UK-Gender-Pay-Gap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gender Pay Gap in the UK\n",
    "\n",
    "### Business Understanding\n",
    "\n",
    "In the UK, employers with 250 or more employees are required to calculate a series of metrics about their gender pay gap and publish them in a yearly report. This [data](https://gender-pay-gap.service.gov.uk/) is publicly available.\n",
    "\n",
    "The reports of 2018/19 will be analyzed since it is the last fully complete dataset available. The steps of CRISP-DM process are followed and indicated with subtitles across this notebook.\n",
    "\n",
    "**Investigation \n",
    "Questions:**\n",
    "1. How balanced are payment amounts by gender?\n",
    "2. What economic activities have largest gender pay gap?\n",
    "3. Which employer characteristics explain the gender pay gap?\n",
    "\n",
    "Apart from exploring answers to the previous questions, further exploratory analysis and alternatives are presented."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Understanding\n",
    "\n",
    "Companies must report the following gender pay gap figures, the calculation details can be found [here](https://www.gov.uk/guidance/gender-pay-gap-reporting-make-your-calculations).\n",
    "\n",
    "\n",
    "| Figure                                                    | Column(s)                                                                                  |\n",
    "|:-----------------------------------------------------------|:------------------------------------------------------------------------------------------|\n",
    "| Mean gender pay gap in hourly pay                         | `DiffMeanHourlyPercent`                                                                    |\n",
    "| Median gender pay gap in hourly pay                       | `DiffMedianHourlyPercent`                                                                  |\n",
    "| Mean bonus gender pay gap                                 | `DiffMeanBonusPercent`                                                                     |\n",
    "| Median bonus gender pay gap                               | `DiffMedianBonusPercent`                                                                   |\n",
    "| Proportion of males and females receiving a bonus payment |  `(M/F)BonusPercent`                                                                |\n",
    "| Proportion of males and females in each pay quartile      | `(M/F)LowQuartile, (M/F)LowerMiddleQuartile, (M/F)UpperMiddleQuartile, (M/FUpperQuartile)` |\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\n",
    "plt.rcParams[\"font.family\"] = \"serif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10828 x 25\n"
     ]
    },
    {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EmployerName</th>\n",
       "      <th>Address</th>\n",
       "      <th>CompanyNumber</th>\n",
       "      <th>SicCodes</th>\n",
       "      <th>DiffMeanHourlyPercent</th>\n",
       "      <th>DiffMedianHourlyPercent</th>\n",
       "      <th>DiffMeanBonusPercent</th>\n",
       "      <th>DiffMedianBonusPercent</th>\n",
       "      <th>MaleBonusPercent</th>\n",
       "      <th>FemaleBonusPercent</th>\n",
       "      <th>...</th>\n",
       "      <th>FemaleUpperMiddleQuartile</th>\n",
       "      <th>MaleTopQuartile</th>\n",
       "      <th>FemaleTopQuartile</th>\n",
       "      <th>CompanyLinkToGPGInfo</th>\n",
       "      <th>ResponsiblePerson</th>\n",
       "      <th>EmployerSize</th>\n",
       "      <th>CurrentName</th>\n",
       "      <th>SubmittedAfterTheDeadline</th>\n",
       "      <th>DueDate</th>\n",
       "      <th>DateSubmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"Bryanston School\",Incorporated</td>\n",
       "      <td>Bryanston House, Blandford, Dorset, DT11 0PX</td>\n",
       "      <td>00226143</td>\n",
       "      <td>85310</td>\n",
       "      <td>19.6</td>\n",
       "      <td>32.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>55.1</td>\n",
       "      <td>53.3</td>\n",
       "      <td>46.7</td>\n",
       "      <td>https://www.bryanston.co.uk/employment</td>\n",
       "      <td>Michael McGovern (Chief Operating Officer)</td>\n",
       "      <td>500 to 999</td>\n",
       "      <td>BRYANSTON SCHOOL INCORPORATED</td>\n",
       "      <td>False</td>\n",
       "      <td>05/04/2019 00:00:00</td>\n",
       "      <td>03/04/2019 13:43:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"RED BAND\" CHEMICAL COMPANY, LIMITED</td>\n",
       "      <td>19 Smith's Place, Leith Walk, Edinburgh, EH6 8NU</td>\n",
       "      <td>SC016876</td>\n",
       "      <td>47730</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>23.7</td>\n",
       "      <td>-75.2</td>\n",
       "      <td>22.2</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.4</td>\n",
       "      <td>20.3</td>\n",
       "      <td>79.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Philip Galt (Managing Director)</td>\n",
       "      <td>250 to 499</td>\n",
       "      <td>\"RED BAND\" CHEMICAL COMPANY, LIMITED</td>\n",
       "      <td>False</td>\n",
       "      <td>05/04/2019 00:00:00</td>\n",
       "      <td>01/04/2019 12:37:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>118 LIMITED</td>\n",
       "      <td>3 Alexandra Gate Ffordd Pengam, Ground Floor, ...</td>\n",
       "      <td>03951948</td>\n",
       "      <td>61900</td>\n",
       "      <td>7.7</td>\n",
       "      <td>12.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>10.9</td>\n",
       "      <td>67.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michael Graham (CEO)</td>\n",
       "      <td>1000 to 4999</td>\n",
       "      <td>118 LIMITED</td>\n",
       "      <td>False</td>\n",
       "      <td>05/04/2019 00:00:00</td>\n",
       "      <td>06/03/2019 13:26:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123 EMPLOYEES LTD</td>\n",
       "      <td>34 Roundhay Road, Leeds, England, LS7 1AB</td>\n",
       "      <td>10530651</td>\n",
       "      <td>78300</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chloe Lines (Financial Controller)</td>\n",
       "      <td>250 to 499</td>\n",
       "      <td>123 EMPLOYEES LTD</td>\n",
       "      <td>False</td>\n",
       "      <td>05/04/2019 00:00:00</td>\n",
       "      <td>03/04/2019 15:43:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1509 GROUP</td>\n",
       "      <td>Royal Grammar School, High Street, Guildford, ...</td>\n",
       "      <td>04104101</td>\n",
       "      <td>85200,\\r\\n85310</td>\n",
       "      <td>13.4</td>\n",
       "      <td>12.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>51.6</td>\n",
       "      <td>61.3</td>\n",
       "      <td>38.7</td>\n",
       "      <td>https://www.rgs-guildford.co.uk</td>\n",
       "      <td>Kate Perceval (Bursar)</td>\n",
       "      <td>250 to 499</td>\n",
       "      <td>1509 GROUP</td>\n",
       "      <td>False</td>\n",
       "      <td>05/04/2019 00:00:00</td>\n",
       "      <td>18/03/2019 14:03:16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           EmployerName  \\\n",
       "0       \"Bryanston School\",Incorporated   \n",
       "1  \"RED BAND\" CHEMICAL COMPANY, LIMITED   \n",
       "2                           118 LIMITED   \n",
       "3                     123 EMPLOYEES LTD   \n",
       "4                            1509 GROUP   \n",
       "\n",
       "                                             Address CompanyNumber  \\\n",
       "0       Bryanston House, Blandford, Dorset, DT11 0PX      00226143   \n",
       "1   19 Smith's Place, Leith Walk, Edinburgh, EH6 8NU      SC016876   \n",
       "2  3 Alexandra Gate Ffordd Pengam, Ground Floor, ...      03951948   \n",
       "3          34 Roundhay Road, Leeds, England, LS7 1AB      10530651   \n",
       "4  Royal Grammar School, High Street, Guildford, ...      04104101   \n",
       "\n",
       "          SicCodes  DiffMeanHourlyPercent  DiffMedianHourlyPercent  \\\n",
       "0            85310                   19.6                     32.5   \n",
       "1            47730                    7.7                     -4.2   \n",
       "2            61900                    7.7                     12.7   \n",
       "3            78300                   30.0                     29.0   \n",
       "4  85200,\\r\\n85310                   13.4                     12.7   \n",
       "\n",
       "   DiffMeanBonusPercent  DiffMedianBonusPercent  MaleBonusPercent  \\\n",
       "0                   NaN                     NaN               0.0   \n",
