Third_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding gender bias in job applications\n",
    "According to this paper (http://gender-decoder.katmatfield.com/static/documents/Gaucher-Friesen-Kay-JPSP-Gendered-Wording-in-Job-ads.pdf) female applicants are less likely to apply to male-biased job advertisement texts.\n",
    "I have found a dataset which includes job advertisements, their requirements and descriptions, and the amount of female/male applicants that responded to the job."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>JOB_CLASS_TITLE</th>\n",
       "      <th>JOB_CLASS_NO</th>\n",
       "      <th>REQUIREMENT_SET_ID</th>\n",
       "      <th>JOB_DUTIES</th>\n",
       "      <th>ENTRY_SALARY_GEN</th>\n",
       "      <th>ENTRY_SALARY_DWP</th>\n",
       "      <th>OPEN_DATE</th>\n",
       "      <th>EDUCATION_YEARS</th>\n",
       "      <th>SCHOOL_TYPE</th>\n",
       "      <th>EDUCATION_MAJOR</th>\n",
       "      <th>...</th>\n",
       "      <th>COURSE_LENGTH</th>\n",
       "      <th>COURSE_SUBJECT</th>\n",
       "      <th>REQUIRED_CERTIFICATE</th>\n",
       "      <th>CERTIFICATE_ISSUED_BY</th>\n",
       "      <th>DRIVERS_LICENSE_REQ</th>\n",
       "      <th>DRIV_LIC_TYPE</th>\n",
       "      <th>EXAM_TYPE</th>\n",
       "      <th>req_list</th>\n",
       "      <th>raw_clean_job_text</th>\n",
       "      <th>REQUIREMENTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
       "      <td>1</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>$48,797-$71,326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>Arts (design, literary, media/communication, p...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "      <td>ARTS ASSOCIATEClass Code:       2454          ...</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
       "      <td>2</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>$48,797-$71,326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>two-year</td>\n",
       "      <td>college with a</td>\n",
       "      <td>Arts (design, literary, media/communication, p...</td>\n",
       "      <td>...</td>\n",
       "      <td>9 semester units or 14 quarter units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>An associate of arts degree from an accredite...</td>\n",
       "      <td>ARTS ASSOCIATEClass Code:       2454          ...</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
       "      <td>3</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>$48,797-$71,326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>Four years of full-time paid experience withi...</td>\n",
       "      <td>ARTS ASSOCIATEClass Code:       2454          ...</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN</td>\n",
       "      <td>3638</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Communications Electrician acts as a ...</td>\n",
       "      <td>$94,941 (flat-rated)</td>\n",
       "      <td>$114,568 (flat-rated)</td>\n",
       "      <td>03-03-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Two years of full-time paid experience in a cl...</td>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN             ...</td>\n",
       "      <td>Two years of full-time paid experience in a cl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SENIOR AUTOMOTIVE SUPERVISOR</td>\n",
       "      <td>3716</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Automotive Supervisor assists in plan...</td>\n",
       "      <td>$103,126-$111,875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-20-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>B,C</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Two years of full-time paid experience with th...</td>\n",
       "      <td>SENIOR AUTOMOTIVE SUPERVISOR  Class Code:     ...</td>\n",
       "      <td>Two years of full-time paid experience with th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER</td>\n",
       "      <td>9425</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Structural Engineer plans, organizes ...</td>\n",
       "      <td>$111,373-$158,500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-07-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>1. Registration as a Structural Engineer with ...</td>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER                    ...</td>\n",
       "      <td>1. Registration as a Structural Engineer with ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER</td>\n",
       "      <td>9425</td>\n",
       "      <td>2</td>\n",
       "      <td>A Senior Structural Engineer plans, organizes ...</td>\n",
       "      <td>$111,373-$158,500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-07-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Two years of full-time paid experience with t...</td>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER                    ...</td>\n",
       "      <td>1. Registration as a Structural Engineer with ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER</td>\n",
       "      <td>9425</td>\n",
       "      <td>3</td>\n",
       "      <td>A Senior Structural Engineer plans, organizes ...</td>\n",
       "      <td>$111,373-$158,500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-07-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Four years of full-time paid professional eng...</td>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER                    ...</td>\n",
       "      <td>1. Registration as a Structural Engineer with ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>EQUIPMENT SUPERVISOR</td>\n",
       "      <td>3527</td>\n",
       "      <td>1</td>\n",
       "      <td>An Equipment Supervisor assigns, reviews and e...</td>\n",
       "      <td>$84,918-$99,931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-13-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Four years of full-time paid experience with t...</td>\n",
       "      <td>EQUIPMENT SUPERVISORClass Code:       3527Open...</td>\n",
       "      <td>Four years of full-time paid experience with t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>COMMUNITY AFFAIRS ADVOCATE</td>\n",
       "      <td>2496</td>\n",
       "      <td>1</td>\n",
       "      <td>Liaison or community relations advocate to com...</td>\n",
       "      <td>$105,966-$131,648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-14-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>college or university</td>\n",
       "      <td>Urban Planning</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>1. Current employment with the City of Los Ang...</td>\n",
       "      <td>COMMUNITY AFFAIRS ADVOCATEClass Code:       24...</td>\n",
       "      <td>1. Current employment with the City of Los Ang...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1102 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        JOB_CLASS_TITLE  JOB_CLASS_NO  REQUIREMENT_SET_ID  \\\n",
       "0                        ARTS ASSOCIATE          2454                   1   \n",
       "1                        ARTS ASSOCIATE          2454                   2   \n",
       "2                        ARTS ASSOCIATE          2454                   3   \n",
       "3     SENIOR COMMUNICATIONS ELECTRICIAN          3638                   1   \n",
       "4          SENIOR AUTOMOTIVE SUPERVISOR          3716                   1   \n",
       "...                                 ...           ...                 ...   \n",
       "1108         SENIOR STRUCTURAL ENGINEER          9425                   1   \n",
       "1109         SENIOR STRUCTURAL ENGINEER          9425                   2   \n",
       "1110         SENIOR STRUCTURAL ENGINEER          9425                   3   \n",
       "1111               EQUIPMENT SUPERVISOR          3527                   1   \n",
       "1112         COMMUNITY AFFAIRS ADVOCATE          2496                   1   \n",
       "\n",
       "                                             JOB_DUTIES      ENTRY_SALARY_GEN  \\\n",
       "0     An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "1     An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "2     An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "3     A Senior Communications Electrician acts as a ...  $94,941 (flat-rated)   \n",
       "4     A Senior Automotive Supervisor assists in plan...     $103,126-$111,875   \n",
       "...                                                 ...                   ...   \n",
       "1108  A Senior Structural Engineer plans, organizes ...     $111,373-$158,500   \n",
       "1109  A Senior Structural Engineer plans, organizes ...     $111,373-$158,500   \n",
       "1110  A Senior Structural Engineer plans, organizes ...     $111,373-$158,500   \n",
       "1111  An Equipment Supervisor assigns, reviews and e...       $84,918-$99,931   \n",
       "1112  Liaison or community relations advocate to com...     $105,966-$131,648   \n",
       "\n",
       "           ENTRY_SALARY_DWP OPEN_DATE EDUCATION_YEARS            SCHOOL_TYPE  \\\n",
       "0                       NaN  07-21-17       four-year  college or university   \n",
       "1                       NaN  07-21-17        two-year         college with a   \n",
       "2                       NaN  07-21-17             NaN                    NaN   \n",
       "3     $114,568 (flat-rated)  03-03-17             NaN                    NaN   \n",
       "4                       NaN  11-20-15             NaN                    NaN   \n",
       "...                     ...       ...             ...                    ...   \n",
       "1108                    NaN  04-07-17             NaN                    NaN   \n",
       "1109                    NaN  04-07-17             NaN                    NaN   \n",
       "1110                    NaN  04-07-17             NaN                    NaN   \n",
