Product_Survey_Data_EDA_by_HM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=left><font size = 8>Product Survey Analysis</font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Before we get the data and start exploring it, let's download all the dependencies that we will need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # library to handle data in a vectorized manner\n",
    "import pandas as pd # library for data analsysis\n",
    "pd.set_option('display.max_columns', 500) \n",
    "pd.set_option('display.max_rows', 500)\n",
    "%matplotlib inline\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import pie, axis\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Prepare the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the Pandas method <b>read_csv()</b> to load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data from CSV File for survey Responses\n",
    "survey_response = pd.read_csv(\"Survey_Resp.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "      <th>5</th>\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",
       "      <th>6</th>\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",
       "      <th>7</th>\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",
       "      <th>8</th>\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",
       "      <th>9</th>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_response.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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_RESPONSE_DATE</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>REGION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "      <th>5</th>\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",
       "      <th>6</th>\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",
       "      <th>7</th>\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",
       "      <th>8</th>\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",
       "      <th>9</th>\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_RESPONSE_DATE  \\\n",
       "0     13071656             8  MTQ5,Dk1,TMy,A==       1/18/2018 8:48   \n",
       "1     13071811            10  MTM0Mjk4,TE0OA==       1/18/2018 8:59   \n",
       "2     13071889             9      MzY4,DgzOTU=       1/18/2018 9:05   \n",
       "3     13071909            10  MTM4MzU5MjY2Mg==       1/18/2018 9:06   \n",
       "4     13071920            10  MzU3ODcz,Tg1OA==       1/18/2018 9:07   \n",
       "5     13071988            10  Mjk1,jk1ODE4OQ==       1/18/2018 9:11   \n",
       "6     13072012             8  MTM2ODUxMTE4,g==       1/18/2018 9:12   \n",
       "7     13072097             7  MzA1ODE0,zYwOQ==       1/18/2018 9:17   \n",
       "8     13072253            10  MTg0,DEwMzMx,A==       1/18/2018 9:27   \n",
       "9     13072647            10  MTI5,zcxMTI0Mg==       1/18/2018 9:50   \n",
       "\n",
       "  COUNTRY_CODE         REGION  \n",
       "0           FI        uusimaa  \n",
       "1           FR  ile-de-france  \n",
       "2           AE          dubai  \n",
       "3           CZ          praha  \n",
       "4           IE            NaN  \n",
       "5           HU       budapest  \n",
       "6           GB            NaN  \n",
       "7           IN  uttar pradesh  \n",
       "8           GB            NaN  \n",
       "9           GB            NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headers = [\"RESPONSE_ID\",\"SURVEY_SCORE\",\"MASKED_IP\",\"SURVEY_RESPONSE_DATE\",\"COUNTRY_CODE\", \"REGION\"]\n",
    "survey_response.columns = headers\n",
    "survey_response.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data from CSV File for User Info\n",
    "user_info = pd.read_csv(\"User_Info.csv\", encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>USER_ID</th>\n",
       "      <th>CUSTOMER_ID</th>\n",
       "      <th>CRM_PRODUCT_TIER</th>\n",
       "      <th>SALES_PRODUCT_TIER</th>\n",
       "      <th>MARKETING_PRODUCT_TIER</th>\n",
       "      <th>PRODUCT_ADDONS</th>\n",
       "      <th>SCREEN_SIZE</th>\n",
       "      <th>USER_ROLE</th>\n",
       "      <th>LANGUAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14248134</td>\n",
       "      <td>803</td>\n",
       "      <td>1473270</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>sites</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Especialista en marketing</td>\n",
       "      <td>es-mx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13502120</td>\n",
       "      <td>804</td>\n",
       "      <td>4641168</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en-us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13954226</td>\n",
       "      <td>805</td>\n",
       "      <td>5654010</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>reporting|ads</td>\n",
       "      <td>1280x800</td>\n",
       "      <td>Salesperson</td>\n",
       "      <td>en-us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14315322</td>\n",
       "      <td>806</td>\n",
       "      <td>4802412</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13042600</td>\n",
       "      <td>807</td>\n",
       "      <td>6746619</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting|ssl</td>\n",
       "      <td>1920x1080</td>\n",
       "      <td>SpÃ©cialiste marketing</td>\n",
       "      <td>fr-fr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13438239</td>\n",
       "      <td>808</td>\n",
       "      <td>5234634</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1280x1024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12981556</td>\n",
       "      <td>809</td>\n",
       "      <td>12595470</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Partner / Agency / Freelancer</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14033087</td>\n",
       "      <td>810</td>\n",
       "      <td>5348841</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1920x1080</td>\n",
       "      <td>Executive / Management</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12977894</td>\n",
       "      <td>811</td>\n",
       "      <td>6355494</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>ssl</td>\n",
       "      <td>2560x1080</td>\n",
       "      <td>I do a little bit of everything</td>\n",
       "      <td>sv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14296992</td>\n",
       "      <td>812</td>\n",
       "      <td>5682909</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pt-br</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RESPONSE_ID  USER_ID  CUSTOMER_ID CRM_PRODUCT_TIER SALES_PRODUCT_TIER  \\\n",
       "0     14248134      803      1473270             free               free   \n",
       "1     13502120      804      4641168             free                pro   \n",
       "2     13954226      805      5654010             free                pro   \n",
       "3     14315322      806      4802412             free                pro   \n",
       "4     13042600      807      6746619             free                pro   \n",
       "5     13438239      808      5234634             free               free   \n",
       "6     12981556      809     12595470             free               free   \n",
       "7     14033087      810      5348841             free                pro   \n",
       "8     12977894      811      6355494             free                pro   \n",
       "9     14296992      812      5682909             free               free   \n",
       "\n",
       "  MARKETING_PRODUCT_TIER PRODUCT_ADDONS SCREEN_SIZE  \\\n",
       "0                    pro          sites    1366x768   \n",
       "1                    pro      reporting    1440x900   \n",
       "2             enterprise  reporting|ads    1280x800   \n",
       "3                    pro      reporting    1440x900   \n",
       "4                    pro  reporting|ssl   1920x1080   \n",
       "5                    pro            NaN   1280x1024   \n",
       "6                   free            NaN    1440x900   \n",
       "7                    pro            NaN   1920x1080   \n",
       "8                    pro            ssl   2560x1080   \n",
       "9                   free            NaN    1366x768   \n",
       "\n",
       "                         USER_ROLE LANGUAGE  \n",
       "0        Especialista en marketing    es-mx  \n",
       "1                              NaN    en-us  \n",
       "2                      Salesperson    en-us  \n",
       "3                              NaN       en  \n",
       "4           SpÃ©cialiste marketing    fr-fr  \n",
       "5                              NaN       en  \n",
       "6    Partner / Agency / Freelancer       en  \n",
       "7           Executive / Management       en  \n",
       "8  I do a little bit of everything       sv  \n",
       "9                              NaN    pt-br  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RESP_ID          int64\n",
       "SUR_SCORE        int64\n",
       "MASKED_IP       object\n",
       "CRT_DT          object\n",
       "COUNTRY_CODE    object\n",
       "REGION          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.dtypes to get the list of data types for all columns in Dataframe\n",
    "survey_response.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RESPONSE_ID                int64\n",
       "USER_ID                    int64\n",
       "CUSTOMER_ID                int64\n",
       "CRM_PRODUCT_TIER          object\n",
       "SALES_PRODUCT_TIER        object\n",
       "MARKETING_PRODUCT_TIER    object\n",
       "PRODUCT_ADDONS            object\n",
       "SCREEN_SIZE               object\n",
       "USER_ROLE                 object\n",
       "LANGUAGE                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### We are trying to find the null values using isnull method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RESP_ID  SUR_SCORE  MASKED_IP  CRT_DT  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"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_surv_resp = survey_response.isnull()\n",
    "missing_data_surv_resp.head(5)"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Name: RESP_ID\n",
      "False    4788\n",
      "Name: RESP_ID, dtype: int64\n",
      "------------------------------------------------------\n",
      "Column Name: SUR_SCORE\n",
      "False    4788\n",
      "Name: SUR_SCORE, dtype: int64\n",
      "------------------------------------------------------\n",
      "Column Name: MASKED_IP\n",
      "False    4788\n",
      "Name: MASKED_IP, dtype: int64\n",
      "------------------------------------------------------\n",
      "Column Name: CRT_DT\n",
      "False    4788\n",
      "Name: CRT_DT, 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 column in missing_data_surv_resp.columns.values.tolist():\n",
    "    print(\"Column Name:\",column)\n",
    "    print(missing_data_surv_resp[column].value_counts())\n",
    "    print(\"------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Survey Response Data Set : Missing data findings\n",
    "Based on the summary above, each column has 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": [
    "##### Now let's repeat the same for the User Info Data-set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>RESPONSE_ID</th>\n",
       "      <th>USER_ID</th>\n",
       "      <th>CUSTOMER_ID</th>\n",
       "      <th>CRM_PRODUCT_TIER</th>\n",
       "      <th>SALES_PRODUCT_TIER</th>\n",
       "      <th>MARKETING_PRODUCT_TIER</th>\n",
       "      <th>PRODUCT_ADDONS</th>\n",
       "      <th>SCREEN_SIZE</th>\n",
       "      <th>USER_ROLE</th>\n",
       "      <th>LANGUAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <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": [
       "   RESPONSE_ID  USER_ID  CUSTOMER_ID  CRM_PRODUCT_TIER  SALES_PRODUCT_TIER  \\\n",
       "0        False    False        False             False               False   \n",
       "1        False    False        False             False               False   \n",
       "2        False    False        False             False               False   \n",
       "3        False    False        False             False               False   \n",
       "4        False    False        False             False               False   \n",
       "\n",
       "   MARKETING_PRODUCT_TIER  PRODUCT_ADDONS  SCREEN_SIZE  USER_ROLE  LANGUAGE  \n",
       "0                   False           False        False      False     False  \n",
       "1                   False           False        False       True     False  \n",
       "2                   False           False        False      False     False  \n",
       "3                   False           False        False       True     False  \n",
       "4                   False           False        False      False     False  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_user_info = user_info.isnull()\n",
    "missing_data_user_info.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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: USER_ID\n",
      "False    4788\n",
      "Name: USER_ID, dtype: int64\n",
      "------------------------------------------------------\n",
      "Column Name: CUSTOMER_ID\n",
      "False    4788\n",
      "Name: CUSTOMER_ID, dtype: int64\n",
      "------------------------------------------------------\n",
      "Column Name: CRM_PRODUCT_TIER\n",
      "False    4788\n",
      "Name: CRM_PRODUCT_TIER, dtype: int64\n",
