2_exploratory_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Exploratory Data Analysis Lab**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the pandas module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # primary data structure library\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np  # useful for many scientific computing in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset into a dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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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       "\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>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",
       "      <th>SONewContent</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Sexuality</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>SurveyLength</th>\n",
       "      <th>SurveyEase</th>\n",
       "      <th>NormalizedAnnualCompensation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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>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",
       "      <td>61000.0</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>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",
       "      <td>138000.0</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>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",
       "      <td>90000.0</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>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",
       "      <td>348000.0</td>\n",
       "    </tr>\n",
       "    <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>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",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 86 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",
       "                                        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",
       "  NormalizedAnnualCompensation  \n",
       "0                      61000.0  \n",
       "1                     138000.0  \n",
       "2                      90000.0  \n",
       "3                     348000.0  \n",
       "4                      90000.0  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m2_survey_data.csv\")\n",
    "df = pd.read_csv(\"survey_data_clean.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11398, 86)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Respondent</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <th>CodeRevHrs</th>\n",
       "      <th>Age</th>\n",
       "      <th>NormalizedAnnualCompensation</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",
       "      <td>1.058900e+04</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",
       "      <td>6.170771e+06</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",
       "      <td>9.842866e+07</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",
       "      <td>0.000000e+00</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",
       "      <td>5.200000e+04</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",
       "      <td>1.000000e+05</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",
