Missing values and Imputation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "# from sklearn.preprocessing import Imputer  ## It is deprecated in the lastest version\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>HURLEY</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>na</td>\n",
       "      <td>2</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH   SQ_FT\n",
       "0  100001000.0   104.0      PUTNAM            Y            3        1  1000.0\n",
       "1  100002000.0   197.0   LEXINGTON            N            3      1.5   100.0\n",
       "2  100003000.0     NaN   LEXINGTON            N          NaN        1   850.0\n",
       "3  100004000.0   201.0    BERKELEY          NaN            1      NaN   700.0\n",
       "4          NaN   203.0    BERKELEY            Y            3        2  1600.0\n",
       "5  100006000.0   207.0    BERKELEY            Y          NaN        1   800.0\n",
       "6  100007000.0     NaN  WASHINGTON          NaN            2   HURLEY   950.0\n",
       "7  100008000.0   213.0     TREMONT            Y            1        1     NaN\n",
       "8  100009000.0   215.0     TREMONT            Y           na        2  1800.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading our dataset\n",
    "\n",
    "data = pd.read_csv('Sample_real_estate_data.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data['ST_NUM'].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>HURLEY</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT\n",
       "0  100001000.0   104.0      PUTNAM            Y           3.0        1  1000.0\n",
       "1  100002000.0   197.0   LEXINGTON            N           3.0      1.5   100.0\n",
       "2  100003000.0     NaN   LEXINGTON            N           NaN        1   850.0\n",
       "3  100004000.0   201.0    BERKELEY          NaN           1.0      NaN   700.0\n",
       "4          NaN   203.0    BERKELEY            Y           3.0        2  1600.0\n",
       "5  100006000.0   207.0    BERKELEY            Y           NaN        1   800.0\n",
       "6  100007000.0     NaN  WASHINGTON          NaN           2.0   HURLEY   950.0\n",
       "7  100008000.0   213.0     TREMONT            Y           1.0        1     NaN\n",
       "8  100009000.0   215.0     TREMONT            Y           NaN        2  1800.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handle missing values while loading dataset\n",
    "missing_value = [\"n/a\",\"na\",\"--\"]\n",
    "data1 = pd.read_csv('Sample_real_estate_data.csv', na_values = missing_value)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'numpy.float64'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# checking the type of the value\n",
    "print(type(data1['NUM_BATH'][0]))\n",
    "print(type(data1['PID'][0]))\n",
    "print(type(data1['OWN_OCCUPIED'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    1.5\n",
       "2    NaN\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    NaN\n",
       "6    NaN\n",
       "7    NaN\n",
       "8    NaN\n",
       "Name: NUM_BATH, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert all string and numerical to nan using regex\n",
    "\n",
    "temp = data1['NUM_BATH'].replace(r'^([A-Za-z]|[0-9]|_)+$', np.NaN, regex=True)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 0         1\n",
       "1       1.5\n",
       "2         1\n",
       "3       NaN\n",
       "4         2\n",
       "5         1\n",
       "6    HURLEY\n",
       "7         1\n",
       "8         2\n",
       "Name: NUM_BATH, dtype: object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the unique values of NUM_BATH cloumn\n",
    "data1['NUM_BATH'].unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1\n",
       "1       1.5\n",
       "2         1\n",
       "3       NaN\n",
       "4         2\n",
       "5         1\n",
       "6    HURLEY\n",
       "7         1\n",
       "8         2\n",
       "Name: NUM_BATH, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = data1.NUM_BATH.astype(object)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PID             float64\n",
       "ST_NUM          float64\n",
       "ST_NAME          object\n",
       "OWN_OCCUPIED     object\n",
       "NUM_BEDROOMS    float64\n",
       "NUM_BATH         object\n",
       "SQ_FT           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking data type of a columns\n",
    "data1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# temp = data1.NUM_BATH.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count=0\n",
    "for row in data1['NUM_BATH']:\n",
    "    try:\n",
    "        float(row)\n",
    "        temp = np.nan\n",
    "    except ValueError:\n",
    "        temp = row\n",
    "    count+=1\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1    1.5\n",
       "2      1\n",
       "3    NaN\n",
       "4      2\n",
       "5      1\n",
       "6    NaN\n",
       "7      1\n",
       "8      2\n",
       "Name: NUM_BATH, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace mixed type columns having all alphabet with NUM\n",
    "# kept all string numerical string\n",
    "temp = data1['NUM_BATH'].replace(r'^([A-Za-z]|_)+$', np.NaN, regex=True)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT\n",
