Handle missing data using MCAR(Missing Complete At Random) ,CCA (Complete Case Analysis).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f4770a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "462cd88b",
   "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>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>Soiewre</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "   society total_sqft  bath  balcony   price  \n",
       "0  Coomee        1056   2.0      1.0   39.07  \n",
       "1  Theanmp       2600   5.0      3.0  120.00  \n",
       "2      NaN       1440   2.0      3.0   62.00  \n",
       "3  Soiewre       1521   3.0      1.0   95.00  \n",
       "4      NaN       1200   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the=pd.read_csv(r'C:\\Users\\DELLS\\OneDrive\\Documents\\Downloads\\BHP.csv')\n",
    "the.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee5f0043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d454cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type        0.000000\n",
       "availability     0.000000\n",
       "location         0.007508\n",
       "size             0.120120\n",
       "society         41.306306\n",
       "total_sqft       0.000000\n",
       "bath             0.548048\n",
       "balcony          4.572072\n",
       "price            0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3786f9bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(the)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "17039dce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['location', 'size', 'bath', 'balcony']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [var for var in the.columns if the[var].isnull().mean() < 0.05 and the[var].isnull().mean() > 0]\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d765913",
   "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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4855</th>\n",
       "      <td>R.T. Nagar</td>\n",
       "      <td>2 Bedroom</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7449</th>\n",
       "      <td>Bharathi Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7133</th>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3816</th>\n",
       "      <td>Lakshminarayana Pura</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  location       size  bath  balcony\n",
       "4855            R.T. Nagar  2 Bedroom   1.0      0.0\n",
       "7449        Bharathi Nagar      2 BHK   2.0      2.0\n",
       "1900           Uttarahalli      2 BHK   2.0      1.0\n",
       "7133     Bannerghatta Road      3 BHK   3.0      2.0\n",
       "3816  Lakshminarayana Pura      2 BHK   2.0      1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the[cols].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b9a4bd9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                  540\n",
       "Sarjapur  Road              399\n",
       "Electronic City             302\n",
       "Kanakpura Road              273\n",
       "Thanisandra                 234\n",
       "                           ... \n",
       "Keerthi Layout                1\n",
       "KG Halli                      1\n",
       "Shanthi Pura                  1\n",
       "Malimakanapura                1\n",
       "Doctor Layout Rayasandra      1\n",
       "Name: location, Length: 1305, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "25895b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9542042042042042"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(the[cols].dropna()) / len(the)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b6ad9168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((13320, 9), (12710, 4))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = the[cols].dropna()\n",