       "      <td>3.600000e+05</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",
       "      <td>8.400000e+09</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  NormalizedAnnualCompensation  \n",
       "count  11111.000000                  1.058900e+04  \n",
       "mean      30.778895                  6.170771e+06  \n",
       "std        7.393686                  9.842866e+07  \n",
       "min       16.000000                  0.000000e+00  \n",
       "25%       25.000000                  5.200000e+04  \n",
       "50%       29.000000                  1.000000e+05  \n",
       "75%       35.000000                  3.600000e+05  \n",
       "max       99.000000                  8.400000e+09  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>ConvertedComp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.058200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.315967e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.947865e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.686800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.774500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ConvertedComp\n",
       "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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['ConvertedComp']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ConvertedComp'].plot(kind='area', alpha=0.45,stacked=False, figsize=(20, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the histogram for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# np.histogram returns 2 values\n",
    "#count, bin_edges = np.histogram(df['ConvertedComp'])\n",
    "\n",
    "df['ConvertedComp'].plot(kind='hist', figsize=(8, 5)) #,  xticks=bin_edges)\n",
    "\n",
    "#plt.title('Histogram of ConvertedComp') # add a title to the histogram\n",
    "#plt.ylabel('Number of Countries') # add y-label\n",
    "#plt.xlabel('Number of Immigrants') # add x-label\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the median of the column `ConvertedComp`?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57745.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ConvertedComp'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many responders identified themselves only as a **Man**?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "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: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 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>ConvertedComp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Man</th>\n",
       "      <td>57744.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Man;Non-binary, genderqueer, or gender non-conforming</th>\n",
       "      <td>59520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-binary, genderqueer, or gender non-conforming</th>\n",
       "      <td>67142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woman</th>\n",
       "      <td>57708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woman;Man</th>\n",
       "      <td>21648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woman;Man;Non-binary, genderqueer, or gender non-conforming</th>\n",