       "1                  23.7                   -75.2              22.2   \n",
       "2                  11.3                    10.9              67.0   \n",
       "3                 -10.0                    69.0              46.0   \n",
       "4                   0.0                     0.0               0.0   \n",
       "\n",
       "   FemaleBonusPercent  ...  FemaleUpperMiddleQuartile  MaleTopQuartile  \\\n",
       "0                 0.0  ...                       55.1             53.3   \n",
       "1                82.0  ...                       88.4             20.3   \n",
       "2                52.0  ...                       50.0             56.0   \n",
       "3                74.0  ...                       83.0             25.0   \n",
       "4                 0.0  ...                       51.6             61.3   \n",
       "\n",
       "   FemaleTopQuartile                    CompanyLinkToGPGInfo  \\\n",
       "0               46.7  https://www.bryanston.co.uk/employment   \n",
       "1               79.7                                     NaN   \n",
       "2               44.0                                     NaN   \n",
       "3               75.0                                     NaN   \n",
       "4               38.7         https://www.rgs-guildford.co.uk   \n",
       "\n",
       "                            ResponsiblePerson  EmployerSize  \\\n",
       "0  Michael McGovern (Chief Operating Officer)    500 to 999   \n",
       "1             Philip Galt (Managing Director)    250 to 499   \n",
       "2                        Michael Graham (CEO)  1000 to 4999   \n",
       "3          Chloe Lines (Financial Controller)    250 to 499   \n",
       "4                      Kate Perceval (Bursar)    250 to 499   \n",
       "\n",
       "                            CurrentName  SubmittedAfterTheDeadline  \\\n",
       "0         BRYANSTON SCHOOL INCORPORATED                      False   \n",
       "1  \"RED BAND\" CHEMICAL COMPANY, LIMITED                      False   \n",
       "2                           118 LIMITED                      False   \n",
       "3                     123 EMPLOYEES LTD                      False   \n",
       "4                            1509 GROUP                      False   \n",
       "\n",
       "               DueDate        DateSubmitted  \n",
       "0  05/04/2019 00:00:00  03/04/2019 13:43:34  \n",
       "1  05/04/2019 00:00:00  01/04/2019 12:37:21  \n",
       "2  05/04/2019 00:00:00  06/03/2019 13:26:24  \n",
       "3  05/04/2019 00:00:00  03/04/2019 15:43:17  \n",
       "4  05/04/2019 00:00:00  18/03/2019 14:03:16  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw = pd.read_csv(\"data/UK-Gender-Pay-Gap-Data-2018-2019.csv\")\n",
    "rows, cols = raw.shape\n",
    "print(rows, \"x\", cols)\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EmployerName', 'Address', 'CompanyNumber', 'SicCodes', 'DiffMeanHourlyPercent', 'DiffMedianHourlyPercent', 'DiffMeanBonusPercent', 'DiffMedianBonusPercent', 'MaleBonusPercent', 'FemaleBonusPercent', 'MaleLowerQuartile', 'FemaleLowerQuartile', 'MaleLowerMiddleQuartile', 'FemaleLowerMiddleQuartile', 'MaleUpperMiddleQuartile', 'FemaleUpperMiddleQuartile', 'MaleTopQuartile', 'FemaleTopQuartile', 'CompanyLinkToGPGInfo', 'ResponsiblePerson', 'EmployerSize', 'CurrentName', 'SubmittedAfterTheDeadline', 'DueDate', 'DateSubmitted']\n"
     ]
    }
   ],
   "source": [
    "print(list(raw.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apart from gender pay gap figures, the dataset includes company information such as name, size (`EmployerSize`), the industry sectors they belong to (`SicCodes`) and a URL to their written report (`CompanyLinkToGPGInfo`). Additionally, submission metadata is included too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EmployerName</th>\n",
       "      <th>Address</th>\n",
       "      <th>CompanyNumber</th>\n",
       "      <th>SicCodes</th>\n",
       "      <th>CompanyLinkToGPGInfo</th>\n",
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       "      <th>DateSubmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10828</td>\n",
       "      <td>10828</td>\n",
       "      <td>9486</td>\n",
       "      <td>10328</td>\n",
       "      <td>7433</td>\n",
       "      <td>9136</td>\n",
       "      <td>10828</td>\n",
       "      <td>10828</td>\n",
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       "      <td>10828</td>\n",
       "      <td>10828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
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       "      <td>9564</td>\n",
       "      <td>9486</td>\n",
       "      <td>2004</td>\n",
       "      <td>6258</td>\n",
       "      <td>7515</td>\n",
       "      <td>7</td>\n",
       "      <td>10827</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Denso Marston Ltd</td>\n",
       "      <td>30 Berners Street, London, England, W1T 3LR</td>\n",
       "      <td>02801404</td>\n",
       "      <td>82990</td>\n",
       "      <td>https://www.capita.com/media/3760/gender-pay-g...</td>\n",
       "      <td>Will Serle (Chief People Officer)</td>\n",
       "      <td>250 to 499</td>\n",
       "      <td>CHOICE SUPPORT</td>\n",
       "      <td>False</td>\n",
       "      <td>05/04/2019 00:00:00</td>\n",
       "      <td>30/03/2019 23:59:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>450</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>4896</td>\n",
       "      <td>2</td>\n",
       "      <td>10320</td>\n",
       "      <td>9058</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             EmployerName                                      Address  \\\n",
       "count               10828                                        10828   \n",
       "unique              10828                                         9564   \n",
       "top     Denso Marston Ltd  30 Berners Street, London, England, W1T 3LR   \n",
       "freq                    1                                           23   \n",
       "\n",
       "       CompanyNumber SicCodes  \\\n",
       "count           9486    10328   \n",
       "unique          9486     2004   \n",
       "top         02801404    82990   \n",
       "freq               1      450   \n",
       "\n",
       "                                     CompanyLinkToGPGInfo  \\\n",
       "count                                                7433   \n",
       "unique                                               6258   \n",
       "top     https://www.capita.com/media/3760/gender-pay-g...   \n",
       "freq                                                   30   \n",
       "\n",
       "                        ResponsiblePerson EmployerSize     CurrentName  \\\n",
       "count                                9136        10828           10828   \n",
       "unique                               7515            7           10827   \n",
       "top     Will Serle (Chief People Officer)   250 to 499  CHOICE SUPPORT   \n",
       "freq                                   30         4896               2   \n",
       "\n",
       "       SubmittedAfterTheDeadline              DueDate        DateSubmitted  \n",
       "count                      10828                10828                10828  \n",
       "unique                         2                    2                10706  \n",
       "top                        False  05/04/2019 00:00:00  30/03/2019 23:59:59  \n",
       "freq                       10320                 9058                    8  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = raw.columns\n",
    "non_numeric_cols = ['EmployerName', 'Address', 'CompanyNumber', 'SicCodes',\n",
    "                    'CompanyLinkToGPGInfo', 'ResponsiblePerson', 'EmployerSize',\n",
    "                    'CurrentName', 'SubmittedAfterTheDeadline', 'DueDate', 'DateSubmitted']\n",
    "raw[non_numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DiffMeanHourlyPercent</th>\n",
       "      <th>DiffMedianHourlyPercent</th>\n",
       "      <th>DiffMeanBonusPercent</th>\n",
       "      <th>DiffMedianBonusPercent</th>\n",
       "      <th>MaleBonusPercent</th>\n",
       "      <th>FemaleBonusPercent</th>\n",
       "      <th>MaleLowerQuartile</th>\n",
       "      <th>FemaleLowerQuartile</th>\n",
       "      <th>MaleLowerMiddleQuartile</th>\n",
       "      <th>FemaleLowerMiddleQuartile</th>\n",
       "      <th>MaleUpperMiddleQuartile</th>\n",
       "      <th>FemaleUpperMiddleQuartile</th>\n",
       "      <th>MaleTopQuartile</th>\n",
       "      <th>FemaleTopQuartile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10828.000000</td>\n",
