05. Missing Values Part 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3f6c4f2",
   "metadata": {},
   "source": [
    "# Missing Values Part 2\n",
    "Here we will deal with categorical columns containing missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "140578be",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imoporting libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f5a7bb0",
   "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>Pclass</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass Cabin Embarked\n",
       "0       3   NaN        S\n",
       "1       1   C85        C\n",
       "2       3   NaN        S\n",
       "3       1  C123        S\n",
       "4       3   NaN        S"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the data with categorical columns\n",
    "df=pd.read_csv('titanic.csv',usecols=['Pclass','Embarked','Cabin'])\n",
    "df.head()\n",
    "#Pclass may seem to be containing numeric values\n",
    "#But those are discrete and represent Passenger Class i.e. 1: First Class, 2: Second Class, 3: Third Class\n",
    "#Therefore it should be treated as an categorical column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fcdc6505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass        0\n",
       "Cabin       687\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the count of null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5bfd3165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0.000000\n",
       "Embarked    0.002245\n",
       "Cabin       0.771044\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percentage of null values in ascending order\n",
    "df.isnull().mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f642d3bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Frequency of the categorical values from 'Embarked' column\n",
    "df['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d357398c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NUc2m5SXZwPx99R8w//0r4G3M/1fXe6rqv0Y43i1n3KVrJHkn8NK999NV9cWl9uv2kuRdwDbm/3Xa01U1NeKRRsK4S1KDvOcuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ36P1XZgwIN2UW5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram of the 'Embarked'\n",
    "df['Embarked'].value_counts().plot.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "328d2e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Frequency of the categorical values from the 'Pclass' column\n",
    "df['Pclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "24d84216",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Histogram of the 'Pclass'\n",
    "df['Pclass'].value_counts().plot.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6926afea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Most recurring value i.e. top value of the Embarked\n",
    "df['Embarked'].value_counts().index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "74596bdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Second most recurring value\n",
    "df['Embarked'].value_counts().index[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "472ff3dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the most recurring value using .mode()\n",
    "df['Embarked'].mode()[0]\n",
    "#Here we are using indexing [0] because one can multiple modes havinf same frequency\n",
    "#Indexing makes sure that the first most recurring value is considered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a538643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'B96 B98'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the most recurring value using .mode()\n",
    "df['Cabin'].mode()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8600ca0",
   "metadata": {},
   "source": [
    "#### Mode Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05ad9b8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining function that fills the missing values with mode while creating a new column\n",
    "def impute_nan(df,variable):\n",
    "    most_frequent_category=df[variable].mode()[0]\n",
    "    df[variable+\"_mode\"]=df[variable].fillna(most_frequent_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "544be84c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying the function on categorical columns\n",
    "for feature in ['Pclass','Embarked','Cabin']:\n",
    "    impute_nan(df,feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1674c60a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass           0.000000\n",
       "Cabin            0.771044\n",
       "Embarked         0.002245\n",
       "Pclass_mode      0.000000\n",
       "Embarked_mode    0.000000\n",
       "Cabin_mode       0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b380ce8",
   "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>Pclass</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Pclass_mode</th>\n",
       "      <th>Embarked_mode</th>\n",
       "      <th>Cabin_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>B96 B98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>C85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>B96 B98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>C123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>B96 B98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass Cabin Embarked  Pclass_mode Embarked_mode Cabin_mode\n",
       "0       3   NaN        S            3             S    B96 B98\n",
       "1       1   C85        C            1             C        C85\n",
       "2       3   NaN        S            3             S    B96 B98\n",
       "3       1  C123        S            1             S       C123\n",
       "4       3   NaN        S            3             S    B96 B98"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5456b3",
   "metadata": {},
   "source": [
    "#### Filling the missing values with 'missing'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b88e8216",
   "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>Pclass</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass Cabin Embarked\n",
       "0       3   NaN        S\n",
       "1       1   C85        C\n",
       "2       3   NaN        S\n",
       "3       1  C123        S\n",
       "4       3   NaN        S"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the data again\n",
    "df=pd.read_csv('titanic.csv', usecols=['Pclass','Embarked','Cabin'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5ae72213",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining a function that fills missing values with 'missing' while creating a new column\n",
    "def impute_nan(df,variable):\n",
    "    df[variable+\"_newvar\"]=np.where(df[variable].isnull(),'Missing',df[variable])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43d4a3ed",
   "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>Pclass</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Pclass_newvar</th>\n",
       "      <th>Embarked_newvar</th>\n",
       "      <th>Cabin_newvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>C85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>C123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Missing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass Cabin Embarked Pclass_newvar Embarked_newvar Cabin_newvar\n",
       "0       3   NaN        S             3               S      Missing\n",
       "1       1   C85        C             1               C          C85\n",
       "2       3   NaN        S             3               S      Missing\n",
       "3       1  C123        S             1               S         C123\n",
       "4       3   NaN        S             3               S      Missing"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying the function on categorical columns\n",
    "for feature in ['Pclass','Embarked','Cabin']:\n",
    "    impute_nan(df,feature)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ee883488",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass_newvar</th>\n",
       "      <th>Embarked_newvar</th>\n",
       "      <th>Cabin_newvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>C85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>C123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>Missing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Pclass_newvar Embarked_newvar Cabin_newvar\n",
       "0             3               S      Missing\n",
       "1             1               C          C85\n",
       "2             3               S      Missing\n",
       "3             1               S         C123\n",
       "4             3               S      Missing"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can drop the columns once the missing values are filled\n",
    "df.drop(['Pclass','Embarked','Cabin'], axis=1, inplace=True)\n",
    "df.head()\n",
    "#Another way :\n",
    "#df=df.drop(['Pclass','Embarked','Cabin'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a1b87d",
   "metadata": {},
   "source": [
    "The End"
   ]
  }
 ],
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