      "------------------------------------------------------\n",
      "Column Name: SALES_PRODUCT_TIER\n",
      "False    4788\n",
      "Name: SALES_PRODUCT_TIER, dtype: int64\n",
      "------------------------------------------------------\n",
      "Column Name: MARKETING_PRODUCT_TIER\n",
      "False    4788\n",
      "Name: MARKETING_PRODUCT_TIER, dtype: int64\n",
      "------------------------------------------------------\n",
      "Column Name: PRODUCT_ADDONS\n",
      "True     2892\n",
      "False    1896\n",
      "Name: PRODUCT_ADDONS, dtype: int64\n",
      "------------------------------------------------------\n",
      "Column Name: SCREEN_SIZE\n",
      "False    4788\n",
      "Name: SCREEN_SIZE, dtype: int64\n",
      "------------------------------------------------------\n",
      "Column Name: USER_ROLE\n",
      "False    2930\n",
      "True     1858\n",
      "Name: USER_ROLE, dtype: int64\n",
      "------------------------------------------------------\n",
      "Column Name: LANGUAGE\n",
      "False    4788\n",
      "Name: LANGUAGE, dtype: int64\n",
      "------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "## Now let's count the missing values in each column. \"True\" represents the missing  values and \"False\" represents the values exists in Dataset\n",
    "for column in missing_data_user_info.columns.values.tolist():\n",
    "    print(\"Column Name:\",column)\n",
    "    print(missing_data_user_info[column].value_counts())\n",
    "    print(\"------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### User Info Data Set : Missing data / Null value findings\n",
    "Based on the summary above, each column has 4788 rows of data, and 2 columns containing missing data:\n",
    "<ol>\n",
    "    <li>\"USER_ROLE\": 1858 missing data</li>\n",
    "    <li>\"PRODUCT_ADDONS\": 1858 responses doesn't have any addons</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistical Summary of each column in Survey Response Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>SUR_SCORE</th>\n",
       "      <th>CRT_DT</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>REGION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4788.000000</td>\n",
       "      <td>4788</td>\n",
       "      <td>3994</td>\n",
       "      <td>3215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4432</td>\n",
       "      <td>101</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\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",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>1468</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.119674</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.435567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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": [
       "          SUR_SCORE          CRT_DT 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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical Summary of each column can be obtained using the Describe Method\n",
    "survey_response[['SUR_SCORE', 'CRT_DT', 'COUNTRY_CODE','REGION']].describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>CRM_PRODUCT_TIER</th>\n",
       "      <th>SALES_PRODUCT_TIER</th>\n",
       "      <th>MARKETING_PRODUCT_TIER</th>\n",
       "      <th>PRODUCT_ADDONS</th>\n",
       "      <th>SCREEN_SIZE</th>\n",
       "      <th>USER_ROLE</th>\n",
       "      <th>LANGUAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4788</td>\n",
       "      <td>4788</td>\n",
       "      <td>4788</td>\n",
       "      <td>1896</td>\n",
       "      <td>4788</td>\n",
       "      <td>2930</td>\n",
       "      <td>4788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>130</td>\n",
       "      <td>41</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting</td>\n",
       "      <td>1920x1080</td>\n",
       "      <td>Executive / Management</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4688</td>\n",
       "      <td>2441</td>\n",
       "      <td>1771</td>\n",
       "      <td>867</td>\n",
       "      <td>1203</td>\n",
       "      <td>844</td>\n",
       "      <td>2285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRM_PRODUCT_TIER SALES_PRODUCT_TIER MARKETING_PRODUCT_TIER  \\\n",
       "count              4788               4788                   4788   \n",
       "unique                2                  4                      6   \n",
       "top                free                pro                    pro   \n",
       "freq               4688               2441                   1771   \n",
       "\n",
       "       PRODUCT_ADDONS SCREEN_SIZE               USER_ROLE LANGUAGE  \n",
       "count            1896        4788                    2930     4788  \n",
       "unique             12         130                      41       56  \n",
       "top         reporting   1920x1080  Executive / Management       en  \n",
       "freq              867        1203                     844     2285  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info[['CRM_PRODUCT_TIER', 'SALES_PRODUCT_TIER','MARKETING_PRODUCT_TIER', 'PRODUCT_ADDONS', 'SCREEN_SIZE', 'USER_ROLE','LANGUAGE']].describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### We have chosen to drop the rows which doesn't have Country Codes for our Survey Response data-set Analysis\n",
    "##### And we will perform an aggregation to get country level counts and percentage of responses by country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "srvresp_cc_counts = survey_response['COUNTRY_CODE'].value_counts(dropna = 'True').to_frame('COUNTS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "srvresp_cc_counts['COUNTRY_CODE'] = srvresp_cc_counts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "srvresp_cc_counts.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COUNTS           int64\n",
       "COUNTRY_CODE    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srvresp_cc_counts.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>COUNTS</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>COUNTRY_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1468</td>\n",
       "      <td>US</td>\n",
       "      <td>36.755133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>499</td>\n",
       "      <td>GB</td>\n",
       "      <td>12.493741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>218</td>\n",
       "      <td>CA</td>\n",
       "      <td>5.458187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>142</td>\n",
       "      <td>FR</td>\n",
       "      <td>3.555333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123</td>\n",
       "      <td>DE</td>\n",
       "      <td>3.079619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>122</td>\n",
       "      <td>AU</td>\n",
       "      <td>3.054582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>117</td>\n",
       "      <td>BR</td>\n",
       "      <td>2.929394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>114</td>\n",
       "      <td>ES</td>\n",
       "      <td>2.854281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100</td>\n",
       "      <td>NL</td>\n",
       "      <td>2.503756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>IN</td>\n",
       "      <td>2.503756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   COUNTS COUNTRY_CODE  COUNTRY_%\n",
       "0    1468           US  36.755133\n",
       "1     499           GB  12.493741\n",
       "2     218           CA   5.458187\n",
       "3     142           FR   3.555333\n",
       "4     123           DE   3.079619\n",
       "5     122           AU   3.054582\n",
       "6     117           BR   2.929394\n",
       "7     114           ES   2.854281\n",
       "8     100           NL   2.503756\n",
       "9     100           IN   2.503756"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srvresp_cc_counts['COUNTRY_%'] = (srvresp_cc_counts.COUNTS / srvresp_cc_counts.COUNTS.sum())*100\n",
    "srvresp_cc_counts.head()\n",
    "srvresp_cc_counts.sort_values(by=['COUNTRY_%'])\n",
    "srvresp_cc_counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "        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>COUNTS</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>COUNTRY_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1468</td>\n",
       "      <td>US</td>\n",
       "      <td>36.755133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>499</td>\n",
       "      <td>GB</td>\n",
       "      <td>12.493741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>218</td>\n",
       "      <td>CA</td>\n",
       "      <td>5.458187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>142</td>\n",
       "      <td>FR</td>\n",
       "      <td>3.555333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123</td>\n",
       "      <td>DE</td>\n",
       "      <td>3.079619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>122</td>\n",
       "      <td>AU</td>\n",
       "      <td>3.054582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>117</td>\n",
       "      <td>BR</td>\n",
       "      <td>2.929394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>114</td>\n",
       "      <td>ES</td>\n",
       "      <td>2.854281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100</td>\n",
       "      <td>NL</td>\n",
       "      <td>2.503756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>IN</td>\n",
       "      <td>2.503756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>82</td>\n",
       "      <td>MX</td>\n",
       "      <td>2.053080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60</td>\n",
       "      <td>SE</td>\n",
       "      <td>1.502253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60</td>\n",
       "      <td>IT</td>\n",
       "      <td>1.502253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>51</td>\n",
       "      <td>IE</td>\n",
       "      <td>1.276915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>51</td>\n",
       "      <td>FI</td>\n",
       "      <td>1.276915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42</td>\n",
       "      <td>NO</td>\n",
       "      <td>1.051577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>37</td>\n",
       "      <td>BE</td>\n",
       "      <td>0.926390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35</td>\n",
       "      <td>CO</td>\n",
       "      <td>0.876314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>35</td>\n",
       "      <td>DK</td>\n",
       "      <td>0.876314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>31</td>\n",
       "      <td>CL</td>\n",
       "      <td>0.776164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>31</td>\n",
       "      <td>CH</td>\n",
       "      <td>0.776164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>28</td>\n",
       "      <td>AE</td>\n",
       "      <td>0.701052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>28</td>\n",
       "      <td>ZA</td>\n",
       "      <td>0.701052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>NZ</td>\n",
       "      <td>0.575864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>22</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.550826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20</td>\n",
       "      <td>PH</td>\n",
       "      <td>0.500751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20</td>\n",
       "      <td>PL</td>\n",
       "      <td>0.500751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>18</td>\n",
       "      <td>SG</td>\n",
       "      <td>0.450676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>17</td>\n",
       "      <td>AR</td>\n",
       "      <td>0.425638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15</td>\n",
       "      <td>AT</td>\n",
       "      <td>0.375563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>14</td>\n",
       "      <td>UA</td>\n",
       "      <td>0.350526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>14</td>\n",
       "      <td>JP</td>\n",
       "      <td>0.350526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13</td>\n",
       "      <td>MY</td>\n",
       "      <td>0.325488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>13</td>\n",
       "      <td>PT</td>\n",
       "      <td>0.325488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>12</td>\n",
       "      <td>RO</td>\n",
       "      <td>0.300451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>12</td>\n",
       "      <td>GR</td>\n",
       "      <td>0.300451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>11</td>\n",
       "      <td>CZ</td>\n",
       "      <td>0.275413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>11</td>\n",
       "      <td>VN</td>\n",
       "      <td>0.275413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>9</td>\n",
       "      <td>PK</td>\n",
       "      <td>0.225338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>8</td>\n",
       "      <td>RS</td>\n",
       "      <td>0.200300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>8</td>\n",
       "      <td>HU</td>\n",
       "      <td>0.200300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>7</td>\n",
       "      <td>EC</td>\n",
       "      <td>0.175263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>7</td>\n",
       "      <td>RU</td>\n",
       "      <td>0.175263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7</td>\n",
       "      <td>CR</td>\n",
       "      <td>0.175263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6</td>\n",
