9.3 Filling in Missing Data.ipynb

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   "source": [
    "![title](img/cover4.png)"
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   "metadata": {},
   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.3 Filling in Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import numpy and pandas libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets read this dataframe with **missing values** from a csv file using the function **pd.read_csv()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "      <td>26.0</td>\n",
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       "      <td>24</td>\n",
       "      <td>35.0</td>\n",
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       "      day1  day2  day3  day4\n",
       "time                        \n",
       "8     21.0  26.0    19  30.0\n",
       "9     23.0  28.0    21   NaN\n",
       "10    25.0  30.0    23  34.0\n",
       "11     NaN  32.0    25  36.0\n",
       "12    27.0  32.0    25  36.0\n",
       "13    29.0  34.0    27  38.0\n",
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       "15    26.0   NaN    24  35.0\n",
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       "17    25.0  30.0    23  34.0\n",
       "18    22.0  27.0    20  31.0\n",
       "19    21.0  26.0    19  30.0\n",
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     "execution_count": 2,
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   "source": [
    "dt1 = pd.read_csv('data/temperature.csv', index_col = 'time')\n",
    "dt1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can fill the missing values with a single value (here 20) using the function **fillna()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "      day1  day2  day3  day4\n",
       "time                        \n",
       "8     21.0  26.0    19  30.0\n",
       "9     23.0  28.0    21  20.0\n",
       "10    25.0  30.0    23  34.0\n",
       "11    20.0  32.0    25  36.0\n",
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       "19    21.0  26.0    19  30.0\n",
       "20    19.0  24.0    17  20.0"
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   "source": [
    "dt1.fillna(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also assign a different value for each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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       "      day1  day2  day3  day4\n",
       "time                        \n",
       "8     21.0  26.0    19  30.0\n",
       "9     23.0  28.0    21  30.0\n",
       "10    25.0  30.0    23  34.0\n",
       "11    20.0  32.0    25  36.0\n",
       "12    27.0  32.0    25  36.0\n",
       "13    29.0  34.0    27  38.0\n",
       "14    29.0  34.0    27  38.0\n",
       "15    26.0  25.0    24  35.0\n",
       "16    26.0  31.0    24  35.0\n",
       "17    25.0  30.0    23  34.0\n",
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       "19    21.0  26.0    19  30.0\n",
       "20    19.0  24.0    17  30.0"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dt1.fillna({'day1':20, 'day2':25, 'day4':30})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the argument **(method = ffill)** to fill the missing values with the values that just precedes it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>26.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>24</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>24</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>22.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>19</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      day1  day2  day3  day4\n",
       "time                        \n",
       "8     21.0  26.0    19  30.0\n",
       "9     23.0  28.0    21  30.0\n",
       "10    25.0  30.0    23  34.0\n",
       "11    25.0  32.0    25  36.0\n",
       "12    27.0  32.0    25  36.0\n",
       "13    29.0  34.0    27  38.0\n",
       "14    29.0  34.0    27  38.0\n",
       "15    26.0  34.0    24  35.0\n",
       "16    26.0  31.0    24  35.0\n",
       "17    25.0  30.0    23  34.0\n",
       "18    22.0  27.0    20  31.0\n",
       "19    21.0  26.0    19  30.0\n",
       "20    19.0  24.0    17  30.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt1.fillna(method ='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the argument **(method = bfill)** to fill the missing values with the values that comes after it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
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   "outputs": [
    {
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       "      day1  day2  day3  day4\n",
       "time                        \n",
       "8     21.0  26.0    19  30.0\n",
       "9     23.0  28.0    21  34.0\n",
       "10    25.0  30.0    23  34.0\n",
       "11    27.0  32.0    25  36.0\n",
       "12    27.0  32.0    25  36.0\n",
       "13    29.0  34.0    27  38.0\n",
       "14    29.0  34.0    27  38.0\n",
       "15    26.0  31.0    24  35.0\n",
       "16    26.0  31.0    24  35.0\n",
       "17    25.0  30.0    23  34.0\n",
       "18    22.0  27.0    20  31.0\n",
       "19    21.0  26.0    19  30.0\n",
       "20    19.0  24.0    17   NaN"
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   "source": [
    "dt1.fillna(method ='bfill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also fill the missing values with the **mean**, the mean will be calculated separately for each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "           day1  day2  day3       day4\n",
       "time                                  \n",
       "8     21.000000  26.0    19  30.000000\n",
       "9     23.000000  28.0    21  34.272727\n",
       "10    25.000000  30.0    23  34.000000\n",
       "11    24.416667  32.0    25  36.000000\n",
       "12    27.000000  32.0    25  36.000000\n",
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       "14    29.000000  34.0    27  38.000000\n",
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       "19    21.000000  26.0    19  30.000000\n",
       "20    19.000000  24.0    17  34.272727"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt1.fillna(dt1.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
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