2. Random Sample Imputation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Sample Imputation\n",
    "\n",
    "-  Random Sample Imputation take a random observation from the feature.\n",
    "-  After that we use random observation to replace NaN in that feature.\n",
    "-  It should be used when data is missing completely at  random (MCAR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Required Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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",
       "    </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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Titanic.csv',usecols=['Age','Fare','Survived'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Age         177\n",
       "Fare          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    - There is 177 null values are present in Age feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Now, we replace this NaN by using Random Sample Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137    37.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].dropna().sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    - where .dropna() function drop all nan values in that feature and .sample() function return any one random value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37     21.0\n",
       "323    22.0\n",
       "492    55.0\n",
       "377    27.0\n",
       "855    18.0\n",
       "       ... \n",
       "646    19.0\n",
       "332    38.0\n",
       "376    22.0\n",
       "44     19.0\n",
       "699    42.0\n",
       "Name: Age, Length: 177, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].dropna().sample(df['Age'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    - this function check where is null values are present and replace Nan with random sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "       ...  \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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].dropna().sample(df['Age'].isnull().sum(),random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    - This function check where is null values are present and replace Nan with random sample\n",
    "    - We use random_state because it replace NaN with specific value only ( if we not use random_state then values change evrytime when we run .)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age'].isnull()].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    - This line of code return a index where Age is NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create A function which Replace NaN with Random Sample\n",
    "- Here we creating a two new features Age_median and Age_random \n",
    "- In Age_median replace NaN with Median and In Age_random replce NaN with Random Sample "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RSI(df,variable,median):\n",
    "    df[variable+'_median'] = df[variable].fillna(median)#replace Nan with median\n",
    "    df[variable+'_random'] = df[variable]#Copy feature into new feature\n",
    "    #calculate random smaple and store into random_sample_values\n",
    "    random_sample_value = df[variable].dropna().sample(df[variable].isnull().sum(),random_state=0)\n",
    "    #in random_sample_value all filled nan values are present now we want to put/merge this all filled values in our dataset\n",
    "    # for this we want to match all nan values index in random_sample_values with df[variavle_'random] \n",
    "    #Pandas need to have same index in order to merge dataset\n",
    "    random_sample_value.index = df[df[variable].isnull()].index#find index of NaN values in feature\n",
    "    #now put a condition where ever it is null with loc function then replace with random_sample_values\n",
    "    df.loc[df[variable].isnull(),variable+'_random'] =random_sample_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- calculate median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "median = df['Age'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Call Function\n",
    "    - RSI('dataframe','Feature','median')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "RSI(df,'Age',median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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",
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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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot graph between Age, Age_median , Age_random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2165179dca0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "df.Age.plot(kind='kde',color='b')\n",
    "df.Age_median.plot(kind='kde',color='y')\n",
    "df.Age_random.plot(kind='kde',color='r')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: \n",
    "- from plot we can say that Random Sample Imputation work better than Mean Median Mode imputation is some cases.\n",
    "- When we use Median to fillna there is Distortion\n",
    "- There is Less distortion in random sample\n",
    "- In every cases random sample imputation wont work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Please drop a comment and share your feedback 👍🏻\n",
    "## Thank you 😇😇 !"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}