    "the.shape, new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "286d8fd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.hist(bins=50, density=True, figsize=(12, 12))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aedd4684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD7CAYAAABDld6xAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQBklEQVR4nO3db4xc9XXG8efBNk3qQUHJ0i3Cdt2qlWWCSsKOCJGlaAZERWkEL8oLI5WWyNFajUKJ1JX6R2qrVKr6xo2UtlFjFKOSlrKJCEQUhbRI8RbFKqSzFFJgSUVo6iylISYNMNsqyNHpi5lrZpdZz93ZnZ1zR9+PtPLcmd+1z/HxPr57595dR4QAAHldMO4CAADnR1ADQHIENQAkR1ADQHIENQAkR1ADQHIDg9r2AdtP9Xy8bvsT21AbAECSN3Idte0dkl6S9IGI+M+RVQUAOGfnBtdfJ+nbg0J6amoq9u/fP1RBKysr2r1791D7ZjMpvUxKHxK9ZDQpfUib62VxcfFMRFzS77WNBvVhSfcNWrR//361Wq0N/tYdCwsLajQaQ+2bzaT0Mil9SPSS0aT0IW2uF9vrHgCXPvVh+0JJ/yXpvRHxvT6vz0qalaTp6emZ+fn5oYptt9uq1WpD7ZvNpPQyKX1I9JLRpPQhba6XZrO5GBH1vi9GRKkPSTdL+scya2dmZmJYJ0+eHHrfbCall0npI4JeMpqUPiI214ukVqyTqRu5PO9WlTjtAQDYWqWC2vZuSddLemC05QAA1ir1ZmJErEh6z4hrAQD0wZ2JAJAcQQ0AyRHUAJAcQQ0AyW30zkQAY1Y/aknSkUPHNHe0ee751nF+/umk4ogaAJIjqAEgOYIaAJIjqAEgOYIaAJIjqAEgOYIaAJLjOmq8TXGdbqG4XpfrdIHx4IgaAJIjqAEgOYIaAJIjqAEgOYIaAJIjqAEgOYIaAJIjqAEgOYIaAJIrFdS2L7Z9v+3nbS/Z/uCoCwMAdJS9hfzTkr4aEbfYvlDST46wJgBAj4FBbftdkj4k6XZJiog3Jb052rIAAAVHnP8b7dh+n6S7JD0n6UpJi5LujIiVNetmJc1K0vT09Mz8/PxQBbXbbdVqtaH2zaaqvSydXly1PbV7j86sLOvgvpkxVbR1qjqTXsV8irkUqjqfSZhJYTO9NJvNxYio93utTFDXJT0u6VBEPGH705Jej4g/WG+fer0erVZrqGIXFhbUaDSG2jebqvbS77vnnTg1NxHfPa+qM+nV+1PIT5yaO/d8VeczCTMpbKYX2+sGdZk3E5clLUfEE93t+yVdNVQlAIANGxjUEfHfkr5r+0D3qevUOQ0CANgGZa/6uEPSvd0rPl6U9JHRlQQA6FUqqCPiKUl9z50AAEaLOxMBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCS21lmke3vSHpD0o8lnY2I+iiLAgC8pVRQdzUj4szIKgEA9MWpDwBIzhExeJH9H5L+R1JIOh4Rd/VZMytpVpKmp6dn5ufnhyqo3W6rVqsNtW82Ve1l6fTiqu2p3Xt0ZmVZB/fNjKmirVPVmfQq5lPMpVDV+UzCTAqb6aXZbC6ud1q5bFBfFhEv2f4pSY9KuiMiHltvfb1ej1arNVSxCwsLajQaQ+2bTVV7qR/1qu0jh47pxKk5tY4P/reSXVVn0quYTzGXQlXnMwkzKWymF9vrBnWpUx8R8VL311ckPSjp6qEqAQBs2MCgtr3b9kXFY0m/JOmZURcGAOgoc9XHtKQHbRfr/y4ivjrSqgAA5wwM6oh4UdKV21ALAKAPLs8DgOQIagBIjqAGgOQIagBIjqAGgOQIagBIjqAGgOQIagBIjqAGgOQIagBIjqAGgOQIagBIjqAGgOQIagBIjqAGgOQIagBIjqAGgOQIagBIjqAGgOQIagBIjqAGgOQIagBIrnRQ295h+19tPzzKggAAq23kiPpOSUujKgQA0F+poLa9R9KvSPrcaMsBAKzliBi8yL5f0p9KukjSXER8uM+aWUmzkjQ9PT0zPz8/VEHtdlu1Wm2ofbOpai9LpxdXbU/t3qMzK8s6uG9mTBVtnarOpFcxn2IuharOZxJmUthML81mczEi6v1eGxjUtj8s6caI+JjthtYJ6l71ej1ardZQxS4sLKjRaAy1bzZV7aV+1Ku2jxw6phOn5tQ6Pvg/9eyqOpNexXyKuRSqOp9JmElhM73YXjeoy5z6OCTpJtvfkTQv6VrbfztUJQCADRsY1BHxexGxJyL2Szos6WsR8WsjrwwAIInrqAEgvZ0bWRwRC5IWRlIJAKAvjqgBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSGxjUtt9h+xu2n7b9rO1PbkdhAICOnSXW/EjStRHRtr1L0tdtPxIRj4+4NgCASgR1RISkdndzV/cjRlkUAOAt7uTwgEX2DkmLkn5e0mci4nf6rJmVNCtJ09PTM/Pz80MV1G63VavVhto3m6r2snR6cdX21O49OrOyrIP7ZsZU0dap6kx6FfMp5lKo6nwmYSaFzfTSbDYXI6Le77VSQX1usX2xpAcl3RERz6y3rl6vR6vV2midkqSFhQU1Go2h9s2mqr3Uj3rV9pFDx3Ti1Jxax6v/hVRVZ9KrmE8xl0JV5zMJMylsphfb6wb1hq76iIgfSjop6YahKgEAbFiZqz4u6R5Jy/Y7JV0v6fkR1wUA6Cpz1celku7pnqe+QNIXI+Lh0ZYFACiUuerjm5Levw21AAD64M5EAEiOoAaA5AhqAEiOoAaA5AhqAEiOoAaA5AhqAEiOoAaA5AhqAEiOoAaA5AhqAEiOoAaA5AhqAEiOoAaA5AhqAEiOoAaA5AhqAEiOoAaA5AhqAEiuzA+3BQD0qB913+eP3XpyJH8eR9QAkBxBDQDJDQxq23ttn7T9nO1nbd+5HYUBADrKnKM+K+m3I+JJ2xdJWrT9aEQ8N+LaAAAqcUQdES9HxJPdx29IWpJ02agLAwB0OCLKL7b3S3pM0hUR8fqa12YlzUrS9PT0zPz8/FAFtdtt1Wq1ofbNpqq9LJ1eXLU9tXuPzqws6+C+mTFVtHWqOpNexXyKuRSqOp8qzmTt50hh77sPDN1Ls9lcjIh6v9dKB7XtmqR/kvQnEfHA+dbW6/VotVobLlSSFhYW1Gg0hto3m6r2svbSoyOHjunEqTm1jpf/Tz