       "1111                    NaN  04-13-18             NaN                    NaN   \n",
       "1112                    NaN  11-14-14             NaN  college or university   \n",
       "\n",
       "                                        EDUCATION_MAJOR  ...  \\\n",
       "0     Arts (design, literary, media/communication, p...  ...   \n",
       "1     Arts (design, literary, media/communication, p...  ...   \n",
       "2                                                   NaN  ...   \n",
       "3                                                   NaN  ...   \n",
       "4                                                   NaN  ...   \n",
       "...                                                 ...  ...   \n",
       "1108                                                NaN  ...   \n",
       "1109                                                NaN  ...   \n",
       "1110                                                NaN  ...   \n",
       "1111                                                NaN  ...   \n",
       "1112                                     Urban Planning  ...   \n",
       "\n",
       "                             COURSE_LENGTH COURSE_SUBJECT  \\\n",
       "0                                      NaN            NaN   \n",
       "1     9 semester units or 14 quarter units            NaN   \n",
       "2                                      NaN            NaN   \n",
       "3                                      NaN            NaN   \n",
       "4                                      NaN            NaN   \n",
       "...                                    ...            ...   \n",
       "1108                                   NaN            NaN   \n",
       "1109                                   NaN            NaN   \n",
       "1110                                   NaN            NaN   \n",
       "1111                                   NaN            NaN   \n",
       "1112                                   NaN            NaN   \n",
       "\n",
       "     REQUIRED_CERTIFICATE CERTIFICATE_ISSUED_BY DRIVERS_LICENSE_REQ  \\\n",
       "0                     NaN                   NaN                   P   \n",
       "1                     NaN                   NaN                   P   \n",
       "2                     NaN                   NaN                   P   \n",
       "3                     NaN                   NaN                   P   \n",
       "4                     NaN                   NaN                   P   \n",
       "...                   ...                   ...                 ...   \n",
       "1108                  NaN                   NaN                   P   \n",
       "1109                  NaN                   NaN                   P   \n",
       "1110                  NaN                   NaN                   P   \n",
       "1111                  NaN                   NaN                   P   \n",
       "1112                  NaN                   NaN                   P   \n",
       "\n",
       "     DRIV_LIC_TYPE      EXAM_TYPE  \\\n",
       "0              NaN           OPEN   \n",
       "1              NaN           OPEN   \n",
       "2              NaN           OPEN   \n",
       "3              NaN  INT_DEPT_PROM   \n",
       "4              B,C  INT_DEPT_PROM   \n",
       "...            ...            ...   \n",
       "1108           NaN  INT_DEPT_PROM   \n",
       "1109           NaN  INT_DEPT_PROM   \n",
       "1110           NaN  INT_DEPT_PROM   \n",
       "1111           NaN  INT_DEPT_PROM   \n",
       "1112           NaN           OPEN   \n",
       "\n",
       "                                               req_list  \\\n",
       "0     1. A bachelor's degree from an accredited four...   \n",
       "1      An associate of arts degree from an accredite...   \n",
       "2      Four years of full-time paid experience withi...   \n",
       "3     Two years of full-time paid experience in a cl...   \n",
       "4     Two years of full-time paid experience with th...   \n",
       "...                                                 ...   \n",
       "1108  1. Registration as a Structural Engineer with ...   \n",
       "1109   Two years of full-time paid experience with t...   \n",
       "1110   Four years of full-time paid professional eng...   \n",
       "1111  Four years of full-time paid experience with t...   \n",
       "1112  1. Current employment with the City of Los Ang...   \n",
       "\n",
       "                                     raw_clean_job_text  \\\n",
       "0     ARTS ASSOCIATEClass Code:       2454          ...   \n",
       "1     ARTS ASSOCIATEClass Code:       2454          ...   \n",
       "2     ARTS ASSOCIATEClass Code:       2454          ...   \n",
       "3     SENIOR COMMUNICATIONS ELECTRICIAN             ...   \n",
       "4     SENIOR AUTOMOTIVE SUPERVISOR  Class Code:     ...   \n",
       "...                                                 ...   \n",
       "1108  SENIOR STRUCTURAL ENGINEER                    ...   \n",
       "1109  SENIOR STRUCTURAL ENGINEER                    ...   \n",
       "1110  SENIOR STRUCTURAL ENGINEER                    ...   \n",
       "1111  EQUIPMENT SUPERVISORClass Code:       3527Open...   \n",
       "1112  COMMUNITY AFFAIRS ADVOCATEClass Code:       24...   \n",
       "\n",
       "                                           REQUIREMENTS  \n",
       "0     1. A bachelor's degree from an accredited four...  \n",
       "1     1. A bachelor's degree from an accredited four...  \n",
       "2     1. A bachelor's degree from an accredited four...  \n",
       "3     Two years of full-time paid experience in a cl...  \n",
       "4     Two years of full-time paid experience with th...  \n",
       "...                                                 ...  \n",
       "1108  1. Registration as a Structural Engineer with ...  \n",
       "1109  1. Registration as a Structural Engineer with ...  \n",
       "1110  1. Registration as a Structural Engineer with ...  \n",
       "1111  Four years of full-time paid experience with t...  \n",
       "1112  1. Current employment with the City of Los Ang...  \n",
       "\n",
       "[1102 rows x 28 columns]"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('job_data/jobs.csv')\n",
    "df = df.dropna(subset=['JOB_DUTIES'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will be using the feminine and masculine coded words from the paper, and relationship related words + superlative based on the ones they used in this notebook: https://www.kaggle.com/nienkevanpoppel/2-encourage-diversity-reduce-bias-cola/edit. This is done because research has shown that superlatives do not invite women to apply for a job whereas relationship related words would."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "masculine_words=[\"active\",\"adventurous\",\"aggress\",\"ambitio\",\n",
    "    \"analy\",\"assert\",\"athlet\",\"autonom\",\"battle\",\"boast\",\"challeng\",\n",
    "    \"champion\",\"compet\",\"confident\",\"courag\",\"decid\",\"decision\",\"decisive\",\n",
    "    \"defend\",\"determin\",\"domina\",\"dominant\",\"driven\",\"fearless\",\"fight\",\n",
    "    \"force\",\"greedy\",\"head-strong\",\"headstrong\",\"hierarch\",\"hostil\",\n",
    "    \"impulsive\",\"independen\",\"individual\",\"intellect\",\"lead\",\"logic\",\n",
    "    \"objective\",\"opinion\",\"outspoken\",\"persist\",\"principle\",\"reckless\",\n",
    "    \"self-confiden\",\"self-relian\",\"self-sufficien\",\"selfconfiden\",\n",
    "    \"selfrelian\",\"selfsufficien\",\"stubborn\",\"superior\",\"unreasonab\"]\n",
    "\n",
    "feminine_words=[ \"agree\",\"affectionate\",\"child\",\"cheer\",\"collab\",\"commit\",\"communal\",\n",
    "    \"compassion\",\"connect\",\"considerate\",\"cooperat\",\"co-operat\",\n",
    "    \"depend\",\"emotiona\",\"empath\",\"feel\",\"flatterable\",\"gentle\",\n",
    "    \"honest\",\"interpersonal\",\"interdependen\",\"interpersona\",\"inter-personal\",\n",
    "    \"inter-dependen\",\"inter-persona\",\"kind\",\"kinship\",\"loyal\",\"modesty\",\n",
    "    \"nag\",\"nurtur\",\"pleasant\",\"polite\",\"quiet\",\"respon\",\"sensitiv\",\n",
    "    \"submissive\",\"support\",\"sympath\",\"tender\",\"together\",\"trust\",\"understand\",\n",
    "    \"warm\",\"whin\",\"enthusias\",\"inclusive\",\"yield\",\"share\",\"sharin\"]\n",
    "\n",
    "superlative_words = ['expert', 'perfection', 'rockstar', 'specialist', 'authority', 'pundit', 'oracle', 'resource person', 'adept', 'maestro', 'virtuoso', 'master', 'past master', 'professional', 'genius', 'wizard', 'connoisseur', 'aficionado', 'cognoscenti', 'cognoscente', 'doyen', 'savant', 'ace', 'buff', 'ninja', 'pro', 'whizz', 'hotshot', 'old hand', 'alpha geek', 'dab hand', 'maven', 'crackerjack']\n",
    "relationship_words = ['family', 'child', 'parent', 'women', 'mother', 'father', 'son', 'daughter', 'kids', 'kid', 'married', 'household', 'home', 'sibling']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the gender biased words in the texts\n",
