M4-ExploratoryDataAnalysis-lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Exploratory Data Analysis Lab**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **30** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this module you get to work with the cleaned dataset from the previous module.\n",
    "\n",
    "In this assignment you will perform the task of exploratory data analysis.\n",
    "You will find out the distribution of data, presence of outliers and also determine the correlation between different columns in the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab you will perform the following:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*   Identify the distribution of data in the dataset.\n",
    "\n",
    "*   Identify outliers in the dataset.\n",
    "\n",
    "*   Remove outliers from the dataset.\n",
    "\n",
    "*   Identify correlation between features in the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hands on Lab\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the pandas module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset into a dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m2_survey_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11398 entries, 0 to 11397\n",
      "Data columns (total 85 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Respondent              11398 non-null  int64  \n",
      " 1   MainBranch              11398 non-null  object \n",
      " 2   Hobbyist                11398 non-null  object \n",
      " 3   OpenSourcer             11398 non-null  object \n",
      " 4   OpenSource              11317 non-null  object \n",
      " 5   Employment              11398 non-null  object \n",
      " 6   Country                 11398 non-null  object \n",
      " 7   Student                 11347 non-null  object \n",
      " 8   EdLevel                 11286 non-null  object \n",
      " 9   UndergradMajor          10661 non-null  object \n",
      " 10  EduOther                11234 non-null  object \n",
      " 11  OrgSize                 11302 non-null  object \n",
      " 12  DevType                 11333 non-null  object \n",
      " 13  YearsCode               11389 non-null  object \n",
      " 14  Age1stCode              11385 non-null  object \n",
      " 15  YearsCodePro            11382 non-null  object \n",
      " 16  CareerSat               11398 non-null  object \n",
      " 17  JobSat                  11397 non-null  object \n",
      " 18  MgrIdiot                10905 non-null  object \n",
      " 19  MgrMoney                10901 non-null  object \n",
      " 20  MgrWant                 10905 non-null  object \n",
      " 21  JobSeek                 11398 non-null  object \n",
      " 22  LastHireDate            11398 non-null  object \n",
      " 23  LastInt                 10985 non-null  object \n",
      " 24  FizzBuzz                11361 non-null  object \n",
      " 25  JobFactors              11395 non-null  object \n",
      " 26  ResumeUpdate            11359 non-null  object \n",
      " 27  CurrencySymbol          11398 non-null  object \n",
      " 28  CurrencyDesc            11398 non-null  object \n",
      " 29  CompTotal               10589 non-null  float64\n",
      " 30  CompFreq                11192 non-null  object \n",
      " 31  ConvertedComp           10582 non-null  float64\n",
      " 32  WorkWeekHrs             11276 non-null  float64\n",
      " 33  WorkPlan                11277 non-null  object \n",
      " 34  WorkChallenge           11234 non-null  object \n",
      " 35  WorkRemote              11390 non-null  object \n",
      " 36  WorkLoc                 11366 non-null  object \n",
      " 37  ImpSyn                  11393 non-null  object \n",
      " 38  CodeRev                 11397 non-null  object \n",
      " 39  CodeRevHrs              8972 non-null   float64\n",
      " 40  UnitTests               11369 non-null  object \n",
      " 41  PurchaseHow             11202 non-null  object \n",
      " 42  PurchaseWhat            11360 non-null  object \n",
      " 43  LanguageWorkedWith      11387 non-null  object \n",
      " 44  LanguageDesireNextYear  11264 non-null  object \n",
      " 45  DatabaseWorkedWith      10945 non-null  object \n",
      " 46  DatabaseDesireNextYear  10356 non-null  object \n",
      " 47  PlatformWorkedWith      10987 non-null  object \n",
      " 48  PlatformDesireNextYear  10854 non-null  object \n",
      " 49  WebFrameWorkedWith      10005 non-null  object \n",
      " 50  WebFrameDesireNextYear  9781 non-null   object \n",
      " 51  MiscTechWorkedWith      9216 non-null   object \n",
      " 52  MiscTechDesireNextYear  9943 non-null   object \n",
      " 53  DevEnviron              11369 non-null  object \n",
      " 54  OpSys                   11364 non-null  object \n",
      " 55  Containers              11316 non-null  object \n",
      " 56  BlockchainOrg           9076 non-null   object \n",
      " 57  BlockchainIs            8788 non-null   object \n",
      " 58  BetterLife              11300 non-null  object \n",
      " 59  ITperson                11363 non-null  object \n",
      " 60  OffOn                   11360 non-null  object \n",
      " 61  SocialMedia             11105 non-null  object \n",
      " 62  Extraversion            11378 non-null  object \n",
      " 63  ScreenName              10891 non-null  object \n",
