Pandas_Practice_May31.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  #import the pandas library to our notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)  #This command sets the display columns in a dataframe to the number specified\n",
    "pd.set_option('display.max_rows', 500)   #This command sets the display rows in a dataframe to the number specified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey = pd.read_csv(\"Survey_Resp.csv\") #this read_csv method in pandas is reading a csv file from local to a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RESP_ID</th>\n",
       "      <th>SUR_SCORE</th>\n",
       "      <th>MASKED_IP</th>\n",
       "      <th>CRT_DT</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>REGION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13071656</td>\n",
       "      <td>8</td>\n",
       "      <td>MTQ5,Dk1,TMy,A==</td>\n",
       "      <td>1/18/2018 8:48</td>\n",
       "      <td>FI</td>\n",
       "      <td>uusimaa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>13071811</td>\n",
       "      <td>10</td>\n",
       "      <td>MTM0Mjk4,TE0OA==</td>\n",
       "      <td>1/18/2018 8:59</td>\n",
       "      <td>FR</td>\n",
       "      <td>ile-de-france</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>13071889</td>\n",
       "      <td>9</td>\n",
       "      <td>MzY4,DgzOTU=</td>\n",
       "      <td>1/18/2018 9:05</td>\n",
       "      <td>AE</td>\n",
       "      <td>dubai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RESP_ID  SUR_SCORE         MASKED_IP          CRT_DT COUNTRY_CODE  \\\n",
       "0  13071656          8  MTQ5,Dk1,TMy,A==  1/18/2018 8:48           FI   \n",
       "1  13071811         10  MTM0Mjk4,TE0OA==  1/18/2018 8:59           FR   \n",
       "2  13071889          9      MzY4,DgzOTU=  1/18/2018 9:05           AE   \n",
       "\n",
       "          REGION  \n",
       "0        uusimaa  \n",
       "1  ile-de-france  \n",
       "2          dubai  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RESP_ID</th>\n",
       "      <th>SUR_SCORE</th>\n",
       "      <th>MASKED_IP</th>\n",
       "      <th>CRT_DT</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>REGION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13071656</td>\n",
       "      <td>8</td>\n",
       "      <td>MTQ5,Dk1,TMy,A==</td>\n",
       "      <td>1/18/2018 8:48</td>\n",
       "      <td>FI</td>\n",
       "      <td>uusimaa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>13071811</td>\n",
       "      <td>10</td>\n",
       "      <td>MTM0Mjk4,TE0OA==</td>\n",
       "      <td>1/18/2018 8:59</td>\n",
       "      <td>FR</td>\n",
       "      <td>ile-de-france</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>13071889</td>\n",
       "      <td>9</td>\n",
       "      <td>MzY4,DgzOTU=</td>\n",
       "      <td>1/18/2018 9:05</td>\n",
       "      <td>AE</td>\n",
       "      <td>dubai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13071909</td>\n",
       "      <td>10</td>\n",
       "      <td>MTM4MzU5MjY2Mg==</td>\n",
       "      <td>1/18/2018 9:06</td>\n",
       "      <td>CZ</td>\n",
       "      <td>praha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>13071920</td>\n",
       "      <td>10</td>\n",
       "      <td>MzU3ODcz,Tg1OA==</td>\n",
       "      <td>1/18/2018 9:07</td>\n",
       "      <td>IE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>13071988</td>\n",
       "      <td>10</td>\n",
       "      <td>Mjk1,jk1ODE4OQ==</td>\n",
       "      <td>1/18/2018 9:11</td>\n",
       "      <td>HU</td>\n",
       "      <td>budapest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>13072012</td>\n",
       "      <td>8</td>\n",
       "      <td>MTM2ODUxMTE4,g==</td>\n",
       "      <td>1/18/2018 9:12</td>\n",
       "      <td>GB</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>13072097</td>\n",
       "      <td>7</td>\n",
       "      <td>MzA1ODE0,zYwOQ==</td>\n",
       "      <td>1/18/2018 9:17</td>\n",
       "      <td>IN</td>\n",
       "      <td>uttar pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>13072253</td>\n",
       "      <td>10</td>\n",
       "      <td>MTg0,DEwMzMx,A==</td>\n",
       "      <td>1/18/2018 9:27</td>\n",
       "      <td>GB</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>13072647</td>\n",
       "      <td>10</td>\n",
       "      <td>MTI5,zcxMTI0Mg==</td>\n",
       "      <td>1/18/2018 9:50</td>\n",
       "      <td>GB</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RESP_ID  SUR_SCORE         MASKED_IP          CRT_DT COUNTRY_CODE  \\\n",
       "0  13071656          8  MTQ5,Dk1,TMy,A==  1/18/2018 8:48           FI   \n",
       "1  13071811         10  MTM0Mjk4,TE0OA==  1/18/2018 8:59           FR   \n",
       "2  13071889          9      MzY4,DgzOTU=  1/18/2018 9:05           AE   \n",
       "3  13071909         10  MTM4MzU5MjY2Mg==  1/18/2018 9:06           CZ   \n",
       "4  13071920         10  MzU3ODcz,Tg1OA==  1/18/2018 9:07           IE   \n",
       "5  13071988         10  Mjk1,jk1ODE4OQ==  1/18/2018 9:11           HU   \n",
       "6  13072012          8  MTM2ODUxMTE4,g==  1/18/2018 9:12           GB   \n",
       "7  13072097          7  MzA1ODE0,zYwOQ==  1/18/2018 9:17           IN   \n",
       "8  13072253         10  MTg0,DEwMzMx,A==  1/18/2018 9:27           GB   \n",
       "9  13072647         10  MTI5,zcxMTI0Mg==  1/18/2018 9:50           GB   \n",
       "\n",
       "          REGION  \n",
       "0        uusimaa  \n",
       "1  ile-de-france  \n",
       "2          dubai  \n",
       "3          praha  \n",
       "4            NaN  \n",
