Feature Engineering- Missing Values Day 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Arbitrary Value Imputation\n",
    "\n",
    "this technique was derived from kaggle competition\n",
    "It consists of replacing NAN by an arbitrary value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
       "    </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",
       "</div>"
      ],
      "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df,variable):\n",
    "    df[variable+'_zero']=df[variable].fillna(0)\n",
    "    df[variable+'_hundred']=df[variable].fillna(100)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfb2ed4c88>"
      ]
     },
     "execution_count": 4,
     "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": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "### Advantages\n",
    "\n",
    "- Easy to implement\n",
    "- Captures the importance of missingess if there is one\n",
    "\n",
    "### Disadvantages\n",
    "\n",
    "- Distorts the original distribution of the variable\n",
    "- If missingess is not important, it may mask the predictive power of the original variable by distorting its distribution\n",
    "- Hard to decide which value to use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### How To Handle Categroical Missing Values\n",
    "\n",
    "##### Frequent Category Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('loan.csv', usecols=['BsmtQual','FireplaceQu','GarageType','SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BsmtQual        37\n",
       "FireplaceQu    690\n",
       "GarageType      81\n",
       "SalePrice        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice      0.000000\n",
       "BsmtQual       0.025342\n",
       "GarageType     0.055479\n",
       "FireplaceQu    0.472603\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean().sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute the frequency with every feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfb73a1be0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8FO+Kfm4fr6p7k1wDbAPeAqxO8m7g3qr6zGjbW7ZOJHkv8EFmP+TuDcCJ0ba07H0c2FNVR5ME+BNmP4LkxtG2tSz9oPvIlgeTvAM4A1wz4p4Gwhum5pDkK1X1qmfUVgLbgddV1W2j6Wx5S3I18IfAL3WlLwDvrqrzo+tqeUtybVV99xm18ap6dFQ9LVdJXgqcZfb6/FuAnwDuuXj5djkz6OeQ5KtVdcuo+2hFkq3Auqp6Vzc+Bowxu6p/a1V9ZJT9LUdJ3lpV7+j2t1fVh3se+6uqetvoultekvxUVT0x6j6GyaCfQ5Jp4J2XeryqLvmYni3Jl4AdVXWqGz8I3Aa8GPhAVW0eZX/LUe9Pnc/8CXSun0h1ac84lx+tqteOuqdB8xr93FYwG0L+AnYwrrwY8p0vVtW3gW93vwfR4uUS+3ONNb/e8/WykXUxRAb93M5U1V+OuomGXNc7qKo39QzHnuNeWlGX2J9rrPnNdy6bYNDPzRXRYN2f5Per6j29xSR/ABwbUU/L3U1Jvsvs/9UXdft046tH19ayNN+5rBZumPIa/RySrOwuLWgAkqwC/hU4D1z8uIOfB64CtlXV2VH1Jv0oMOj1nElyG////u7jVfXZUfYj/agw6CWpcX4EgiQ1zqCXpMYZ9JLUOINekhpn0EtS4/4PQAweCs6UK/kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['BsmtQual'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfb71f3320>"
      ]
     },
     "execution_count": 21,
     "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.groupby(['BsmtQual'])['BsmtQual'].count().sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfb73d0908>"
      ]
     },
     "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": [
    "df['GarageType'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfb74132e8>"
      ]
     },
     "execution_count": 26,
     "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['FireplaceQu'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Attchd'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['GarageType'].value_counts().index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Attchd'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['GarageType'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df,variable):\n",
    "    most_frequent_category=df[variable].mode()[0]\n",
    "    df[variable].fillna(most_frequent_category,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in ['BsmtQual','FireplaceQu','GarageType']:\n",
    "    impute_nan(df,feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BsmtQual       0.0\n",
       "FireplaceQu    0.0\n",
       "GarageType     0.0\n",
       "SalePrice      0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Advantages\n",
    "1. Easy To implement\n",
    "2. Fater way to implement\n",
    "#### Disadvantages\n",
    "1. Since we are using the more frequent labels, it may use them in an over respresented way, if there are many nan's\n",
    "2. It distorts the relation of the most frequent label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Adding a variable to capture NAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>BsmtQual</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice\n",
       "0       Gd         NaN     Attchd     208500\n",
       "1       Gd          TA     Attchd     181500\n",
       "2       Gd          TA     Attchd     223500\n",
       "3       TA          Gd     Detchd     140000\n",
       "4       Gd          TA     Attchd     250000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('loan.csv', usecols=['BsmtQual','FireplaceQu','GarageType','SalePrice'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df['BsmtQual_Var']=np.where(df['BsmtQual'].isnull(),1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>BsmtQual</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>BsmtQual_Var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>208500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>181500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>223500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>140000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>250000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice  BsmtQual_Var\n",
