private Rental Housing               0.0            0.0   \n",
       "Rent Payment Assistance                                    0.0            2.0   \n",
       "General Crisis Intervention Hotlines                       0.0           38.0   \n",
       "Directory Assistance                                       0.0            0.0   \n",
       "Mental Health Crisis Lines                                 0.0           26.0   \n",
       "\n",
       "Primary                                       Other  \\\n",
       "taxonomy_name                                         \n",
       "Talklines/Warmlines                            26.0   \n",
       "Food Pantries                                  29.0   \n",
       "Community Shelters                             37.0   \n",
       "Housing Related Coordinated Entry              17.0   \n",
       "Electric Service Payment Assistance             2.0   \n",
       "Low Income/Subsidized Private Rental Housing   38.0   \n",
       "Rent Payment Assistance                        13.0   \n",
       "General Crisis Intervention Hotlines           16.0   \n",
       "Directory Assistance                           11.0   \n",
       "Mental Health Crisis Lines                      1.0   \n",
       "\n",
       "Primary                                       Other Financial Assistance  \\\n",
       "taxonomy_name                                                              \n",
       "Talklines/Warmlines                                                  1.0   \n",
       "Food Pantries                                                        0.0   \n",
       "Community Shelters                                                   0.0   \n",
       "Housing Related Coordinated Entry                                    0.0   \n",
       "Electric Service Payment Assistance                                  2.0   \n",
       "Low Income/Subsidized Private Rental Housing                         0.0   \n",
       "Rent Payment Assistance                                              1.0   \n",
       "General Crisis Intervention Hotlines                                 0.0   \n",
       "Directory Assistance                                                 0.0   \n",
       "Mental Health Crisis Lines                                           0.0   \n",
       "\n",
       "Primary                                       Shelter  Substance Use  \\\n",
       "taxonomy_name                                                          \n",
       "Talklines/Warmlines                               8.0            0.0   \n",
       "Food Pantries                                     4.0            0.0   \n",
       "Community Shelters                              325.0            0.0   \n",
       "Housing Related Coordinated Entry               274.0            0.0   \n",
       "Electric Service Payment Assistance               1.0            0.0   \n",
       "Low Income/Subsidized Private Rental Housing     28.0            0.0   \n",
       "Rent Payment Assistance                           6.0            0.0   \n",
       "General Crisis Intervention Hotlines              1.0            0.0   \n",
       "Directory Assistance                              0.0            0.0   \n",
       "Mental Health Crisis Lines                        0.0            0.0   \n",
       "\n",
       "Primary                                       Supportive Listening  \\\n",
       "taxonomy_name                                                        \n",
       "Talklines/Warmlines                                         2006.0   \n",
       "Food Pantries                                                  1.0   \n",
       "Community Shelters                                             0.0   \n",
       "Housing Related Coordinated Entry                              0.0   \n",
       "Electric Service Payment Assistance                            0.0   \n",
       "Low Income/Subsidized Private Rental Housing                   0.0   \n",
       "Rent Payment Assistance                                        0.0   \n",
       "General Crisis Intervention Hotlines                           7.0   \n",
       "Directory Assistance                                           0.0   \n",
       "Mental Health Crisis Lines                                     5.0   \n",
       "\n",
       "Primary                                       Transportation  Utility/Energy  \\\n",
       "taxonomy_name                                                                  \n",
       "Talklines/Warmlines                                      0.0             0.0   \n",
       "Food Pantries                                            1.0             1.0   \n",
       "Community Shelters                                       3.0             0.0   \n",
       "Housing Related Coordinated Entry                        0.0             2.0   \n",
       "Electric Service Payment Assistance                      0.0           282.0   \n",
       "Low Income/Subsidized Private Rental Housing             0.0             1.0   \n",
       "Rent Payment Assistance                                  0.0             6.0   \n",
       "General Crisis Intervention Hotlines                     0.0             0.0   \n",
       "Directory Assistance                                     1.0             0.0   \n",
       "Mental Health Crisis Lines                               0.0             0.0   \n",
       "\n",
       "Primary                                       impact_on_primary  \n",
       "taxonomy_name                                                    \n",
       "Talklines/Warmlines                                      2485.0  \n",
       "Food Pantries                                             477.0  \n",
       "Community Shelters                                        379.0  \n",
       "Housing Related Coordinated Entry                         320.0  \n",
       "Electric Service Payment Assistance                       290.0  \n",
       "Low Income/Subsidized Private Rental Housing              115.0  \n",
       "Rent Payment Assistance                                   112.0  \n",
       "General Crisis Intervention Hotlines                      107.0  \n",
       "Directory Assistance                                       98.0  \n",
       "Mental Health Crisis Lines                                 55.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxonomy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e92230df-568a-4693-8227-df998a61581b",
   "metadata": {},
   "source": [
    "### Output task #2: Taxonomy names vs primary needs visualization\n",
    "<a id='taxonomy_vs_primary'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "74b260a3-db90-4771-bbc4-4a084d67c872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(11, 7), gridspec_kw={'width_ratios': [2, 1]})\n",
    "\n",
    "heatmap_taxonomy = sns.heatmap(taxonomy.drop('impact_on_primary', axis=1),\n",
    "               #annot=True,\n",
    "               cmap=\"YlGnBu\",\n",
    "               ax=axes[0]\n",
    "               )\n",
    "_ = heatmap_taxonomy.set(ylabel='Taxonomy Names')\n",
    "_ = heatmap_taxonomy.set(xlabel='Primary Needs')\n",
    "_ = heatmap_taxonomy.set(title='Top 10 Taxonomies impacting primary needs')\n",
    "\n",
    "bar_taxonomy = sns.barplot(x=taxonomy.impact_on_primary, \n",
    "            y=taxonomy.index, \n",
    "            color='gray',\n",
    "            ax=axes[1]\n",
    "            )\n",
    "\n",
    "_ = bar_taxonomy.set(yticklabels=[])\n",
    "_ = bar_taxonomy.set(ylabel=None)\n",
    "_ = bar_taxonomy.set(xlabel='No. Primary needs impacted')\n",
    "\n",
    "fig.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}