    "Both the job_duties text (which is the job description) and the requirements will be checked for gender biased words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"An Arts Associate performs a broad range of entry-level professional arts administration work in the areas of: community and public arts program coordination; cultural grant administration; performing arts and festival production; preparation and development of marketing materials; project management in public art, mural commissioning, and conservation of artworks; and art collection management and/or archives, while receiving on-the-job training and experience in preparation for career advancement in various art administration areas. An associate of arts degree from an accredited two-year college with a major in Arts (design, literary, media/communication, performing, and/or visual), Cultural Studies, Literature, English, Art History, Art Administration, or a closely related field, including completion of 9 semester units or 14 quarter units in the fields listed; and two years of full-time paid experience within the last ten years in assisting and coordinating a program in an organization responsible for fine arts, public art, or cultural programs and activities; 1. A bachelor's degree from an accredited four-year college or university with a major in Arts (design, literary, media/communication, performing, and/or visual), Cultural Studies, Literature, English, Art History, Art Administration, or a closely related field; or 2. An associate of arts degree from an accredited two-year college with a major in Arts (design, literary, media/communication, performing, and/or visual), Cultural Studies, Literature, English, Art History, Art Administration, or a closely related field, including completion of 9 semester units or 14 quarter units in the fields listed; and two years of full-time paid experience within the last ten years in assisting and coordinating a program in an organization responsible for fine arts, public art, or cultural programs and activities; or 3. Four years of full-time paid experience within the last ten years in assisting and coordinating a program in an organization responsible for fine arts, public art, or cultural programs and activities.  \""
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['all_text'] = df[\"JOB_DUTIES\"] + df[\"req_list\"] + df[\"REQUIREMENTS\"]\n",
    "df['all_text'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "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>masc_words</th>\n",
       "      <th>fem_words</th>\n",
       "      <th>amount_masc</th>\n",
       "      <th>amount_fem</th>\n",
       "      <th>genderbias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lead</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>analy force principle</td>\n",
       "      <td>respon</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</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>1108</th>\n",
       "      <td>force principle professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>force principle professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>force principle professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>force principle pro</td>\n",
       "      <td>respon</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>professional pro</td>\n",
       "      <td>nag son</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1102 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            masc_words   fem_words  amount_masc  amount_fem  \\\n",
       "0                     professional pro  nag respon            2           2   \n",
       "1                     professional pro  nag respon            2           2   \n",
       "2                     professional pro  nag respon            2           2   \n",
       "3                                 lead                        1           0   \n",
       "4                analy force principle      respon            3           1   \n",
       "...                                ...         ...          ...         ...   \n",
       "1108  force principle professional pro  nag respon            4           2   \n",
       "1109  force principle professional pro  nag respon            4           2   \n",
       "1110  force principle professional pro  nag respon            4           2   \n",
       "1111               force principle pro      respon            3           1   \n",
       "1112                  professional pro     nag son            2           2   \n",
       "\n",
       "     genderbias  \n",
       "0       neutral  \n",
       "1       neutral  \n",
       "2       neutral  \n",
       "3          male  \n",
       "4          male  \n",
       "...         ...  \n",
       "1108       male  \n",
       "1109       male  \n",
       "1110       male  \n",
       "1111       male  \n",
       "1112    neutral  \n",
       "\n",
       "[1102 rows x 5 columns]"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sex = pd.DataFrame({})\n",
    "for index, row in df.iterrows(): \n",
    "    masculine_matches = []\n",
    "    for x in masculine_words:\n",
    "        if x in row['all_text'] and x not in masculine_matches:\n",
    "            masculine_matches.append(x)\n",
    "    for x in superlative_words: \n",
    "        if x in row['all_text'] and x not in masculine_matches:\n",
    "            masculine_matches.append(x)\n",
    "            \n",
    "    feminine_matches = []\n",
    "    for x in feminine_words:\n",
    "        if x in row['all_text'] and x not in feminine_matches:\n",
    "            feminine_matches.append(x)\n",
    "    for x in relationship_words: \n",
    "        if x in row['all_text'] and x not in feminine_matches:\n",
    "            feminine_matches.append(x)\n",
    "            \n",
    "    difference = int(len(masculine_matches)- len(feminine_matches))\n",
    "    if difference == 0:\n",
    "        genderbias = 'neutral';\n",
    "    else :\n",
    "        if difference < 0:\n",
    "            genderbias = 'female';\n",
    "        else:\n",
    "            if difference >0:\n",
    "                genderbias = 'male';\n",
    "    \n",
    "    data = pd.DataFrame({'masc_words': ' '.join(masculine_matches), 'fem_words': ' '.join(feminine_matches), 'amount_masc': len(masculine_matches), 'amount_fem': len(feminine_matches), 'genderbias': genderbias}, index=[index])\n",
    "    df_sex = df_sex.append(data)\n",
    "df_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>JOB_CLASS_TITLE</th>\n",
       "      <th>JOB_CLASS_NO</th>\n",
       "      <th>REQUIREMENT_SET_ID</th>\n",
       "      <th>JOB_DUTIES</th>\n",
       "      <th>ENTRY_SALARY_GEN</th>\n",
       "      <th>ENTRY_SALARY_DWP</th>\n",
       "      <th>OPEN_DATE</th>\n",
       "      <th>EDUCATION_YEARS</th>\n",
       "      <th>SCHOOL_TYPE</th>\n",
       "      <th>EDUCATION_MAJOR</th>\n",
       "      <th>...</th>\n",
       "      <th>EXAM_TYPE</th>\n",
       "      <th>req_list</th>\n",
       "      <th>raw_clean_job_text</th>\n",
       "      <th>REQUIREMENTS</th>\n",
       "      <th>all_text</th>\n",
       "      <th>masc_words</th>\n",
       "      <th>fem_words</th>\n",
       "      <th>amount_masc</th>\n",
       "      <th>amount_fem</th>\n",
       "      <th>genderbias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
       "      <td>1</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>$48,797-$71,326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>Arts (design, literary, media/communication, p...</td>\n",
       "      <td>...</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "      <td>ARTS ASSOCIATEClass Code:       2454          ...</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
       "      <td>2</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>$48,797-$71,326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>two-year</td>\n",
       "      <td>college with a</td>\n",
       "      <td>Arts (design, literary, media/communication, p...</td>\n",
       "      <td>...</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>An associate of arts degree from an accredite...</td>\n",
       "      <td>ARTS ASSOCIATEClass Code:       2454          ...</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
       "      <td>3</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>$48,797-$71,326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>Four years of full-time paid experience withi...</td>\n",
       "      <td>ARTS ASSOCIATEClass Code:       2454          ...</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN</td>\n",
       "      <td>3638</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Communications Electrician acts as a ...</td>\n",
       "      <td>$94,941 (flat-rated)</td>\n",
       "      <td>$114,568 (flat-rated)</td>\n",
       "      <td>03-03-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Two years of full-time paid experience in a cl...</td>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN             ...</td>\n",
       "      <td>Two years of full-time paid experience in a cl...</td>\n",
       "      <td>A Senior Communications Electrician acts as a ...</td>\n",
       "      <td>lead</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SENIOR AUTOMOTIVE SUPERVISOR</td>\n",
       "      <td>3716</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Automotive Supervisor assists in plan...</td>\n",
       "      <td>$103,126-$111,875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-20-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Two years of full-time paid experience with th...</td>\n",
       "      <td>SENIOR AUTOMOTIVE SUPERVISOR  Class Code:     ...</td>\n",
       "      <td>Two years of full-time paid experience with th...</td>\n",
       "      <td>A Senior Automotive Supervisor assists in plan...</td>\n",
       "      <td>analy force principle</td>\n",
       "      <td>respon</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER</td>\n",
       "      <td>9425</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Structural Engineer plans, organizes ...</td>\n",
       "      <td>$111,373-$158,500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-07-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>1. Registration as a Structural Engineer with ...</td>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER                    ...</td>\n",
       "      <td>1. Registration as a Structural Engineer with ...</td>\n",
       "      <td>A Senior Structural Engineer plans, organizes ...</td>\n",
       "      <td>force principle professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER</td>\n",