      " 64  SOVisit1st              11073 non-null  object \n",
      " 65  SOVisitFreq             11393 non-null  object \n",
      " 66  SOVisitTo               11397 non-null  object \n",
      " 67  SOFindAnswer            11395 non-null  object \n",
      " 68  SOTimeSaved             11348 non-null  object \n",
      " 69  SOHowMuchTime           9481 non-null   object \n",
      " 70  SOAccount               11397 non-null  object \n",
      " 71  SOPartFreq              10270 non-null  object \n",
      " 72  SOJobs                  11392 non-null  object \n",
      " 73  EntTeams                11393 non-null  object \n",
      " 74  SOComm                  11398 non-null  object \n",
      " 75  WelcomeChange           11313 non-null  object \n",
      " 76  SONewContent            9433 non-null   object \n",
      " 77  Age                     11111 non-null  float64\n",
      " 78  Gender                  11325 non-null  object \n",
      " 79  Trans                   11275 non-null  object \n",
      " 80  Sexuality               10856 non-null  object \n",
      " 81  Ethnicity               10723 non-null  object \n",
      " 82  Dependents              11258 non-null  object \n",
      " 83  SurveyLength            11379 non-null  object \n",
      " 84  SurveyEase              11384 non-null  object \n",
      "dtypes: float64(5), int64(1), object(79)\n",
      "memory usage: 7.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "      <th>MainBranch</th>\n",
       "      <th>Hobbyist</th>\n",
       "      <th>OpenSourcer</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Country</th>\n",
       "      <th>Student</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>UndergradMajor</th>\n",
       "      <th>...</th>\n",
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       "      <th>Gender</th>\n",
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       "      <th>Sexuality</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>SurveyLength</th>\n",
       "      <th>SurveyEase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech articles written by other developers;Indu...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Once a month or more often</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>No</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Bisexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Less than once a month but more than once per ...</td>\n",
       "      <td>OSS is, on average, of HIGHER quality than pro...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat more welcome now than last year</td>\n",
       "      <td>Tech articles written by other developers;Cour...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Never</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech articles written by other developers;Indu...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Less than once a month but more than once per ...</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Australia</td>\n",
       "      <td>No</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech articles written by other developers;Indu...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>Hispanic or Latino/Latina;Multiracial</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent                      MainBranch Hobbyist  \\\n",
       "0           4  I am a developer by profession       No   \n",
       "1           9  I am a developer by profession      Yes   \n",
       "2          13  I am a developer by profession      Yes   \n",
       "3          16  I am a developer by profession      Yes   \n",
       "4          17  I am a developer by profession      Yes   \n",
       "\n",
       "                                         OpenSourcer  \\\n",
       "0                                              Never   \n",
       "1                         Once a month or more often   \n",
       "2  Less than once a month but more than once per ...   \n",
       "3                                              Never   \n",
       "4  Less than once a month but more than once per ...   \n",
       "\n",
       "                                          OpenSource          Employment  \\\n",
       "0  The quality of OSS and closed source software ...  Employed full-time   \n",
       "1  The quality of OSS and closed source software ...  Employed full-time   \n",
       "2  OSS is, on average, of HIGHER quality than pro...  Employed full-time   \n",
       "3  The quality of OSS and closed source software ...  Employed full-time   \n",
       "4  The quality of OSS and closed source software ...  Employed full-time   \n",
       "\n",
       "          Country Student                                            EdLevel  \\\n",
       "0   United States      No           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "1     New Zealand      No  Some college/university study without earning ...   \n",
       "2   United States      No        Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
       "3  United Kingdom      No        Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
       "4       Australia      No           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "\n",
       "                                      UndergradMajor  ...  \\\n",
       "0  Computer science, computer engineering, or sof...  ...   \n",
       "1  Computer science, computer engineering, or sof...  ...   \n",