       "5       budapest  \n",
       "6            NaN  \n",
       "7  uttar pradesh  \n",
       "8            NaN  \n",
       "9            NaN  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RESP_ID', 'SUR_SCORE', 'MASKED_IP', 'CRT_DT', 'COUNTRY_CODE',\n",
       "       'REGION'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.columns   #will get all the column headers available in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "headers = [\"RESPONSE_ID\",\"SURVEY_SCORE\", \"MASKED_IP\", \"SURVEY_DATE\", \"COUNTRY_CODE\", \"REGION\"]\n",
    "survey.columns = headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RESPONSE_ID', 'SURVEY_SCORE', 'MASKED_IP', 'SURVEY_DATE',\n",
       "       'COUNTRY_CODE', 'REGION'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RESPONSE_ID      int64\n",
       "SURVEY_SCORE     int64\n",
       "MASKED_IP       object\n",
       "SURVEY_DATE     object\n",
       "COUNTRY_CODE    object\n",
       "REGION          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.dtypes   #This command will get the datatypes for all the columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RESPONSE_ID</th>\n",
       "      <th>SURVEY_SCORE</th>\n",
       "      <th>MASKED_IP</th>\n",
       "      <th>SURVEY_DATE</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>REGION</th>\n",
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       "      <td>0</td>\n",
       "      <td>13071656</td>\n",
       "      <td>8</td>\n",
       "      <td>MTQ5,Dk1,TMy,A==</td>\n",
       "      <td>1/18/2018 8:48</td>\n",
       "      <td>FI</td>\n",
       "      <td>uusimaa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>13071811</td>\n",
       "      <td>10</td>\n",
       "      <td>MTM0Mjk4,TE0OA==</td>\n",
       "      <td>1/18/2018 8:59</td>\n",
       "      <td>FR</td>\n",
       "      <td>ile-de-france</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>13071889</td>\n",
       "      <td>9</td>\n",
       "      <td>MzY4,DgzOTU=</td>\n",
       "      <td>1/18/2018 9:05</td>\n",
       "      <td>AE</td>\n",
       "      <td>dubai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13071909</td>\n",
       "      <td>10</td>\n",
       "      <td>MTM4MzU5MjY2Mg==</td>\n",
       "      <td>1/18/2018 9:06</td>\n",
       "      <td>CZ</td>\n",
       "      <td>praha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>13071920</td>\n",
       "      <td>10</td>\n",
       "      <td>MzU3ODcz,Tg1OA==</td>\n",
       "      <td>1/18/2018 9:07</td>\n",
       "      <td>IE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>13071988</td>\n",
       "      <td>10</td>\n",
       "      <td>Mjk1,jk1ODE4OQ==</td>\n",
       "      <td>1/18/2018 9:11</td>\n",
       "      <td>HU</td>\n",
       "      <td>budapest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>13072012</td>\n",
       "      <td>8</td>\n",
       "      <td>MTM2ODUxMTE4,g==</td>\n",
       "      <td>1/18/2018 9:12</td>\n",
       "      <td>GB</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>13072097</td>\n",
       "      <td>7</td>\n",
       "      <td>MzA1ODE0,zYwOQ==</td>\n",
       "      <td>1/18/2018 9:17</td>\n",
       "      <td>IN</td>\n",
       "      <td>uttar pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>13072253</td>\n",
       "      <td>10</td>\n",
       "      <td>MTg0,DEwMzMx,A==</td>\n",
       "      <td>1/18/2018 9:27</td>\n",
       "      <td>GB</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>13072647</td>\n",
       "      <td>10</td>\n",
       "      <td>MTI5,zcxMTI0Mg==</td>\n",
       "      <td>1/18/2018 9:50</td>\n",
       "      <td>GB</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RESPONSE_ID  SURVEY_SCORE         MASKED_IP     SURVEY_DATE COUNTRY_CODE  \\\n",
       "0     13071656             8  MTQ5,Dk1,TMy,A==  1/18/2018 8:48           FI   \n",
       "1     13071811            10  MTM0Mjk4,TE0OA==  1/18/2018 8:59           FR   \n",
       "2     13071889             9      MzY4,DgzOTU=  1/18/2018 9:05           AE   \n",
       "3     13071909            10  MTM4MzU5MjY2Mg==  1/18/2018 9:06           CZ   \n",
       "4     13071920            10  MzU3ODcz,Tg1OA==  1/18/2018 9:07           IE   \n",
       "5     13071988            10  Mjk1,jk1ODE4OQ==  1/18/2018 9:11           HU   \n",
       "6     13072012             8  MTM2ODUxMTE4,g==  1/18/2018 9:12           GB   \n",
       "7     13072097             7  MzA1ODE0,zYwOQ==  1/18/2018 9:17           IN   \n",
       "8     13072253            10  MTg0,DEwMzMx,A==  1/18/2018 9:27           GB   \n",
       "9     13072647            10  MTI5,zcxMTI0Mg==  1/18/2018 9:50           GB   \n",
       "\n",
       "          REGION  \n",
       "0        uusimaa  \n",
       "1  ile-de-france  \n",
       "2          dubai  \n",
       "3          praha  \n",
       "4            NaN  \n",
       "5       budapest  \n",
       "6            NaN  \n",
       "7  uttar pradesh  \n",
       "8            NaN  \n",
       "9            NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>RESPONSE_ID</th>\n",
       "      <th>SURVEY_SCORE</th>\n",
       "      <th>MASKED_IP</th>\n",
       "      <th>SURVEY_DATE</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>REGION</th>\n",
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       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4783</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4784</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4785</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4786</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4787</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4788 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RESPONSE_ID  SURVEY_SCORE  MASKED_IP  SURVEY_DATE  COUNTRY_CODE  REGION\n",