       "      <td>9425</td>\n",
       "      <td>2</td>\n",
       "      <td>A Senior Structural Engineer plans, organizes ...</td>\n",
       "      <td>$111,373-$158,500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-07-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Two years of full-time paid experience with t...</td>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER                    ...</td>\n",
       "      <td>1. Registration as a Structural Engineer with ...</td>\n",
       "      <td>A Senior Structural Engineer plans, organizes ...</td>\n",
       "      <td>force principle professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER</td>\n",
       "      <td>9425</td>\n",
       "      <td>3</td>\n",
       "      <td>A Senior Structural Engineer plans, organizes ...</td>\n",
       "      <td>$111,373-$158,500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-07-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Four years of full-time paid professional eng...</td>\n",
       "      <td>SENIOR STRUCTURAL ENGINEER                    ...</td>\n",
       "      <td>1. Registration as a Structural Engineer with ...</td>\n",
       "      <td>A Senior Structural Engineer plans, organizes ...</td>\n",
       "      <td>force principle professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>EQUIPMENT SUPERVISOR</td>\n",
       "      <td>3527</td>\n",
       "      <td>1</td>\n",
       "      <td>An Equipment Supervisor assigns, reviews and e...</td>\n",
       "      <td>$84,918-$99,931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-13-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Four years of full-time paid experience with t...</td>\n",
       "      <td>EQUIPMENT SUPERVISORClass Code:       3527Open...</td>\n",
       "      <td>Four years of full-time paid experience with t...</td>\n",
       "      <td>An Equipment Supervisor assigns, reviews and e...</td>\n",
       "      <td>force principle pro</td>\n",
       "      <td>respon</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>COMMUNITY AFFAIRS ADVOCATE</td>\n",
       "      <td>2496</td>\n",
       "      <td>1</td>\n",
       "      <td>Liaison or community relations advocate to com...</td>\n",
       "      <td>$105,966-$131,648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-14-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>college or university</td>\n",
       "      <td>Urban Planning</td>\n",
       "      <td>...</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>1. Current employment with the City of Los Ang...</td>\n",
       "      <td>COMMUNITY AFFAIRS ADVOCATEClass Code:       24...</td>\n",
       "      <td>1. Current employment with the City of Los Ang...</td>\n",
       "      <td>Liaison or community relations advocate to com...</td>\n",
       "      <td>professional pro</td>\n",
       "      <td>nag son</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1102 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        JOB_CLASS_TITLE  JOB_CLASS_NO  REQUIREMENT_SET_ID  \\\n",
       "0                        ARTS ASSOCIATE          2454                   1   \n",
       "1                        ARTS ASSOCIATE          2454                   2   \n",
       "2                        ARTS ASSOCIATE          2454                   3   \n",
       "3     SENIOR COMMUNICATIONS ELECTRICIAN          3638                   1   \n",
       "4          SENIOR AUTOMOTIVE SUPERVISOR          3716                   1   \n",
       "...                                 ...           ...                 ...   \n",
       "1097         SENIOR STRUCTURAL ENGINEER          9425                   1   \n",
       "1098         SENIOR STRUCTURAL ENGINEER          9425                   2   \n",
       "1099         SENIOR STRUCTURAL ENGINEER          9425                   3   \n",
       "1100               EQUIPMENT SUPERVISOR          3527                   1   \n",
       "1101         COMMUNITY AFFAIRS ADVOCATE          2496                   1   \n",
       "\n",
       "                                             JOB_DUTIES      ENTRY_SALARY_GEN  \\\n",
       "0     An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "1     An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "2     An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "3     A Senior Communications Electrician acts as a ...  $94,941 (flat-rated)   \n",
       "4     A Senior Automotive Supervisor assists in plan...     $103,126-$111,875   \n",
       "...                                                 ...                   ...   \n",
       "1097  A Senior Structural Engineer plans, organizes ...     $111,373-$158,500   \n",
       "1098  A Senior Structural Engineer plans, organizes ...     $111,373-$158,500   \n",
       "1099  A Senior Structural Engineer plans, organizes ...     $111,373-$158,500   \n",
       "1100  An Equipment Supervisor assigns, reviews and e...       $84,918-$99,931   \n",
       "1101  Liaison or community relations advocate to com...     $105,966-$131,648   \n",
       "\n",
       "           ENTRY_SALARY_DWP OPEN_DATE EDUCATION_YEARS            SCHOOL_TYPE  \\\n",
       "0                       NaN  07-21-17       four-year  college or university   \n",
       "1                       NaN  07-21-17        two-year         college with a   \n",
       "2                       NaN  07-21-17             NaN                    NaN   \n",
       "3     $114,568 (flat-rated)  03-03-17             NaN                    NaN   \n",
       "4                       NaN  11-20-15             NaN                    NaN   \n",
       "...                     ...       ...             ...                    ...   \n",
       "1097                    NaN  04-07-17             NaN                    NaN   \n",
       "1098                    NaN  04-07-17             NaN                    NaN   \n",
       "1099                    NaN  04-07-17             NaN                    NaN   \n",
       "1100                    NaN  04-13-18             NaN                    NaN   \n",
       "1101                    NaN  11-14-14             NaN  college or university   \n",
       "\n",
       "                                        EDUCATION_MAJOR  ...      EXAM_TYPE  \\\n",
       "0     Arts (design, literary, media/communication, p...  ...           OPEN   \n",
       "1     Arts (design, literary, media/communication, p...  ...           OPEN   \n",
       "2                                                   NaN  ...           OPEN   \n",
       "3                                                   NaN  ...  INT_DEPT_PROM   \n",
       "4                                                   NaN  ...  INT_DEPT_PROM   \n",
       "...                                                 ...  ...            ...   \n",
       "1097                                                NaN  ...  INT_DEPT_PROM   \n",
       "1098                                                NaN  ...  INT_DEPT_PROM   \n",
       "1099                                                NaN  ...  INT_DEPT_PROM   \n",
       "1100                                                NaN  ...  INT_DEPT_PROM   \n",
       "1101                                     Urban Planning  ...           OPEN   \n",
       "\n",
       "                                               req_list  \\\n",
       "0     1. A bachelor's degree from an accredited four...   \n",
       "1      An associate of arts degree from an accredite...   \n",
       "2      Four years of full-time paid experience withi...   \n",
       "3     Two years of full-time paid experience in a cl...   \n",
       "4     Two years of full-time paid experience with th...   \n",
       "...                                                 ...   \n",
       "1097  1. Registration as a Structural Engineer with ...   \n",
       "1098   Two years of full-time paid experience with t...   \n",
       "1099   Four years of full-time paid professional eng...   \n",
       "1100  Four years of full-time paid experience with t...   \n",
       "1101  1. Current employment with the City of Los Ang...   \n",
       "\n",
       "                                     raw_clean_job_text  \\\n",
       "0     ARTS ASSOCIATEClass Code:       2454          ...   \n",
       "1     ARTS ASSOCIATEClass Code:       2454          ...   \n",
       "2     ARTS ASSOCIATEClass Code:       2454          ...   \n",
       "3     SENIOR COMMUNICATIONS ELECTRICIAN             ...   \n",
       "4     SENIOR AUTOMOTIVE SUPERVISOR  Class Code:     ...   \n",
       "...                                                 ...   \n",
       "1097  SENIOR STRUCTURAL ENGINEER                    ...   \n",
       "1098  SENIOR STRUCTURAL ENGINEER                    ...   \n",
       "1099  SENIOR STRUCTURAL ENGINEER                    ...   \n",
       "1100  EQUIPMENT SUPERVISORClass Code:       3527Open...   \n",
       "1101  COMMUNITY AFFAIRS ADVOCATEClass Code:       24...   \n",
       "\n",
       "                                           REQUIREMENTS  \\\n",
       "0     1. A bachelor's degree from an accredited four...   \n",
       "1     1. A bachelor's degree from an accredited four...   \n",
       "2     1. A bachelor's degree from an accredited four...   \n",
       "3     Two years of full-time paid experience in a cl...   \n",
       "4     Two years of full-time paid experience with th...   \n",
       "...                                                 ...   \n",
       "1097  1. Registration as a Structural Engineer with ...   \n",
       "1098  1. Registration as a Structural Engineer with ...   \n",
       "1099  1. Registration as a Structural Engineer with ...   \n",
       "1100  Four years of full-time paid experience with t...   \n",
       "1101  1. Current employment with the City of Los Ang...   \n",