       "      <td>TR</td>\n",
       "      <td>0.150225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6</td>\n",
       "      <td>GT</td>\n",
       "      <td>0.150225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6</td>\n",
       "      <td>PE</td>\n",
       "      <td>0.150225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>IR</td>\n",
       "      <td>0.125188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5</td>\n",
       "      <td>SI</td>\n",
       "      <td>0.125188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5</td>\n",
       "      <td>LK</td>\n",
       "      <td>0.125188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5</td>\n",
       "      <td>HK</td>\n",
       "      <td>0.125188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5</td>\n",
       "      <td>ID</td>\n",
       "      <td>0.125188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>5</td>\n",
       "      <td>MA</td>\n",
       "      <td>0.125188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4</td>\n",
       "      <td>KE</td>\n",
       "      <td>0.100150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>4</td>\n",
       "      <td>AM</td>\n",
       "      <td>0.100150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>BG</td>\n",
       "      <td>0.100150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4</td>\n",
       "      <td>TH</td>\n",
       "      <td>0.100150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4</td>\n",
       "      <td>BS</td>\n",
       "      <td>0.100150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>4</td>\n",
       "      <td>EE</td>\n",
       "      <td>0.100150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>4</td>\n",
       "      <td>EG</td>\n",
       "      <td>0.100150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>4</td>\n",
       "      <td>DO</td>\n",
       "      <td>0.100150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>3</td>\n",
       "      <td>UY</td>\n",
       "      <td>0.075113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>3</td>\n",
       "      <td>VE</td>\n",
       "      <td>0.075113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>3</td>\n",
       "      <td>TN</td>\n",
       "      <td>0.075113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2</td>\n",
       "      <td>MU</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2</td>\n",
       "      <td>LV</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2</td>\n",
       "      <td>NI</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2</td>\n",
       "      <td>LT</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2</td>\n",
       "      <td>BO</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2</td>\n",
       "      <td>RE</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2</td>\n",
       "      <td>LU</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2</td>\n",
       "      <td>PA</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2</td>\n",
       "      <td>MN</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2</td>\n",
       "      <td>TW</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2</td>\n",
       "      <td>NG</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2</td>\n",
       "      <td>KH</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2</td>\n",
       "      <td>CY</td>\n",
       "      <td>0.050075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1</td>\n",
       "      <td>MZ</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>ME</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1</td>\n",
       "      <td>ZM</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1</td>\n",
       "      <td>BY</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "      <td>HR</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1</td>\n",
       "      <td>SA</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1</td>\n",
       "      <td>PY</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1</td>\n",
       "      <td>PR</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1</td>\n",
       "      <td>HN</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1</td>\n",
       "      <td>MT</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1</td>\n",
       "      <td>KW</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1</td>\n",
       "      <td>PS</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1</td>\n",
       "      <td>SN</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1</td>\n",
       "      <td>SK</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1</td>\n",
       "      <td>SV</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1</td>\n",
       "      <td>AO</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1</td>\n",
       "      <td>DZ</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "      <td>AI</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1</td>\n",
       "      <td>NP</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1</td>\n",
       "      <td>BH</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "      <td>CN</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "      <td>IS</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1</td>\n",
       "      <td>AZ</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1</td>\n",
       "      <td>GH</td>\n",
       "      <td>0.025038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     COUNTS COUNTRY_CODE  COUNTRY_%\n",
       "0      1468           US  36.755133\n",
       "1       499           GB  12.493741\n",
       "2       218           CA   5.458187\n",
       "3       142           FR   3.555333\n",
       "4       123           DE   3.079619\n",
       "5       122           AU   3.054582\n",
       "6       117           BR   2.929394\n",
       "7       114           ES   2.854281\n",
       "8       100           NL   2.503756\n",
       "9       100           IN   2.503756\n",
       "10       82           MX   2.053080\n",
       "11       60           SE   1.502253\n",
       "12       60           IT   1.502253\n",
       "13       51           IE   1.276915\n",
       "14       51           FI   1.276915\n",
       "15       42           NO   1.051577\n",
       "16       37           BE   0.926390\n",
       "17       35           CO   0.876314\n",
       "18       35           DK   0.876314\n",
       "19       31           CL   0.776164\n",
       "20       31           CH   0.776164\n",
       "21       28           AE   0.701052\n",
       "22       28           ZA   0.701052\n",
       "23       23           NZ   0.575864\n",
       "24       22           IL   0.550826\n",
       "25       20           PH   0.500751\n",
       "26       20           PL   0.500751\n",
       "27       18           SG   0.450676\n",
       "28       17           AR   0.425638\n",
       "29       15           AT   0.375563\n",
       "30       14           UA   0.350526\n",
       "31       14           JP   0.350526\n",
       "32       13           MY   0.325488\n",
       "33       13           PT   0.325488\n",
       "34       12           RO   0.300451\n",
       "35       12           GR   0.300451\n",
       "36       11           CZ   0.275413\n",
       "37       11           VN   0.275413\n",
       "38        9           PK   0.225338\n",
       "39        8           RS   0.200300\n",
       "40        8           HU   0.200300\n",
       "41        7           EC   0.175263\n",
       "42        7           RU   0.175263\n",
       "43        7           CR   0.175263\n",
       "44        6           TR   0.150225\n",
       "45        6           GT   0.150225\n",
       "46        6           PE   0.150225\n",
       "47        5           IR   0.125188\n",
       "48        5           SI   0.125188\n",
       "49        5           LK   0.125188\n",
       "50        5           HK   0.125188\n",
       "51        5           ID   0.125188\n",
       "52        5           MA   0.125188\n",
       "53        4           KE   0.100150\n",
       "54        4           AM   0.100150\n",
       "55        4           BG   0.100150\n",
       "56        4           TH   0.100150\n",
       "57        4           BS   0.100150\n",
       "58        4           EE   0.100150\n",
       "59        4           EG   0.100150\n",
       "60        4           DO   0.100150\n",
       "61        3           UY   0.075113\n",
       "62        3           VE   0.075113\n",
       "63        3           TN   0.075113\n",
       "64        2           MU   0.050075\n",
       "65        2           LV   0.050075\n",
       "66        2           NI   0.050075\n",
       "67        2           LT   0.050075\n",
       "68        2           BO   0.050075\n",
       "69        2           RE   0.050075\n",
       "70        2           LU   0.050075\n",
       "71        2           PA   0.050075\n",
       "72        2           MN   0.050075\n",
       "73        2           TW   0.050075\n",
       "74        2           NG   0.050075\n",
       "75        2           KH   0.050075\n",
       "76        2           CY   0.050075\n",
       "77        1           MZ   0.025038\n",
       "78        1           ME   0.025038\n",
       "79        1           ZM   0.025038\n",
       "80        1           BY   0.025038\n",
       "81        1           HR   0.025038\n",
       "82        1           SA   0.025038\n",
       "83        1           PY   0.025038\n",
       "84        1           PR   0.025038\n",
       "85        1           HN   0.025038\n",
       "86        1           MT   0.025038\n",
       "87        1           KW   0.025038\n",
       "88        1           PS   0.025038\n",
       "89        1           SN   0.025038\n",
       "90        1           SK   0.025038\n",
       "91        1           SV   0.025038\n",
       "92        1           AO   0.025038\n",
       "93        1           DZ   0.025038\n",
       "94        1           AI   0.025038\n",
       "95        1           NP   0.025038\n",
       "96        1           BH   0.025038\n",
       "97        1           CN   0.025038\n",
       "98        1           IS   0.025038\n",
       "99        1           AZ   0.025038\n",
       "100       1           GH   0.025038"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srvresp_cc_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's plot piechart for visual representation of the country wide responses on the survey response data-set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axis('equal');\n",
    "\n",
    "pie(srvresp_cc_counts['COUNTRY_%'],radius = 3,startangle = 270,labels=srvresp_cc_counts['COUNTRY_CODE'], autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.3);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we will calculate the mean survey score by each country and use the same for the purpose of binning the countries into three categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_country_gb = survey_response[['SUR_SCORE', 'COUNTRY_CODE']].groupby(['COUNTRY_CODE']).mean().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>SUR_SCORE</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>AE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>AI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>AO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SUR_SCORE COUNTRY_CODE\n",
       "0        8.0           AE\n",
       "1       10.0           AI\n",
       "2        8.0           AM\n",
       "3        7.0           AO\n",
       "4        7.0           AR"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country_gb['COUNTRY_CODE']=df_country_gb.index\n",
    "df_country_gb.reset_index(drop=True,inplace=True)\n",
    "df_country_gb.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SUR_SCORE       float64\n",
       "COUNTRY_CODE     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country_gb.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>COUNTRY_CODE</th>\n",
       "      <th>SURVEY_SCORE_MEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AE</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AI</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AM</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AO</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AR</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AT</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AU</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AZ</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BE</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BG</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  COUNTRY_CODE  SURVEY_SCORE_MEAN\n",
       "0           AE                8.0\n",
       "1           AI               10.0\n",
       "2           AM                8.0\n",
       "3           AO                7.0\n",
       "4           AR                7.0\n",
       "5           AT                8.0\n",
       "6           AU                8.0\n",
       "7           AZ                9.0\n",
       "8           BE                8.0\n",
       "9           BG                9.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country_gb.columns=['SURVEY_SCORE_MEAN','COUNTRY_CODE']\n",
    "df_country_gb.reset_index(drop=True,inplace=True)\n",
    "df_country_gb = df_country_gb[['COUNTRY_CODE', 'SURVEY_SCORE_MEAN']]\n",