       "      <td>30244.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woman;Non-binary, genderqueer, or gender non-conforming</th>\n",
       "      <td>65535.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    ConvertedComp\n",
       "Gender                                                           \n",
       "Man                                                       57744.0\n",
       "Man;Non-binary, genderqueer, or gender non-conf...        59520.0\n",
       "Non-binary, genderqueer, or gender non-conforming         67142.0\n",
       "Woman                                                     57708.0\n",
       "Woman;Man                                                 21648.0\n",
       "Woman;Man;Non-binary, genderqueer, or gender no...        30244.0\n",
       "Woman;Non-binary, genderqueer, or gender non-co...        65535.5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['ConvertedComp','Gender']].groupby('Gender').median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Give the five number summary for the column `Age`?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.778895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.393686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Age']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a histogram of the column `Age`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C+fn5mTWLFy/WqFGjzLvRkpOTlZWV5fXrAQAAdZPNMAzD103UByUlJbLb7XK5XKwnknT9xI993YJHfpnWz9ctAAC86FJ/f/v8OUQAAAC+RiACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACW51Eg2r17d033AQAA4DMeBaI2bdqoR48eeuutt3Ty5Mma7gkAAMCrPApE3333nW6//XaNHTtWDodDQ4cO1bp162q6NwAAAK/wKBDFxsYqMzNTBw4c0Pz581VYWKhu3brplltuUWZmpg4fPlzTfQIAANSaK1pU7e/vr/79++vdd9/V9OnTtWvXLo0bN04tWrTQY489poKCgprqEwAAoNZcUSDasGGDhg0bpubNmyszM1Pjxo3Trl27tGrVKh04cEAPPPBATfUJAABQa/w9OSgzM1Pz58/Xjh07dN9992nRokW677771KDB6XzVunVrzZ07VzfffHONNgsAAFAbPApEc+bM0ZNPPqknnnhCDofjnDUtW7bUvHnzrqg5AAAAb/AoEO3cufOiNYGBgRo8eLAnpwcAAPAqj9YQzZ8/X//85z+rjf/zn//UwoULr7gpAAAAb/IoEE2bNk3NmjWrNh4REaGpU6decVMAAADe5FEg2rNnj1q3bl1tvFWrVtq7d+8VNwUAAOBNHgWiiIgIbd68udr4d999p/Dw8CtuCgAAwJs8CkQPP/ywRo0apc8//1yVlZWqrKzUqlWrNHr0aD388MM13SMAAECt8ugusxdeeEF79uxRz5495e9/+hRVVVV67LHHWEMEAADqHY9miAIDA/XOO+/ohx9+0OLFi7Vs2TLt2rVLb7zxhgIDAy/5PHPmzFGHDh0UFhamsLAwxcfH65NPPjH3G4ahyZMny+l0Kjg4WN27d9e2bdvczlFWVqaRI0eqWbNmatSokZKTk7V//363muLiYqWmpsput8tutys1NVVHjhzx5NIBAMBV6Iq+uuOmm27Sf/7nfyopKUmtWrW67ONbtGihadOmacOGDdqwYYP+8Ic/6IEHHjBDz4wZM5SZmamsrCytX79eDodDvXv31tGjR81zpKena/ny5Vq6dKlWr16t0tJSJSUlqbKy0qxJSUlRfn6+srOzlZ2drfz8fKWmpl7JpQMAgKuIzTAM43IPqqys1IIFC/TZZ5+pqKhIVVVVbvtXrVrlcUNNmzbVzJkz9eSTT8rpdCo9PV0TJkyQdHo2KDIyUtOnT9fQoUPlcrl07bXX6s0339TAgQMlSQcPHlRUVJRWrFihxMREbd++Xe3atVNeXp7i4uIkSXl5eYqPj9cPP/ygmJiYS+qrpKREdrtdLpdLYWFhHl/f1eL6iR/7ugWP/DKtn69bAAB40aX+/vZohmj06NEaPXq0KisrFRsbq1tvvdVt80RlZaWWLl2qY8eOKT4+Xrt371ZhYaH69Olj1gQFBSkhIUFr1qyRJG3cuFEVFRVuNU6nU7GxsWbNN998I7vdboYhSerSpYvsdrtZcy5lZWUqKSlx2wAAwNXJo0XVS5cu1bvvvqv77rvvihvYsmWL4uPjdfLkSV1zzTVavny52rVrZ4aVyMhIt/rIyEjt2bNHklRYWKjAwEA1adKkWk1hYaFZExERUe19IyIizJpzycjI0JQpU67o2gAAQP3g8aLqNm3a1EgDMTExys/PV15enp5++mkNHjxY33