       "\n",
       "                                               all_text  \\\n",
       "0     An Arts Associate performs a broad range of en...   \n",
       "1     An Arts Associate performs a broad range of en...   \n",
       "2     An Arts Associate performs a broad range of en...   \n",
       "3     A Senior Communications Electrician acts as a ...   \n",
       "4     A Senior Automotive Supervisor assists in plan...   \n",
       "...                                                 ...   \n",
       "1097  A Senior Structural Engineer plans, organizes ...   \n",
       "1098  A Senior Structural Engineer plans, organizes ...   \n",
       "1099  A Senior Structural Engineer plans, organizes ...   \n",
       "1100  An Equipment Supervisor assigns, reviews and e...   \n",
       "1101  Liaison or community relations advocate to com...   \n",
       "\n",
       "                            masc_words   fem_words amount_masc amount_fem  \\\n",
       "0                     professional pro  nag respon           2          2   \n",
       "1                     professional pro  nag respon           2          2   \n",
       "2                     professional pro  nag respon           2          2   \n",
       "3                                 lead                       1          0   \n",
       "4                analy force principle      respon           3          1   \n",
       "...                                ...         ...         ...        ...   \n",
       "1097  force principle professional pro  nag respon           4          2   \n",
       "1098  force principle professional pro  nag respon           4          2   \n",
       "1099  force principle professional pro  nag respon           4          2   \n",
       "1100               force principle pro      respon           3          1   \n",
       "1101                  professional pro     nag son           2          2   \n",
       "\n",
       "     genderbias  \n",
       "0       neutral  \n",
       "1       neutral  \n",
       "2       neutral  \n",
       "3          male  \n",
       "4          male  \n",
       "...         ...  \n",
       "1097       male  \n",
       "1098       male  \n",
       "1099       male  \n",
       "1100       male  \n",
       "1101    neutral  \n",
       "\n",
       "[1102 rows x 34 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df.reset_index(drop=True), df_sex.reset_index(drop=True)],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'JOB_CLASS_NO':'Job Number'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data\n",
    "I've combined the data now, so it is time to gather some insights from graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'How many job applications were female/male biased or neutrally formulated?')"
      ]
     },
     "execution_count": 275,
     "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": [
    "import seaborn as sns #this is the plotting library I'll be using \n",
    "import matplotlib.pyplot as plt #we need Matplotlib for setting the labels in the Seaborn graphs\n",
    "sns.countplot(x=\"genderbias\", data=df)\n",
    "plt.title('How many job applications were female/male biased or neutrally formulated?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "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>JOB_CLASS_TITLE</th>\n",
       "      <th>Job Number</th>\n",
       "      <th>REQUIREMENT_SET_ID</th>\n",
       "      <th>JOB_DUTIES</th>\n",
       "      <th>ENTRY_SALARY_GEN</th>\n",
       "      <th>ENTRY_SALARY_DWP</th>\n",
       "      <th>OPEN_DATE</th>\n",
       "      <th>EDUCATION_YEARS</th>\n",
       "      <th>SCHOOL_TYPE</th>\n",
       "      <th>EDUCATION_MAJOR</th>\n",
       "      <th>...</th>\n",
       "      <th>EXAM_TYPE</th>\n",
       "      <th>req_list</th>\n",
       "      <th>raw_clean_job_text</th>\n",
       "      <th>REQUIREMENTS</th>\n",
       "      <th>all_text</th>\n",
       "      <th>masc_words</th>\n",
       "      <th>fem_words</th>\n",
       "      <th>amount_masc</th>\n",
       "      <th>amount_fem</th>\n",
       "      <th>genderbias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
       "      <td>1</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>$48,797-$71,326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>Arts (design, literary, media/communication, p...</td>\n",
       "      <td>...</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "      <td>ARTS ASSOCIATEClass Code:       2454          ...</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
       "      <td>2</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>$48,797-$71,326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>two-year</td>\n",
       "      <td>college with a</td>\n",
       "      <td>Arts (design, literary, media/communication, p...</td>\n",
       "      <td>...</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>An associate of arts degree from an accredite...</td>\n",
       "      <td>ARTS ASSOCIATEClass Code:       2454          ...</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
       "      <td>3</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>$48,797-$71,326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>OPEN</td>\n",
       "      <td>Four years of full-time paid experience withi...</td>\n",
       "      <td>ARTS ASSOCIATEClass Code:       2454          ...</td>\n",
       "      <td>1. A bachelor's degree from an accredited four...</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>professional pro</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN</td>\n",
       "      <td>3638</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Communications Electrician acts as a ...</td>\n",
       "      <td>$94,941 (flat-rated)</td>\n",
       "      <td>$114,568 (flat-rated)</td>\n",
       "      <td>03-03-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Two years of full-time paid experience in a cl...</td>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN             ...</td>\n",
       "      <td>Two years of full-time paid experience in a cl...</td>\n",
       "      <td>A Senior Communications Electrician acts as a ...</td>\n",
       "      <td>lead</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SENIOR AUTOMOTIVE SUPERVISOR</td>\n",
       "      <td>3716</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Automotive Supervisor assists in plan...</td>\n",
       "      <td>$103,126-$111,875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11-20-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>INT_DEPT_PROM</td>\n",
       "      <td>Two years of full-time paid experience with th...</td>\n",
       "      <td>SENIOR AUTOMOTIVE SUPERVISOR  Class Code:     ...</td>\n",
       "      <td>Two years of full-time paid experience with th...</td>\n",
       "      <td>A Senior Automotive Supervisor assists in plan...</td>\n",
       "      <td>analy force principle</td>\n",
       "      <td>respon</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     JOB_CLASS_TITLE  Job Number  REQUIREMENT_SET_ID  \\\n",
       "0                     ARTS ASSOCIATE        2454                   1   \n",
       "1                     ARTS ASSOCIATE        2454                   2   \n",
       "2                     ARTS ASSOCIATE        2454                   3   \n",
       "3  SENIOR COMMUNICATIONS ELECTRICIAN        3638                   1   \n",
       "4       SENIOR AUTOMOTIVE SUPERVISOR        3716                   1   \n",
       "\n",
       "                                          JOB_DUTIES      ENTRY_SALARY_GEN  \\\n",
       "0  An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "1  An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "2  An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "3  A Senior Communications Electrician acts as a ...  $94,941 (flat-rated)   \n",
       "4  A Senior Automotive Supervisor assists in plan...     $103,126-$111,875   \n",
       "\n",
       "        ENTRY_SALARY_DWP OPEN_DATE EDUCATION_YEARS            SCHOOL_TYPE  \\\n",
       "0                    NaN  07-21-17       four-year  college or university   \n",
       "1                    NaN  07-21-17        two-year         college with a   \n",
       "2                    NaN  07-21-17             NaN                    NaN   \n",
       "3  $114,568 (flat-rated)  03-03-17             NaN                    NaN   \n",
       "4                    NaN  11-20-15             NaN                    NaN   \n",
       "\n",
       "                                     EDUCATION_MAJOR  ...      EXAM_TYPE  \\\n",
       "0  Arts (design, literary, media/communication, p...  ...           OPEN   \n",
       "1  Arts (design, literary, media/communication, p...  ...           OPEN   \n",
       "2                                                NaN  ...           OPEN   \n",
       "3                                                NaN  ...  INT_DEPT_PROM   \n",
       "4                                                NaN  ...  INT_DEPT_PROM   \n",
       "\n",
       "                                            req_list  \\\n",
       "0  1. A bachelor's degree from an accredited four...   \n",
       "1   An associate of arts degree from an accredite...   \n",
       "2   Four years of full-time paid experience withi...   \n",
       "3  Two years of full-time paid experience in a cl...   \n",
       "4  Two years of full-time paid experience with th...   \n",
       "\n",
       "                                  raw_clean_job_text  \\\n",
       "0  ARTS ASSOCIATEClass Code:       2454          ...   \n",
       "1  ARTS ASSOCIATEClass Code:       2454          ...   \n",
       "2  ARTS ASSOCIATEClass Code:       2454          ...   \n",
       "3  SENIOR COMMUNICATIONS ELECTRICIAN             ...   \n",