2rqs6kVzGfYi6Fqs6nijM53+V5w/Zie92gLnXVh+1dkr4k6d5BIQ0A2FplrvqwpBOSliLiU6MvCQDQq8wR9SFJt0m61vZT3Y8bR1wXAKBr4OV5EfF1Sf1PyAAARo47EwEgOYIaAJIjqAEgOYIaAJIjqAEgOYIaAJIjqAEgOYIaAJIjqAEgOYIaAJIjqAEgOYIaAJIjqAEgOYIaAJIr81PIt9XS6UXNHW2+7fmq/pghANgsjqgBIDmCGgCSI6gBIDmCGgCSI6gBIDmCGgCSI6gBILmBQW37btuv2H5mOwoCAKxW5oj6ryXdMOI6AADrGBjUEfGYpB9sQy0AgD4cMfjWbNv7JT0cEVecZ82spFlJmp6enpmfnx+qoO+/+j2dWVl+2/MH980M9fuNU7vdVq1WG3cZG7Z0enHV9tTuPTqzslzJGaxV1Zn0KuZTzKVQ1flUcSZrP0cKe999YOhems3mYkTU+722ZUHdq16vR6vV2lCRhb/6/J/pxKm5tz1fxe/1sbCwoEajMe4yNqx+1Ku2jxw6phOn5io5g7WqOpNexXyKuRSqOp8qzmTt50jh2K0nh+7F9rpBzVUfAJAcQQ0AyZW5PO8+Sf8s6YDtZdtHRl8WAKAw8PtRR8St21EIAKA/Tn0AQHIENQAkR1ADQHIENQAkR1ADQHIENQAkR1ADQHIENQAkR1ADQHIENQAkR1ADQHIENQAkR1ADQHIENQAkR1ADQHIENQAkR1ADQHIENQAkR1ADQHIENQAkR1ADQHKlgtr2Dba/ZfsF27876qIAAG8ZGNS2d0j6jKRflnS5pFttXz7qwgAAHWWOqK+W9EJEvBgRb0qal3TzaMsCABTKBPVlkr7bs73cfQ4AsA0cEedfYN8i6YaI+Gh3+zZJH4iIj69ZNytptrt5QNK3hqxpStKZIffNZlJ6mZQ+JHrJaFL6kDbXy89ExCX9XthZYueXJO3t2d7TfW6ViLhL0l1DldfDdisi6pv9fTKYlF4mpQ+JXjKalD6k0fVS5tTHv0j6Bds/a/tCSYclPbTVhQAA+ht4RB0RZ21/XNI/SNoh6e6IeHbklQEAJJU79aGI+Iqkr4y4lsKmT58kMim9TEofEr1kNCl9SCPqZeCbiQCA8eIWcgBIbmxBPei2dNs/YfsL3defsL1/DGUOVKKP221/3/ZT3Y+PjqPOQWzfbfsV28+s87pt/3m3z2/avmq7ayyrRC8N26/1zOQPt7vGsmzvtX3S9nO2n7V9Z5816WdTso9KzMX2O2x/w/bT3V4+2WfN1uZXRGz7hzpvSn5b0s9JulDS05IuX7PmY5I+2318WNIXxlHrFvRxu6S/HHetJXr5kKSrJD2zzus3SnpEkiVdI+mJcde8iV4akh4ed50le7lU0lXdxxdJ+vc+/8bSz6ZkH5WYS/fvudZ9vEvSE5KuWbNmS/NrXEfUZW5Lv1nSPd3H90u6zra3scYyJub2+oh4TNIPzrPkZkmfj47HJV1s+9LtqW5jSvRSGRHxckQ82X38hqQlvf3O4PSzKdlHJXT/ntvdzV3dj7Vv9m1pfo0rqMvcln5uTUSclfSapPdsS3Xllb29/le7X5Leb3tvn9erYNK+lcAHu1+6PmL7veMupozul8/vV+cIrlelZnOePqSKzMX2DttPSXpF0qMRse5MtiK/eDNx9P5e0v6I+EVJj+qt/2UxPk+qc7vulZL+QtKXx1vOYLZrkr4k6RMR8fq46xnWgD4qM5eI+HFEvE+dO7Wvtn3FKP+8cQV1mdvSz62xvVPSuyS9ui3VlTewj4h4NSJ+1N38nKSZbaptq5X6VgJVEBGvF1+6RucegV22p8Zc1rps71In3O6NiAf6LKnEbAb1UbW5SFJE/FDSSUk3rHlpS/NrXEFd5rb0hyT9RvfxLZK+Ft0z84kM7GPNucKb1Dk3V0UPSfr17hUG10h6LSJeHndRw7D908X5QttXq/N5kO0gQFLnig5JJyQtRcSn1lmWfjZl+qjKXGxfYvvi7uN3Srpe0vNrlm1pfpW6M3GrxTq3pdv+Y0mtiHhInaH+je0X1Hlj6PA4aj2fkn38lu2bJJ1Vp4/bx1bwedi+T5133adsL0v6I3XeJFFEfFadO1NvlPSCpP+V9JHxVDpYiV5ukfSbts9K+j9JhxMeBBQOSbpN0r91z4lK0u9L2idVajZl+qjKXC6VdI87P1TlAklfjIiHR5lf3JkIAMnxZiIAJEdQA0ByBDUAJEdQA0ByBDUAJEdQA0ByBDUAJEdQA0By/w+RzVvvPoQdWgAAAABJRU5ErkJggg==\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",
    "\n",
    "# original data\n",
    "the['balcony'].hist(bins=50, ax=ax, density=True, color='red')\n",
    "\n",
    "# data after cca, the argument alpha makes the color transparent, so we can\n",
    "# see the overlay of the 2 distributions\n",
    "new_df['balcony'].hist(bins=50, ax=ax, color='green', density=True, alpha=0.8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7ec29277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 24,
     "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",
    "\n",
    "# original data\n",
    "the['balcony'].plot.density(color='red')\n",
    "\n",
    "# data after cca\n",
    "new_df['balcony'].plot.density(color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a28f2350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "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",
    "\n",
    "# original data\n",
    "the['bath'].hist(bins=50, ax=ax, density=True, color='red')\n",
    "\n",
    "# data after cca, the argument alpha makes the color transparent, so we can\n",
    "# see the overlay of the 2 distributions\n",
    "new_df['bath'].hist(bins=50, ax=ax, color='green', density=True, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ac98009b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 27,