    "df_country_gb.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting a Histogram to visualize the country counts by mean survey score on 'Y' and 'X' axes respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Binning of Country Survey Score means')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_country_gb[\"SURVEY_SCORE_MEAN\"])\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.xlabel(\"SURVEY SCORE AVERAGE\")\n",
    "plt.ylabel(\"COUNTRY COUNT\")\n",
    "plt.title(\"Binning of Country Survey Score means\", fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  6.,  8., 10.])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.linspace(min(df_country_gb[\"SURVEY_SCORE_MEAN\"]), max(df_country_gb[\"SURVEY_SCORE_MEAN\"]), 4)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_names = ['LOW(0-6)', 'MEDIUM(6-8)', 'HIGH(8-10)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>COUNTRY_CODE</th>\n",
       "      <th>SURVEY_SCORE_MEAN</th>\n",
       "      <th>SURVEY_SCORE_BINS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AE</td>\n",
       "      <td>8.0</td>\n",
       "      <td>MEDIUM(6-8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AI</td>\n",
       "      <td>10.0</td>\n",
       "      <td>HIGH(8-10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AM</td>\n",
       "      <td>8.0</td>\n",
       "      <td>MEDIUM(6-8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AO</td>\n",
       "      <td>7.0</td>\n",
       "      <td>MEDIUM(6-8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AR</td>\n",
       "      <td>7.0</td>\n",
       "      <td>MEDIUM(6-8)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  COUNTRY_CODE  SURVEY_SCORE_MEAN SURVEY_SCORE_BINS\n",
       "0           AE                8.0       MEDIUM(6-8)\n",
       "1           AI               10.0        HIGH(8-10)\n",
       "2           AM                8.0       MEDIUM(6-8)\n",
       "3           AO                7.0       MEDIUM(6-8)\n",
       "4           AR                7.0       MEDIUM(6-8)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country_gb['SURVEY_SCORE_BINS'] = pd.cut(df_country_gb['SURVEY_SCORE_MEAN'], bins, labels=group_names, include_lowest=True )\n",
    "df_country_gb.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDIUM(6-8)    52\n",
       "HIGH(8-10)     44\n",
       "LOW(0-6)        5\n",
       "Name: SURVEY_SCORE_BINS, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country_gb[\"SURVEY_SCORE_BINS\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SURVEY_SCORE BINS')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_country_gb[\"SURVEY_SCORE_BINS\"])\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.xlabel(\"SURVEY_SCORE\")\n",
    "plt.ylabel(\"COUNTRY COUNT\")\n",
    "plt.title(\"SURVEY_SCORE BINS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['COUNTRY_CODE', 'SURVEY_SCORE_MEAN', 'SURVEY_SCORE_BINS'], dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country_gb.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>COUNTRY_CODE</th>\n",
       "      <th>SURVEY_SCORE_MEAN</th>\n",
       "      <th>SURVEY_SCORE_BINS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>EG</td>\n",
       "      <td>6.0</td>\n",
       "      <td>LOW(0-6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>LU</td>\n",
       "      <td>6.0</td>\n",
       "      <td>LOW(0-6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>PS</td>\n",
       "      <td>5.0</td>\n",
       "      <td>LOW(0-6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>TH</td>\n",
       "      <td>4.0</td>\n",
       "      <td>LOW(0-6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>VN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>LOW(0-6)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   COUNTRY_CODE  SURVEY_SCORE_MEAN SURVEY_SCORE_BINS\n",
       "29           EG                6.0          LOW(0-6)\n",
       "54           LU                6.0          LOW(0-6)\n",
       "76           PS                5.0          LOW(0-6)\n",
       "90           TH                4.0          LOW(0-6)\n",
       "98           VN                6.0          LOW(0-6)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country_gb.loc[df_country_gb['SURVEY_SCORE_BINS'] == 'LOW(0-6)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      3\n",
       "10     3\n",
       "14     3\n",
       "21     3\n",
       "25     3\n",
       "26     3\n",
       "39     3\n",
       "49     3\n",
       "53     3\n",
       "57     3\n",
       "59     3\n",
       "63     3\n",
       "75     3\n",
       "78     3\n",
       "79     3\n",
       "83     3\n",
       "87     3\n",
       "89     3\n",
       "97     3\n",
       "100    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country_gb.loc[df_country_gb['SURVEY_SCORE_MEAN'] == 10.0].count(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_response.rename(columns = {'RESP_ID':'RESPONSE_ID'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RESPONSE_ID', 'SURVEY_SCORE', 'MASKED_IP', 'CRT_DT', 'COUNTRY_CODE',\n",
       "       'REGION'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_response.rename(columns = {'SUR_SCORE':'SURVEY_SCORE'}, inplace = True)\n",
    "survey_response.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Join the Data-frames to perform more detailed analysis on data-sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_survey_resp=pd.merge(user_info, survey_response, on='RESPONSE_ID', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>RESPONSE_ID</th>\n",
       "      <th>USER_ID</th>\n",
       "      <th>CUSTOMER_ID</th>\n",
       "      <th>CRM_PRODUCT_TIER</th>\n",
       "      <th>SALES_PRODUCT_TIER</th>\n",
       "      <th>MARKETING_PRODUCT_TIER</th>\n",
       "      <th>PRODUCT_ADDONS</th>\n",
       "      <th>SCREEN_SIZE</th>\n",
       "      <th>USER_ROLE</th>\n",
       "      <th>LANGUAGE</th>\n",
       "      <th>SURVEY_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",
       "      <th>0</th>\n",
       "      <td>14248134</td>\n",
       "      <td>803</td>\n",
       "      <td>1473270</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>sites</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Especialista en marketing</td>\n",
       "      <td>es-mx</td>\n",
       "      <td>5</td>\n",
       "      <td>MzE4,DI5OTE0,Q==</td>\n",
       "      <td>2/27/2018 21:52</td>\n",
       "      <td>MX</td>\n",
       "      <td>jalisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13502120</td>\n",
       "      <td>804</td>\n",
       "      <td>4641168</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en-us</td>\n",
       "      <td>10</td>\n",
       "      <td>,jQ2,zE4OTMw</td>\n",
       "      <td>2/2/2018 15:40</td>\n",
       "      <td>US</td>\n",
       "      <td>new york</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13954226</td>\n",
       "      <td>805</td>\n",
       "      <td>5654010</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>reporting|ads</td>\n",
       "      <td>1280x800</td>\n",
       "      <td>Salesperson</td>\n",
       "      <td>en-us</td>\n",
       "      <td>2</td>\n",
       "      <td>MTYy,jcwMTMwMw==</td>\n",
       "      <td>2/18/2018 5:25</td>\n",
       "      <td>US</td>\n",
       "      <td>pennsylvania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14315322</td>\n",
       "      <td>806</td>\n",
       "      <td>4802412</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>5</td>\n",
       "      <td>MTA4,jA1MTQ0OA==</td>\n",
       "      <td>3/1/2018 18:49</td>\n",
       "      <td>US</td>\n",
       "      <td>california</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13042600</td>\n",
       "      <td>807</td>\n",
       "      <td>6746619</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting|ssl</td>\n",
       "      <td>1920x1080</td>\n",
       "      <td>SpÃ©cialiste marketing</td>\n",
       "      <td>fr-fr</td>\n",
       "      <td>6</td>\n",
       "      <td>MTQ5OTE5OTI1Mg==</td>\n",
       "      <td>1/17/2018 10:10</td>\n",
       "      <td>FR</td>\n",
       "      <td>auvergne-rhone-alpes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13438239</td>\n",
       "      <td>808</td>\n",
       "      <td>5234634</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1280x1024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>10</td>\n",
       "      <td>MzQ1MjkyMDg2MA==</td>\n",
       "      <td>1/31/2018 14:58</td>\n",
       "      <td>US</td>\n",
       "      <td>maryland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12981556</td>\n",
       "      <td>809</td>\n",
       "      <td>12595470</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Partner / Agency / Freelancer</td>\n",
       "      <td>en</td>\n",
       "      <td>8</td>\n",
       "      <td>MTQxMjEzMjMw,Q==</td>\n",
       "      <td>1/15/2018 10:50</td>\n",
       "      <td>GB</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14033087</td>\n",
       "      <td>810</td>\n",
       "      <td>5348841</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1920x1080</td>\n",
       "      <td>Executive / Management</td>\n",
       "      <td>en</td>\n",
       "      <td>10</td>\n",
       "      <td>MTIxMjgzMDk5,Q==</td>\n",
       "      <td>2/20/2018 19:46</td>\n",
       "      <td>US</td>\n",
       "      <td>massachusetts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12977894</td>\n",
       "      <td>811</td>\n",
       "      <td>6355494</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>ssl</td>\n",
       "      <td>2560x1080</td>\n",
       "      <td>I do a little bit of everything</td>\n",
       "      <td>sv</td>\n",
       "      <td>10</td>\n",
       "      <td>MTM4,zcz,Dk5,A==</td>\n",
       "      <td>1/15/2018 7:34</td>\n",
       "      <td>SE</td>\n",
       "      <td>stockholms lan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14296992</td>\n",
       "      <td>812</td>\n",
       "      <td>5682909</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pt-br</td>\n",
       "      <td>10</td>\n",
       "      <td>MzE0MTY4MjQ3Mw==</td>\n",
       "      <td>3/1/2018 12:29</td>\n",
       "      <td>BR</td>\n",
       "      <td>sao paulo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RESPONSE_ID  USER_ID  CUSTOMER_ID CRM_PRODUCT_TIER SALES_PRODUCT_TIER  \\\n",
       "0     14248134      803      1473270             free               free   \n",
       "1     13502120      804      4641168             free                pro   \n",
       "2     13954226      805      5654010             free                pro   \n",
       "3     14315322      806      4802412             free                pro   \n",
       "4     13042600      807      6746619             free                pro   \n",
       "5     13438239      808      5234634             free               free   \n",
       "6     12981556      809     12595470             free               free   \n",
       "7     14033087      810      5348841             free                pro   \n",
       "8     12977894      811      6355494             free                pro   \n",
       "9     14296992      812      5682909             free               free   \n",
       "\n",
       "  MARKETING_PRODUCT_TIER PRODUCT_ADDONS SCREEN_SIZE  \\\n",
       "0                    pro          sites    1366x768   \n",
       "1                    pro      reporting    1440x900   \n",
       "2             enterprise  reporting|ads    1280x800   \n",
       "3                    pro      reporting    1440x900   \n",
       "4                    pro  reporting|ssl   1920x1080   \n",
       "5                    pro            NaN   1280x1024   \n",
       "6                   free            NaN    1440x900   \n",
       "7                    pro            NaN   1920x1080   \n",
       "8                    pro            ssl   2560x1080   \n",
       "9                   free            NaN    1366x768   \n",
       "\n",
       "                         USER_ROLE LANGUAGE  SURVEY_SCORE         MASKED_IP  \\\n",
       "0        Especialista en marketing    es-mx             5  MzE4,DI5OTE0,Q==   \n",
       "1                              NaN    en-us            10      ,jQ2,zE4OTMw   \n",
       "2                      Salesperson    en-us             2  MTYy,jcwMTMwMw==   \n",
       "3                              NaN       en             5  MTA4,jA1MTQ0OA==   \n",
       "4           SpÃ©cialiste marketing    fr-fr             6  MTQ5OTE5OTI1Mg==   \n",
       "5                              NaN       en            10  MzQ1MjkyMDg2MA==   \n",
       "6    Partner / Agency / Freelancer       en             8  MTQxMjEzMjMw,Q==   \n",
       "7           Executive / Management       en            10  MTIxMjgzMDk5,Q==   \n",
       "8  I do a little bit of everything       sv            10  MTM4,zcz,Dk5,A==   \n",
       "9                              NaN    pt-br            10  MzE0MTY4MjQ3Mw==   \n",
       "\n",
       "            CRT_DT COUNTRY_CODE                REGION  \n",
       "0  2/27/2018 21:52           MX               jalisco  \n",
       "1   2/2/2018 15:40           US              new york  \n",
       "2   2/18/2018 5:25           US          pennsylvania  \n",
       "3   3/1/2018 18:49           US            california  \n",
       "4  1/17/2018 10:10           FR  auvergne-rhone-alpes  \n",
       "5  1/31/2018 14:58           US              maryland  \n",
       "6  1/15/2018 10:50           GB                   NaN  \n",
       "7  2/20/2018 19:46           US         massachusetts  \n",