       "4  SENIOR AUTOMOTIVE SUPERVISOR  Class Code:     ...   \n",
       "\n",
       "                                        REQUIREMENTS  \\\n",
       "0  1. A bachelor's degree from an accredited four...   \n",
       "1  1. A bachelor's degree from an accredited four...   \n",
       "2  1. A bachelor's degree from an accredited four...   \n",
       "3  Two years of full-time paid experience in a cl...   \n",
       "4  Two years of full-time paid experience with th...   \n",
       "\n",
       "                                            all_text             masc_words  \\\n",
       "0  An Arts Associate performs a broad range of en...       professional pro   \n",
       "1  An Arts Associate performs a broad range of en...       professional pro   \n",
       "2  An Arts Associate performs a broad range of en...       professional pro   \n",
       "3  A Senior Communications Electrician acts as a ...                   lead   \n",
       "4  A Senior Automotive Supervisor assists in plan...  analy force principle   \n",
       "\n",
       "    fem_words amount_masc amount_fem genderbias  \n",
       "0  nag respon           2          2    neutral  \n",
       "1  nag respon           2          2    neutral  \n",
       "2  nag respon           2          2    neutral  \n",
       "3                       1          0       male  \n",
       "4      respon           3          1       male  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The job classification number overlaps with the other dataset we found of jobs in Los Angeles. This dataset includes number of sollicitants, female/male, but does not inclue a job description. We need to merge both datasets for this reason."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Job Number</th>\n",
       "      <th>Job Description</th>\n",
       "      <th>Apps Received</th>\n",
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       "      <th>0</th>\n",
       "      <td>2013-2014</td>\n",
       "      <td>9206 OP 2014/04/18</td>\n",
       "      <td>311 DIRECTOR 9206</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
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       "      <td>2013-2014</td>\n",
       "      <td>1223 P 2013/08/09</td>\n",
       "      <td>ACCOUNTING CLERK 1223</td>\n",
       "      <td>648</td>\n",
       "      <td>488</td>\n",
       "      <td>152</td>\n",
       "      <td>8</td>\n",
       "      <td>151</td>\n",
       "      <td>204</td>\n",
       "      <td>123</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>79</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-2014</td>\n",
       "      <td>7260 OP 2014/02/14</td>\n",
       "      <td>AIRPORT MANAGER 7260</td>\n",
       "      <td>51</td>\n",
       "      <td>13</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-2014</td>\n",
       "      <td>3227 P 2013/11/15</td>\n",
       "      <td>AIRPORT POLICE LIEUTENANT 2013</td>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>2013-2014</td>\n",
       "      <td>2400 O 2014/05/02</td>\n",
       "      <td>AQUARIST 2400</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2014-2015</td>\n",
       "      <td>7840 P 2014/7/25-ARCHIVE</td>\n",
       "      <td>WASTEWATER TREATMENT LABORATORY MANAGER 7840 -...</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <th>183</th>\n",
       "      <td>2014-2015</td>\n",
       "      <td>4123 O 2014/07/04-ARCHIVE</td>\n",
       "      <td>WASTEWATER TREATMENT OPERATOR 4123 - ARCHIVE</td>\n",
       "      <td>125</td>\n",
       "      <td>9</td>\n",
       "      <td>113</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2014-2015</td>\n",
       "      <td>7857 O 2014/7/18-ARCHIVE</td>\n",
       "      <td>WATER MICROBIOLOGIST 7857 - ARCHIVE</td>\n",
       "      <td>179</td>\n",
       "      <td>89</td>\n",
       "      <td>82</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>37</td>\n",
       "      <td>64</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>2014-2015</td>\n",
       "      <td>3912/P/2014/07/25-ARCHIVE</td>\n",
       "      <td>WATER UTILITY WORKER 3912 - ARCHIVE</td>\n",
       "      <td>96</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>48</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>2014-2015</td>\n",
       "      <td>1774 OP 2014/7/18-ARCHIVE</td>\n",
       "      <td>WORKERS' COMPENSATION ANALYST 1774 - ARCHIVE</td>\n",
       "      <td>166</td>\n",
       "      <td>100</td>\n",
       "      <td>61</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fiscal Year                 Job Number  \\\n",
       "0     2013-2014         9206 OP 2014/04/18   \n",
       "1     2013-2014          1223 P 2013/08/09   \n",
       "2     2013-2014         7260 OP 2014/02/14   \n",
       "3     2013-2014          3227 P 2013/11/15   \n",
       "4     2013-2014          2400 O 2014/05/02   \n",
       "..          ...                        ...   \n",
       "182   2014-2015   7840 P 2014/7/25-ARCHIVE   \n",
       "183   2014-2015  4123 O 2014/07/04-ARCHIVE   \n",
       "184   2014-2015   7857 O 2014/7/18-ARCHIVE   \n",
       "185   2014-2015  3912/P/2014/07/25-ARCHIVE   \n",
       "186   2014-2015  1774 OP 2014/7/18-ARCHIVE   \n",
       "\n",
       "                                       Job Description  Apps Received  Female  \\\n",
       "0                                    311 DIRECTOR 9206             54      20   \n",
       "1                                ACCOUNTING CLERK 1223            648     488   \n",
       "2                                 AIRPORT MANAGER 7260             51      13   \n",
       "3                       AIRPORT POLICE LIEUTENANT 2013             48       9   \n",
       "4                                        AQUARIST 2400             40      15   \n",
       "..                                                 ...            ...     ...   \n",
       "182  WASTEWATER TREATMENT LABORATORY MANAGER 7840 -...             16       6   \n",
       "183       WASTEWATER TREATMENT OPERATOR 4123 - ARCHIVE            125       9   \n",
       "184                WATER MICROBIOLOGIST 7857 - ARCHIVE            179      89   \n",
       "185                WATER UTILITY WORKER 3912 - ARCHIVE             96       2   \n",
       "186       WORKERS' COMPENSATION ANALYST 1774 - ARCHIVE            166     100   \n",
       "\n",
       "     Male  Unknown_Gender  Black  Hispanic  Asian  Caucasian  \\\n",
       "0      31               3     25        18      1          6   \n",
       "1     152               8    151       204    123         62   \n",
       "2      37               1      8        12      9         20   \n",
       "3      38               1     21        14      3          7   \n",
       "4      24               1      3         7      7         19   \n",
       "..    ...             ...    ...       ...    ...        ...   \n",
       "182     9               1      3         0      5          7   \n",
       "183   113               3     29        38     10         32   \n",
       "184    82               8     13        37     64         25   \n",
       "185    92               2      8        48      6         23   \n",
       "186    61               5     44        61     14         21   \n",
       "\n",
       "     American Indian/ Alaskan Native  Filipino  Unknown_Ethnicity  \n",
       "0                                  0         0                  4  \n",
       "1                                  3        79                 26  \n",
       "2                                  0         0                  2  \n",
       "3                                  0         1                  2  \n",
       "4                                  1         1                  2  \n",
       "..                               ...       ...                ...  \n",
       "182                                0         0                  1  \n",
       "183                                7         4                  5  \n",
       "184                                0        18                 22  \n",
       "185                                1         7                  3  \n",
       "186                                0        11                 15  \n",
       "\n",
       "[187 rows x 14 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_applicants = pd.read_csv('job_data/rows.csv')\n",
    "df_applicants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-2014</td>\n",
       "      <td>3227</td>\n",
       "      <td>AIRPORT POLICE LIEUTENANT 2013</td>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-2014</td>\n",
       "      <td>2400</td>\n",
       "      <td>AQUARIST 2400</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2014-2015</td>\n",
       "      <td>7840</td>\n",
       "      <td>WASTEWATER TREATMENT LABORATORY MANAGER 7840 -...</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2014-2015</td>\n",
       "      <td>4123</td>\n",
       "      <td>WASTEWATER TREATMENT OPERATOR 4123 - ARCHIVE</td>\n",
       "      <td>125</td>\n",
       "      <td>9</td>\n",
       "      <td>113</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2014-2015</td>\n",
       "      <td>7857</td>\n",
       "      <td>WATER MICROBIOLOGIST 7857 - ARCHIVE</td>\n",
       "      <td>179</td>\n",
       "      <td>89</td>\n",
       "      <td>82</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>37</td>\n",
       "      <td>64</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>2014-2015</td>\n",
       "      <td>3912</td>\n",
       "      <td>WATER UTILITY WORKER 3912 - ARCHIVE</td>\n",
       "      <td>96</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>48</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>2014-2015</td>\n",
       "      <td>1774</td>\n",
       "      <td>WORKERS' COMPENSATION ANALYST 1774 - ARCHIVE</td>\n",