       "0  100001000.0   104.0      PUTNAM            Y           3.0        1  1000.0\n",
       "1  100002000.0   197.0   LEXINGTON            N           3.0      1.5   100.0\n",
       "2  100003000.0     NaN   LEXINGTON            N           NaN        1   850.0\n",
       "3  100004000.0   201.0    BERKELEY          NaN           1.0      NaN   700.0\n",
       "4          NaN   203.0    BERKELEY            Y           3.0        2  1600.0\n",
       "5  100006000.0   207.0    BERKELEY            Y           NaN        1   800.0\n",
       "6  100007000.0     NaN  WASHINGTON          NaN           2.0      NaN   950.0\n",
       "7  100008000.0   213.0     TREMONT            Y           1.0        1     NaN\n",
       "8  100009000.0   215.0     TREMONT            Y           NaN        2  1800.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply on original dataframe :D\n",
    "\n",
    "data1['NUM_BATH'] = data1['NUM_BATH'].replace(r'^([A-Za-z]|_)+$', np.NaN, regex=True)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PID             float64\n",
       "ST_NUM          float64\n",
       "ST_NAME          object\n",
       "OWN_OCCUPIED     object\n",
       "NUM_BEDROOMS    float64\n",
       "NUM_BATH         object\n",
       "SQ_FT           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking data type of a columns\n",
    "data1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    1.5\n",
       "2    1.0\n",
       "3    NaN\n",
       "4    2.0\n",
       "5    1.0\n",
       "6    NaN\n",
       "7    1.0\n",
       "8    2.0\n",
       "Name: NUM_BATH, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = data1.NUM_BATH.astype(float)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PID             float64\n",
       "ST_NUM          float64\n",
       "ST_NAME          object\n",
       "OWN_OCCUPIED     object\n",
       "NUM_BEDROOMS    float64\n",
       "NUM_BATH        float64\n",
       "SQ_FT           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the str type to float\n",
    "\n",
    "data1.NUM_BATH = data1.NUM_BATH.astype(float)\n",
    "data1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PID             1\n",
      "ST_NUM          2\n",
      "ST_NAME         0\n",
      "OWN_OCCUPIED    2\n",
      "NUM_BEDROOMS    3\n",
      "NUM_BATH        2\n",
      "SQ_FT           1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# checking how many null values in every column\n",
    "print(data1.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS  NUM_BATH  \\\n",
       "0  100001000.0   104.0      PUTNAM            Y           3.0       1.0   \n",
       "1  100002000.0   197.0   LEXINGTON            N           3.0       1.5   \n",
       "2  100003000.0     NaN   LEXINGTON            N           NaN       1.0   \n",
       "3  100004000.0   201.0    BERKELEY          NaN           1.0       NaN   \n",
       "4          NaN   203.0    BERKELEY            Y           3.0       2.0   \n",
       "5  100006000.0   207.0    BERKELEY            Y           NaN       1.0   \n",
       "6  100007000.0     NaN  WASHINGTON          NaN           2.0       NaN   \n",
       "7  100008000.0   213.0     TREMONT            Y           1.0       1.0   \n",
       "8  100009000.0   215.0     TREMONT            Y           NaN       2.0   \n",
       "\n",
       "    SQ_FT  \n",
       "0  1000.0  \n",
       "1   100.0  \n",
       "2   850.0  \n",
       "3   700.0  \n",
       "4  1600.0  \n",
       "5   800.0  \n",
       "6   950.0  \n",
       "7     NaN  \n",
       "8  1800.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # handling missing value using imputer\n",
    "# X = data.iloc[:,:].values  # get the all values in an array\n",
    "# Y = data.iloc[:,6] # getting all the rows of last column\n",
    "# imput = SimpleImputer(missing_values = 'NaN', strategy= 'mean')\n",
    "# imput = imput.fit(X[:,1:2])\n",
    "# X[:,1:2] = imput.transform(X[:,1:2])\n",
    "# X[:,1:2]\n",
    "# # X\n",
    "# # Y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.0\n",
       "1    3.0\n",
       "2    2.5\n",
       "3    1.0\n",
       "4    3.0\n",
       "5    2.5\n",
       "6    2.0\n",
       "7    1.0\n",
       "8    2.5\n",
       "Name: NUM_BEDROOMS, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing the Nan value with median\n",
    "\n",
    "median = data1['NUM_BEDROOMS'].median()\n",
    "data1['NUM_BEDROOMS'].fillna(median)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS  NUM_BATH  \\\n",
       "0  100001000.0   104.0      PUTNAM            Y           3.0       1.0   \n",
       "1  100002000.0   197.0   LEXINGTON            N           3.0       1.5   \n",
       "2  100003000.0     NaN   LEXINGTON            N           2.5       1.0   \n",
       "3  100004000.0   201.0    BERKELEY          NaN           1.0       NaN   \n",
       "4          NaN   203.0    BERKELEY            Y           3.0       2.0   \n",
       "5  100006000.0   207.0    BERKELEY            Y           2.5       1.0   \n",
       "6  100007000.0     NaN  WASHINGTON          NaN           2.0       NaN   \n",
       "7  100008000.0   213.0     TREMONT            Y           1.0       1.0   \n",