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       "      <td>9072.000000</td>\n",
       "      <td>9072.000000</td>\n",
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       "      <td>10828.000000</td>\n",
       "      <td>10828.000000</td>\n",
       "      <td>10828.000000</td>\n",
       "      <td>10828.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.195447</td>\n",
       "      <td>11.897848</td>\n",
       "      <td>18.405037</td>\n",
       "      <td>-1.038779</td>\n",
       "      <td>35.707453</td>\n",
       "      <td>34.380181</td>\n",
       "      <td>46.092372</td>\n",
       "      <td>53.907628</td>\n",
       "      <td>50.158146</td>\n",
       "      <td>49.841854</td>\n",
       "      <td>54.376090</td>\n",
       "      <td>45.623910</td>\n",
       "      <td>60.241365</td>\n",
       "      <td>39.758635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.243800</td>\n",
       "      <td>15.515081</td>\n",
       "      <td>218.986173</td>\n",
       "      <td>295.327384</td>\n",
       "      <td>36.675303</td>\n",
       "      <td>36.378251</td>\n",
       "      <td>24.118756</td>\n",
       "      <td>24.118756</td>\n",
       "      <td>26.191420</td>\n",
       "      <td>26.191420</td>\n",
       "      <td>26.328965</td>\n",
       "      <td>26.328965</td>\n",
       "      <td>24.496928</td>\n",
       "      <td>24.496928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-98.700000</td>\n",
       "      <td>-92.300000</td>\n",
       "      <td>-13934.000000</td>\n",
       "      <td>-14490.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.100000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>17.150000</td>\n",
       "      <td>44.600000</td>\n",
       "      <td>55.400000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.400000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>56.600000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>75.200000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>134.200000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DiffMeanHourlyPercent  DiffMedianHourlyPercent  DiffMeanBonusPercent  \\\n",
       "count           10828.000000             10828.000000           9072.000000   \n",
       "mean               14.195447                11.897848             18.405037   \n",
       "std                14.243800                15.515081            218.986173   \n",
       "min               -98.700000               -92.300000         -13934.000000   \n",
       "25%                 4.900000                 0.900000              0.000000   \n",
       "50%                13.100000                 9.500000             32.100000   \n",
       "75%                22.400000                21.000000             56.600000   \n",
       "max               100.000000               100.000000            134.200000   \n",
       "\n",
       "       DiffMedianBonusPercent  MaleBonusPercent  FemaleBonusPercent  \\\n",
       "count             9072.000000      10828.000000        10828.000000   \n",
       "mean                -1.038779         35.707453           34.380181   \n",
       "std                295.327384         36.675303           36.378251   \n",
       "min             -14490.800000          0.000000            0.000000   \n",
       "25%                  0.000000          0.700000            0.300000   \n",
       "50%                 15.550000         19.200000           17.150000   \n",
       "75%                 43.000000         75.200000           72.000000   \n",
       "max               4000.000000        100.000000          100.000000   \n",
       "\n",
       "       MaleLowerQuartile  FemaleLowerQuartile  MaleLowerMiddleQuartile  \\\n",
       "count       10828.000000         10828.000000             10828.000000   \n",
       "mean           46.092372            53.907628                50.158146   \n",
       "std            24.118756            24.118756                26.191420   \n",
       "min             0.000000             0.000000                 0.000000   \n",
       "25%            27.000000            35.500000                29.000000   \n",
       "50%            44.600000            55.400000                48.000000   \n",
       "75%            64.500000            73.000000                72.000000   \n",
       "max           100.000000           100.000000               100.000000   \n",
       "\n",
       "       FemaleLowerMiddleQuartile  MaleUpperMiddleQuartile  \\\n",
       "count               10828.000000             10828.000000   \n",
       "mean                   49.841854                54.376090   \n",
       "std                    26.191420                26.328965   \n",
       "min                     0.000000                 0.000000   \n",
       "25%                    28.000000                33.000000   \n",
       "50%                    52.000000                53.000000   \n",
       "75%                    71.000000                78.000000   \n",
       "max                   100.000000               100.000000   \n",
       "\n",
       "       FemaleUpperMiddleQuartile  MaleTopQuartile  FemaleTopQuartile  \n",
       "count               10828.000000     10828.000000       10828.000000  \n",
       "mean                   45.623910        60.241365          39.758635  \n",
       "std                    26.328965        24.496928          24.496928  \n",
       "min                     0.000000         0.000000           0.000000  \n",
       "25%                    22.000000        41.000000          18.000000  \n",
       "50%                    47.000000        62.000000          38.000000  \n",
       "75%                    67.000000        82.000000          59.000000  \n",
       "max                   100.000000       100.000000         100.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cols = [col for col in columns if col not in non_numeric_cols]\n",
    "raw[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bonus data have minimum and maximum outliers. This will not be fixed since bonuses are not in the scope of the business questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10828 entries, 0 to 10827\n",
      "Data columns (total 25 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   EmployerName               10828 non-null  object \n",
      " 1   Address                    10828 non-null  object \n",
      " 2   CompanyNumber              9486 non-null   object \n",
      " 3   SicCodes                   10328 non-null  object \n",
      " 4   DiffMeanHourlyPercent      10828 non-null  float64\n",
      " 5   DiffMedianHourlyPercent    10828 non-null  float64\n",
      " 6   DiffMeanBonusPercent       9072 non-null   float64\n",
      " 7   DiffMedianBonusPercent     9072 non-null   float64\n",
      " 8   MaleBonusPercent           10828 non-null  float64\n",
      " 9   FemaleBonusPercent         10828 non-null  float64\n",
      " 10  MaleLowerQuartile          10828 non-null  float64\n",
      " 11  FemaleLowerQuartile        10828 non-null  float64\n",
      " 12  MaleLowerMiddleQuartile    10828 non-null  float64\n",
      " 13  FemaleLowerMiddleQuartile  10828 non-null  float64\n",
      " 14  MaleUpperMiddleQuartile    10828 non-null  float64\n",
      " 15  FemaleUpperMiddleQuartile  10828 non-null  float64\n",
      " 16  MaleTopQuartile            10828 non-null  float64\n",
      " 17  FemaleTopQuartile          10828 non-null  float64\n",
      " 18  CompanyLinkToGPGInfo       7433 non-null   object \n",
      " 19  ResponsiblePerson          9136 non-null   object \n",
      " 20  EmployerSize               10828 non-null  object \n",
      " 21  CurrentName                10828 non-null  object \n",
      " 22  SubmittedAfterTheDeadline  10828 non-null  bool   \n",
      " 23  DueDate                    10828 non-null  object \n",
      " 24  DateSubmitted              10828 non-null  object \n",
      "dtypes: bool(1), float64(14), object(10)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "raw.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crucial colummns do not miss values (employer size, hourly pay gaps and payment quartiles). `SicCodes` has 4.6% values missing (500/10.828), this issue will be address in the data preparation stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean vs Mean Hourly Pay Gap**\n",
    "\n",
    "Both `DiffMedianHourlyPercent` and `DiffMeanHourlyPercent` indicate the gender pay gap of the company."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c1c6410>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pay_gap_indicators = raw[['DiffMeanHourlyPercent', 'DiffMedianHourlyPercent']]\n",