       "      <td>166</td>\n",
       "      <td>100</td>\n",
       "      <td>61</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fiscal Year Job Number                                    Job Description  \\\n",
       "0     2013-2014       9206                                  311 DIRECTOR 9206   \n",
       "1     2013-2014       1223                              ACCOUNTING CLERK 1223   \n",
       "2     2013-2014       7260                               AIRPORT MANAGER 7260   \n",
       "3     2013-2014       3227                     AIRPORT POLICE LIEUTENANT 2013   \n",
       "4     2013-2014       2400                                      AQUARIST 2400   \n",
       "..          ...        ...                                                ...   \n",
       "182   2014-2015       7840  WASTEWATER TREATMENT LABORATORY MANAGER 7840 -...   \n",
       "183   2014-2015       4123       WASTEWATER TREATMENT OPERATOR 4123 - ARCHIVE   \n",
       "184   2014-2015       7857                WATER MICROBIOLOGIST 7857 - ARCHIVE   \n",
       "185   2014-2015       3912                WATER UTILITY WORKER 3912 - ARCHIVE   \n",
       "186   2014-2015       1774       WORKERS' COMPENSATION ANALYST 1774 - ARCHIVE   \n",
       "\n",
       "     Apps Received  Female  Male  Unknown_Gender  Black  Hispanic  Asian  \\\n",
       "0               54      20    31               3     25        18      1   \n",
       "1              648     488   152               8    151       204    123   \n",
       "2               51      13    37               1      8        12      9   \n",
       "3               48       9    38               1     21        14      3   \n",
       "4               40      15    24               1      3         7      7   \n",
       "..             ...     ...   ...             ...    ...       ...    ...   \n",
       "182             16       6     9               1      3         0      5   \n",
       "183            125       9   113               3     29        38     10   \n",
       "184            179      89    82               8     13        37     64   \n",
       "185             96       2    92               2      8        48      6   \n",
       "186            166     100    61               5     44        61     14   \n",
       "\n",
       "     Caucasian  American Indian/ Alaskan Native  Filipino  Unknown_Ethnicity  \n",
       "0            6                                0         0                  4  \n",
       "1           62                                3        79                 26  \n",
       "2           20                                0         0                  2  \n",
       "3            7                                0         1                  2  \n",
       "4           19                                1         1                  2  \n",
       "..         ...                              ...       ...                ...  \n",
       "182          7                                0         0                  1  \n",
       "183         32                                7         4                  5  \n",
       "184         25                                0        18                 22  \n",
       "185         23                                1         7                  3  \n",
       "186         21                                0        11                 15  \n",
       "\n",
       "[182 rows x 14 columns]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_applicants[\"Job Number\"]= df_applicants[\"Job Number\"].astype(str) \n",
    "\n",
    "df_applicants['Job Number'] = df_applicants['Job Number'].str.slice(0,4)\n",
    "\n",
    "\n",
    "df_applicants = df_applicants.drop([27, 64, 69, 72, 75])\n",
    "df_applicants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>two-year</td>\n",
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       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>240</td>\n",
       "      <td>32</td>\n",
       "      <td>96</td>\n",
       "      <td>173</td>\n",
       "      <td>84</td>\n",
       "      <td>211</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN</td>\n",
       "      <td>3638</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Communications Electrician acts as a ...</td>\n",
       "      <td>$94,941 (flat-rated)</td>\n",
       "      <td>$114,568 (flat-rated)</td>\n",
       "      <td>03-03-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>76</td>\n",
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       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
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       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PIPEFITTER</td>\n",
       "      <td>3433</td>\n",
       "      <td>1</td>\n",
       "      <td>A Pipefitter does skilled work in the installa...</td>\n",
       "      <td>$94,455 (flat-rated)</td>\n",
       "      <td>$101,622 (flat-rated)</td>\n",
       "      <td>08-03-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>apprenticeship</td>\n",
       "      <td>pipefitter</td>\n",
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       "      <td>215</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>104</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     JOB_CLASS_TITLE  Job Number  REQUIREMENT_SET_ID  \\\n",
       "0                     ARTS ASSOCIATE        2454                   1   \n",
       "1                     ARTS ASSOCIATE        2454                   2   \n",
       "2                     ARTS ASSOCIATE        2454                   3   \n",
       "3  SENIOR COMMUNICATIONS ELECTRICIAN        3638                   1   \n",
       "4                         PIPEFITTER        3433                   1   \n",
       "\n",
       "                                          JOB_DUTIES      ENTRY_SALARY_GEN  \\\n",
       "0  An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "1  An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "2  An Arts Associate performs a broad range of en...       $48,797-$71,326   \n",
       "3  A Senior Communications Electrician acts as a ...  $94,941 (flat-rated)   \n",
       "4  A Pipefitter does skilled work in the installa...  $94,455 (flat-rated)   \n",
       "\n",
       "        ENTRY_SALARY_DWP OPEN_DATE EDUCATION_YEARS            SCHOOL_TYPE  \\\n",
       "0                    NaN  07-21-17       four-year  college or university   \n",
       "1                    NaN  07-21-17        two-year         college with a   \n",
       "2                    NaN  07-21-17             NaN                    NaN   \n",
       "3  $114,568 (flat-rated)  03-03-17             NaN                    NaN   \n",
       "4  $101,622 (flat-rated)  08-03-18             NaN         apprenticeship   \n",
       "\n",
       "                                     EDUCATION_MAJOR  ... Female Male  \\\n",
       "0  Arts (design, literary, media/communication, p...  ...    430  240   \n",
       "1  Arts (design, literary, media/communication, p...  ...    430  240   \n",
       "2                                                NaN  ...    430  240   \n",
       "3                                                NaN  ...      1   76   \n",
       "4                                         pipefitter  ...      3  215   \n",
       "\n",
       "  Unknown_Gender Black Hispanic Asian Caucasian  \\\n",
       "0             32    96      173    84       211   \n",
       "1             32    96      173    84       211   \n",
       "2             32    96      173    84       211   \n",
       "3              0    12       17    14        26   \n",
       "4              5    29      104     2        75   \n",
       "\n",
       "  American Indian/ Alaskan Native Filipino Unknown_Ethnicity  \n",
       "0                               5       40                93  \n",
       "1                               5       40                93  \n",
       "2                               5       40                93  \n",
       "3                               0        5                 3  \n",
       "4                               1        5                 7  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_applicants['Job Number'] = df_applicants['Job Number'].astype(int)\n",
    "resulting_df = df.merge(\n",
    "    df_applicants, left_on='Job Number', right_on='Job Number')\n",
    "resulting_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>nag respon</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>ARTS ASSOCIATE 2454</td>\n",
       "      <td>702</td>\n",
       "      <td>430</td>\n",
       "      <td>240</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN</td>\n",
       "      <td>3638</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Communications Electrician acts as a ...</td>\n",
       "      <td>$94,941 (flat-rated)</td>\n",
       "      <td>$114,568 (flat-rated)</td>\n",
       "      <td>03-03-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN (3638)</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PIPEFITTER</td>\n",
       "      <td>3433</td>\n",
       "      <td>1</td>\n",
       "      <td>A Pipefitter does skilled work in the installa...</td>\n",
       "      <td>$94,455 (flat-rated)</td>\n",
       "      <td>$101,622 (flat-rated)</td>\n",
       "      <td>08-03-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>apprenticeship</td>\n",
       "      <td>pipefitter</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>PIPEFITTER 3433 - ARCHIVE</td>\n",
       "      <td>223</td>\n",
       "      <td>3</td>\n",
       "      <td>215</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC</td>\n",
       "      <td>3812</td>\n",
       "      <td>2</td>\n",
       "      <td>An Underground Distribution Construction Mecha...</td>\n",
