Fill The Missing Values From Data Set.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Series Name</th>\n",
       "      <th>Series Code</th>\n",
       "      <th>2005 [YR2005]</th>\n",
       "      <th>2006 [YR2006]</th>\n",
       "      <th>2007 [YR2007]</th>\n",
       "      <th>2008 [YR2008]</th>\n",
       "      <th>2009 [YR2009]</th>\n",
       "      <th>2010 [YR2010]</th>\n",
       "      <th>2011 [YR2011]</th>\n",
       "      <th>2012 [YR2012]</th>\n",
       "      <th>2013 [YR2013]</th>\n",
       "      <th>2014 [YR2014]</th>\n",
       "      <th>2015 [YR2015]</th>\n",
       "      <th>2016 [YR2016]</th>\n",
       "      <th>2017 [YR2017]</th>\n",
       "      <th>2018 [YR2018]</th>\n",
       "      <th>2019 [YR2019]</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>A woman can apply for a passport in the same w...</td>\n",
       "      <td>SG.APL.PSPT.EQ</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>A woman can be head of household in the same w...</td>\n",
       "      <td>SG.HLD.HEAD.EQ</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>A woman can choose where to live in the same w...</td>\n",
       "      <td>SG.LOC.LIVE.EQ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>A woman can get a job in the same way as a man...</td>\n",
       "      <td>SG.GET.JOBS.EQ</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>A woman can obtain a judgment of divorce in th...</td>\n",
       "      <td>SG.OBT.DVRC.EQ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "  Country Name Country Code  \\\n",
       "0  Afghanistan          AFG   \n",
       "1  Afghanistan          AFG   \n",
       "2  Afghanistan          AFG   \n",
       "3  Afghanistan          AFG   \n",
       "4  Afghanistan          AFG   \n",
       "\n",
       "                                         Series Name     Series Code  \\\n",
       "0  A woman can apply for a passport in the same w...  SG.APL.PSPT.EQ   \n",
       "1  A woman can be head of household in the same w...  SG.HLD.HEAD.EQ   \n",
       "2  A woman can choose where to live in the same w...  SG.LOC.LIVE.EQ   \n",
       "3  A woman can get a job in the same way as a man...  SG.GET.JOBS.EQ   \n",
       "4  A woman can obtain a judgment of divorce in th...  SG.OBT.DVRC.EQ   \n",
       "\n",
       "  2005 [YR2005] 2006 [YR2006] 2007 [YR2007] 2008 [YR2008] 2009 [YR2009]  \\\n",
       "0             0             0             0             0             0   \n",
       "1             1             1             1             1             1   \n",
       "2             0             0             0             0             0   \n",
       "3             1             1             1             1             1   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "  2010 [YR2010] 2011 [YR2011] 2012 [YR2012] 2013 [YR2013] 2014 [YR2014]  \\\n",
       "0             0             0             0             0             0   \n",
       "1             1             1             1             1             1   \n",
       "2             0             0             0             0             0   \n",
       "3             1             1             1             1             1   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "  2015 [YR2015] 2016 [YR2016] 2017 [YR2017] 2018 [YR2018] 2019 [YR2019]  \n",
       "0             0             1             1             1             1  \n",
       "1             1             1             1             1             1  \n",
       "2             0             0             0             0             0  \n",
       "3             1             1             1             1             1  \n",
       "4             0             0             0             0             0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the Data Set from CSV File\n",
    "df = pd.read_csv('CountryData.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Series Name</th>\n",
       "      <th>Series Code</th>\n",
       "      <th>2005 [YR2005]</th>\n",
       "      <th>2006 [YR2006]</th>\n",
       "      <th>2007 [YR2007]</th>\n",
       "      <th>2008 [YR2008]</th>\n",
       "      <th>2009 [YR2009]</th>\n",
       "      <th>2010 [YR2010]</th>\n",
       "      <th>2011 [YR2011]</th>\n",
       "      <th>2012 [YR2012]</th>\n",
       "      <th>2013 [YR2013]</th>\n",
       "      <th>2014 [YR2014]</th>\n",
       "      <th>2015 [YR2015]</th>\n",
       "      <th>2016 [YR2016]</th>\n",
       "      <th>2017 [YR2017]</th>\n",
       "      <th>2018 [YR2018]</th>\n",
       "      <th>2019 [YR2019]</th>\n",
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       "      <th>162008</th>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>162010</th>\n",
       "      <td>True</td>\n",
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       "      <th>162011</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>162012</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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      "text/plain": [
       "        Country Name  Country Code  Series Name  Series Code  2005 [YR2005]  \\\n",
       "162008          True          True         True         True           True   \n",
       "162009          True          True         True         True           True   \n",
       "162010          True          True         True         True           True   \n",
       "162011         False          True         True         True           True   \n",
       "162012         False          True         True         True           True   \n",
       "\n",
       "        2006 [YR2006]  2007 [YR2007]  2008 [YR2008]  2009 [YR2009]  \\\n",
       "162008           True           True           True           True   \n",
       "162009           True           True           True           True   \n",
       "162010           True           True           True           True   \n",
       "162011           True           True           True           True   \n",
       "162012           True           True           True           True   \n",
       "\n",
       "        2010 [YR2010]  2011 [YR2011]  2012 [YR2012]  2013 [YR2013]  \\\n",
       "162008           True           True           True           True   \n",
       "162009           True           True           True           True   \n",
       "162010           True           True           True           True   \n",
       "162011           True           True           True           True   \n",
       "162012           True           True           True           True   \n",
       "\n",