       "8   1/15/2018 7:34           SE        stockholms lan  \n",
       "9   3/1/2018 12:29           BR             sao paulo  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_survey_resp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>free</th>\n",
       "      <th>none</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   free  none\n",
       "0     1     0\n",
       "1     1     0\n",
       "2     1     0\n",
       "3     1     0\n",
       "4     1     0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_1 = pd.get_dummies(user_survey_resp[\"CRM_PRODUCT_TIER\"])\n",
    "dummy_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_survey_resp = pd.concat([user_survey_resp, dummy_1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .dataframe thead th {\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>RESPONSE_ID</th>\n",
       "      <th>USER_ID</th>\n",
       "      <th>CUSTOMER_ID</th>\n",
       "      <th>CRM_PRODUCT_TIER</th>\n",
       "      <th>SALES_PRODUCT_TIER</th>\n",
       "      <th>MARKETING_PRODUCT_TIER</th>\n",
       "      <th>PRODUCT_ADDONS</th>\n",
       "      <th>SCREEN_SIZE</th>\n",
       "      <th>USER_ROLE</th>\n",
       "      <th>LANGUAGE</th>\n",
       "      <th>SURVEY_SCORE</th>\n",
       "      <th>MASKED_IP</th>\n",
       "      <th>CRT_DT</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>REGION</th>\n",
       "      <th>CRM_PROD_TIER_FREE</th>\n",
       "      <th>CRM_PROD_TIER_NONE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14248134</td>\n",
       "      <td>803</td>\n",
       "      <td>1473270</td>\n",
       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>sites</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Especialista en marketing</td>\n",
       "      <td>es-mx</td>\n",
       "      <td>5</td>\n",
       "      <td>MzE4,DI5OTE0,Q==</td>\n",
       "      <td>2/27/2018 21:52</td>\n",
       "      <td>MX</td>\n",
       "      <td>jalisco</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13502120</td>\n",
       "      <td>804</td>\n",
       "      <td>4641168</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en-us</td>\n",
       "      <td>10</td>\n",
       "      <td>,jQ2,zE4OTMw</td>\n",
       "      <td>2/2/2018 15:40</td>\n",
       "      <td>US</td>\n",
       "      <td>new york</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13954226</td>\n",
       "      <td>805</td>\n",
       "      <td>5654010</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>reporting|ads</td>\n",
       "      <td>1280x800</td>\n",
       "      <td>Salesperson</td>\n",
       "      <td>en-us</td>\n",
       "      <td>2</td>\n",
       "      <td>MTYy,jcwMTMwMw==</td>\n",
       "      <td>2/18/2018 5:25</td>\n",
       "      <td>US</td>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14315322</td>\n",
       "      <td>806</td>\n",
       "      <td>4802412</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>5</td>\n",
       "      <td>MTA4,jA1MTQ0OA==</td>\n",
       "      <td>3/1/2018 18:49</td>\n",
       "      <td>US</td>\n",
       "      <td>california</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13042600</td>\n",
       "      <td>807</td>\n",
       "      <td>6746619</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting|ssl</td>\n",
       "      <td>1920x1080</td>\n",
       "      <td>SpÃ©cialiste marketing</td>\n",
       "      <td>fr-fr</td>\n",
       "      <td>6</td>\n",
       "      <td>MTQ5OTE5OTI1Mg==</td>\n",
       "      <td>1/17/2018 10:10</td>\n",
       "      <td>FR</td>\n",
       "      <td>auvergne-rhone-alpes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RESPONSE_ID  USER_ID  CUSTOMER_ID CRM_PRODUCT_TIER SALES_PRODUCT_TIER  \\\n",
       "0     14248134      803      1473270             free               free   \n",
       "1     13502120      804      4641168             free                pro   \n",
       "2     13954226      805      5654010             free                pro   \n",
       "3     14315322      806      4802412             free                pro   \n",
       "4     13042600      807      6746619             free                pro   \n",
       "\n",
       "  MARKETING_PRODUCT_TIER PRODUCT_ADDONS SCREEN_SIZE  \\\n",
       "0                    pro          sites    1366x768   \n",
       "1                    pro      reporting    1440x900   \n",
       "2             enterprise  reporting|ads    1280x800   \n",
       "3                    pro      reporting    1440x900   \n",
       "4                    pro  reporting|ssl   1920x1080   \n",
       "\n",
       "                   USER_ROLE LANGUAGE  SURVEY_SCORE         MASKED_IP  \\\n",
       "0  Especialista en marketing    es-mx             5  MzE4,DI5OTE0,Q==   \n",
       "1                        NaN    en-us            10      ,jQ2,zE4OTMw   \n",
       "2                Salesperson    en-us             2  MTYy,jcwMTMwMw==   \n",
       "3                        NaN       en             5  MTA4,jA1MTQ0OA==   \n",
       "4     SpÃ©cialiste marketing    fr-fr             6  MTQ5OTE5OTI1Mg==   \n",
       "\n",
       "            CRT_DT COUNTRY_CODE                REGION  CRM_PROD_TIER_FREE  \\\n",
       "0  2/27/2018 21:52           MX               jalisco                   1   \n",
       "1   2/2/2018 15:40           US              new york                   1   \n",
       "2   2/18/2018 5:25           US          pennsylvania                   1   \n",
       "3   3/1/2018 18:49           US            california                   1   \n",
       "4  1/17/2018 10:10           FR  auvergne-rhone-alpes                   1   \n",
       "\n",
       "   CRM_PROD_TIER_NONE  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_survey_resp.rename(columns={'free':'CRM_PROD_TIER_FREE', 'none':'CRM_PROD_TIER_NONE'}, inplace=True)\n",
    "user_survey_resp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   free  none  pro  starter\n",
       "0     1     0    0        0\n",
       "1     0     0    1        0\n",
       "2     0     0    1        0\n",
       "3     0     0    1        0\n",
       "4     0     0    1        0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_2 = pd.get_dummies(user_survey_resp[\"SALES_PRODUCT_TIER\"])\n",
    "dummy_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   RESPONSE_ID  USER_ID  CUSTOMER_ID CRM_PRODUCT_TIER SALES_PRODUCT_TIER  \\\n",
       "0     14248134      803      1473270             free               free   \n",
       "1     13502120      804      4641168             free                pro   \n",
       "2     13954226      805      5654010             free                pro   \n",
       "3     14315322      806      4802412             free                pro   \n",
       "4     13042600      807      6746619             free                pro   \n",
       "\n",
       "  MARKETING_PRODUCT_TIER PRODUCT_ADDONS SCREEN_SIZE  \\\n",
       "0                    pro          sites    1366x768   \n",
       "1                    pro      reporting    1440x900   \n",
       "2             enterprise  reporting|ads    1280x800   \n",
       "3                    pro      reporting    1440x900   \n",
       "4                    pro  reporting|ssl   1920x1080   \n",
       "\n",
       "                   USER_ROLE LANGUAGE  SURVEY_SCORE         MASKED_IP  \\\n",
       "0  Especialista en marketing    es-mx             5  MzE4,DI5OTE0,Q==   \n",
       "1                        NaN    en-us            10      ,jQ2,zE4OTMw   \n",
       "2                Salesperson    en-us             2  MTYy,jcwMTMwMw==   \n",
       "3                        NaN       en             5  MTA4,jA1MTQ0OA==   \n",
       "4     SpÃ©cialiste marketing    fr-fr             6  MTQ5OTE5OTI1Mg==   \n",
       "\n",
       "            CRT_DT COUNTRY_CODE                REGION  CRM_PROD_TIER_FREE  \\\n",
       "0  2/27/2018 21:52           MX               jalisco                   1   \n",
       "1   2/2/2018 15:40           US              new york                   1   \n",
       "2   2/18/2018 5:25           US          pennsylvania                   1   \n",
       "3   3/1/2018 18:49           US            california                   1   \n",
       "4  1/17/2018 10:10           FR  auvergne-rhone-alpes                   1   \n",
       "\n",
       "   CRM_PROD_TIER_NONE  SALES_PROD_TIER_FREE  SALES_PROD_TIER_NONE  \\\n",
       "0                   0                     1                     0   \n",
       "1                   0                     0                     0   \n",
       "2                   0                     0                     0   \n",
       "3                   0                     0                     0   \n",
       "4                   0                     0                     0   \n",
       "\n",
       "   SALES_PROD_TIER_PRO  SALES_PROD_TIER_STARTER  \n",
       "0                    0                        0  \n",
       "1                    1                        0  \n",
       "2                    1                        0  \n",
       "3                    1                        0  \n",
       "4                    1                        0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_survey_resp = pd.concat([user_survey_resp, dummy_2], axis=1)\n",
    "user_survey_resp.rename(columns={'free':'SALES_PROD_TIER_FREE', 'none':'SALES_PROD_TIER_NONE', 'pro':'SALES_PROD_TIER_PRO', 'starter':'SALES_PROD_TIER_STARTER'}, inplace=True)\n",
    "\n",
    "user_survey_resp.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "   basic  enterprise  free  none  pro  starter\n",
       "0      0           0     0     0    1        0\n",
       "1      0           0     0     0    1        0\n",
       "2      0           1     0     0    0        0\n",
       "3      0           0     0     0    1        0\n",
       "4      0           0     0     0    1        0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_3 = pd.get_dummies(user_survey_resp[\"MARKETING_PRODUCT_TIER\"])\n",
    "dummy_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>USER_ID</th>\n",
       "      <th>CUSTOMER_ID</th>\n",
       "      <th>CRM_PRODUCT_TIER</th>\n",
       "      <th>SALES_PRODUCT_TIER</th>\n",
       "      <th>MARKETING_PRODUCT_TIER</th>\n",
       "      <th>PRODUCT_ADDONS</th>\n",
       "      <th>SCREEN_SIZE</th>\n",
       "      <th>USER_ROLE</th>\n",
       "      <th>LANGUAGE</th>\n",
       "      <th>SURVEY_SCORE</th>\n",
       "      <th>MASKED_IP</th>\n",
       "      <th>CRT_DT</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>REGION</th>\n",
       "      <th>CRM_PROD_TIER_FREE</th>\n",
       "      <th>CRM_PROD_TIER_NONE</th>\n",
       "      <th>SALES_PROD_TIER_FREE</th>\n",
       "      <th>SALES_PROD_TIER_NONE</th>\n",
       "      <th>SALES_PROD_TIER_PRO</th>\n",
       "      <th>SALES_PROD_TIER_STARTER</th>\n",
       "      <th>MARKETING_PROD_TIER_BASIC</th>\n",
       "      <th>MARKETING_PROD_TIER_ENTERPRISE</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14248134</td>\n",
       "      <td>803</td>\n",
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       "      <td>free</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>sites</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Especialista en marketing</td>\n",
       "      <td>es-mx</td>\n",
       "      <td>5</td>\n",
       "      <td>MzE4,DI5OTE0,Q==</td>\n",
       "      <td>2/27/2018 21:52</td>\n",
       "      <td>MX</td>\n",
       "      <td>jalisco</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>13502120</td>\n",
       "      <td>804</td>\n",
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       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en-us</td>\n",
       "      <td>10</td>\n",
       "      <td>,jQ2,zE4OTMw</td>\n",
       "      <td>2/2/2018 15:40</td>\n",
       "      <td>US</td>\n",
       "      <td>new york</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13954226</td>\n",
       "      <td>805</td>\n",
       "      <td>5654010</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>enterprise</td>\n",
       "      <td>reporting|ads</td>\n",
       "      <td>1280x800</td>\n",
       "      <td>Salesperson</td>\n",
       "      <td>en-us</td>\n",
       "      <td>2</td>\n",
       "      <td>MTYy,jcwMTMwMw==</td>\n",
       "      <td>2/18/2018 5:25</td>\n",
       "      <td>US</td>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14315322</td>\n",
       "      <td>806</td>\n",
       "      <td>4802412</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>5</td>\n",
       "      <td>MTA4,jA1MTQ0OA==</td>\n",
       "      <td>3/1/2018 18:49</td>\n",
       "      <td>US</td>\n",
       "      <td>california</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13042600</td>\n",
       "      <td>807</td>\n",
       "      <td>6746619</td>\n",
       "      <td>free</td>\n",
       "      <td>pro</td>\n",
       "      <td>pro</td>\n",
       "      <td>reporting|ssl</td>\n",
       "      <td>1920x1080</td>\n",
       "      <td>SpÃ©cialiste marketing</td>\n",
       "      <td>fr-fr</td>\n",
       "      <td>6</td>\n",
       "      <td>MTQ5OTE5OTI1Mg==</td>\n",
       "      <td>1/17/2018 10:10</td>\n",
       "      <td>FR</td>\n",
       "      <td>auvergne-rhone-alpes</td>\n",
       "      <td>1</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",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   RESPONSE_ID  USER_ID  CUSTOMER_ID CRM_PRODUCT_TIER SALES_PRODUCT_TIER  \\\n",
