Feature Engineering- Missing Values Day 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Sample Imputation\n",
    "\n",
    "Aim: Random sample imputation consists of taking random observation from the dataset and we use this observation to replace the nan values\n",
    "\n",
    "When should it be used? \n",
    "It assumes that the data are missing completely at random(MCAR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('titanic.csv', usecols=['Age','Fare','Survived'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Age         177\n",
       "Fare          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0.000000\n",
       "Age         0.198653\n",
       "Fare        0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "423    28.00\n",
       "177    50.00\n",
       "305     0.92\n",
       "292    36.00\n",
       "889    26.00\n",
       "595    36.00\n",
       "637    31.00\n",
       "54     65.00\n",
       "456    65.00\n",
       "110    47.00\n",
       "287    22.00\n",
       "315    26.00\n",
       "691     4.00\n",
       "811    39.00\n",
       "861    21.00\n",
       "15     55.00\n",
       "239    33.00\n",
       "644     0.75\n",
       "394    24.00\n",
       "795    39.00\n",
       "131    20.00\n",
       "346    40.00\n",
       "9      14.00\n",
       "477    29.00\n",
       "606    30.00\n",
       "469     0.75\n",
       "325    36.00\n",
       "72     21.00\n",
       "724    27.00\n",
       "40     40.00\n",
       "       ...  \n",
       "396    31.00\n",
       "452    30.00\n",
       "395    22.00\n",
       "882    22.00\n",
       "821    27.00\n",
       "314    43.00\n",
       "622    20.00\n",
       "204    18.00\n",
       "60     22.00\n",
       "731    11.00\n",
       "81     29.00\n",
       "676    24.50\n",
       "433    17.00\n",
       "378    20.00\n",
       "174    56.00\n",
       "624    21.00\n",
       "642     2.00\n",
       "157    30.00\n",
       "427    19.00\n",
       "698    49.00\n",
       "179    36.00\n",
       "24      8.00\n",
       "630    80.00\n",
       "458    50.00\n",
       "49     18.00\n",
       "539    22.00\n",
       "267    25.00\n",
       "352    15.00\n",
       "99     34.00\n",
       "689    15.00\n",
       "Name: Age, Length: 177, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].dropna().sample(df['Age'].isnull().sum(),random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  5,  17,  19,  26,  28,  29,  31,  32,  36,  42,\n",
       "            ...\n",
       "            832, 837, 839, 846, 849, 859, 863, 868, 878, 888],\n",
       "           dtype='int64', length=177)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age'].isnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df,variable,median):\n",
    "    df[variable+\"_median\"]=df[variable].fillna(median)\n",
    "    df[variable+\"_random\"]=df[variable]\n",
    "    ##It will have the random sample to fill the na\n",
    "    random_sample=df[variable].dropna().sample(df[variable].isnull().sum(),random_state=0)\n",
    "    ##pandas need to have same index in order to merge the dataset\n",
    "    random_sample.index=df[df[variable].isnull()].index\n",
    "    df.loc[df[variable].isnull(),variable+'_random']=random_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "median=df.Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "impute_nan(df,\"Age\",median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age_median</th>\n",
       "      <th>Age_random</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_median  Age_random\n",
       "0         0  22.0   7.2500        22.0        22.0\n",
       "1         1  38.0  71.2833        38.0        38.0\n",
       "2         1  26.0   7.9250        26.0        26.0\n",
       "3         1  35.0  53.1000        35.0        35.0\n",
       "4         0  35.0   8.0500        35.0        35.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2861b07e438>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "df['Age'].plot(kind='kde', ax=ax)\n",
    "df.Age_median.plot(kind='kde', ax=ax, color='red')\n",
    "df.Age_random.plot(kind='kde', ax=ax, color='green')\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(lines, labels, loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Advantages\n",
    "1. Easy To implement\n",
    "2. There is less distortion in variance\n",
    "\n",
    "##### Disadvantage\n",
    "1. Every situation randomness wont work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Capturing NAN values with a new feature\n",
    "\n",