       "        2014 [YR2014]  2015 [YR2015]  2016 [YR2016]  2017 [YR2017]  \\\n",
       "162008           True           True           True           True   \n",
       "162009           True           True           True           True   \n",
       "162010           True           True           True           True   \n",
       "162011           True           True           True           True   \n",
       "162012           True           True           True           True   \n",
       "\n",
       "        2018 [YR2018]  2019 [YR2019]  \n",
       "162008           True           True  \n",
       "162009           True           True  \n",
       "162010           True           True  \n",
       "162011           True           True  \n",
       "162012           True           True  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets try to find if values are missing from the dataset\n",
    "\n",
    "df.isnull().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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       "      <th>Series Code</th>\n",
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       "      <th>162008</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162010</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162011</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    </tr>\n",
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       "      <th>162012</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country Name  Country Code  Series Name  Series Code  2005 [YR2005]  \\\n",
       "162008         False         False        False        False          False   \n",
       "162009         False         False        False        False          False   \n",
       "162010         False         False        False        False          False   \n",
       "162011          True         False        False        False          False   \n",
       "162012          True         False        False        False          False   \n",
       "\n",
       "        2006 [YR2006]  2007 [YR2007]  2008 [YR2008]  2009 [YR2009]  \\\n",
       "162008          False          False          False          False   \n",
       "162009          False          False          False          False   \n",
       "162010          False          False          False          False   \n",
       "162011          False          False          False          False   \n",
       "162012          False          False          False          False   \n",
       "\n",
       "        2010 [YR2010]  2011 [YR2011]  2012 [YR2012]  2013 [YR2013]  \\\n",
       "162008          False          False          False          False   \n",
       "162009          False          False          False          False   \n",
       "162010          False          False          False          False   \n",
       "162011          False          False          False          False   \n",
       "162012          False          False          False          False   \n",
       "\n",
       "        2014 [YR2014]  2015 [YR2015]  2016 [YR2016]  2017 [YR2017]  \\\n",
       "162008          False          False          False          False   \n",
       "162009          False          False          False          False   \n",
       "162010          False          False          False          False   \n",
       "162011          False          False          False          False   \n",
       "162012          False          False          False          False   \n",
       "\n",
       "        2018 [YR2018]  2019 [YR2019]  \n",
       "162008          False          False  \n",
       "162009          False          False  \n",
       "162010          False          False  \n",
       "162011          False          False  \n",
       "162012          False          False  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets use the function notnull() to check which values are not null\n",
    "\n",
    "df.notnull().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country Name     3\n",
       "Country Code     5\n",
       "Series Name      5\n",
       "Series Code      5\n",
       "2005 [YR2005]    5\n",
       "2006 [YR2006]    5\n",
       "2007 [YR2007]    5\n",
       "2008 [YR2008]    5\n",
       "2009 [YR2009]    5\n",
       "2010 [YR2010]    5\n",
       "2011 [YR2011]    5\n",
       "2012 [YR2012]    5\n",
       "2013 [YR2013]    5\n",
       "2014 [YR2014]    5\n",
       "2015 [YR2015]    5\n",
       "2016 [YR2016]    5\n",
       "2017 [YR2017]    5\n",
       "2018 [YR2018]    5\n",
       "2019 [YR2019]    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's do another operation to find number of missing values in each columns\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removing Complete Null Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([162008, 162009, 162010], dtype='int64')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we want to find that which rows are complete null and remove them\n",
    "data = df\n",
    "null_rows = data.index[data.isnull().all(1)]\n",
    "null_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(162013, 19)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop all the Rows that are complete Null\n",
    "\n",
    "df.drop(null_rows, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(162010, 19)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we want to drop all the values which are missing in our data set\n",
    "# Drop the row if any of them have 'NaN' Values\n",
    "\n",
    "df.dropna(how = 'any', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(162008, 19)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets check that how many rows and Columns we have after cleaning the data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country Name     0\n",
       "Country Code     0\n",
       "Series Name      0\n",
       "Series Code      0\n",
       "2005 [YR2005]    0\n",
       "2006 [YR2006]    0\n",
       "2007 [YR2007]    0\n",
       "2008 [YR2008]    0\n",
       "2009 [YR2009]    0\n",
       "2010 [YR2010]    0\n",
       "2011 [YR2011]    0\n",
       "2012 [YR2012]    0\n",
       "2013 [YR2013]    0\n",
       "2014 [YR2014]    0\n",
       "2015 [YR2015]    0\n",
       "2016 [YR2016]    0\n",
       "2017 [YR2017]    0\n",
       "2018 [YR2018]    0\n",
       "2019 [YR2019]    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Again, ensure that If the dataset contain any missing values\n",
    "# We can see that there are no Columns that contain any missing values\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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