       "0           False         False      False        False         False   False\n",
       "1           False         False      False        False         False   False\n",
       "2           False         False      False        False         False   False\n",
       "3           False         False      False        False         False   False\n",
       "4           False         False      False        False         False    True\n",
       "...           ...           ...        ...          ...           ...     ...\n",
       "4783        False         False      False        False         False   False\n",
       "4784        False         False      False        False         False   False\n",
       "4785        False         False      False        False         False   False\n",
       "4786        False         False      False        False          True    True\n",
       "4787        False         False      False        False         False   False\n",
       "\n",
       "[4788 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.isnull() #Identify the missed values and this method will return the a boolean at cell level based on value exists or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "surv_miss_val = survey.isnull() #We are creating anew dataframe based on isnull method acting upon the original dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RESPONSE_ID',\n",
       " 'SURVEY_SCORE',\n",
       " 'MASKED_IP',\n",
       " 'SURVEY_DATE',\n",
       " 'COUNTRY_CODE',\n",
       " 'REGION']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surv_miss_val.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    3215\n",
       "True     1573\n",
       "Name: REGION, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surv_miss_val[\"REGION\"].value_counts()  #This method will return the counts for distinct values existing in the column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now let's count the missing values in each column. \"True\" represents the missing  values and \"False\" represents the values exists in Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Name RESPONSE_ID\n",
      "False    4788\n",
      "Name: RESPONSE_ID, dtype: int64\n",
      "------------------------------------------------\n",
      "Column Name SURVEY_SCORE\n",
      "False    4788\n",
      "Name: SURVEY_SCORE, dtype: int64\n",
      "------------------------------------------------\n",
      "Column Name MASKED_IP\n",
      "False    4788\n",
      "Name: MASKED_IP, dtype: int64\n",
      "------------------------------------------------\n",
      "Column Name SURVEY_DATE\n",
      "False    4788\n",
      "Name: SURVEY_DATE, dtype: int64\n",
      "------------------------------------------------\n",
      "Column Name COUNTRY_CODE\n",
      "False    3994\n",
      "True      794\n",
      "Name: COUNTRY_CODE, dtype: int64\n",
      "------------------------------------------------\n",
      "Column Name REGION\n",
      "False    3215\n",
      "True     1573\n",
      "Name: REGION, dtype: int64\n",
      "------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for col_name in surv_miss_val.columns.values.tolist():  # We are taking each column name into this existing variable\n",
    "    print(\"Column Name\",col_name)                       # Within the loop we will be print each column name\n",
    "    print(surv_miss_val[col_name].value_counts())        # And he we are passing each column name to the method to get distinct value counts\n",
    "    print('------------------------------------------------')    #Just a custom line seperator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Survey Response Data Set : Missing data findings\n",
    "Based on the summary above, out of 4788 rows of data, and 2 columns containing missing data:\n",
    "<ol>\n",
    "    <li>\"COUNTRY_CODE\": 794 missing data</li>\n",
    "    <li>\"REGION\": 1573 missing data</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RESPONSE_ID', 'SURVEY_SCORE', 'MASKED_IP', 'SURVEY_DATE',\n",
       "       'COUNTRY_CODE', 'REGION'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>SURVEY_SCORE</th>\n",
       "      <th>SURVEY_DATE</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>REGION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>4788.000000</td>\n",
       "      <td>4788</td>\n",
       "      <td>3994</td>\n",
       "      <td>3215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4432</td>\n",
       "      <td>101</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/9/2018 17:56</td>\n",
       "      <td>US</td>\n",
       "      <td>california</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>1468</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>8.119674</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.435567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        SURVEY_SCORE     SURVEY_DATE COUNTRY_CODE      REGION\n",
       "count    4788.000000            4788         3994        3215\n",
       "unique           NaN            4432          101         365\n",
       "top              NaN  1/9/2018 17:56           US  california\n",
       "freq             NaN               6         1468         146\n",
       "mean        8.119674             NaN          NaN         NaN\n",
       "std         2.435567             NaN          NaN         NaN\n",
       "min         0.000000             NaN          NaN         NaN\n",