       "0     14248134      803      1473270             free               free   \n",
       "1     13502120      804      4641168             free                pro   \n",
       "2     13954226      805      5654010             free                pro   \n",
       "3     14315322      806      4802412             free                pro   \n",
       "4     13042600      807      6746619             free                pro   \n",
       "\n",
       "  MARKETING_PRODUCT_TIER PRODUCT_ADDONS SCREEN_SIZE  \\\n",
       "0                    pro          sites    1366x768   \n",
       "1                    pro      reporting    1440x900   \n",
       "2             enterprise  reporting|ads    1280x800   \n",
       "3                    pro      reporting    1440x900   \n",
       "4                    pro  reporting|ssl   1920x1080   \n",
       "\n",
       "                   USER_ROLE LANGUAGE  SURVEY_SCORE         MASKED_IP  \\\n",
       "0  Especialista en marketing    es-mx             5  MzE4,DI5OTE0,Q==   \n",
       "1                        NaN    en-us            10      ,jQ2,zE4OTMw   \n",
       "2                Salesperson    en-us             2  MTYy,jcwMTMwMw==   \n",
       "3                        NaN       en             5  MTA4,jA1MTQ0OA==   \n",
       "4     SpÃ©cialiste marketing    fr-fr             6  MTQ5OTE5OTI1Mg==   \n",
       "\n",
       "            CRT_DT COUNTRY_CODE                REGION  CRM_PROD_TIER_FREE  \\\n",
       "0  2/27/2018 21:52           MX               jalisco                   1   \n",
       "1   2/2/2018 15:40           US              new york                   1   \n",
       "2   2/18/2018 5:25           US          pennsylvania                   1   \n",
       "3   3/1/2018 18:49           US            california                   1   \n",
       "4  1/17/2018 10:10           FR  auvergne-rhone-alpes                   1   \n",
       "\n",
       "   CRM_PROD_TIER_NONE  SALES_PROD_TIER_FREE  SALES_PROD_TIER_NONE  \\\n",
       "0                   0                     1                     0   \n",
       "1                   0                     0                     0   \n",
       "2                   0                     0                     0   \n",
       "3                   0                     0                     0   \n",
       "4                   0                     0                     0   \n",
       "\n",
       "   SALES_PROD_TIER_PRO  SALES_PROD_TIER_STARTER  MARKETING_PROD_TIER_BASIC  \\\n",
       "0                    0                        0                          0   \n",
       "1                    1                        0                          0   \n",
       "2                    1                        0                          0   \n",
       "3                    1                        0                          0   \n",
       "4                    1                        0                          0   \n",
       "\n",
       "   MARKETING_PROD_TIER_ENTERPRISE  MARKETING_PROD_TIER_FREE  \\\n",
       "0                               0                         0   \n",
       "1                               0                         0   \n",
       "2                               1                         0   \n",
       "3                               0                         0   \n",
       "4                               0                         0   \n",
       "\n",
       "   MARKETING_PROD_TIER_NONE  MARKETING_PROD_TIER_PRO  \\\n",
       "0                         0                        1   \n",
       "1                         0                        1   \n",
       "2                         0                        0   \n",
       "3                         0                        1   \n",
       "4                         0                        1   \n",
       "\n",
       "   MARKETING_PROD_TIER_STARTER  \n",
       "0                            0  \n",
       "1                            0  \n",
       "2                            0  \n",
       "3                            0  \n",
       "4                            0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_survey_resp = pd.concat([user_survey_resp, dummy_3], axis=1)\n",
    "user_survey_resp.rename(columns={'basic':'MARKETING_PROD_TIER_BASIC','enterprise':'MARKETING_PROD_TIER_ENTERPRISE','free':'MARKETING_PROD_TIER_FREE', 'none':'MARKETING_PROD_TIER_NONE', 'pro':'MARKETING_PROD_TIER_PRO', 'starter':'MARKETING_PROD_TIER_STARTER'}, inplace=True)\n",
    "user_survey_resp.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RESPONSE_ID                        int64\n",
       "USER_ID                            int64\n",
       "CUSTOMER_ID                        int64\n",
       "CRM_PRODUCT_TIER                  object\n",
       "SALES_PRODUCT_TIER                object\n",
       "MARKETING_PRODUCT_TIER            object\n",
       "PRODUCT_ADDONS                    object\n",
       "SCREEN_SIZE                       object\n",
       "USER_ROLE                         object\n",
       "LANGUAGE                          object\n",
       "SURVEY_SCORE                       int64\n",
       "MASKED_IP                         object\n",
       "CRT_DT                            object\n",
       "COUNTRY_CODE                      object\n",
       "REGION                            object\n",
       "CRM_PROD_TIER_FREE                 uint8\n",
       "CRM_PROD_TIER_NONE                 uint8\n",
       "SALES_PROD_TIER_FREE               uint8\n",
       "SALES_PROD_TIER_NONE               uint8\n",
       "SALES_PROD_TIER_PRO                uint8\n",
       "SALES_PROD_TIER_STARTER            uint8\n",
       "MARKETING_PROD_TIER_BASIC          uint8\n",
       "MARKETING_PROD_TIER_ENTERPRISE     uint8\n",
       "MARKETING_PROD_TIER_FREE           uint8\n",
       "MARKETING_PROD_TIER_NONE           uint8\n",
       "MARKETING_PROD_TIER_PRO            uint8\n",
       "MARKETING_PROD_TIER_STARTER        uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_survey_resp.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.026124</td>\n",
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       "      <td>-0.199087</td>\n",
       "      <td>0.343895</td>\n",
       "      <td>0.052489</td>\n",
       "      <td>-0.182148</td>\n",
       "      <td>-0.091211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SALES_PROD_TIER_NONE</th>\n",
       "      <td>0.012628</td>\n",
       "      <td>-0.121206</td>\n",
       "      <td>-0.096149</td>\n",
       "      <td>-0.017742</td>\n",
       "      <td>-0.893962</td>\n",
       "      <td>0.893962</td>\n",
       "      <td>-0.139261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.164905</td>\n",
       "      <td>-0.032508</td>\n",
       "      <td>0.053252</td>\n",
       "      <td>0.018040</td>\n",
       "      <td>-0.099203</td>\n",
       "      <td>0.156425</td>\n",
       "      <td>0.018873</td>\n",
       "      <td>-0.022381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SALES_PROD_TIER_PRO</th>\n",
       "      <td>0.008185</td>\n",
       "      <td>-0.031733</td>\n",
       "      <td>-0.231601</td>\n",
       "      <td>-0.085285</td>\n",
       "      <td>0.146026</td>\n",
       "      <td>-0.146026</td>\n",
       "      <td>-0.878312</td>\n",
       "      <td>-0.164905</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.205027</td>\n",
       "      <td>-0.047188</td>\n",
       "      <td>0.210145</td>\n",
       "      <td>-0.329801</td>\n",
       "      <td>-0.108138</td>\n",
       "      <td>0.185296</td>\n",
       "      <td>0.095725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SALES_PROD_TIER_STARTER</th>\n",
       "      <td>0.057474</td>\n",
       "      <td>0.114440</td>\n",
       "      <td>0.109832</td>\n",
       "      <td>0.024739</td>\n",
       "      <td>0.029362</td>\n",
       "      <td>-0.029362</td>\n",
       "      <td>-0.173143</td>\n",
       "      <td>-0.032508</td>\n",
       "      <td>-0.205027</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018214</td>\n",
       "      <td>-0.048916</td>\n",
       "      <td>0.054103</td>\n",
       "      <td>0.017879</td>\n",
       "      <td>-0.028654</td>\n",
       "      <td>0.004009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKETING_PROD_TIER_BASIC</th>\n",
       "      <td>0.018399</td>\n",
       "      <td>-0.043302</td>\n",
       "      <td>-0.002806</td>\n",
       "      <td>-0.005430</td>\n",
       "      <td>-0.057984</td>\n",
       "      <td>0.057984</td>\n",
       "      <td>0.023618</td>\n",
       "      <td>0.053252</td>\n",
       "      <td>-0.047188</td>\n",
       "      <td>0.018214</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.099050</td>\n",
       "      <td>-0.155383</td>\n",
       "      <td>-0.032496</td>\n",
       "      <td>-0.158709</td>\n",
       "      <td>-0.028671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKETING_PROD_TIER_ENTERPRISE</th>\n",
       "      <td>0.027487</td>\n",
       "      <td>-0.042203</td>\n",
       "      <td>-0.249779</td>\n",
       "      <td>-0.075063</td>\n",
       "      <td>-0.038995</td>\n",
       "      <td>0.038995</td>\n",
       "      <td>-0.199087</td>\n",
       "      <td>0.018040</td>\n",
       "      <td>0.210145</td>\n",
       "      <td>-0.048916</td>\n",
       "      <td>-0.099050</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.358672</td>\n",
       "      <td>-0.075011</td>\n",
       "      <td>-0.366349</td>\n",
       "      <td>-0.066182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKETING_PROD_TIER_FREE</th>\n",
       "      <td>-0.048388</td>\n",
       "      <td>0.215776</td>\n",
       "      <td>0.397785</td>\n",
       "      <td>0.040142</td>\n",
       "      <td>0.091299</td>\n",
       "      <td>-0.091299</td>\n",
       "      <td>0.343895</td>\n",
       "      <td>-0.099203</td>\n",
       "      <td>-0.329801</td>\n",
       "      <td>0.054103</td>\n",
       "      <td>-0.155383</td>\n",
       "      <td>-0.358672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117673</td>\n",
       "      <td>-0.574706</td>\n",
       "      <td>-0.103822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKETING_PROD_TIER_NONE</th>\n",
       "      <td>0.009642</td>\n",
       "      <td>-0.072988</td>\n",
       "      <td>-0.044402</td>\n",
       "      <td>-0.003788</td>\n",
       "      <td>-0.024784</td>\n",
       "      <td>0.024784</td>\n",
       "      <td>0.052489</td>\n",
       "      <td>0.156425</td>\n",
       "      <td>-0.108138</td>\n",
       "      <td>0.017879</td>\n",
       "      <td>-0.032496</td>\n",
       "      <td>-0.075011</td>\n",
       "      <td>-0.117673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.120191</td>\n",
       "      <td>-0.021713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKETING_PROD_TIER_PRO</th>\n",
       "      <td>0.016174</td>\n",
       "      <td>-0.144355</td>\n",
       "      <td>-0.194731</td>\n",
       "      <td>0.026124</td>\n",
       "      <td>-0.033313</td>\n",
       "      <td>0.033313</td>\n",
       "      <td>-0.182148</td>\n",
       "      <td>0.018873</td>\n",
       "      <td>0.185296</td>\n",
       "      <td>-0.028654</td>\n",
       "      <td>-0.158709</td>\n",
       "      <td>-0.366349</td>\n",
       "      <td>-0.574706</td>\n",
       "      <td>-0.120191</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.106044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKETING_PROD_TIER_STARTER</th>\n",
       "      <td>-0.003014</td>\n",
       "      <td>0.017044</td>\n",
       "      <td>0.056185</td>\n",
       "      <td>-0.007433</td>\n",
       "      <td>0.020215</td>\n",
       "      <td>-0.020215</td>\n",
       "      <td>-0.091211</td>\n",
       "      <td>-0.022381</td>\n",
       "      <td>0.095725</td>\n",
       "      <td>0.004009</td>\n",
       "      <td>-0.028671</td>\n",
       "      <td>-0.066182</td>\n",
       "      <td>-0.103822</td>\n",
       "      <td>-0.021713</td>\n",
       "      <td>-0.106044</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                RESPONSE_ID   USER_ID  CUSTOMER_ID  SUR_SCORE  \\\n",
       "RESPONSE_ID                        1.000000  0.080140    -0.014714   0.013771   \n",
       "USER_ID                            0.080140  1.000000     0.508068  -0.059964   \n",
       "CUSTOMER_ID                       -0.014714  0.508068     1.000000   0.048987   \n",
       "SUR_SCORE                          0.013771 -0.059964     0.048987   1.000000   \n",
       "CRM_PROD_TIER_FREE                -0.020840  0.090668     0.079355   0.009576   \n",
       "CRM_PROD_TIER_NONE                 0.020840 -0.090668    -0.079355  -0.009576   \n",
       "SALES_PROD_TIER_FREE              -0.034759  0.025987     0.221866   0.082208   \n",
       "SALES_PROD_TIER_NONE               0.012628 -0.121206    -0.096149  -0.017742   \n",
       "SALES_PROD_TIER_PRO                0.008185 -0.031733    -0.231601  -0.085285   \n",
       "SALES_PROD_TIER_STARTER            0.057474  0.114440     0.109832   0.024739   \n",
       "MARKETING_PROD_TIER_BASIC          0.018399 -0.043302    -0.002806  -0.005430   \n",
       "MARKETING_PROD_TIER_ENTERPRISE     0.027487 -0.042203    -0.249779  -0.075063   \n",
       "MARKETING_PROD_TIER_FREE          -0.048388  0.215776     0.397785   0.040142   \n",