     "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",
    "\n",
    "# original data\n",
    "the['bath'].plot.density(color='red')\n",
    "\n",
    "# data after cca\n",
    "new_df['bath'].plot.density(color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a4f99239",
   "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>original</th>\n",
       "      <th>cca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Whitefield</th>\n",
       "      <td>0.040541</td>\n",
       "      <td>0.040441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarjapur  Road</th>\n",
       "      <td>0.029955</td>\n",
       "      <td>0.029268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic City</th>\n",
       "      <td>0.022673</td>\n",
       "      <td>0.023603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kanakpura Road</th>\n",
       "      <td>0.020495</td>\n",
       "      <td>0.020535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thanisandra</th>\n",
       "      <td>0.017568</td>\n",
       "      <td>0.018175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 original       cca\n",
       "Whitefield       0.040541  0.040441\n",
       "Sarjapur  Road   0.029955  0.029268\n",
       "Electronic City  0.022673  0.023603\n",
       "Kanakpura Road   0.020495  0.020535\n",
       "Thanisandra      0.017568  0.018175"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.concat([\n",
    "            # percentage of observations per category, original data\n",
    "            the['location'].value_counts() / len(the),\n",
    "\n",
    "            # percentage of observations per category, cca data\n",
    "            new_df['location'].value_counts() / len(new_df)\n",
    "        ],\n",
    "        axis=1)\n",
    "\n",
    "# add column names\n",
    "temp.columns = ['original', 'cca']\n",
    "\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fd0942c2",
   "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>original</th>\n",
       "      <th>cca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2 BHK</th>\n",
       "      <td>0.390315</td>\n",
       "      <td>0.405350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 BHK</th>\n",
       "      <td>0.323574</td>\n",
       "      <td>0.324784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 Bedroom</th>\n",
       "      <td>0.062012</td>\n",
       "      <td>0.058930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 BHK</th>\n",
       "      <td>0.044369</td>\n",
       "      <td>0.038474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 Bedroom</th>\n",
       "      <td>0.041066</td>\n",
       "      <td>0.041463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           original       cca\n",
       "2 BHK      0.390315  0.405350\n",
       "3 BHK      0.323574  0.324784\n",
       "4 Bedroom  0.062012  0.058930\n",
       "4 BHK      0.044369  0.038474\n",
       "3 Bedroom  0.041066  0.041463"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.concat([\n",
    "            # percentage of observations per category, original data\n",
    "            the['size'].value_counts() / len(the),\n",
    "\n",
    "            # percentage of observations per category, cca data\n",
    "            new_df['size'].value_counts() / len(new_df)\n",
    "        ],\n",
    "        axis=1)\n",
    "\n",
    "# add column names\n",
    "temp.columns = ['original', 'cca']\n",
    "\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d21606a",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}