       "25%         7.000000             NaN          NaN         NaN\n",
       "50%         9.000000             NaN          NaN         NaN\n",
       "75%        10.000000             NaN          NaN         NaN\n",
       "max        10.000000             NaN          NaN         NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey[['SURVEY_SCORE','SURVEY_DATE','COUNTRY_CODE','REGION']].describe(include='all')\n",
    "# We are tryiong to bring the the descriptive stats in the given dataset based on the columns of our interest \n",
    "# and we are using describe method from Pandas Library to achieve that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>RESPONSE_ID</th>\n",
       "      <th>REGION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>FI</td>\n",
       "      <td>13071656</td>\n",
       "      <td>uusimaa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>FR</td>\n",
       "      <td>13071811</td>\n",
       "      <td>ile-de-france</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>AE</td>\n",
       "      <td>13071889</td>\n",
       "      <td>dubai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>CZ</td>\n",
       "      <td>13071909</td>\n",
       "      <td>praha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>IE</td>\n",
       "      <td>13071920</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4783</td>\n",
       "      <td>US</td>\n",
       "      <td>14279849</td>\n",
       "      <td>georgia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4784</td>\n",
       "      <td>US</td>\n",
       "      <td>14280186</td>\n",
       "      <td>florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4785</td>\n",
       "      <td>US</td>\n",
       "      <td>14280278</td>\n",
       "      <td>ohio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4786</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14281021</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4787</td>\n",
       "      <td>US</td>\n",
       "      <td>14281427</td>\n",
       "      <td>texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4788 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     COUNTRY_CODE  RESPONSE_ID         REGION\n",
       "0              FI     13071656        uusimaa\n",
       "1              FR     13071811  ile-de-france\n",
       "2              AE     13071889          dubai\n",
       "3              CZ     13071909          praha\n",
       "4              IE     13071920            NaN\n",
       "...           ...          ...            ...\n",
       "4783           US     14279849        georgia\n",
       "4784           US     14280186        florida\n",
       "4785           US     14280278           ohio\n",
       "4786          NaN     14281021            NaN\n",
       "4787           US     14281427          texas\n",
       "\n",
       "[4788 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey[['COUNTRY_CODE','RESPONSE_ID','REGION']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = survey.sort_values(by=['COUNTRY_CODE'],ascending=False, na_position='last')\n",
    "# The result of the sorting operation is being passed into a dataframe object 'a'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey.sort_values(by=['COUNTRY_CODE'],ascending=False, inplace=True, na_position='last')\n",
    "# this will  notsave the results of the sorting operation to the actual dataframe \"Survey\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <td>10</td>\n",
       "      <td>MjY5OTIwMjgxOA==</td>\n",
       "      <td>1/19/2018 8:37</td>\n",
       "      <td>ZA</td>\n",
       "      <td>kwazulu-natal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RESPONSE_ID  SURVEY_SCORE         MASKED_IP      SURVEY_DATE  \\\n",
       "1214     13010614            10  Mjc2MTk1Mjk0OQ==   1/16/2018 9:39   \n",
       "2542     13433279             9      ,jk4,jYzODkw  1/31/2018 11:45   \n",
       "351      13167333             9  Mjgz,TQxOTU0,g==  1/22/2018 10:06   \n",
       "1161     12987247            10  MzMwMjQzMjkzOQ==  1/15/2018 14:59   \n",
       "1946     13257960             8      ,jg4,zI3MTMw   1/25/2018 8:45   \n",
       "1360     13045031            10  MzMx,zE4MTQyMA==  1/17/2018 12:18   \n",
       "646      12840351            10  Mjgz,TM4MjQ2,A==   1/10/2018 5:59   \n",
       "359      13168301             6      ,jg5Mjc0OTg2  1/22/2018 10:58   \n",
       "1447     13071463            10  MzMwMjExODI4OQ==   1/18/2018 8:36   \n",
       "119      13101159            10  MjY5OTIwMjgxOA==   1/19/2018 8:37   \n",
       "\n",
       "     COUNTRY_CODE         REGION  \n",
       "1214           ZM         lusaka  \n",
       "2542           ZA   western cape  \n",
       "351            ZA            NaN  \n",
       "1161           ZA   western cape  \n",
       "1946           ZA            NaN  \n",
       "1360           ZA            NaN  \n",
       "646            ZA            NaN  \n",
       "359            ZA            NaN  \n",
       "1447           ZA            NaN  \n",
       "119            ZA  kwazulu-natal  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey.sort_values(by=['COUNTRY_CODE'],ascending=False, inplace=True, na_position='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RESPONSE_ID</th>\n",
       "      <th>SURVEY_SCORE</th>\n",
       "      <th>MASKED_IP</th>\n",
       "      <th>SURVEY_DATE</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>REGION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1214</td>\n",
       "      <td>13010614</td>\n",
       "      <td>10</td>\n",
       "      <td>Mjc2MTk1Mjk0OQ==</td>\n",
       "      <td>1/16/2018 9:39</td>\n",