       "0       Gd         NaN     Attchd     208500             0\n",
       "1       Gd          TA     Attchd     181500             0\n",
       "2       Gd          TA     Attchd     223500             0\n",
       "3       TA          Gd     Detchd     140000             0\n",
       "4       Gd          TA     Attchd     250000             0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TA'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BsmtQual'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BsmtQual'].fillna(frequent,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>FireplaceQu</th>\n",
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       "      <td>181500</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>TA</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice  BsmtQual_Var\n",
       "0       Gd         NaN     Attchd     208500             0\n",
       "1       Gd          TA     Attchd     181500             0\n",
       "2       Gd          TA     Attchd     223500             0\n",
       "3       TA          Gd     Detchd     140000             0\n",
       "4       Gd          TA     Attchd     250000             0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['FireplaceQu_Var']=np.where(df['FireplaceQu'].isnull(),1,0)\n",
    "frequent=df['FireplaceQu'].mode()[0]\n",
    "df['FireplaceQu'].fillna(frequent,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>GarageType</th>\n",
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       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>140000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice  BsmtQual_Var  FireplaceQu_Var\n",
       "0       Gd          Gd     Attchd     208500             0                1\n",
       "1       Gd          TA     Attchd     181500             0                0\n",
       "2       Gd          TA     Attchd     223500             0                0\n",
       "3       TA          Gd     Detchd     140000             0                0\n",
       "4       Gd          TA     Attchd     250000             0                0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Suppose if you have more frequent categories, we just replace NAN with a new category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('loan.csv', usecols=['BsmtQual','FireplaceQu','GarageType','SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>250000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice\n",
       "0       Gd         NaN     Attchd     208500\n",
       "1       Gd          TA     Attchd     181500\n",
       "2       Gd          TA     Attchd     223500\n",
       "3       TA          Gd     Detchd     140000\n",
       "4       Gd          TA     Attchd     250000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df,variable):\n",
    "    df[variable+\"newvar\"]=np.where(df[variable].isnull(),\"Missing\",df[variable])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in ['BsmtQual','FireplaceQu','GarageType']:\n",
    "    impute_nan(df,feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>3</th>\n",
       "      <td>TA</td>\n",
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       "      <td>140000</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
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       "      <th>4</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>250000</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice BsmtQualnewvar FireplaceQunewvar  \\\n",
       "0       Gd         NaN     Attchd     208500             Gd           Missing   \n",
       "1       Gd          TA     Attchd     181500             Gd                TA   \n",
       "2       Gd          TA     Attchd     223500             Gd                TA   \n",
       "3       TA          Gd     Detchd     140000             TA                Gd   \n",
       "4       Gd          TA     Attchd     250000             Gd                TA   \n",
       "\n",
       "  GarageTypenewvar  \n",
       "0           Attchd  \n",
       "1           Attchd  \n",
       "2           Attchd  \n",
       "3           Detchd  \n",
       "4           Attchd  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['BsmtQual','FireplaceQu','GarageType'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>250000</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   SalePrice BsmtQualnewvar FireplaceQunewvar GarageTypenewvar\n",
       "0     208500             Gd           Missing           Attchd\n",
       "1     181500             Gd                TA           Attchd\n",
       "2     223500             Gd                TA           Attchd\n",
       "3     140000             TA                Gd           Detchd\n",
       "4     250000             Gd                TA           Attchd"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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