       "8  100009000.0   215.0     TREMONT            Y           2.5       2.0   \n",
       "\n",
       "    SQ_FT  \n",
       "0  1000.0  \n",
       "1   100.0  \n",
       "2   850.0  \n",
       "3   700.0  \n",
       "4  1600.0  \n",
       "5   800.0  \n",
       "6   950.0  \n",
       "7     NaN  \n",
       "8  1800.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now do the above job in place\n",
    "median = data1['NUM_BEDROOMS'].median()\n",
    "data1['NUM_BEDROOMS'].fillna(median, inplace=True)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.357143</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.357143</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS  NUM_BATH  \\\n",
       "0  100001000.0   104.0      PUTNAM            Y           3.0  1.000000   \n",
       "1  100002000.0   197.0   LEXINGTON            N           3.0  1.500000   \n",
       "2  100003000.0     NaN   LEXINGTON            N           2.5  1.000000   \n",
       "3  100004000.0   201.0    BERKELEY          NaN           1.0  1.357143   \n",
       "4          NaN   203.0    BERKELEY            Y           3.0  2.000000   \n",
       "5  100006000.0   207.0    BERKELEY            Y           2.5  1.000000   \n",
       "6  100007000.0     NaN  WASHINGTON          NaN           2.0  1.357143   \n",
       "7  100008000.0   213.0     TREMONT            Y           1.0  1.000000   \n",
       "8  100009000.0   215.0     TREMONT            Y           2.5  2.000000   \n",
       "\n",
       "    SQ_FT  \n",
       "0  1000.0  \n",
       "1   100.0  \n",
       "2   850.0  \n",
       "3   700.0  \n",
       "4  1600.0  \n",
       "5   800.0  \n",
       "6   950.0  \n",
       "7     NaN  \n",
       "8  1800.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now do the above job in place\n",
    "median = data1['NUM_BATH'].mean()\n",
    "data1['NUM_BATH'].fillna(median, inplace=True)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Y\n",
       "1      N\n",
       "2      N\n",
       "3      Y\n",
       "4      Y\n",
       "5      Y\n",
       "6    NaN\n",
       "7      Y\n",
       "8      Y\n",
       "Name: OWN_OCCUPIED, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['OWN_OCCUPIED'].fillna('Y', limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['OWN_OCCUPIED'].fillna('Y', limit=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.357143</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.357143</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS  NUM_BATH  \\\n",
       "0  100001000.0   104.0      PUTNAM            Y           3.0  1.000000   \n",
       "1  100002000.0   197.0   LEXINGTON            N           3.0  1.500000   \n",
       "2  100003000.0     NaN   LEXINGTON            N           2.5  1.000000   \n",
       "3  100004000.0   201.0    BERKELEY            Y           1.0  1.357143   \n",
       "4          NaN   203.0    BERKELEY            Y           3.0  2.000000   \n",
       "5  100006000.0   207.0    BERKELEY            Y           2.5  1.000000   \n",
       "6  100007000.0     NaN  WASHINGTON          NaN           2.0  1.357143   \n",
       "7  100008000.0   213.0     TREMONT            Y           1.0  1.000000   \n",
       "8  100009000.0   215.0     TREMONT            Y           2.5  2.000000   \n",
       "\n",
       "    SQ_FT  \n",
       "0  1000.0  \n",
       "1   100.0  \n",
       "2   850.0  \n",
       "3   700.0  \n",
       "4  1600.0  \n",
       "5   800.0  \n",
       "6   950.0  \n",
       "7     NaN  \n",
       "8  1800.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    6\n",
       "N    2\n",
       "Name: OWN_OCCUPIED, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the unique values\n",
    "data1['OWN_OCCUPIED'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.357143</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.357143</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS  NUM_BATH  \\\n",
       "0  100001000.0   104.0      PUTNAM            Y           3.0  1.000000   \n",
       "1  100002000.0   197.0   LEXINGTON            N           3.0  1.500000   \n",
       "2  100003000.0     NaN   LEXINGTON            N           2.5  1.000000   \n",
       "3  100004000.0   201.0    BERKELEY            Y           1.0  1.357143   \n",
       "4          NaN   203.0    BERKELEY            Y           3.0  2.000000   \n",
       "5  100006000.0   207.0    BERKELEY            Y           2.5  1.000000   \n",
       "6  100007000.0     NaN  WASHINGTON            Y           2.0  1.357143   \n",
       "7  100008000.0   213.0     TREMONT            Y           1.0  1.000000   \n",
       "8  100009000.0   215.0     TREMONT            Y           2.5  2.000000   \n",
       "\n",
       "    SQ_FT  \n",
       "0  1000.0  \n",
       "1   100.0  \n",
       "2   850.0  \n",
       "3   700.0  \n",
       "4  1600.0  \n",
       "5   800.0  \n",
       "6   950.0  \n",
       "7     NaN  \n",
       "8  1800.0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace again max no of yes\n",
    "data1['OWN_OCCUPIED'].fillna('Y', limit=1, inplace=True)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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