       "      <td>ZM</td>\n",
       "      <td>lusaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3606</td>\n",
       "      <td>13843072</td>\n",
       "      <td>9</td>\n",
       "      <td>,jg3OTEzOTAw</td>\n",
       "      <td>2/14/2018 13:27</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3593</td>\n",
       "      <td>13837512</td>\n",
       "      <td>9</td>\n",
       "      <td>MTc2MzQ2,zk0MA==</td>\n",
       "      <td>2/14/2018 9:22</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4256</td>\n",
       "      <td>14082532</td>\n",
       "      <td>9</td>\n",
       "      <td>,jk3,TAw,TE4</td>\n",
       "      <td>2/22/2018 11:48</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>675</td>\n",
       "      <td>12843137</td>\n",
       "      <td>10</td>\n",
       "      <td>MTc3ODM3,zA0Mw==</td>\n",
       "      <td>1/10/2018 9:22</td>\n",
       "      <td>ZA</td>\n",
       "      <td>western cape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>12844909</td>\n",
       "      <td>10</td>\n",
       "      <td>MzMyMDE0,jMz,g==</td>\n",
       "      <td>1/10/2018 11:02</td>\n",
       "      <td>ZA</td>\n",
       "      <td>western cape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3273</td>\n",
       "      <td>14337959</td>\n",
       "      <td>10</td>\n",
       "      <td>MTcy,zU4MzI2,w==</td>\n",
       "      <td>3/2/2018 10:40</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1679</td>\n",
       "      <td>13693382</td>\n",
       "      <td>10</td>\n",
       "      <td>,jk2OTMz,zk1</td>\n",
       "      <td>2/9/2018 11:27</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2900</td>\n",
       "      <td>13566543</td>\n",
       "      <td>10</td>\n",
       "      <td>MjYw,jU5OTU2OQ==</td>\n",
       "      <td>2/5/2018 10:38</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2455</td>\n",
       "      <td>13405934</td>\n",
       "      <td>4</td>\n",
       "      <td>,jk4,jIwMzc4</td>\n",
       "      <td>1/30/2018 14:37</td>\n",
       "      <td>ZA</td>\n",
       "      <td>western cape</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RESPONSE_ID  SURVEY_SCORE         MASKED_IP      SURVEY_DATE  \\\n",
       "1214     13010614            10  Mjc2MTk1Mjk0OQ==   1/16/2018 9:39   \n",
       "3606     13843072             9      ,jg3OTEzOTAw  2/14/2018 13:27   \n",
       "3593     13837512             9  MTc2MzQ2,zk0MA==   2/14/2018 9:22   \n",
       "4256     14082532             9      ,jk3,TAw,TE4  2/22/2018 11:48   \n",
       "675      12843137            10  MTc3ODM3,zA0Mw==   1/10/2018 9:22   \n",
       "700      12844909            10  MzMyMDE0,jMz,g==  1/10/2018 11:02   \n",
       "3273     14337959            10  MTcy,zU4MzI2,w==   3/2/2018 10:40   \n",
       "1679     13693382            10      ,jk2OTMz,zk1   2/9/2018 11:27   \n",
       "2900     13566543            10  MjYw,jU5OTU2OQ==   2/5/2018 10:38   \n",
       "2455     13405934             4      ,jk4,jIwMzc4  1/30/2018 14:37   \n",
       "\n",
       "     COUNTRY_CODE        REGION  \n",
       "1214           ZM        lusaka  \n",
       "3606           ZA           NaN  \n",
       "3593           ZA           NaN  \n",
       "4256           ZA           NaN  \n",
       "675            ZA  western cape  \n",
       "700            ZA  western cape  \n",
       "3273           ZA           NaN  \n",
       "1679           ZA           NaN  \n",
       "2900           ZA           NaN  \n",
       "2455           ZA  western cape  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.to_csv('surv_sorted.csv')   # The syntax is <dataframe>.to_csv('path/filename.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=pd.read_csv('surv_sorted.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>RESPONSE_ID</th>\n",
       "      <th>SURVEY_SCORE</th>\n",
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       "      <td>1/16/2018 9:39</td>\n",
       "      <td>ZM</td>\n",
       "      <td>lusaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3606</td>\n",
       "      <td>13843072</td>\n",
       "      <td>9</td>\n",
       "      <td>,jg3OTEzOTAw</td>\n",
       "      <td>2/14/2018 13:27</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3593</td>\n",
       "      <td>13837512</td>\n",
       "      <td>9</td>\n",
       "      <td>MTc2MzQ2,zk0MA==</td>\n",
       "      <td>2/14/2018 9:22</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4256</td>\n",
       "      <td>14082532</td>\n",
       "      <td>9</td>\n",
       "      <td>,jk3,TAw,TE4</td>\n",
       "      <td>2/22/2018 11:48</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>675</td>\n",
       "      <td>12843137</td>\n",
       "      <td>10</td>\n",
       "      <td>MTc3ODM3,zA0Mw==</td>\n",
       "      <td>1/10/2018 9:22</td>\n",
       "      <td>ZA</td>\n",
       "      <td>western cape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>700</td>\n",
       "      <td>12844909</td>\n",
       "      <td>10</td>\n",
       "      <td>MzMyMDE0,jMz,g==</td>\n",
       "      <td>1/10/2018 11:02</td>\n",
       "      <td>ZA</td>\n",
       "      <td>western cape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3273</td>\n",
       "      <td>14337959</td>\n",
       "      <td>10</td>\n",
       "      <td>MTcy,zU4MzI2,w==</td>\n",
       "      <td>3/2/2018 10:40</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1679</td>\n",
       "      <td>13693382</td>\n",
       "      <td>10</td>\n",
       "      <td>,jk2OTMz,zk1</td>\n",
       "      <td>2/9/2018 11:27</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2900</td>\n",