       "2  Computer science, computer engineering, or sof...  ...   \n",
       "3                                                NaN  ...   \n",
       "4  Computer science, computer engineering, or sof...  ...   \n",
       "\n",
       "                              WelcomeChange  \\\n",
       "0   Just as welcome now as I felt last year   \n",
       "1   Just as welcome now as I felt last year   \n",
       "2  Somewhat more welcome now than last year   \n",
       "3   Just as welcome now as I felt last year   \n",
       "4   Just as welcome now as I felt last year   \n",
       "\n",
       "                                        SONewContent   Age Gender Trans  \\\n",
       "0  Tech articles written by other developers;Indu...  22.0    Man    No   \n",
       "1                                                NaN  23.0    Man    No   \n",
       "2  Tech articles written by other developers;Cour...  28.0    Man    No   \n",
       "3  Tech articles written by other developers;Indu...  26.0    Man    No   \n",
       "4  Tech articles written by other developers;Indu...  29.0    Man    No   \n",
       "\n",
       "                 Sexuality                              Ethnicity Dependents  \\\n",
       "0  Straight / Heterosexual           White or of European descent         No   \n",
       "1                 Bisexual           White or of European descent         No   \n",
       "2  Straight / Heterosexual           White or of European descent        Yes   \n",
       "3  Straight / Heterosexual           White or of European descent         No   \n",
       "4  Straight / Heterosexual  Hispanic or Latino/Latina;Multiracial         No   \n",
       "\n",
       "            SurveyLength                  SurveyEase  \n",
       "0  Appropriate in length                        Easy  \n",
       "1  Appropriate in length  Neither easy nor difficult  \n",
       "2  Appropriate in length                        Easy  \n",
       "3  Appropriate in length  Neither easy nor difficult  \n",
       "4  Appropriate in length                        Easy  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine how the data is distributed\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column `ConvertedComp` contains Salary converted to annual USD salaries using the exchange rate on 2019-02-01.\n",
    "\n",
    "This assumes 12 working months and 50 working weeks.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution curve for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(a=df[\"ConvertedComp\"],bins=20, hist=False, kde=True)\n",
    "\n",
    "# formatting the plot\n",
    "plt.title('Density Curve of ConvertedComp')\n",
    "plt.xlabel('Annual USD Salaries')\n",
    "plt.ylabel('Number of Respondents')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the histogram for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(a=df[\"ConvertedComp\"],bins=20, kde=False, hist=True)\n",
    "\n",
    "# formatting the plot\n",
    "plt.title('Histogram of ConvertedComp')\n",
    "plt.xlabel('Annual USD Salaries')\n",
    "plt.ylabel('Number of Respondents')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#or\n",
    "df['ConvertedComp'].plot(kind='hist', figsize=(10, 5))\n",
    "\n",
    "# add a title to the histogram\n",
    "plt.title('Histogram of Converted Compensation')\n",
    "# add y-label\n",
    "plt.ylabel('Number of Respondents')\n",
    "# add x-label\n",
    "plt.xlabel('Annual USD Salaries')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the median of the column `ConvertedComp`?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57745.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df[\"ConvertedComp\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Respondent</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <th>CodeRevHrs</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11398.000000</td>\n",
       "      <td>1.058900e+04</td>\n",
       "      <td>1.058200e+04</td>\n",
       "      <td>11276.000000</td>\n",
       "      <td>8972.000000</td>\n",
       "      <td>11111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12490.392437</td>\n",
       "      <td>7.570477e+05</td>\n",
       "      <td>1.315967e+05</td>\n",
       "      <td>42.064606</td>\n",
       "      <td>4.781071</td>\n",
       "      <td>30.778895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7235.461999</td>\n",
       "      <td>9.705598e+06</td>\n",
       "      <td>2.947865e+05</td>\n",
       "      <td>24.672741</td>\n",
       "      <td>4.567060</td>\n",
       "      <td>7.393686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6264.250000</td>\n",
       "      <td>2.500000e+04</td>\n",
       "      <td>2.686800e+04</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12484.000000</td>\n",
       "      <td>6.500000e+04</td>\n",
       "      <td>5.774500e+04</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18784.750000</td>\n",
       "      <td>1.200000e+05</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25142.000000</td>\n",
       "      <td>7.000000e+08</td>\n",
       "      <td>2.000000e+06</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Respondent     CompTotal  ConvertedComp   WorkWeekHrs   CodeRevHrs  \\\n",
       "count  11398.000000  1.058900e+04   1.058200e+04  11276.000000  8972.000000   \n",
       "mean   12490.392437  7.570477e+05   1.315967e+05     42.064606     4.781071   \n",