    "It works well if the data are not missing completely at random "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
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      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic.csv', usecols=['Age','Fare','Survived'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df['Age_NAN']=np.where(df['Age'].isnull(),1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_NAN\n",
       "0         0  22.0   7.2500        0\n",
       "1         1  38.0  71.2833        0\n",
       "2         1  26.0   7.9250        0\n",
       "3         1  35.0  53.1000        0\n",
       "4         0  35.0   8.0500        0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'].fillna(df.Age.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7.9250</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_NAN\n",
       "0         0  22.0   7.2500        0\n",
       "1         1  38.0  71.2833        0\n",
       "2         1  26.0   7.9250        0\n",
       "3         1  35.0  53.1000        0\n",
       "4         0  35.0   8.0500        0\n",
       "5         0  28.0   8.4583        1\n",
       "6         0  54.0  51.8625        0\n",
       "7         0   2.0  21.0750        0\n",
       "8         1  27.0  11.1333        0\n",
       "9         1  14.0  30.0708        0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Advantages\n",
    "1. Easy to implement\n",
    "2. Captures the importance of missing values\n",
    "\n",
    "##### Disadvantages\n",
    "1. Creating Additional Features(Curse of Dimensionality)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### End of Distribution imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic.csv', usecols=['Age','Fare','Survived'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28618d75f28>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1EfGpiPhW9V57Y0Ny3VA9Vxd+fhgRH2hItt+t3vdHI+Kh6t/DyHM1ttAbeGqBfcDOS+btBeYzcyswX02vpvPAnsx8LXAr8L7qORp3LoAfA7dn5o3ATcDOiLi1IdkA7gWOdUw3JddMZt7UsXtbE3J9BHg0M38RuJH28zb2XJn5ZPVc3QRsB/4L+My4s0XEZuB3gKnMfD3tnUbuWpVcmdnIH+CNwOc7pu8D7htzpi3A0Y7pJ4FN1fVNwJNjzvcI8OYG5no58DXgl5uQjfYxE/PA7cDBpryWwAng6kvmjTUX8ErgKarP25qSa4mcbwH+uQnZuHgU/UbaO54crPKNPFdjt9C5PE4tMJmZpwCqy2vHFSQitgA3A19uSq5qWONx4AxwKDObku3Pgd8H/qdjXhNyJfCFiDhcHWHdhFyvAb4P/FU1RPXRiFjbgFyXugt4qLo+1myZeRL4M+Bp4BRwNjO/sBq5mlzoscQ8d8lZQkRMAH8PfCAzfzjuPBdk5ovZ/nP4OuCWiHj9uDNFxDuAM5l5eNxZlnBbZr6B9jDj+yLi18YdiPYW5huAv8zMm4FzjG84aknVgY3vBD457iwA1dj4LuB64FXA2oh492o8dpMLvadTC4zZ6YjYBFBdnlntABHxUtpl/jeZ+emm5OqUmc8DLdqfQYw7223AOyPiBO0zhN4eEZ9oQC4y83vV5RnaY8G3NCDXM8Az1V9XAJ+iXfDjztXprcDXMvN0NT3ubG8CnsrM72fmT4BPA7+yGrmaXOiXw6kFDgC7q+u7aY9hr5qICOBjwLHM/HBTclXZromI9dX1q2i/yb817myZeV9mXpeZW2i/p76Yme8ed66IWBsRr7hwnfaY69Fx58rM/wC+GxE3VLN2AP867lyXeBcXh1tg/NmeBm6NiJdX/0Z30P4gefS5xvlBRg8fLrwN+Dfg34E/HHOWh2iPh/2E9lbLPcDP0/5w7Xh1uXGVM/0q7WGobwKPVz9vG3euKtsvAV+vsh0F/qiaP/ZsHRmnufih6Lhfy9cA36h+nrjwfh93rirDTcBC9Vr+A7ChCbmqbC8HngXWdcwbezbgQ7Q3YI4Cfw383Grk8khRSSpEk4dcJEk1WOiSVAgLXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXifwHiRLeOK0TDzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "extreme=df.Age.mean()+3*df.Age.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2861de7a160>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.boxplot('Age',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df,variable,median,extreme):\n",
    "    df[variable+\"_end_distribution\"]=df[variable].fillna(extreme)\n",
    "    df[variable].fillna(median,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "impute_nan(df,'Age',df.Age.median(),extreme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age_end_distribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_end_distribution\n",
       "0         0  22.0   7.2500                  22.0\n",
       "1         1  38.0  71.2833                  38.0\n",
       "2         1  26.0   7.9250                  26.0\n",
       "3         1  35.0  53.1000                  35.0\n",
       "4         0  35.0   8.0500                  35.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2861debb128>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2861debd2e8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age_end_distribution'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2861dffa2b0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5IuLFmfm/3VXhLbSXJXqo77mkucA1ZG3v6u7JDC+kXRUaY2lMvELWbouIS2j/l3nYRZn5iT10/1fSXmhy2F9m5nV74v6lagyyJBXhN/UkqQiDLElFGGRJKsIgS1IRBlmSivg/fjxt/clvICoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot('Age_end_distribution',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}