       "      <td>13566543</td>\n",
       "      <td>10</td>\n",
       "      <td>MjYw,jU5OTU2OQ==</td>\n",
       "      <td>2/5/2018 10:38</td>\n",
       "      <td>ZA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2455</td>\n",
       "      <td>13405934</td>\n",
       "      <td>4</td>\n",
       "      <td>,jk4,jIwMzc4</td>\n",
       "      <td>1/30/2018 14:37</td>\n",
       "      <td>ZA</td>\n",
       "      <td>western cape</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  RESPONSE_ID  SURVEY_SCORE         MASKED_IP      SURVEY_DATE  \\\n",
       "0        1214     13010614            10  Mjc2MTk1Mjk0OQ==   1/16/2018 9:39   \n",
       "1        3606     13843072             9      ,jg3OTEzOTAw  2/14/2018 13:27   \n",
       "2        3593     13837512             9  MTc2MzQ2,zk0MA==   2/14/2018 9:22   \n",
       "3        4256     14082532             9      ,jk3,TAw,TE4  2/22/2018 11:48   \n",
       "4         675     12843137            10  MTc3ODM3,zA0Mw==   1/10/2018 9:22   \n",
       "5         700     12844909            10  MzMyMDE0,jMz,g==  1/10/2018 11:02   \n",
       "6        3273     14337959            10  MTcy,zU4MzI2,w==   3/2/2018 10:40   \n",
       "7        1679     13693382            10      ,jk2OTMz,zk1   2/9/2018 11:27   \n",
       "8        2900     13566543            10  MjYw,jU5OTU2OQ==   2/5/2018 10:38   \n",
       "9        2455     13405934             4      ,jk4,jIwMzc4  1/30/2018 14:37   \n",
       "\n",
       "  COUNTRY_CODE        REGION  \n",
       "0           ZM        lusaka  \n",
       "1           ZA           NaN  \n",
       "2           ZA           NaN  \n",
       "3           ZA           NaN  \n",
       "4           ZA  western cape  \n",
       "5           ZA  western cape  \n",
       "6           ZA           NaN  \n",
       "7           ZA           NaN  \n",
       "8           ZA           NaN  \n",
       "9           ZA  western cape  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.to_json('surv_sorted.json')   # The syntax is <dataframe>.to_json('path/filename.csv')\n",
    "# This will create a json file from existing dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_survey = pd.read_json('surv_sorted.json')\n",
    "#We are trying to read the Json file that we have created in the previous step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <td>1214</td>\n",
       "      <td>13010614</td>\n",
       "      <td>10</td>\n",
       "      <td>Mjc2MTk1Mjk0OQ==</td>\n",
       "      <td>1/16/2018 9:39</td>\n",
       "      <td>ZM</td>\n",
       "      <td>lusaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2542</td>\n",
       "      <td>13433279</td>\n",
       "      <td>9</td>\n",
       "      <td>,jk4,jYzODkw</td>\n",
       "      <td>1/31/2018 11:45</td>\n",
       "      <td>ZA</td>\n",
       "      <td>western cape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>351</td>\n",
       "      <td>13167333</td>\n",
       "      <td>9</td>\n",
       "      <td>Mjgz,TQxOTU0,g==</td>\n",
       "      <td>1/22/2018 10:06</td>\n",
       "      <td>ZA</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1161</td>\n",
       "      <td>12987247</td>\n",
       "      <td>10</td>\n",
       "      <td>MzMwMjQzMjkzOQ==</td>\n",
       "      <td>1/15/2018 14:59</td>\n",
       "      <td>ZA</td>\n",
       "      <td>western cape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1946</td>\n",
       "      <td>13257960</td>\n",
       "      <td>8</td>\n",
       "      <td>,jg4,zI3MTMw</td>\n",
       "      <td>1/25/2018 8:45</td>\n",
       "      <td>ZA</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1360</td>\n",
       "      <td>13045031</td>\n",
       "      <td>10</td>\n",
       "      <td>MzMx,zE4MTQyMA==</td>\n",
       "      <td>1/17/2018 12:18</td>\n",
       "      <td>ZA</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>646</td>\n",
       "      <td>12840351</td>\n",
       "      <td>10</td>\n",
       "      <td>Mjgz,TM4MjQ2,A==</td>\n",
       "      <td>1/10/2018 5:59</td>\n",
       "      <td>ZA</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>359</td>\n",
       "      <td>13168301</td>\n",
       "      <td>6</td>\n",
       "      <td>,jg5Mjc0OTg2</td>\n",
       "      <td>1/22/2018 10:58</td>\n",
       "      <td>ZA</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1447</td>\n",
       "      <td>13071463</td>\n",
       "      <td>10</td>\n",
       "      <td>MzMwMjExODI4OQ==</td>\n",
       "      <td>1/18/2018 8:36</td>\n",
       "      <td>ZA</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>13101159</td>\n",
       "      <td>10</td>\n",
       "      <td>MjY5OTIwMjgxOA==</td>\n",
       "      <td>1/19/2018 8:37</td>\n",
       "      <td>ZA</td>\n",
       "      <td>kwazulu-natal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RESPONSE_ID  SURVEY_SCORE         MASKED_IP      SURVEY_DATE  \\\n",
       "1214     13010614            10  Mjc2MTk1Mjk0OQ==   1/16/2018 9:39   \n",
       "2542     13433279             9      ,jk4,jYzODkw  1/31/2018 11:45   \n",
       "351      13167333             9  Mjgz,TQxOTU0,g==  1/22/2018 10:06   \n",
       "1161     12987247            10  MzMwMjQzMjkzOQ==  1/15/2018 14:59   \n",
       "1946     13257960             8      ,jg4,zI3MTMw   1/25/2018 8:45   \n",
       "1360     13045031            10  MzMx,zE4MTQyMA==  1/17/2018 12:18   \n",
       "646      12840351            10  Mjgz,TM4MjQ2,A==   1/10/2018 5:59   \n",
       "359      13168301             6      ,jg5Mjc0OTg2  1/22/2018 10:58   \n",
       "1447     13071463            10  MzMwMjExODI4OQ==   1/18/2018 8:36   \n",