    "pay_gap_indicators.plot.hist(bins=100, alpha=0.5, figsize=(14,7),\n",
    "                             grid=True, title=\"Pay gap indicators histogram\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that negative values indicate the company has a gender pay gap that favours woman. This might be due to gender representation or discrimination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DiffMeanHourlyPercent</th>\n",
       "      <th>DiffMedianHourlyPercent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10828.000000</td>\n",
       "      <td>10828.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.195447</td>\n",
       "      <td>11.897848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.243800</td>\n",
       "      <td>15.515081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-98.700000</td>\n",
       "      <td>-92.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.900000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.100000</td>\n",
       "      <td>9.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.400000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DiffMeanHourlyPercent  DiffMedianHourlyPercent\n",
       "count           10828.000000             10828.000000\n",
       "mean               14.195447                11.897848\n",
       "std                14.243800                15.515081\n",
       "min               -98.700000               -92.300000\n",
       "25%                 4.900000                 0.900000\n",
       "50%                13.100000                 9.500000\n",
       "75%                22.400000                21.000000\n",
       "max               100.000000               100.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_gap_indicators.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average of the mean curve is 14.92 and the median value of the median curve is 9.5. For further analysis the **Mean** gap will not be used because that metric is very sensitive to statistical outliers (very low of very high salaries) which might be missleading. the **Median** draws a better representation of data like raw salaries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation\n",
    "**Remove Irrelevant columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10828, 17)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = raw.drop(columns=['Address','CompanyNumber', 'CompanyLinkToGPGInfo',\n",
    "                       'CurrentName', 'SubmittedAfterTheDeadline',\n",
    "                       'DueDate', 'DateSubmitted', 'ResponsiblePerson'\n",
    "                      ])\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Company Size** The `EmployerSize` column contain 1 of 7 categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Companies')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "emp_size_dist = df.EmployerSize.value_counts().sort_values()\n",
    "ax = emp_size_dist.plot.barh(title='EmployerSize distribution')\n",
    "ax.set_xlabel(\"Number of Companies\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`EmployerSize` categorical texts will be replaced with the midpoint of the ranges they represent to have numerical values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1           9\n",
       "150       373\n",
       "375      4896\n",
       "750      2663\n",
       "3000     2326\n",
       "12500     500\n",
       "40000      61\n",
       "Name: EmployerSize, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_replace     = ['Not Provided', 'Less than 250',  '250 to 499',  '500 to 999',\n",
    "                  '1000 to 4999', '5000 to 19,999', '20,000 or more']\n",
    "replace_values = [1, 150, 375, 750, 3000, 12500, 40000]\n",
    "df.EmployerSize = df.EmployerSize.replace(to_replace, replace_values).astype(int)\n",
    "df.EmployerSize.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference between the initial categories (i.e. 1, 150) and the last categories (i.e. 12.500, 40.000) are significantly big. Hence, a logarithmic representation is added in order to obtain a linear representation of the sizes, this might be helfpul for the modelling phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EmployerSizeLog'] = pd.Series(np.log(df.EmployerSize), index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12bf9fa50>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(11,4))\n",
    "axs[0].set_title(\"EmployerSize\")\n",
    "axs[0].set_ylabel(\"Regular scale\")\n",
    "axs[0].set_xticks(range(8))\n",
    "axs[0].set_xticklabels(to_replace, rotation=40, ha=\"right\")\n",
    "axs[0].grid()\n",
    "\n",
    "axs[0].plot(np.sort(df.EmployerSize.unique()), marker=\".\")\n",
    "axs[1].set_title(\"EmployerSizeLog\")\n",
    "axs[1].set_ylabel(\"Logarigthmic scale\")\n",
    "axs[1].set_xticks(range(8))\n",
    "axs[1].set_xticklabels(to_replace, rotation=40, ha=\"right\")\n",
    "axs[1].grid()\n",
    "axs[1].plot(range(0,7), np.sort(df.EmployerSizeLog.unique()), marker=\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1: Payment Balance**\n",
    "A [violin plot](https://seaborn.pydata.org/generated/seaborn.violinplot.html) is suitable to represent how companies are balancing salaries based in building four quartiles. Data granularization is required to plot this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_quartiles_by_gender(gender_str, df):\n",
    "    \"\"\"Returns a data frame only with a gender specific quartile percentages.\n",
    "    \"\"\"\n",
    "    raw_cols = ['LowerQuartile', 'LowerMiddleQuartile', 'UpperMiddleQuartile', 'TopQuartile']\n",
    "    quartile_cols = [gender_str + quartile for quartile in raw_cols]\n",
    "    quartiles = df[quartile_cols].copy()\n",
    "    quartiles.loc[:, 'Sex'] = gender_str\n",
    "    quartiles.rename(columns={\n",
    "        quartile_cols[0]: \"Lower\",\n",
    "        quartile_cols[1]: \"LowerMiddle\",\n",
    "        quartile_cols[2]: \"UpperMiddle\",\n",
    "        quartile_cols[3]: \"Top\",\n",
    "    }, inplace=True)\n",
    "    return quartiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21656, 5)\n"
     ]
    },
    {
     "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>Lower</th>\n",
       "      <th>LowerMiddle</th>\n",
       "      <th>UpperMiddle</th>\n",
       "      <th>Top</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9016</th>\n",
       "      <td>41.2</td>\n",
       "      <td>41.2</td>\n",
       "      <td>39.6</td>\n",
       "      <td>21.7</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>49.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2678</th>\n",
       "      <td>80.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5141</th>\n",
       "      <td>75.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9743</th>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Lower  LowerMiddle  UpperMiddle   Top     Sex\n",
       "9016   41.2         41.2         39.6  21.7  Female\n",
       "1304   49.0         44.0         44.0  35.0  Female\n",
       "2678   80.0         75.0         70.0  58.0  Female\n",
       "5141   75.0         76.0         83.0  92.0    Male\n",
       "9743   27.0         15.0          5.0   4.0  Female"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_quartile   = extract_quartiles_by_gender(\"Male\", df)\n",
    "female_quartile = extract_quartiles_by_gender(\"Female\", df)\n",
    "full_quartiles  = male_quartile.append(female_quartile)\n",
    "print(full_quartiles.shape)\n",
    "full_quartiles.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(86624, 3)\n"
     ]
    },
    {
     "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>Sex</th>\n",
       "      <th>Quartile</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24654</th>\n",
       "      <td>Male</td>\n",
       "      <td>LowerMiddle</td>\n",
       "      <td>76.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74639</th>\n",
       "      <td>Male</td>\n",
       "      <td>Top</td>\n",
       "      <td>98.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11850</th>\n",
       "      <td>Female</td>\n",
       "      <td>Lower</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35802</th>\n",
       "      <td>Female</td>\n",
       "      <td>LowerMiddle</td>\n",
       "      <td>24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>Male</td>\n",
       "      <td>Lower</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sex     Quartile  Percentage\n",
       "24654    Male  LowerMiddle        76.3\n",
       "74639    Male          Top        98.2\n",
       "11850  Female        Lower        14.0\n",