//vbnfZrO51RuGUW3sbGfXnKv+YueZNGmSXC6Xue3bt+9SLwkAANQzHgWisWPH6qWXXpIHy4+qOROu7rjjDmVkZOjWW2/VSy+9JIfDIUnVZnGKiorMWSOHw6Hy8nIVFxdfsObQoUPV3vfw4cPVZp9+LygoyLz77cwGAACuTh4FotWrV2vx4sW68cYbdf/992vAgAFu25UwDENlZWVq3bq1HA6HcnJyzH3l5eXKzc1V165dJUmdOnVSQECAW01BQYG2bt1q1sTHx8vlcmndunVmzdq1a+VyucwaAABgbR6tIWrcuLH69+9/xW/+X//1X7r33nsVFRWlo0ePaunSpfriiy+UnZ0tm82m9PR0TZ06VdHR0YqOjtbUqVMVEhKilJQUSZLdbldaWprGjh2r8PBwNW3aVOPGjVP79u3Vq1cvSVLbtm3Vt29fDRkyRHPnzpUkPfXUU0pKSrrkO8wAAMDVzaNANH/+/Bp580OHDik1NVUFBQWy2+3q0KGDsrOz1bt3b0nS+PHjdeLECQ0bNkzFxcWKi4vTypUrFRoaap5j9uzZ8vf310MPPaQTJ06oZ8+eWrBggfz8/MyaxYsXa9SoUebdaMnJycrKyqqRawAAAPWfR88hkqRTp07piy++0K5du5SSkqLQ0FAdPHhQYWFhuuaaa2q6T5/jOUTueA4RAKA+uNTf3x7NEO3Zs0d9+/bV3r17VVZWpt69eys0NFQzZszQyZMn9eqrr3rcOAAAgLd5/GDGO+64Q8XFxQoODjbH+/fvr88++6zGmgMAAPAGj2aIVq9era+//rraF7m2atVKBw4cqJHGAAAAvMWjGaKqqiq3L089Y//+/W4LngEAAOoDjwJR79699eKLL5qvbTabSktL9de//rVGvs4DAADAmzz6yGz27Nnq0aOH2rVrp5MnTyolJUU7d+5Us2bN9Pbbb9d0jwAAALXKo0DkdDqVn5+vt99+W5s2bVJVVZXS0tL06KOPui2yBgAAqA88CkSSFBwcrCeffFJPPvlkTfYDAADgdR4FokWLFl1w/2OPPeZRMwAAAL7gUSAaPXq02+uKigodP35cgYGBCgkJIRABAIB6xaO7zIqLi9220tJS7dixQ926dWNRNQAAqHc8CkTnEh0drWnTplWbPQIAAKjraiwQSZKfn58OHjxYk6cEAACodR6tIfrwww/dXhuGoYKCAmVlZemuu+6qkcYAAAC8xaNA9OCDD7q9ttlsuvbaa/WHP/xBs2bNqom+AAAAvMajQFRVVVXTfQAAAPhMja4hAgAAqI88miEaM2bMJddmZmZ68hYAAABe41Eg+vbbb7Vp0yadOnVKMTExkqQff/xRfn5+6tixo1lns9lqpksAAIBa5FEguv/++xUaGqqFCxeqSZMmkk4/rPGJJ57Q3XffrbFjx9ZokwAAALXJozVEs2bNUkZGhhmGJKlJkyZ64YUXuMsMAADUOx4FopKSEh06dKjaeFFRkY4ePXrFTQEAAHiTR4Gof//+euKJJ/Tee+9p//792r9/v9577z2lpaVpwIABNd0jAABArfJoDdGrr76qcePGadCgQaqoqDh9In9/paWlaebMmTXaIFCTrp/4sa9buGy/TOvn6xYA4KrnUSAKCQnRK6+8opkzZ2rXrl0yDENt2rRRo0aNaro/AACAWndFD2YsKChQQUGBbrrpJjVq1EiGYdRUXwAAAF7jUSD697//rZ49e+qmm27Sfffdp4KCAknSn/70J265BwAA9Y5HgeiZZ55RQECA9u7dq5CQEHN84MCBys7OrrHmAAAAvMGjNUQrV67U//3f/6lFixZu49HR0dqzZ0+NNAYAAOAtHs0QHTt2zG1m6Ixff/1VQUFBV9wUAACAN3kUiO655x4tWrTIfG2z2VRVVaWZM2eqR48eNdYcAACAN3j0kdnMmTPVvXt3bdiwQeXl5Ro/fry2bdum3377TV9//XVN9wgAAFCrPJohateunTZv3qw777xTvXv31rFjxzRgwAB9++23uvHGG2u6RwAAgFp12TNEFRUV6tOnj+bOnaspU6bURk8AAABeddkzRAEBAdq6datsNltt9AMAAOB1Hn1k9thjj2nevHk13QsAAIBPeLSoury8XP/4xz+Uk5OjO+64o9p3mGVmZtZIcwAAAN5wWYHo559/1vXXX6+tW7eqY8eOkqQff/zRrYaP0gAAQH1zWYEoOjpaBQUF+vzzzyWd/qqOl19+WZGRkbXSHAAAgDdc1hqis7/N/pNPPtGxY8dqtCEAAABv82hR9RlnByQAAID66LICkc1mq7ZGiDVDAACgvrusNUSGYejxxx83v8D15MmT+vOf/1ztLrNly5bVXIcAAAC17LIC0eDBg91eDxo0qEabAQAA8IXLCkTz58+vrT4AAAB85ooWVQMAAFwNCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyLuvBjKgd10/82NctAABgacwQAQAAy/NpIMrIyFDnzp0VGhqqiIgIPfjgg9qxY4dbjWEYmjx5spxOp4KDg9W9e3dt27bNraasrEwjR45Us2bN1KhRIyUnJ2v//v1uNcXFxUpNTZXdbpfdbldqaqqOHDlS25cIAADqAZ8GotzcXA0fPlx5eXnKycnRqVOn1KdPHx07dsysmTFjhjIzM5WVlaX169fL4XCod+/eOnr0qFmTnp6u5cuXa+nSpVq9erVKS0uVlJSkyspKsyYlJUX5+fnKzs5Wdna28vPzlZqa6tXrBQAAdZPNMAzD102ccfjwYUVERCg3N1f33HOPDMOQ0+lUenq6JkyYIOn0bFBkZKSmT5+uoUOHyuVy6dprr9Wbb76pgQMHSpIOHjyoqKgorVixQomJidq+fbvatWunvLw8xcXFSZLy8vIUHx+vH374QTExMRftraSkRHa7XS6XS2FhYTV63awhwoX8Mq2fr1sAgHrrUn9/16k1RC6XS5LUtGlTSdLu3btVWFioPn36mDVBQUFKSEjQmjVrJEkbN25URUWFW43T6VRsbKxZ880338hut5thSJK6dOkiu91u1pytrKxMJSUlbhsAALg61ZlAZBiGxowZo27duik2NlaSVFhYKEmKjIx0q42MjDT3FRYWKjAwUE2aNLlgTURERLX3jIiIMGvOlpGRYa43stvtioqKurILBAAAdVadCUQjRozQ5s2b9fbbb1fbZ7PZ3F4bhlFt7Gxn15yr/kLnmTRpklwul7nt27fvUi4DAADUQ3UiEI0cOVIffvihPv/8c7Vo0cIcdzgcklRtFqeoqMicNXI4HCovL1dxcfEFaw4dOlTtfQ8fPlxt9umMoKAghYWFuW0AAODq5NNAZBiGRowYoWXLlmnVqlVq3bq12/7WrVvL4XAoJyfHHCsvL1dubq66du0qSerUqZMCAgLcagoKCrR161azJj4+Xi6XS+vWrTNr1q5dK5fLZdYAAADr8umTqocPH64lS5bogw8+UGhoqDkTZLfbFRwcLJvNpvT0dE2dOlXR0dGKjo7W1KlTFRISopSUFLM2LS1NY8eOVXh4uJo2bapx48apffv26tWrlySpbdu26tu3r4YMGaK5c+dKkp566iklJSVd0h1mAADg6ubTQDRnzhxJUvfu3d3G58+fr8cff1ySNH78eJ04cULDhg1TcXGx4uLitHLlSoWGhpr1s2fPlr+/vx566CGdOHFCPXv21IIFC+Tn52fWLF68WKNGjTLvRktOTlZWVlbtXiAAAKgX6tRziOoynkMEX+E5RADguXr5HCIAAABfIBABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADL82kg+vLLL3X//ffL6XTKZrPp/fffd9tvGIYmT54sp9Op4OBgde/eXdu2bXOrKSsr08iRI9WsWTM1atRIycnJ2r9/v1tNcXGxUlNTZbfbZbfblZqaqiNHjtTy1QEAgPrCp4Ho2LFjuvXWW5WVlXXO/TNmzFBmZqaysrK0fv16ORwO9e7dW0ePHjVr0tPTtXz5ci1dulSrV69WaWmpkpKSVFlZadakpKQoPz9f2dnZys7OVn5+vlJTU2v9+gAAQP1gMwzD8HUTkmSz2bR8+XI9+OCDkk7PDjmdTqWnp2vChAmSTs8GRUZGavr06Ro6dKhcLpeuvfZavfnmmxo4cKAk6eDBg4qKitKKFSuUmJio7du3q127dsrLy1NcXJwkKS8vT/Hx8frhhx8UExNzSf2VlJTIbrfL5XIpLCysRq/9+okf1+j5cHX5ZVo/X7cAAPXWpf7+rrNriHbv3q3CwkL16dPHHAsKClJCQoLWrFkjSdq4caMqKircapxOp2JjY82ab775Rna73QxDktSlSxfZ7Xaz5lzKyspUUlLitgEAgKtTnQ1EhYWFkqTIyEi38cjISHNfYWGhAgMD1aRJkwvWREREVDt/RESEWXMuGRkZ5poju92uqKioK7oeAABQd9XZQHSGzWZze20YRrWxs51dc676i51n0qRJcrlc5rZv377L7BwAANQXdTYQORwOSao2i1NUVGTOGjkcDpWXl6u4uPiCNYcOHap2/sOHD1ebffq9oKAghYWFuW0AAODqVGcDUevWreVwOJSTk2OOlZeXKzc3V127dpUkderUSQEBAW41BQUF2rp1q1kTHx8vl8uldevWmTVr166Vy+UyawAAgLX5+/LNS0tL9dNPP5mvd+/erfz8fDVt2lQtW7ZUenq6pk6dqujoaEVHR2vq1KkKCQlRSkqKJMlutystLU1jx45VeHi4mjZtqnHjxql9+/bq1auXJKlt27bq27evhgwZorlz50qSnnrqKSUlJV3yHWYAAODq5tNAtGHDBvXo0cN8PWbMGEnS4MGDtWDBAo0fP14nTpzQsGHDVFxcrLi4OK1cuVKhoaHmMbNnz5a/v78eeughnThxQj179tSCBQvk5+dn1ixevFijRo0y70ZLTk4+77OPAACA9dSZ5xDVdTyHCL7Cc4gAwHP1/jlEAAAA3kIgAgAAlkcgAgAAlkcgAgAAlufTu8wAXFx9XHTPQnAA9Q0zRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPL8fd0AgKvP9RM/9nULl+2Xaf183QIAH2KGCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWJ6lAtErr7yi1q1bq2HDhurUqZO++uorX7cEAADqAMt8l9k777yj9PR0vfLKK7rrrrs0d+5c3Xvvvfr+++/VsmVLX7cHwMf4/jXA2iwzQ5SZmam0tDT96U9/Utu2bfXiiy8qKipKc+bM8XVrAADAxywxQ1ReXq6NGzdq4sSJbuN9+vTRmjVrznlMWVmZysrKzNcul0uSVFJSUuP9VZUdr/FzArj61cZ/j4CrzZl/TwzDuGCdJQLRr7/+qsrKSkVGRrqNR0ZGqrCw8JzHZGRkaMqUKdXGo6KiaqVHALhc9hd93QFQfxw9elR2u/28+y0RiM6w2Wxurw3DqDZ2xqRJkzRmzBjzdVVVlX777TeFh4ef9xhPlJSUKCoqSvv27VNYWFiNnbe21Kd+Dxw4oHbt2un777/Xdddd5+t2Lqo+9Vuf/h4AsDbDMHT06FE5nc4L1lkiEDVr1kx+fn7VZoOKioqqzRqdERQUpKCgILexxo0b11aLCgsLq1e/WOpDv2emSUNDQ+t8r1L961eqH38PAOBCM0NnWGJRdWBgoDp16qScnBy38ZycHHXt2tVHXQEAgLrCEjNEkjRmzBilpqbqjjvuUHx8vF577TXt3btXf/7zn33dGgAA8DHLBKKBAwfq3//+t55//nkVFBQoNjZWK1asUKtWrXzaV1BQkP76179W+3iurqpP/YaFhSkhIaHefKRTn/qtT38PAOBS2IyL3YcGAABwlbPEGiIAAIALIRABAADLIxABAADLIxABAADLIxB5yZdffqn7779fTqdTNptN77//frWa7du3Kzk5WXa7XaGhoerSpYv27t3r9V4zMjLUuXNnhYaGKiIiQg8++KB27Nhx3vqhQ4fKZrPpxRdf9F6T/79HHnlEwcHBstlsstlsCg0N1fPPPy9JOn78uOLi4tSwYUPZbDb5+fnpxhtv1KZNm7ze5/kkJibKZrPp9ttvN8eqqqrUvXt3+fn5yWazqXHjxvrggw982OVpGRkZstlsSk9PN8dKS0s1YsQItWjRQsHBwWrbti1fmAygXiIQecmxY8d06623Kisr65z7d+3apW7duunmm2/WF198oe+++05/+ctf1LBhQy93KuXm5mr48OHKy8tTTk6OTp06pT59+ujYsWPVat9//32tXbv2oo9Ery1t2rTRxIkTlZ2drezsbHXo0EF//etf9cEHH+jXX3/VTz/9pKefflorVqzQ3LlzdejQId19990+6fVsCxcu1KpVq6r9Gffr10+5ubkaO3asli1bprCwMA0YMEAHDx70UafS+vXr9dprr6lDhw5u488884yys7P11ltvafv27XrmmWc0cuTIOhHgAOCyGPA6Scby5cvdxgYOHGgMGjTINw1dRFFRkSHJyM3NdRvfv3+/cd111xlbt241WrVqZcyePds3DZ7FZrMZgwcPPue+BQsWGJKMNWvWeLepsxQUFBgBAQHG9OnTDbvdbtx2222GYRhGZWWl0aBBA6Nv375mrcvlMiQZKSkpPun16NGjRnR0tJGTk2MkJCQYo0ePNvfdcsstxvPPP+9W37FjR+O///u/vdwlAFwZZojqgKqqKn388ce66aablJiYqIiICMXFxZ3zYzVfcLlckqSmTZuaY1VVVUpNTdWzzz6rW265xVetuSkvL9eoUaNkGIYGDBhwzppDhw5JkqKiorzZWjV33XWXYmNjNX78eLfxL7/8UlVVVXrsscfMsbCwMDkcDuXl5Xm7TUnS8OHD1a9fP/Xq1avavm7duunDDz/UgQMHZBiGPv/8c/34449KTEz0QacA4DkCUR1QVFSk0tJSTZs2TX379tXKlSvVv39/DRgwQLm5uT7tzTAMjRkzRt26dVNsbKw5Pn36dPn7+2vUqFE+7O60//3f/5XNZlNQUJD+/ve/a/LkyUpOTq5Wd+TIEU2ZMkXXX3+9WrRo4YNOTxs1apQOHDigVatWVdt3Zq1WTEyM23jjxo3NYOpNS5cu1aZNm5SRkXHO/S+//LLatWunFi1aKDAwUH379tUrr7yibt26eblTALgylvnqjrqsqqpKkvTAAw/omWeekSTddtttWrNmjV599VUlJCT4rLcRI0Zo8+bNWr16tTm2ceNGvfTSS9q0aZNsNpvPejsjMTFRn376qQ4cOKA5c+ZoypQpuv32291C0fHjx9W2bVsZhqGvvvrKZ72uXbtWWVlZWrp0qRo3bnzeugYN3P9fxfDBA+X37dun0aNHa+XKleddy/byyy8rLy9PH374oVq1aqUvv/xSw4YNU/Pmzc85owQAdZZPP7CzKJ21hqisrMzw9/c3/ud//setbvz48UbXrl293N3/M2LECKNFixbGzz//7DY+e/Zsw2azGX5+fuYmyWjQoIHRqlUr3zT7O02aNDFuvvlm8/WxY8eM5s2bGw0bNjR+/PFHH3ZmGBMnTjQknXf77LPPDEnGkiVL3I5zOBzGDTfc4NVely9fbkiq9ud85s++tLTUCAgIMD766CO349LS0ozExESv9goAV4oZojogMDBQnTt3rnZr+48//uiTL581DEMjR47U8uXL9cUXX6h169Zu+1NTU6v9339iYqJSU1P1xBNPeLPV86qoqJB0emaoTZs2+u233/Ttt98qOjrap32NHDlSd955p9vYoEGDFBkZqVmzZql79+5q0KCB3nzzTT3yyCOSTt/aXlhYqJSUFK/22rNnT23ZssVt7IknntDNN9+sCRMmqLKyUhUVFdVms/z8/MxZTwCoLwhEXlJaWqqffvrJfL17927l5+eradOmatmypZ599lkNHDhQ99xzj3r06KHs7Gz961//0hdffOH1XocPH64lS5bogw8+UGhoqAoLCyVJdrtdwcHBCg8PV3h4uNsxAQEBcjgc1da+1LauXbsqJSVFt912mw4dOqTMzEwVFxdr/PjxOnnypG688UYdPnxYS5YsUUVFhTZv3ixJuuGGG3TNNdd4tVdJcjqd6t+/v9tYQECA7Ha7Od6nTx998sknmjBhgrp06aJnnnlGDRo00MyZM73aa2hoqNu6MUlq1KiRwsPDzfGEhAQ9++yzCg4OVqtWrZSbm6tFixYpMzPTq70CwBXz9RSVVXz++efn/Ijk97eHz5s3z2jTpo3RsGFD49ZbbzXef/99n/R6rj4lGfPnzz/vMb667T46Otrto5wmTZoYGRkZhmEYxldffXXea6krjwgwDMPttnvDOH3rfUJCgtGgQQNDkmG3241ly5b5sMP/5+zb7gsKCozHH3/ccDqdRsOGDY2YmBhj1qxZRlVVle+aBAAP2AzDB6s1AQAA6hBuuwcAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJb3/wGJtjpizJ5vFwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_edge = [16,24,32,40,48,32,40]\n",
    "df[['Age']].plot(kind = 'hist',xticks = bin_edge)\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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ConvertedComp'].plot(kind = 'box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the Inter Quartile Range for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ConvertedComp'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inter Quartile Range for the column `ConvertedComp` 73132.0\n"
     ]
    }
   ],
   "source": [
    "countV,meanV,stdV ,minV ,q1,q2,q3, maxV = df['ConvertedComp'].describe()\n",
    "iqr = q3-q1\n",
    "print('Inter Quartile Range for the column `ConvertedComp`', iqr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median of ConvertedComp  57745.0\n"