       "std     7235.461999  9.705598e+06   2.947865e+05     24.672741     4.567060   \n",
       "min        4.000000  0.000000e+00   0.000000e+00      3.000000     0.000000   \n",
       "25%     6264.250000  2.500000e+04   2.686800e+04     40.000000     2.000000   \n",
       "50%    12484.000000  6.500000e+04   5.774500e+04     40.000000     4.000000   \n",
       "75%    18784.750000  1.200000e+05   1.000000e+05     43.000000     5.000000   \n",
       "max    25142.000000  7.000000e+08   2.000000e+06   1012.000000    99.000000   \n",
       "\n",
       "                Age  \n",
       "count  11111.000000  \n",
       "mean      30.778895  \n",
       "std        7.393686  \n",
       "min       16.000000  \n",
       "25%       25.000000  \n",
       "50%       29.000000  \n",
       "75%       35.000000  \n",
       "max       99.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many responders identified themselves only as a **Man**?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Man                                                            10480\n",
       "Woman                                                            731\n",
       "Non-binary, genderqueer, or gender non-conforming                 63\n",
       "Man;Non-binary, genderqueer, or gender non-conforming             26\n",
       "Woman;Non-binary, genderqueer, or gender non-conforming           14\n",
       "Woman;Man                                                          9\n",
       "Woman;Man;Non-binary, genderqueer, or gender non-conforming        2\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df[\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the  median ConvertedComp of responders identified themselves only as a **Woman**?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57708.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "woman = df[df[\"Gender\"] == \"Woman\"]\n",
    "woman[\"ConvertedComp\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57708.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#or\n",
    "df.loc[(df['Gender'] == 'Woman'), 'ConvertedComp'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Give the five number summary for the column `Age`?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Double click here for hint**.\n",
    "\n",
    "<!--\n",
    "min,q1,median,q3,max of a column are its five number summary.\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11111.000000\n",
       "mean        30.778895\n",
       "std          7.393686\n",
       "min         16.000000\n",
       "25%         25.000000\n",
       "50%         29.000000\n",
       "75%         35.000000\n",
       "max         99.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df[\"Age\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a histogram of the column `Age`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['Age'].plot(kind='hist', figsize=(10, 5))\n",
    "\n",
    "# add a title to the histogram\n",
    "plt.title(\"Histogram of Respondent's Age\")\n",
    "# add y-label\n",
    "plt.ylabel('Number of Respondents')\n",
    "# add x-label\n",
    "plt.xlabel('Interval Age')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#or\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(a=df[\"ConvertedComp\"],bins=20, hist=True, kde=False)\n",
    "\n",
    "# add a title to the histogram\n",
    "plt.title(\"Histogram of Respondent's Age\")\n",
    "# add y-label\n",
    "plt.ylabel('Number of Respondents')\n",
    "# add x-label\n",
    "plt.xlabel('Interval Age')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding outliers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out if outliers exist in the column `ConvertedComp` using a box plot?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x=df.ConvertedComp, data=df)\n",
    "\n",
    "#format the plot\n",
    "plt.title('Distribution Data of Converted Compensation')\n",
    "plt.ylabel('Annual USD Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#or\n",
    "df['ConvertedComp'].plot(kind='box', figsize=(10, 5))\n",
    "\n",
    "plt.title('Distribution Data of Converted Compensation')\n",
    "plt.ylabel('Annual USD Salary')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the Inter Quartile Range for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.058200e+04\n",
       "mean     1.315967e+05\n",
       "std      2.947865e+05\n",
       "min      0.000000e+00\n",
       "25%      2.686800e+04\n",
       "50%      5.774500e+04\n",
       "75%      1.000000e+05\n",
       "max      2.000000e+06\n",
       "Name: ConvertedComp, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df[\"ConvertedComp\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73132.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#inter quartile range (IQR)\n",
    "Q1 = df['ConvertedComp'].quantile(0.25)\n",
    "Q3 = df['ConvertedComp'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "IQR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the upper and lower bounds.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upper Bounds : 209698.0\n",
      "Lower Bounds : -82830.0\n"
     ]
    }
   ],
   "source": [
    "# your code goes here\n",
    "upper_bounds = Q3 + 1.5 * IQR\n",
    "print('Upper Bounds :', upper_bounds)\n",
    "lower_bounds = Q1 - 1.5*IQR\n",