       "35802  Female  LowerMiddle        24.6\n",
       "2157     Male        Lower        53.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melt_quartiles = pd.melt(full_quartiles, id_vars=['Sex'], var_name='Quartile',\n",
    "    value_vars=['Lower', 'LowerMiddle', 'UpperMiddle', 'Top'],\n",
    "    value_name='Percentage'\n",
    ")\n",
    "print(melt_quartiles.shape)\n",
    "melt_quartiles.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "ax.set_title(\"Quartile percentage distribution by gender\", fontsize=14, pad=20)\n",
    "# sns.despine(left=True, bottom=True)\n",
    "ax = sns.violinplot(data=melt_quartiles, x=\"Quartile\", y=\"Percentage\", ax=ax, hue=\"Sex\", \n",
    "                     cut=0, split=True, inner=None, scale=\"count\", palette=\"Set2\")\n",
    "plt.tick_params(axis='y', labelsize=12)\n",
    "plt.tick_params(axis='x', labelsize=12)\n",
    "plt.grid()\n",
    "plt.legend(loc='lower center', fontsize=11, framealpha=1)\n",
    "ax.set_xlabel(None)\n",
    "ax.set_ylabel('% Density distribution' , fontsize=12)\n",
    "fig.savefig(\"plots/quartile-percentage-dist.png\", dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1 Answer** A balance is found when both curves draw rainbow arcs, meaning that male and female employees are equally present in that payment quartile (smooth mode at 50%). From left to right (also better to worse), the lower quartile is the most balanced one with a slight preference on woman. The greatest imbalance is in the high wage quarile, where men take 80 to 100% of the salaries in the top quartile."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2 Economic activities** Sic Codes are five digit encodings used to classify companies into 21 economic sections which are subdivided into 731 economic sectors. `SicCodes` column contains a list of these codes, with some missing, clutter and incorrect values (`1`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1375               96090\n",
       "4883               42110\n",
       "2107               78200\n",
       "10177              62090\n",
       "2853               68209\n",
       "2769     87900,\\r\\n88990\n",
       "8980                 NaN\n",
       "7903     70100,\\r\\n86102\n",
       "8674               85310\n",
       "482                47640\n",
       "Name: SicCodes, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10).SicCodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to understand the industrial context of the companies, this data should be extended with human interpretable descriptions. For that, [this](https://datahub.io/core/uk-sic-2007-condensed) dataset is available in `data/sic_codes.csv`, it includes the mapping between industrial sections and sectors for company classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>sic_code</th>\n",
       "      <th>sic_description</th>\n",
       "      <th>section</th>\n",
       "      <th>section_description</th>\n",
       "      <th>sic_version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1110</td>\n",
       "      <td>Growing of cereals (except rice), leguminous c...</td>\n",
       "      <td>A</td>\n",
       "      <td>Agriculture, Forestry and Fishing</td>\n",
       "      <td>SIC 2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1120</td>\n",
       "      <td>Growing of rice</td>\n",
       "      <td>A</td>\n",
       "      <td>Agriculture, Forestry and Fishing</td>\n",
       "      <td>SIC 2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1130</td>\n",
       "      <td>Growing of vegetables and melons, roots and tu...</td>\n",
       "      <td>A</td>\n",
       "      <td>Agriculture, Forestry and Fishing</td>\n",
       "      <td>SIC 2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1140</td>\n",
       "      <td>Growing of sugar cane</td>\n",
       "      <td>A</td>\n",
       "      <td>Agriculture, Forestry and Fishing</td>\n",
       "      <td>SIC 2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1150</td>\n",
       "      <td>Growing of tobacco</td>\n",
       "      <td>A</td>\n",
       "      <td>Agriculture, Forestry and Fishing</td>\n",
       "      <td>SIC 2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>98000</td>\n",
       "      <td>Residents property management</td>\n",
       "      <td>T</td>\n",
       "      <td>Activities of households as employers; undiffe...</td>\n",
       "      <td>SIC 2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>98100</td>\n",
       "      <td>Undifferentiated goods-producing activities of...</td>\n",
       "      <td>T</td>\n",
       "      <td>Activities of households as employers; undiffe...</td>\n",
       "      <td>SIC 2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>98200</td>\n",
       "      <td>Undifferentiated service-producing activities ...</td>\n",
       "      <td>T</td>\n",
       "      <td>Activities of households as employers; undiffe...</td>\n",
       "      <td>SIC 2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>99000</td>\n",
       "      <td>Activities of extraterritorial organisations a...</td>\n",
       "      <td>U</td>\n",
       "      <td>Activities of extraterritorial organisations a...</td>\n",
       "      <td>SIC 2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>99999</td>\n",
       "      <td>Dormant Company</td>\n",
       "      <td>U</td>\n",
       "      <td>Activities of extraterritorial organisations a...</td>\n",
       "      <td>SIC 2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sic_code                                    sic_description section  \\\n",
       "0        1110  Growing of cereals (except rice), leguminous c...       A   \n",
       "1        1120                                    Growing of rice       A   \n",
       "2        1130  Growing of vegetables and melons, roots and tu...       A   \n",
       "3        1140                              Growing of sugar cane       A   \n",
       "4        1150                                 Growing of tobacco       A   \n",
       "..        ...                                                ...     ...   \n",
       "726     98000                      Residents property management       T   \n",
       "727     98100  Undifferentiated goods-producing activities of...       T   \n",
       "728     98200  Undifferentiated service-producing activities ...       T   \n",
       "729     99000  Activities of extraterritorial organisations a...       U   \n",
       "730     99999                                    Dormant Company       U   \n",
       "\n",
       "                                   section_description sic_version  \n",
       "0                    Agriculture, Forestry and Fishing    SIC 2007  \n",
       "1                    Agriculture, Forestry and Fishing    SIC 2007  \n",
       "2                    Agriculture, Forestry and Fishing    SIC 2007  \n",
       "3                    Agriculture, Forestry and Fishing    SIC 2007  \n",
       "4                    Agriculture, Forestry and Fishing    SIC 2007  \n",
       "..                                                 ...         ...  \n",
       "726  Activities of households as employers; undiffe...    SIC 2007  \n",
       "727  Activities of households as employers; undiffe...    SIC 2007  \n",
       "728  Activities of households as employers; undiffe...    SIC 2007  \n",
       "729  Activities of extraterritorial organisations a...    SIC 2007  \n",
       "730  Activities of extraterritorial organisations a...    SIC 2007  \n",
       "\n",
       "[731 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sic_data = pd.read_csv('data/sic_codes.csv')\n",
    "sic_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to manage SIC sectors and section codes, an independent module was implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "import sic_codes as sc\n",
    "%autoreload 1\n",
    "%aimport sic_codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10328, 18)\n"
     ]
    },
    {
     "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>SicCodes</th>\n",
       "      <th>SicSections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>[47520]</td>\n",
       "      <td>[G]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7069</th>\n",
       "      <td>[68310]</td>\n",
       "      <td>[L]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073</th>\n",
       "      <td>[6100, 6200]</td>\n",
       "      <td>[B]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2437</th>\n",
       "      <td>[10110, 46320]</td>\n",
       "      <td>[C, G]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8826</th>\n",
