visualization_of_missing_value_location.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of Missing Value Location\n",
    "This notebook explains visualization techniques for the location of missing values using the followings:\n",
    " 1. seaborn.heatmap function\n",
    "   - Just-one-line, easy, and instant method.\n",
    " 2. [missingno](https://github.com/ResidentMario/missingno) module\n",
    "   - Requires an additional module, but gives us more insight on missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Library import, setup, and data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv') # Use Titanic train dataset from Kaggle as an example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# df.info() shows the number of non-null values (and hence the number of missing values).\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Using seaborn.heatmap function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15cedc60470>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Following command shows missing/non-missing values in two colors.\n",
    "sns.heatmap(df.isnull(), cbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Using [missingno](https://github.com/ResidentMario/missingno) module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15cefd750f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# missingno.matrix function shows missing/non-missing values in two colors.\n",
    "# The result is similar to seaborn.heatmap function above, \n",
    "# plus the bar on the right side which indicates completeness of values in each entry \n",
    "# (ranges from 10 to 12 in this dataset.)\n",
    "msno.matrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15cefe04898>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# missingno.heatmap function gives nullity correlation\n",
    "# (how strongly the presence or absence of one variable affects the presence of another).\n",
    "# For Titanic train dataset, there is no strong correlation observed for nullity.\n",
    "msno.heatmap(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And more...\n",
    "\n",
    "See [the documentation](https://github.com/ResidentMario/missingno)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}