       "119      13101159            10  MjY5OTIwMjgxOA==   1/19/2018 8:37   \n",
       "\n",
       "     COUNTRY_CODE         REGION  \n",
       "1214           ZM         lusaka  \n",
       "2542           ZA   western cape  \n",
       "351            ZA           None  \n",
       "1161           ZA   western cape  \n",
       "1946           ZA           None  \n",
       "1360           ZA           None  \n",
       "646            ZA           None  \n",
       "359            ZA           None  \n",
       "1447           ZA           None  \n",
       "119            ZA  kwazulu-natal  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_survey.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
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      "text/plain": [
       "   0  1  2  3  4  5     6    7\n",
       "0  3  4  6  8  9  0  24.0  6.0\n",
       "1  a  b  c  d  e  f   NaN  NaN"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = [[3,4,6,8,9,0,24,6], ['a','b','c','d','e','f']]\n",
    "pd.DataFrame(d)\n",
    "# This is how we can convert a two dimesnional list into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "e={'CA':{'Prod-1':1, 'Prod-2':2,'Prod-3':3},'NAME':{'Prod-1':'HM','Prod-2':'RV','Prod-3':'SG'},'TX':{'Prod-1':5, 'Prod-2':6,'Prod-3':7},\\\n",
    "   'VT':{'Prod-1':8, 'Prod-2':9,'Prod-3':6}}\n",
    "df1=pd.DataFrame(e)\n",
    "#This is how we can create a dataframe from a dictionary\n",
    "#DataFrame is a predefined class in Pandass library which can convert the Python data structures into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>CA</th>\n",
       "      <th>NAME</th>\n",
       "      <th>TX</th>\n",
       "      <th>VT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Prod-1</td>\n",
       "      <td>1</td>\n",
       "      <td>HM</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Prod-2</td>\n",
       "      <td>2</td>\n",
       "      <td>RV</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Prod-3</td>\n",
       "      <td>3</td>\n",
       "      <td>SG</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CA NAME  TX  VT\n",
       "Prod-1   1   HM   5   8\n",
       "Prod-2   2   RV   6   9\n",
       "Prod-3   3   SG   7   6"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    2.000000\n",
       "TX    6.000000\n",
       "VT    7.666667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.mean()\n",
    "#mean function will return the mean for all the numeric columns in the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CA</th>\n",
       "      <th>TX</th>\n",
       "      <th>VT</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.654654</td>\n",
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       "    <tr>\n",
       "      <td>TX</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.654654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VT</td>\n",
       "      <td>-0.654654</td>\n",
       "      <td>-0.654654</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "          CA        TX        VT\n",
       "CA  1.000000  1.000000 -0.654654\n",
       "TX  1.000000  1.000000 -0.654654\n",
       "VT -0.654654 -0.654654  1.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.corr()\n",
    "# Correlation is a process in which we can find and establish a relationship between two or more measures.\n",
    "# Measures: Measures are the columns which will hold numeric values.\n",
    "# Dimensions: Dimesions are the columns which will hold non-numeric or string values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA      3\n",
       "NAME    3\n",
       "TX      3\n",
       "VT      3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA       3\n",
       "NAME    SG\n",
       "TX       7\n",
       "VT       9\n",
       "dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA       1\n",
       "NAME    HM\n",
       "TX       5\n",
       "VT       6\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    2.0\n",
       "TX    6.0\n",
       "VT    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    1.000000\n",
       "TX    1.000000\n",
       "VT    1.527525\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>Prod-2</td>\n",
       "      <td>2</td>\n",
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       "      <td>9</td>\n",
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      "text/plain": [
       "        CA NAME  TX  VT\n",
       "Prod-1   1   HM   5   8\n",
       "Prod-2   2   RV   6   9\n",
       "Prod-3   3   SG   7   6"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "df1"
   ]
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  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1.iloc[2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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      "text/plain": [
       "        CA NAME  TX  VT\n",
       "Prod-1   1   HM   5   8"
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     "execution_count": 118,
     "metadata": {},
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   "source": [
    "df1.iloc[-3:-2]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 107,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "  <tbody>\n",
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       "      <td>5</td>\n",
       "      <td>8</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "        CA NAME  TX  VT\n",
       "Prod-1   1   HM   5   8"
      ]
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     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1.iloc[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AB'"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v='BABA'\n",
    "v[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CA</th>\n",
       "      <th>NAME</th>\n",
       "      <th>TX</th>\n",
       "      <th>VT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Prod-2</td>\n",
       "      <td>2</td>\n",
       "      <td>RV</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Prod-3</td>\n",
       "      <td>3</td>\n",
       "      <td>SG</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CA NAME  TX  VT\n",
       "Prod-2   2   RV   6   9\n",
       "Prod-3   3   SG   7   6"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RESPONSE_ID', 'SURVEY_SCORE', 'MASKED_IP', 'SURVEY_DATE',\n",
       "       'COUNTRY_CODE', 'REGION'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.columns\n",
    "#This will print all the columns in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['aargau', 'abu dhabi', 'aichi', 'akershus', 'al asimah',\n",
       "       'al iskandariyah', 'al kuwayt', 'al qahirah', 'alabama', 'alajuela',\n",
       "       ...\n",
       "       'wilayah persekutuan kuala lump', 'wisconsin', 'wyoming', 'yerevan',\n",
       "       'yucatan', 'zeeland', 'zlinsky kraj', 'zug', 'zuid-holland', 'zurich'],\n",
       "      dtype='object', name='REGION', length=365)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b= a[['SURVEY_SCORE','REGION']].groupby(['REGION']).count()\n",
    "# This group by operation will return an object which will groupby based on a column \n",
    "# and that column will in turn become an index of resultant datafarme \n",
    "b.index\n",
    "# Index method will return the index of a given dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>SURVEY_SCORE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGION</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>aargau</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>abu dhabi</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>aichi</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SURVEY_SCORE\n",
       "REGION                 \n",
       "aargau                2\n",
       "abu dhabi             3\n",
       "aichi                 1"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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>SURVEY_SCORE</th>\n",
       "      <th>REGION_CREATED_FROM_INDEX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGION</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>aargau</td>\n",
       "      <td>2</td>\n",
       "      <td>aargau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>abu dhabi</td>\n",
       "      <td>3</td>\n",
       "      <td>abu dhabi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>aichi</td>\n",
       "      <td>1</td>\n",
       "      <td>aichi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SURVEY_SCORE REGION_CREATED_FROM_INDEX\n",
       "REGION                                           \n",
       "aargau                2                    aargau\n",
       "abu dhabi             3                 abu dhabi\n",
       "aichi                 1                     aichi"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b['REGION_CREATED_FROM_INDEX']=b.index\n",
    "# In the above comman we are trying to copy the index values into a column which we are trying to add to the dataframe\n",
    "b.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>SURVEY_SCORE</th>\n",
       "      <th>REGION_CREATED_FROM_INDEX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>aargau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>abu dhabi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>aichi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SURVEY_SCORE REGION_CREATED_FROM_INDEX\n",
       "0             2                    aargau\n",
       "1             3                 abu dhabi\n",
       "2             1                     aichi"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.reset_index(drop=True,inplace=True)\n",
    "#We are trying to drop and recreate the index based on pandas default indexing\n",
    "b.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>SURVEY_COUNT</th>\n",
       "      <th>REGION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>aargau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>abu dhabi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>aichi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SURVEY_COUNT     REGION\n",
       "0             2     aargau\n",
       "1             3  abu dhabi\n",
       "2             1      aichi"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headers1 = ['SURVEY_COUNT', 'REGION']\n",
    "# Create the new column headers we needed to replace in the data frame\n",
    "b.columns = headers1\n",
    "# Once we have the headers defined we can pass the headers ito the columns of the dataframe.\n",
    "b.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'SURVEY_ID'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2896\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2897\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'SURVEY_ID'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-152-0336f2748e66>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SURVEY_ID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2978\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2979\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2980\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2981\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'SURVEY_ID'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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