       "      <td>[46900]</td>\n",
       "      <td>[G]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SicCodes SicSections\n",
       "1485         [47520]         [G]\n",
       "7069         [68310]         [L]\n",
       "2073    [6100, 6200]         [B]\n",
       "2437  [10110, 46320]      [C, G]\n",
       "8826         [46900]         [G]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = sc.drop_sic_codes_na(df) # drops rows with NaN in the SicCodes column.\n",
    "print(df2.shape)\n",
    "df2 = sc.clean_sic_codes(df2) # Transforms cluttered SicCodes into clean lists.\n",
    "df2 = sc.add_sections(df2)    # Extends the dataset with a SicSection column.\n",
    "df2[[\"SicCodes\", \"SicSections\"]].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`SicCodes` correspond to sectors (731 elements), which are grouped in sections (21 elements). For this reason, some `SicSections` lists might be shorter than codes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11300, 20)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_section_dist(df, title, filename, save=False):\n",
    "    \"\"\"Plots Industry sections distribution of companies\"\"\"\n",
    "    exploded_sections = sc.explode_sections(df)\n",
    "    print(exploded_sections.shape)\n",
    "\n",
    "    fig, ax = plt.subplots(1,1, figsize=(9,8))\n",
    "    ax.set_title(title, fontsize='14', loc=\"left\", pad=18)\n",
    "    ax.spines['top'].set_visible(False); ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False); ax.spines['bottom'].set_visible(False)\n",
    "    plt.tick_params(axis='both', which='both', bottom=False, top=False,\n",
    "        labelbottom=False, right=False, left=False, labelleft=True, labelsize=12)\n",
    "\n",
    "    section_bars = exploded_sections.SectDesc.value_counts().sort_values()\n",
    "\n",
    "    section_bars.plot(kind='barh', ax=ax)\n",
    "\n",
    "    for container in ax.containers:\n",
    "        plt.setp(container, height=0.75)\n",
    "\n",
    "    for i, v in enumerate(section_bars):\n",
    "        ax.text(v + 4, i, str(v))\n",
    "    if save:\n",
    "        fig.savefig(f\"plots/{filename}-industry-dist.png\", bbox_inches=\"tight\", dpi=100)\n",
    "\n",
    "plot_section_dist(df2, title=\"Company distribution by Industry Section\", filename=\"general\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous plot shows the distribution of all companies. The total sum of the companies is bigger thant the original rows because companies with 2 or more industrial sections have been exploded.\n",
    "\n",
    "The following sub-distributions have been extracted with a sample of 1000 companies:\n",
    "- Companies with largest pay Gap in favour of man\n",
    "- Companies with largest pay Gap in favour of woman\n",
    "- Companies without median pay gap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1084, 21)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_size = 1000\n",
    "sorted_median_paygap = df2.sort_values(by=['DiffMedianHourlyPercent']).copy().reset_index()\n",
    "male_biased   = sorted_median_paygap.tail(sample_size)\n",
    "plot_section_dist(male_biased, \"Industry section distribution of 1000 companies with largest pay gap\", filename=\"male\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2 Answer** Education sector conforms the 30% of the \"bad\" companies. The following four groups conform 10 to 12%. Three involve technical activities such as finances, insurances and construction. Also administrative roles but the reason might be because of the large general number of occurrences. The rest of the companies groups are less than 10%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1097, 21)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_biased = sorted_median_paygap.head(sample_size)\n",
    "plot_section_dist(female_biased, title=\"Industry sections of 1000 companies with largest pay gap in favour of women\", filename=\"female\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "895 have zero median pay gap\n",
      "Sample balanced companies index range(1378, 2378)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1000.00000\n",
       "mean       -0.00090\n",
       "std         0.04279\n",
       "min        -0.20000\n",
       "25%         0.00000\n",
       "50%         0.00000\n",
       "75%         0.00000\n",
       "max         0.20000\n",
       "Name: DiffMedianHourlyPercent, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find how many reports claim 0 median pay gap.\n",
    "zero_median_gap = sorted_median_paygap[sorted_median_paygap.DiffMedianHourlyPercent == 0]\n",
    "zero_median_gap_count = zero_median_gap.shape[0]\n",
    "print(zero_median_gap_count, \"have zero median pay gap\")\n",
    "\n",
    "# Include companies close to zero median gap \n",
    "# to build a new index with 1000 elements.\n",
    "side_pad = int((sample_size - zero_median_gap_count) / 2) + 1\n",
    "balanced_companies_idx = range(zero_median_gap.index[0] - side_pad,\n",
    "                               zero_median_gap.index[-1]+ side_pad)\n",
    "\n",
    "print(\"Sample balanced companies index\", balanced_companies_idx)\n",
    "balanced = sorted_median_paygap.iloc[balanced_companies_idx] \n",
    "balanced.DiffMedianHourlyPercent.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1081, 21)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_section_dist(balanced, title=\"Industry sections of 1000 companies with less pay gap\", filename=\"balance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The services area is the section with more companies that claim having no median gap. Interestingly, Human and social oriented activities are present in high proportions in this sample of \"good\" companies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Split Industry Sections**\n",
    "\n",
    "The `sc.split_sections` function distributes the list of industry codes and extends the DataFrame with 21 new columns `SectA`, `SectB` … `SectU` which indicate whether the company belongs to a particular section (1) or not (0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10328, 40)\n"
     ]
    },
    {
     "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>EmployerName</th>\n",
       "      <th>SicCodes</th>\n",
       "      <th>DiffMeanHourlyPercent</th>\n",
       "      <th>DiffMedianHourlyPercent</th>\n",
       "      <th>DiffMeanBonusPercent</th>\n",
       "      <th>DiffMedianBonusPercent</th>\n",
       "      <th>MaleBonusPercent</th>\n",
       "      <th>FemaleBonusPercent</th>\n",
       "      <th>MaleLowerQuartile</th>\n",
       "      <th>FemaleLowerQuartile</th>\n",
       "      <th>...</th>\n",
       "      <th>SectL</th>\n",
       "      <th>SectM</th>\n",
       "      <th>SectN</th>\n",
       "      <th>SectO</th>\n",
       "      <th>SectP</th>\n",
       "      <th>SectQ</th>\n",
       "      <th>SectR</th>\n",
       "      <th>SectS</th>\n",
       "      <th>SectT</th>\n",
       "      <th>SectU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"Bryanston School\",Incorporated</td>\n",
       "      <td>[85310]</td>\n",
       "      <td>19.6</td>\n",
       "      <td>32.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>67.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"RED BAND\" CHEMICAL COMPANY, LIMITED</td>\n",
       "      <td>[47730]</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>23.7</td>\n",
       "      <td>-75.2</td>\n",
       "      <td>22.2</td>\n",
       "      <td>82.0</td>\n",
       "      <td>31.6</td>\n",
       "      <td>68.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>118 LIMITED</td>\n",
       "      <td>[61900]</td>\n",
       "      <td>7.7</td>\n",
       "      <td>12.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>10.9</td>\n",
       "      <td>67.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123 EMPLOYEES LTD</td>\n",
       "      <td>[78300]</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1509 GROUP</td>\n",
       "      <td>[85200, 85310]</td>\n",
       "      <td>13.4</td>\n",
       "      <td>12.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.1</td>\n",
       "      <td>61.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           EmployerName        SicCodes  \\\n",
       "0       \"Bryanston School\",Incorporated         [85310]   \n",
       "1  \"RED BAND\" CHEMICAL COMPANY, LIMITED         [47730]   \n",
       "2                           118 LIMITED         [61900]   \n",
       "3                     123 EMPLOYEES LTD         [78300]   \n",