       "MARKETING_PROD_TIER_NONE           0.009642 -0.072988    -0.044402  -0.003788   \n",
       "MARKETING_PROD_TIER_PRO            0.016174 -0.144355    -0.194731   0.026124   \n",
       "MARKETING_PROD_TIER_STARTER       -0.003014  0.017044     0.056185  -0.007433   \n",
       "\n",
       "                                CRM_PROD_TIER_FREE  CRM_PROD_TIER_NONE  \\\n",
       "RESPONSE_ID                              -0.020840            0.020840   \n",
       "USER_ID                                   0.090668           -0.090668   \n",
       "CUSTOMER_ID                               0.079355           -0.079355   \n",
       "SUR_SCORE                                 0.009576           -0.009576   \n",
       "CRM_PROD_TIER_FREE                        1.000000           -1.000000   \n",
       "CRM_PROD_TIER_NONE                       -1.000000            1.000000   \n",
       "SALES_PROD_TIER_FREE                      0.125785           -0.125785   \n",
       "SALES_PROD_TIER_NONE                     -0.893962            0.893962   \n",
       "SALES_PROD_TIER_PRO                       0.146026           -0.146026   \n",
       "SALES_PROD_TIER_STARTER                   0.029362           -0.029362   \n",
       "MARKETING_PROD_TIER_BASIC                -0.057984            0.057984   \n",
       "MARKETING_PROD_TIER_ENTERPRISE           -0.038995            0.038995   \n",
       "MARKETING_PROD_TIER_FREE                  0.091299           -0.091299   \n",
       "MARKETING_PROD_TIER_NONE                 -0.024784            0.024784   \n",
       "MARKETING_PROD_TIER_PRO                  -0.033313            0.033313   \n",
       "MARKETING_PROD_TIER_STARTER               0.020215           -0.020215   \n",
       "\n",
       "                                SALES_PROD_TIER_FREE  SALES_PROD_TIER_NONE  \\\n",
       "RESPONSE_ID                                -0.034759              0.012628   \n",
       "USER_ID                                     0.025987             -0.121206   \n",
       "CUSTOMER_ID                                 0.221866             -0.096149   \n",
       "SUR_SCORE                                   0.082208             -0.017742   \n",
       "CRM_PROD_TIER_FREE                          0.125785             -0.893962   \n",
       "CRM_PROD_TIER_NONE                         -0.125785              0.893962   \n",
       "SALES_PROD_TIER_FREE                        1.000000             -0.139261   \n",
       "SALES_PROD_TIER_NONE                       -0.139261              1.000000   \n",
       "SALES_PROD_TIER_PRO                        -0.878312             -0.164905   \n",
       "SALES_PROD_TIER_STARTER                    -0.173143             -0.032508   \n",
       "MARKETING_PROD_TIER_BASIC                   0.023618              0.053252   \n",
       "MARKETING_PROD_TIER_ENTERPRISE             -0.199087              0.018040   \n",
       "MARKETING_PROD_TIER_FREE                    0.343895             -0.099203   \n",
       "MARKETING_PROD_TIER_NONE                    0.052489              0.156425   \n",
       "MARKETING_PROD_TIER_PRO                    -0.182148              0.018873   \n",
       "MARKETING_PROD_TIER_STARTER                -0.091211             -0.022381   \n",
       "\n",
       "                                SALES_PROD_TIER_PRO  SALES_PROD_TIER_STARTER  \\\n",
       "RESPONSE_ID                                0.008185                 0.057474   \n",
       "USER_ID                                   -0.031733                 0.114440   \n",
       "CUSTOMER_ID                               -0.231601                 0.109832   \n",
       "SUR_SCORE                                 -0.085285                 0.024739   \n",
       "CRM_PROD_TIER_FREE                         0.146026                 0.029362   \n",
       "CRM_PROD_TIER_NONE                        -0.146026                -0.029362   \n",
       "SALES_PROD_TIER_FREE                      -0.878312                -0.173143   \n",
       "SALES_PROD_TIER_NONE                      -0.164905                -0.032508   \n",
       "SALES_PROD_TIER_PRO                        1.000000                -0.205027   \n",
       "SALES_PROD_TIER_STARTER                   -0.205027                 1.000000   \n",
       "MARKETING_PROD_TIER_BASIC                 -0.047188                 0.018214   \n",
       "MARKETING_PROD_TIER_ENTERPRISE             0.210145                -0.048916   \n",
       "MARKETING_PROD_TIER_FREE                  -0.329801                 0.054103   \n",
       "MARKETING_PROD_TIER_NONE                  -0.108138                 0.017879   \n",
       "MARKETING_PROD_TIER_PRO                    0.185296                -0.028654   \n",
       "MARKETING_PROD_TIER_STARTER                0.095725                 0.004009   \n",
       "\n",
       "                                MARKETING_PROD_TIER_BASIC  \\\n",
       "RESPONSE_ID                                      0.018399   \n",
       "USER_ID                                         -0.043302   \n",
       "CUSTOMER_ID                                     -0.002806   \n",
       "SUR_SCORE                                       -0.005430   \n",
       "CRM_PROD_TIER_FREE                              -0.057984   \n",
       "CRM_PROD_TIER_NONE                               0.057984   \n",
       "SALES_PROD_TIER_FREE                             0.023618   \n",
       "SALES_PROD_TIER_NONE                             0.053252   \n",
       "SALES_PROD_TIER_PRO                             -0.047188   \n",
       "SALES_PROD_TIER_STARTER                          0.018214   \n",
       "MARKETING_PROD_TIER_BASIC                        1.000000   \n",
       "MARKETING_PROD_TIER_ENTERPRISE                  -0.099050   \n",
       "MARKETING_PROD_TIER_FREE                        -0.155383   \n",
       "MARKETING_PROD_TIER_NONE                        -0.032496   \n",
       "MARKETING_PROD_TIER_PRO                         -0.158709   \n",
       "MARKETING_PROD_TIER_STARTER                     -0.028671   \n",
       "\n",
       "                                MARKETING_PROD_TIER_ENTERPRISE  \\\n",
       "RESPONSE_ID                                           0.027487   \n",
       "USER_ID                                              -0.042203   \n",
       "CUSTOMER_ID                                          -0.249779   \n",
       "SUR_SCORE                                            -0.075063   \n",
       "CRM_PROD_TIER_FREE                                   -0.038995   \n",
       "CRM_PROD_TIER_NONE                                    0.038995   \n",
       "SALES_PROD_TIER_FREE                                 -0.199087   \n",
       "SALES_PROD_TIER_NONE                                  0.018040   \n",
       "SALES_PROD_TIER_PRO                                   0.210145   \n",
       "SALES_PROD_TIER_STARTER                              -0.048916   \n",
       "MARKETING_PROD_TIER_BASIC                            -0.099050   \n",
       "MARKETING_PROD_TIER_ENTERPRISE                        1.000000   \n",
       "MARKETING_PROD_TIER_FREE                             -0.358672   \n",
       "MARKETING_PROD_TIER_NONE                             -0.075011   \n",
       "MARKETING_PROD_TIER_PRO                              -0.366349   \n",
       "MARKETING_PROD_TIER_STARTER                          -0.066182   \n",
       "\n",
       "                                MARKETING_PROD_TIER_FREE  \\\n",
       "RESPONSE_ID                                    -0.048388   \n",
       "USER_ID                                         0.215776   \n",
       "CUSTOMER_ID                                     0.397785   \n",
       "SUR_SCORE                                       0.040142   \n",
       "CRM_PROD_TIER_FREE                              0.091299   \n",
       "CRM_PROD_TIER_NONE                             -0.091299   \n",
       "SALES_PROD_TIER_FREE                            0.343895   \n",
       "SALES_PROD_TIER_NONE                           -0.099203   \n",
       "SALES_PROD_TIER_PRO                            -0.329801   \n",
       "SALES_PROD_TIER_STARTER                         0.054103   \n",
       "MARKETING_PROD_TIER_BASIC                      -0.155383   \n",
       "MARKETING_PROD_TIER_ENTERPRISE                 -0.358672   \n",
       "MARKETING_PROD_TIER_FREE                        1.000000   \n",
       "MARKETING_PROD_TIER_NONE                       -0.117673   \n",
       "MARKETING_PROD_TIER_PRO                        -0.574706   \n",
       "MARKETING_PROD_TIER_STARTER                    -0.103822   \n",
       "\n",
       "                                MARKETING_PROD_TIER_NONE  \\\n",
       "RESPONSE_ID                                     0.009642   \n",
       "USER_ID                                        -0.072988   \n",
       "CUSTOMER_ID                                    -0.044402   \n",
       "SUR_SCORE                                      -0.003788   \n",
       "CRM_PROD_TIER_FREE                             -0.024784   \n",
       "CRM_PROD_TIER_NONE                              0.024784   \n",
       "SALES_PROD_TIER_FREE                            0.052489   \n",
       "SALES_PROD_TIER_NONE                            0.156425   \n",
       "SALES_PROD_TIER_PRO                            -0.108138   \n",
       "SALES_PROD_TIER_STARTER                         0.017879   \n",
       "MARKETING_PROD_TIER_BASIC                      -0.032496   \n",
       "MARKETING_PROD_TIER_ENTERPRISE                 -0.075011   \n",
       "MARKETING_PROD_TIER_FREE                       -0.117673   \n",
       "MARKETING_PROD_TIER_NONE                        1.000000   \n",
       "MARKETING_PROD_TIER_PRO                        -0.120191   \n",
       "MARKETING_PROD_TIER_STARTER                    -0.021713   \n",
       "\n",
       "                                MARKETING_PROD_TIER_PRO  \\\n",
       "RESPONSE_ID                                    0.016174   \n",
       "USER_ID                                       -0.144355   \n",
       "CUSTOMER_ID                                   -0.194731   \n",
       "SUR_SCORE                                      0.026124   \n",
       "CRM_PROD_TIER_FREE                            -0.033313   \n",
       "CRM_PROD_TIER_NONE                             0.033313   \n",
       "SALES_PROD_TIER_FREE                          -0.182148   \n",
       "SALES_PROD_TIER_NONE                           0.018873   \n",
       "SALES_PROD_TIER_PRO                            0.185296   \n",
       "SALES_PROD_TIER_STARTER                       -0.028654   \n",
       "MARKETING_PROD_TIER_BASIC                     -0.158709   \n",
       "MARKETING_PROD_TIER_ENTERPRISE                -0.366349   \n",
       "MARKETING_PROD_TIER_FREE                      -0.574706   \n",
       "MARKETING_PROD_TIER_NONE                      -0.120191   \n",
       "MARKETING_PROD_TIER_PRO                        1.000000   \n",
       "MARKETING_PROD_TIER_STARTER                   -0.106044   \n",
       "\n",
       "                                MARKETING_PROD_TIER_STARTER  \n",
       "RESPONSE_ID                                       -0.003014  \n",
       "USER_ID                                            0.017044  \n",
       "CUSTOMER_ID                                        0.056185  \n",
       "SUR_SCORE                                         -0.007433  \n",
       "CRM_PROD_TIER_FREE                                 0.020215  \n",
       "CRM_PROD_TIER_NONE                                -0.020215  \n",
       "SALES_PROD_TIER_FREE                              -0.091211  \n",
       "SALES_PROD_TIER_NONE                              -0.022381  \n",
       "SALES_PROD_TIER_PRO                                0.095725  \n",
       "SALES_PROD_TIER_STARTER                            0.004009  \n",
       "MARKETING_PROD_TIER_BASIC                         -0.028671  \n",
       "MARKETING_PROD_TIER_ENTERPRISE                    -0.066182  \n",
       "MARKETING_PROD_TIER_FREE                          -0.103822  \n",
       "MARKETING_PROD_TIER_NONE                          -0.021713  \n",
       "MARKETING_PROD_TIER_PRO                           -0.106044  \n",
       "MARKETING_PROD_TIER_STARTER                        1.000000  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_survey_resp.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>P-value</b>: \n",
    "<p>What is this P-value? The P-value is the probability value that the correlation between these two variables is statistically significant. Normally, we choose a significance level of 0.05, which means that we are 95% confident that the correlation between the variables is significant.</p>\n",
    "\n",
    "By convention, when the\n",
    "<ul>\n",
    "    <li>p-value is $<$ 0.001: we say there is strong evidence that the correlation is significant.</li>\n",
    "    <li>the p-value is $<$ 0.05: there is moderate evidence that the correlation is significant.</li>\n",
    "    <li>the p-value is $<$ 0.1: there is weak evidence that the correlation is significant.</li>\n",
    "    <li>the p-value is $>$ 0.1: there is no evidence that the correlation is significant.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Pearson Correlation</b>:\n",
    "<p>The Pearson Correlation measures the linear dependence between two variables X and Y.</p>\n",
    "<p>The resulting coefficient is a value between -1 and 1 inclusive, where:</p>\n",
    "<ul>\n",