     ]
    }
   ],
   "source": [
    "print('Median of ConvertedComp ',q2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the upper and lower bounds.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upper Bounds 209698.0\n",
      "Lower Bounds -82830.0\n"
     ]
    }
   ],
   "source": [
    "LowerB = q1 - iqr *1.5\n",
    "UpperB = q3 + iqr *1.5\n",
    "print('Upper Bounds', UpperB)\n",
    "print('Lower Bounds', LowerB)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify how many outliers are there in the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "879"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['ConvertedComp']>UpperB].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new dataframe by removing the outliers from the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df[df['ConvertedComp']<UpperB]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median of ConvertedComp removing the outliers  52704.0\n"
     ]
    }
   ],
   "source": [
    "countV,meanV,stdV ,minV ,q1,q2,q3, maxV = new_df['ConvertedComp'].describe()\n",
    "print('Median of ConvertedComp removing the outliers ',q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of ConvertedComp removing the outliers  59883.20838915799\n"
     ]
    }
   ],
   "source": [
    "print('Mean of ConvertedComp removing the outliers ',meanV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age Outliner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countV,meanV,stdV ,minV ,q1,q2,q3, maxV = df['Age'].describe()\n",
    "iqr = q3-q1\n",
    "LowerB = q1 - iqr *1.5\n",
    "UpperB = q3 + iqr *1.5\n",
    "\n",
    "df[df['Age']<LowerB].shape[0]"
   ]
  },
  {
   "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": 23,
   "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",
       "      <th>NormalizedAnnualCompensation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Respondent</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019354</td>\n",
       "      <td>0.010878</td>\n",
       "      <td>-0.016221</td>\n",
       "      <td>0.005293</td>\n",
       "      <td>0.002180</td>\n",
       "      <td>0.001313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompTotal</th>\n",
       "      <td>-0.019354</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.063561</td>\n",
       "      <td>0.004910</td>\n",
       "      <td>0.017007</td>\n",
       "      <td>0.006337</td>\n",
       "      <td>0.703651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConvertedComp</th>\n",
       "      <td>0.010878</td>\n",
       "      <td>-0.063561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034351</td>\n",
       "      <td>-0.088934</td>\n",
       "      <td>0.401821</td>\n",
       "      <td>-0.086286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <td>-0.016221</td>\n",
       "      <td>0.004910</td>\n",
       "      <td>0.034351</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038935</td>\n",
       "      <td>0.032032</td>\n",
       "      <td>0.009938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CodeRevHrs</th>\n",
       "      <td>0.005293</td>\n",