       "4                            1509 GROUP  [85200, 85310]   \n",
       "\n",
       "   DiffMeanHourlyPercent  DiffMedianHourlyPercent  DiffMeanBonusPercent  \\\n",
       "0                   19.6                     32.5                   NaN   \n",
       "1                    7.7                     -4.2                  23.7   \n",
       "2                    7.7                     12.7                  11.3   \n",
       "3                   30.0                     29.0                 -10.0   \n",
       "4                   13.4                     12.7                   0.0   \n",
       "\n",
       "   DiffMedianBonusPercent  MaleBonusPercent  FemaleBonusPercent  \\\n",
       "0                     NaN               0.0                 0.0   \n",
       "1                   -75.2              22.2                82.0   \n",
       "2                    10.9              67.0                52.0   \n",
       "3                    69.0              46.0                74.0   \n",
       "4                     0.0               0.0                 0.0   \n",
       "\n",
       "   MaleLowerQuartile  FemaleLowerQuartile  ...  SectL  SectM  SectN  SectO  \\\n",
       "0               32.4                 67.6  ...      0      0      0      0   \n",
       "1               31.6                 68.4  ...      0      0      0      0   \n",
       "2               41.0                 59.0  ...      0      0      0      0   \n",
       "3                0.0                100.0  ...      0      0      1      0   \n",
       "4               38.1                 61.9  ...      0      0      0      0   \n",
       "\n",
       "   SectP  SectQ  SectR  SectS SectT  SectU  \n",
       "0      1      0      0      0     0      0  \n",
       "1      0      0      0      0     0      0  \n",
       "2      0      0      0      0     0      0  \n",
       "3      0      0      0      0     0      0  \n",
       "4      1      0      0      0     0      0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = sc.split_sectors(df2) # Add 21 new columns for Industry sections.\n",
    "print(df3.shape)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q3: Which company characteristics influence the pay gap** `DiffHourlyMedianPercent` will be modelled with Linear Regression and Random Forest algorithms in order to find the most influencial characteristics of company when estimating the pay gap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['MaleLowerQuartile', 'FemaleLowerQuartile',\n",
    "#             'DiffMeanBonusPercent', 'DiffMedianBonusPercent',\n",
    "#             'MaleBonusPercent', 'FemaleBonusPercent',\n",
    "            'MaleLowerMiddleQuartile', 'FemaleLowerMiddleQuartile',\n",
    "            'MaleUpperMiddleQuartile', 'FemaleUpperMiddleQuartile',\n",
    "            'MaleTopQuartile', 'FemaleTopQuartile', \n",
    "            'EmployerSize', 'EmployerSizeLog',\n",
    "            'SectA', 'SectB', 'SectC', 'SectD', 'SectE', 'SectF', 'SectG',\n",
    "            'SectH', 'SectI', 'SectJ', 'SectK', 'SectL', 'SectM', 'SectN',\n",
    "            'SectO', 'SectP', 'SectQ', 'SectR', 'SectS', 'SectT', 'SectU']\n",
    "\n",
    "target = 'DiffMedianHourlyPercent'\n",
    "X = df3[features]\n",
    "y = df3[target]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Modelling with Bonuses data does not improve the results. However, further analysis could be carried out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Linear Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4540445585261679"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_reg = LinearRegression(normalize=True)\n",
    "linear_reg.fit(X_train, y_train)\n",
    "\n",
    "y_preds = linear_reg.predict(X_test)\n",
    "score = r2_score(y_test, y_preds)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Column</th>\n",
       "      <th>Coeff</th>\n",
       "      <th>AbsCoeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MaleUpperMiddleQuartile</td>\n",
       "      <td>337477764488.445</td>\n",
       "      <td>337477764488.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FemaleUpperMiddleQuartile</td>\n",
       "      <td>337477764488.147</td>\n",
       "      <td>337477764488.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MaleLowerMiddleQuartile</td>\n",
       "      <td>-231440150897.072</td>\n",
       "      <td>231440150897.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FemaleLowerMiddleQuartile</td>\n",
       "      <td>-231440150896.794</td>\n",
       "      <td>231440150896.794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MaleTopQuartile</td>\n",
       "      <td>2630754441.182</td>\n",
       "      <td>2630754441.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FemaleTopQuartile</td>\n",
       "      <td>2630754440.711</td>\n",
       "      <td>2630754440.711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FemaleLowerQuartile</td>\n",
       "      <td>79466049.488</td>\n",
       "      <td>79466049.488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MaleLowerQuartile</td>\n",
       "      <td>79466048.960</td>\n",
       "      <td>79466048.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SectF</td>\n",
       "      <td>7.630</td>\n",
       "      <td>7.630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SectP</td>\n",
       "      <td>6.692</td>\n",
       "      <td>6.692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SectB</td>\n",
       "      <td>5.821</td>\n",
       "      <td>5.821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SectI</td>\n",
       "      <td>-5.775</td>\n",
       "      <td>5.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SectR</td>\n",
       "      <td>-5.681</td>\n",
       "      <td>5.681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SectA</td>\n",
       "      <td>-4.757</td>\n",
       "      <td>4.757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SectE</td>\n",
       "      <td>-3.955</td>\n",
       "      <td>3.955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SectL</td>\n",
       "      <td>-3.652</td>\n",
       "      <td>3.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SectQ</td>\n",
       "      <td>-3.524</td>\n",
       "      <td>3.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SectG</td>\n",
       "      <td>-3.331</td>\n",
       "      <td>3.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SectN</td>\n",
       "      <td>-2.984</td>\n",
       "      <td>2.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SectS</td>\n",
       "      <td>-2.966</td>\n",
       "      <td>2.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SectO</td>\n",
       "      <td>-2.842</td>\n",
       "      <td>2.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SectJ</td>\n",
       "      <td>2.268</td>\n",
       "      <td>2.268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SectU</td>\n",
       "      <td>2.264</td>\n",
       "      <td>2.264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SectC</td>\n",
       "      <td>-2.188</td>\n",
       "      <td>2.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SectK</td>\n",
       "      <td>1.510</td>\n",
       "      <td>1.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SectH</td>\n",
       "      <td>-1.408</td>\n",
       "      <td>1.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SectT</td>\n",
       "      <td>-1.123</td>\n",
       "      <td>1.123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SectM</td>\n",
       "      <td>0.997</td>\n",
       "      <td>0.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SectD</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EmployerSizeLog</td>\n",
       "      <td>-0.050</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EmployerSize</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Column             Coeff         AbsCoeff\n",
       "4     MaleUpperMiddleQuartile  337477764488.445 337477764488.445\n",
       "5   FemaleUpperMiddleQuartile  337477764488.147 337477764488.147\n",
       "2     MaleLowerMiddleQuartile -231440150897.072 231440150897.072\n",
       "3   FemaleLowerMiddleQuartile -231440150896.794 231440150896.794\n",
       "6             MaleTopQuartile    2630754441.182   2630754441.182\n",
       "7           FemaleTopQuartile    2630754440.711   2630754440.711\n",
       "1         FemaleLowerQuartile      79466049.488     79466049.488\n",
       "0           MaleLowerQuartile      79466048.960     79466048.960\n",
       "15                      SectF             7.630            7.630\n",