    "    <li><b>1</b>: Total positive linear correlation.</li>\n",
    "    <li><b>0</b>: No linear correlation, the two variables most likely do not affect each other.</li>\n",
    "    <li><b>-1</b>: Total negative linear correlation.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>CRM_PRODUCT_TIER vs SURVEY_SCORE</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the  Pearson Correlation Coefficient and P-value of 'CRM_PRODUCT_TIER' and 'SURVEY_SCORE'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'SURVEY_SCORE'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/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_SCORE'",
      "\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-59-208981a7bca0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpearson_coef\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpearsonr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_survey_resp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CRM_PROD_TIER_FREE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muser_survey_resp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SURVEY_SCORE'\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      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The Pearson Correlation Coefficient is\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpearson_coef\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" with a P-value of P =\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2993\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   2994\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-> 2995\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   2996\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   2997\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~/conda/envs/python/lib/python3.6/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_SCORE'"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['CRM_PROD_TIER_FREE'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $>$ 0.1, the correlation between CRM Product Tier-Free and Survey Score is not evident, and the linear relationship doesn't exist (0.009, close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.009576043981805497  with a P-value of P = 0.5076763738191643\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['CRM_PROD_TIER_NONE'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $>$ 0.1, the correlation between CRM Product Tier-None and Survey Score is not evident, and there is no linear relationship (close to 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>SALES_PRODUCT_TIER vs SURVEY_SCORE</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the  Pearson Correlation Coefficient and P-value of 'SALES_PRODUCT_TIER' and 'SURVEY_SCORE'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.08220783876241307  with a P-value of P = 1.2224833547784806e-08\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['SALES_PROD_TIER_FREE'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $<$ 0.001, the correlation between SALES Product Tier-Free and Survey Score is statistically significant, although the linear relationship doesn't exist (0.08, close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.01774246186717904  with a P-value of P = 0.21964510003265664\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['SALES_PROD_TIER_NONE'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $>$ 0.1, the correlation between SALES Product Tier-None and Survey Score is not evident, and the linear relationship is also negative (0.009, close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.024739282270331443  with a P-value of P = 0.0869595445271282\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['SALES_PROD_TIER_STARTER'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $<$ 0.1, the correlation between SALES Product Tier-STARTER and Survey Score is not significantly strong, and the linear relationship doesn't exist (0.02, close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.08528450242182532  with a P-value of P = 3.4102346168242675e-09\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['SALES_PROD_TIER_PRO'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $<$ 0.001, the correlation between SALES Product Tier-PRO and Survey Score is statistically significant, and the linear relationship doesn't exist (-0.08, close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2760067b70>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"MARKETING_PROD_TIER_ENTERPRISE\", y=\"SURVEY_SCORE\", data=user_survey_resp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>MARKETING_PRODUCT_TIER vs SURVEY_SCORE</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the  Pearson Correlation Coefficient and P-value of 'MARKETING_PRODUCT_TIER' and 'SURVEY_SCORE'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.04014208464741162  with a P-value of P = 0.005468770906780102\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['MARKETING_PROD_TIER_FREE'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $<$ 0.05, the correlation between MARKETING Product Tier-FREE and Survey Score is moderately certain, and the linear relationship doesn't exist (0.005, close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.0037879802286661483  with a P-value of P = 0.7932881233883492\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['MARKETING_PROD_TIER_NONE'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $>$ 0.1, the correlation between MARKETING Product Tier-NONE and Survey Score is not certain, and the linear relationship doesn't exist (-0.003, close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.007433081492798794  with a P-value of P = 0.6071068022453392\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['MARKETING_PROD_TIER_STARTER'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $>$ 0.1, the correlation between SALES Product Tier-PRO and Survey Score is not certain, and the linear relationship doesn't exist (-0.007, close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.02612366117582166  with a P-value of P = 0.07068855872986965\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['MARKETING_PROD_TIER_PRO'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $<$ 0.1, the correlation between MARKETING Product Tier-PRO and Survey Score is weakly certain, and the linear relationship doesn't exist (0.07, close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.005429934472630359  with a P-value of P = 0.7071912250174903\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['MARKETING_PROD_TIER_BASIC'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $>$ 0.1, the correlation between MARKETING Product Tier-BASIC and Survey Score is not certain, and the linear relationship doesn't exist (-0.005, close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.07506261331417365  with a P-value of P = 1.9932451831871978e-07\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(user_survey_resp['MARKETING_PROD_TIER_ENTERPRISE'], user_survey_resp['SURVEY_SCORE'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Conclusion:</h5>\n",
    "\n",
    "Since the p-value is $<$ 0.001, the correlation between MARKETING Product Tier-ENTERPRISE and Survey Score is Statistically significant, although linear relationship doesn't exist (-0.07, close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f27542fdb70>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"MARKETING_PROD_TIER_ENTERPRISE\", y=\"SURVEY_SCORE\", data=user_survey_resp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>MARKETING_PRODUCT_TIER</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>basic</th>\n",
       "      <td>8.055838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enterprise</th>\n",
       "      <td>7.737374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free</th>\n",
       "      <td>8.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>none</th>\n",
       "      <td>8.060870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pro</th>\n",
       "      <td>8.202710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>starter</th>\n",
       "      <td>7.988889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        SURVEY_SCORE\n",
       "MARKETING_PRODUCT_TIER              \n",
       "basic                       8.055838\n",
       "enterprise                  7.737374\n",
       "free                        8.250000\n",
       "none                        8.060870\n",
       "pro                         8.202710\n",
       "starter                     7.988889"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_group_one = user_survey_resp[user_survey_resp['SURVEY_SCORE'] & (user_survey_resp['MARKETING_PRODUCT_TIER'])]\n",
    "df_group_one= user_survey_resp[['SURVEY_SCORE', 'MARKETING_PRODUCT_TIER']].groupby(['MARKETING_PRODUCT_TIER']).mean()\n",
    "df_group_one.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>SALES_PRODUCT_TIER</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>free</th>\n",
       "      <td>8.352133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>none</th>\n",
       "      <td>7.852459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pro</th>\n",
       "      <td>7.916018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>starter</th>\n",
       "      <td>8.419355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SURVEY_SCORE\n",
       "SALES_PRODUCT_TIER              \n",
       "free                    8.352133\n",
       "none                    7.852459\n",
       "pro                     7.916018\n",
       "starter                 8.419355"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_two= user_survey_resp[['SURVEY_SCORE', 'SALES_PRODUCT_TIER']].groupby(['SALES_PRODUCT_TIER']).mean()\n",
    "df_group_two.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>CRM_PRODUCT_TIER</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>free</th>\n",
       "      <td>8.12308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>none</th>\n",
       "      <td>7.96000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  SURVEY_SCORE\n",
       "CRM_PRODUCT_TIER              \n",
       "free                   8.12308\n",
       "none                   7.96000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_three= user_survey_resp[['SURVEY_SCORE', 'CRM_PRODUCT_TIER']].groupby(['CRM_PRODUCT_TIER']).mean()\n",
    "df_group_three.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>CUSTOMER_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2973</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30462</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31341</th>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32100</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51219</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61875</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62175</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82467</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86523</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92064</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SURVEY_SCORE\n",
       "CUSTOMER_ID              \n",
       "2973                  8.0\n",
       "30462                 9.0\n",
       "31341                 7.5\n",
       "32100                 9.0\n",
       "51219                10.0\n",
       "61875                10.0\n",
       "62175                 9.0\n",
       "82467                10.0\n",
       "86523                 3.0\n",
       "92064                10.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_four= user_survey_resp[['SURVEY_SCORE', 'CUSTOMER_ID']].groupby(['CUSTOMER_ID']).mean()\n",
    "df_group_four.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SURVEY_SCORE    483\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_four.loc[df_group_four['SURVEY_SCORE'] < 6.0].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 483 Customers have Average Survey Score less than 6.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SURVEY_SCORE    2355\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_four.loc[df_group_four['SURVEY_SCORE'] > 8.0].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2355 customers responded with average score greater than 8.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>USER_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SURVEY_SCORE\n",
       "USER_ID              \n",
       "1                   9\n",
       "2                  10\n",
       "3                  10\n",
       "4                  10\n",
       "5                  10\n",
       "6                   8\n",
       "7                   8\n",
       "8                   8\n",
       "9                  10\n",
       "10                  6"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_five= user_survey_resp[['SURVEY_SCORE', 'USER_ID']].groupby(['USER_ID']).mean()\n",
    "df_group_five.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SURVEY_SCORE    619\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_five.loc[df_group_five['SURVEY_SCORE'] < 6.0].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 619 users have Average Survey Score less than 6.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SURVEY_SCORE    2674\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_five.loc[df_group_five['SURVEY_SCORE'] > 8.0].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2674 users responded with average score greater than 8.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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