    "print('Lower Bounds :', lower_bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify how many outliers are there in the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " outlier in the ConvertedComp column is 879\n"
     ]
    }
   ],
   "source": [
    "# your code goes here\n",
    "outlier =[]\n",
    "for x in df['ConvertedComp']:\n",
    "  if ((x > upper_bounds) or (x < lower_bounds)):\n",
    "    outlier.append(x)\n",
    "print(' outlier in the ConvertedComp column is', len(outlier))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    10519\n",
      "True       879\n",
      "Name: ConvertedComp, dtype: int64\n",
      "False    11398\n",
      "Name: ConvertedComp, dtype: int64\n",
      "False    10519\n",
      "True       879\n",
      "Name: ConvertedComp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# or\n",
    "outliers = (df[\"ConvertedComp\"] < (Q1 - 1.5 * IQR)) | (df[\"ConvertedComp\"] > (Q3 + 1.5 * IQR))\n",
    "print(outliers.value_counts())\n",
    "less = (df[\"ConvertedComp\"] < (Q1 - 1.5 * IQR))\n",
    "print(less.value_counts())\n",
    "more = (df[\"ConvertedComp\"] > (Q3 + 1.5 * IQR))\n",
    "print(more.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new dataframe by removing the outliers from the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      9703.000000\n",
       "mean      59883.208389\n",
       "std       43394.336755\n",
       "min           0.000000\n",
       "25%       24060.000000\n",
       "50%       52704.000000\n",
       "75%       85574.500000\n",
       "max      209356.000000\n",
       "Name: ConvertedComp, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df_outliers_removed = df['ConvertedComp'][~((df['ConvertedComp'] < lower_bounds) | (df['ConvertedComp'] > upper_bounds))]\n",
    "df_outliers_removed.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding correlation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the correlation between `Age` and all other numerical columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Respondent       0.004041\n",
       "CompTotal        0.006970\n",
       "ConvertedComp    0.105386\n",
       "WorkWeekHrs      0.036518\n",
       "CodeRevHrs      -0.020469\n",
       "Age              1.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.corr()['Age']"
   ]
  },
  {
   "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>Respondent</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <th>CodeRevHrs</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Respondent</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.013490</td>\n",
       "      <td>0.002181</td>\n",
       "      <td>-0.015314</td>\n",
       "      <td>0.004621</td>\n",
       "      <td>0.004041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompTotal</th>\n",
       "      <td>-0.013490</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>0.003510</td>\n",
       "      <td>0.007063</td>\n",
       "      <td>0.006970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConvertedComp</th>\n",
       "      <td>0.002181</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021143</td>\n",
       "      <td>-0.033865</td>\n",
       "      <td>0.105386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <td>-0.015314</td>\n",
       "      <td>0.003510</td>\n",
       "      <td>0.021143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026517</td>\n",
       "      <td>0.036518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CodeRevHrs</th>\n",
       "      <td>0.004621</td>\n",
       "      <td>0.007063</td>\n",
       "      <td>-0.033865</td>\n",
       "      <td>0.026517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.004041</td>\n",
       "      <td>0.006970</td>\n",
       "      <td>0.105386</td>\n",
       "      <td>0.036518</td>\n",
       "      <td>-0.020469</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Respondent  CompTotal  ConvertedComp  WorkWeekHrs  CodeRevHrs  \\\n",
       "Respondent       1.000000  -0.013490       0.002181    -0.015314    0.004621   \n",
       "CompTotal       -0.013490   1.000000       0.001037     0.003510    0.007063   \n",
       "ConvertedComp    0.002181   0.001037       1.000000     0.021143   -0.033865   \n",
       "WorkWeekHrs     -0.015314   0.003510       0.021143     1.000000    0.026517   \n",
       "CodeRevHrs       0.004621   0.007063      -0.033865     0.026517    1.000000   \n",
       "Age              0.004041   0.006970       0.105386     0.036518   -0.020469   \n",
       "\n",
       "                    Age  \n",
       "Respondent     0.004041  \n",
       "CompTotal      0.006970  \n",
       "ConvertedComp  0.105386  \n",
       "WorkWeekHrs    0.036518  \n",
       "CodeRevHrs    -0.020469  \n",
       "Age            1.000000  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ramesh Sannareddy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Contributors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rav Ahuja\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By        | Change Description                 |\n",
    "| ----------------- | ------- | ----------------- | ---------------------------------- |\n",
    "| 2020-10-17        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01&cm_mmc=Email_Newsletter-\\_-Developer_Ed%2BTech-\\_-WW_WW-\\_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
   ]
  }
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