       "25                      SectP             6.692            6.692\n",
       "11                      SectB             5.821            5.821\n",
       "18                      SectI            -5.775            5.775\n",
       "27                      SectR            -5.681            5.681\n",
       "10                      SectA            -4.757            4.757\n",
       "14                      SectE            -3.955            3.955\n",
       "21                      SectL            -3.652            3.652\n",
       "26                      SectQ            -3.524            3.524\n",
       "16                      SectG            -3.331            3.331\n",
       "23                      SectN            -2.984            2.984\n",
       "28                      SectS            -2.966            2.966\n",
       "24                      SectO            -2.842            2.842\n",
       "19                      SectJ             2.268            2.268\n",
       "30                      SectU             2.264            2.264\n",
       "12                      SectC            -2.188            2.188\n",
       "20                      SectK             1.510            1.510\n",
       "17                      SectH            -1.408            1.408\n",
       "29                      SectT            -1.123            1.123\n",
       "22                      SectM             0.997            0.997\n",
       "13                      SectD             0.222            0.222\n",
       "9             EmployerSizeLog            -0.050            0.050\n",
       "8                EmployerSize            -0.000            0.000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeffs = pd.DataFrame({'Column': list(X_train.columns), 'Coeff': linear_reg.coef_})\n",
    "coeffs['AbsCoeff'] = abs(coeffs['Coeff'])\n",
    "coeffs.sort_values(by=[\"AbsCoeff\"], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5050968271334224"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_for = RandomForestRegressor(random_state=1)\n",
    "rand_for.fit(X_train, y_train)\n",
    "\n",
    "y_preds = rand_for.predict(X_test)\n",
    "score = r2_score(y_test, y_preds)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = list(X_train.columns)\n",
    "columns[25] = \"EducationSector\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MaleLowerQuartile</td>\n",
       "      <td>18.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FemaleLowerQuartile</td>\n",
       "      <td>16.885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MaleLowerMiddleQuartile</td>\n",
       "      <td>5.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FemaleLowerMiddleQuartile</td>\n",
       "      <td>5.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MaleUpperMiddleQuartile</td>\n",
       "      <td>4.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FemaleUpperMiddleQuartile</td>\n",
       "      <td>4.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MaleTopQuartile</td>\n",
       "      <td>15.773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FemaleTopQuartile</td>\n",
       "      <td>14.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EmployerSize</td>\n",
       "      <td>0.934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EmployerSizeLog</td>\n",
       "      <td>0.901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SectA</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SectB</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SectC</td>\n",
       "      <td>0.553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SectD</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SectE</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SectF</td>\n",
       "      <td>0.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SectG</td>\n",
       "      <td>0.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SectH</td>\n",
       "      <td>0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SectI</td>\n",
       "      <td>0.567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SectJ</td>\n",
       "      <td>0.604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SectK</td>\n",
       "      <td>0.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SectL</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SectM</td>\n",
       "      <td>0.647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SectN</td>\n",
       "      <td>0.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SectO</td>\n",
       "      <td>0.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>EducationSector</td>\n",
       "      <td>6.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SectQ</td>\n",
       "      <td>0.539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SectR</td>\n",
       "      <td>0.279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SectS</td>\n",
       "      <td>0.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SectT</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SectU</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Feature  Importance (%)\n",
       "0           MaleLowerQuartile          18.299\n",
       "1         FemaleLowerQuartile          16.885\n",
       "2     MaleLowerMiddleQuartile           5.179\n",
       "3   FemaleLowerMiddleQuartile           5.154\n",
       "4     MaleUpperMiddleQuartile           4.980\n",
       "5   FemaleUpperMiddleQuartile           4.866\n",
       "6             MaleTopQuartile          15.773\n",
       "7           FemaleTopQuartile          14.729\n",
       "8                EmployerSize           0.934\n",
       "9             EmployerSizeLog           0.901\n",
       "10                      SectA           0.031\n",
       "11                      SectB           0.041\n",
       "12                      SectC           0.553\n",
       "13                      SectD           0.056\n",
       "14                      SectE           0.025\n",
       "15                      SectF           0.436\n",
       "16                      SectG           0.481\n",
       "17                      SectH           0.221\n",
       "18                      SectI           0.567\n",
       "19                      SectJ           0.604\n",
       "20                      SectK           0.405\n",
       "21                      SectL           0.103\n",
       "22                      SectM           0.647\n",
       "23                      SectN           0.538\n",
       "24                      SectO           0.165\n",
       "25            EducationSector           6.248\n",
       "26                      SectQ           0.539\n",
       "27                      SectR           0.279\n",
       "28                      SectS           0.342\n",
       "29                      SectT           0.001\n",
       "30                      SectU           0.018"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeffs2 = pd.DataFrame({'Feature': list(columns),\n",
    "                        \"Importance (%)\": rand_for.feature_importances_ * 100})\n",
    "coeffs2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1341b6290>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coeffs2.sort_values(by=[\"Importance (%)\"], ascending=False) \\\n",
    "    .reset_index().plot.barh(x=\"Feature\", y=\"Importance (%)\", figsize=(10,9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Results Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best trained model was Random Forest, it performed 0.505 in $R2$ score with test data. Even though this is not a high value, the feature importance complement the previous analysis and added new insights to the analysis. \n",
    "\n",
    "**Q3 Answer** The most influencing characteristics of the company are the pay balance in the lower and top quartiles, followed by the question wether the company belongs to education or not. Next, mid quartiles are relevant for the decision. Surprisingly, the Employer size is irrelevant together with the rest of the industry sections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Key insights:\n",
    "- Man predominantly are in the top quartile taking the highest salaries.\n",
    "- Employers in education and other technical economic activities tend to have large pay gaps.\n",
    "- Lower quartiles and top quartiles are key indicators to predict a company's pay gap.\n",
    "- The size of the company does not influence the pay gap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.4 64-bit ('base': conda)",
   "language": "python",
   "name": "python37464bitbasecondad572fd0cb7ab471fbfff7fdb64f304d8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}