       "      <td>0.017007</td>\n",
       "      <td>-0.088934</td>\n",
       "      <td>0.038935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012878</td>\n",
       "      <td>0.039096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.002180</td>\n",
       "      <td>0.006337</td>\n",
       "      <td>0.401821</td>\n",
       "      <td>0.032032</td>\n",
       "      <td>-0.012878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NormalizedAnnualCompensation</th>\n",
       "      <td>0.001313</td>\n",
       "      <td>0.703651</td>\n",
       "      <td>-0.086286</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>0.039096</td>\n",
       "      <td>-0.016299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Respondent  CompTotal  ConvertedComp  \\\n",
       "Respondent                      1.000000  -0.019354       0.010878   \n",
       "CompTotal                      -0.019354   1.000000      -0.063561   \n",
       "ConvertedComp                   0.010878  -0.063561       1.000000   \n",
       "WorkWeekHrs                    -0.016221   0.004910       0.034351   \n",
       "CodeRevHrs                      0.005293   0.017007      -0.088934   \n",
       "Age                             0.002180   0.006337       0.401821   \n",
       "NormalizedAnnualCompensation    0.001313   0.703651      -0.086286   \n",
       "\n",
       "                              WorkWeekHrs  CodeRevHrs       Age  \\\n",
       "Respondent                      -0.016221    0.005293  0.002180   \n",
       "CompTotal                        0.004910    0.017007  0.006337   \n",
       "ConvertedComp                    0.034351   -0.088934  0.401821   \n",
       "WorkWeekHrs                      1.000000    0.038935  0.032032   \n",
       "CodeRevHrs                       0.038935    1.000000 -0.012878   \n",
       "Age                              0.032032   -0.012878  1.000000   \n",
       "NormalizedAnnualCompensation     0.009938    0.039096 -0.016299   \n",
       "\n",
       "                              NormalizedAnnualCompensation  \n",
       "Respondent                                        0.001313  \n",
       "CompTotal                                         0.703651  \n",
       "ConvertedComp                                    -0.086286  \n",
       "WorkWeekHrs                                       0.009938  \n",
       "CodeRevHrs                                        0.039096  \n",
       "Age                                              -0.016299  \n",
       "NormalizedAnnualCompensation                      1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.corr(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rav Ahuja\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?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&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&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&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&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&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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