       "      <td>$70,783-$87,946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10-19-18</td>\n",
       "      <td>One year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>respon son</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...</td>\n",
       "      <td>591</td>\n",
       "      <td>7</td>\n",
       "      <td>573</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC</td>\n",
       "      <td>3812</td>\n",
       "      <td>3</td>\n",
       "      <td>An Underground Distribution Construction Mecha...</td>\n",
       "      <td>$70,783-$87,946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10-19-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>respon son</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...</td>\n",
       "      <td>591</td>\n",
       "      <td>7</td>\n",
       "      <td>573</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC</td>\n",
       "      <td>3812</td>\n",
       "      <td>4</td>\n",
       "      <td>An Underground Distribution Construction Mecha...</td>\n",
       "      <td>$70,783-$87,946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10-19-18</td>\n",
       "      <td>One year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>respon son</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...</td>\n",
       "      <td>591</td>\n",
       "      <td>7</td>\n",
       "      <td>573</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>SENIOR PLUMBING INSPECTOR</td>\n",
       "      <td>4233</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Plumbing Inspector supervises and tra...</td>\n",
       "      <td>$90,410-$109,306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-18-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>nag respon son</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR PLUMBING INSPECTOR 4233 REVISED 4-10-14</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>SENIOR PLUMBING INSPECTOR</td>\n",
       "      <td>4233</td>\n",
       "      <td>2</td>\n",
       "      <td>A Senior Plumbing Inspector supervises and tra...</td>\n",
       "      <td>$90,410-$109,306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-18-18</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>Industrial Technology, Industrial Science</td>\n",
       "      <td>...</td>\n",
       "      <td>nag respon son</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR PLUMBING INSPECTOR 4233 REVISED 4-10-14</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>293 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    JOB_CLASS_TITLE  Job Number  \\\n",
       "0                                    ARTS ASSOCIATE        2454   \n",
       "1                                    ARTS ASSOCIATE        2454   \n",
       "2                                    ARTS ASSOCIATE        2454   \n",
       "3                 SENIOR COMMUNICATIONS ELECTRICIAN        3638   \n",
       "4                                        PIPEFITTER        3433   \n",
       "..                                              ...         ...   \n",
       "288  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC        3812   \n",
       "289  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC        3812   \n",
       "290  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC        3812   \n",
       "291                       SENIOR PLUMBING INSPECTOR        4233   \n",
       "292                       SENIOR PLUMBING INSPECTOR        4233   \n",
       "\n",
       "     REQUIREMENT_SET_ID                                         JOB_DUTIES  \\\n",
       "0                     1  An Arts Associate performs a broad range of en...   \n",
       "1                     2  An Arts Associate performs a broad range of en...   \n",
       "2                     3  An Arts Associate performs a broad range of en...   \n",
       "3                     1  A Senior Communications Electrician acts as a ...   \n",
       "4                     1  A Pipefitter does skilled work in the installa...   \n",
       "..                  ...                                                ...   \n",
       "288                   2  An Underground Distribution Construction Mecha...   \n",
       "289                   3  An Underground Distribution Construction Mecha...   \n",
       "290                   4  An Underground Distribution Construction Mecha...   \n",
       "291                   1  A Senior Plumbing Inspector supervises and tra...   \n",
       "292                   2  A Senior Plumbing Inspector supervises and tra...   \n",
       "\n",
       "         ENTRY_SALARY_GEN       ENTRY_SALARY_DWP OPEN_DATE EDUCATION_YEARS  \\\n",
       "0         $48,797-$71,326                    NaN  07-21-17       four-year   \n",
       "1         $48,797-$71,326                    NaN  07-21-17        two-year   \n",
       "2         $48,797-$71,326                    NaN  07-21-17             NaN   \n",
       "3    $94,941 (flat-rated)  $114,568 (flat-rated)  03-03-17             NaN   \n",
       "4    $94,455 (flat-rated)  $101,622 (flat-rated)  08-03-18             NaN   \n",
       "..                    ...                    ...       ...             ...   \n",
       "288       $70,783-$87,946                    NaN  10-19-18        One year   \n",
       "289       $70,783-$87,946                    NaN  10-19-18             NaN   \n",
       "290       $70,783-$87,946                    NaN  10-19-18        One year   \n",
       "291      $90,410-$109,306                    NaN  05-18-18             NaN   \n",
       "292      $90,410-$109,306                    NaN  05-18-18       four-year   \n",
       "\n",
       "               SCHOOL_TYPE                                    EDUCATION_MAJOR  \\\n",
       "0    college or university  Arts (design, literary, media/communication, p...   \n",
       "1           college with a  Arts (design, literary, media/communication, p...   \n",
       "2                      NaN                                                NaN   \n",
       "3                      NaN                                                NaN   \n",
       "4           apprenticeship                                         pipefitter   \n",
       "..                     ...                                                ...   \n",
       "288                    NaN                                                NaN   \n",
       "289                    NaN                                                NaN   \n",
       "290                    NaN                                                NaN   \n",
       "291                    NaN                                                NaN   \n",
       "292  college or university          Industrial Technology, Industrial Science   \n",
       "\n",
       "     ...       fem_words amount_masc amount_fem genderbias Fiscal Year  \\\n",
       "0    ...      nag respon           2          2    neutral   2013-2014   \n",
       "1    ...      nag respon           2          2    neutral   2013-2014   \n",
       "2    ...      nag respon           2          2    neutral   2013-2014   \n",
       "3    ...                           1          0       male   2013-2014   \n",
       "4    ...                           1          0       male   2014-2015   \n",
       "..   ...             ...         ...        ...        ...         ...   \n",
       "288  ...      respon son           2          2    neutral   2014-2015   \n",
       "289  ...      respon son           2          2    neutral   2014-2015   \n",
       "290  ...      respon son           2          2    neutral   2014-2015   \n",
       "291  ...  nag respon son           3          3    neutral   2013-2014   \n",
       "292  ...  nag respon son           3          3    neutral   2013-2014   \n",
       "\n",
       "                                       Job Description Apps Received Female  \\\n",
       "0                                  ARTS ASSOCIATE 2454           702    430   \n",
       "1                                  ARTS ASSOCIATE 2454           702    430   \n",
       "2                                  ARTS ASSOCIATE 2454           702    430   \n",
       "3             SENIOR COMMUNICATIONS ELECTRICIAN (3638)            77      1   \n",
       "4                            PIPEFITTER 3433 - ARCHIVE           223      3   \n",
       "..                                                 ...           ...    ...   \n",
       "288  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...           591      7   \n",
       "289  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...           591      7   \n",
       "290  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...           591      7   \n",
       "291     SENIOR PLUMBING INSPECTOR 4233 REVISED 4-10-14            25      1   \n",
       "292     SENIOR PLUMBING INSPECTOR 4233 REVISED 4-10-14            25      1   \n",
       "\n",
       "    Male Unknown_Gender  \n",
       "0    240             32  \n",
       "1    240             32  \n",
       "2    240             32  \n",
       "3     76              0  \n",
       "4    215              5  \n",
       "..   ...            ...  \n",
       "288  573             11  \n",
       "289  573             11  \n",
       "290  573             11  \n",
       "291   24              0  \n",
       "292   24              0  \n",
       "\n",
       "[293 rows x 40 columns]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#as I will not be using the ethnicity I will delete this out of the dataframe\n",
    "resulting_df = resulting_df.drop(['Black', 'Hispanic', 'Caucasian', 'Asian', 'American Indian/ Alaskan Native', \n",
    "                                  'Filipino', 'Unknown_Ethnicity' ], axis=1)\n",
    "resulting_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>JOB_CLASS_TITLE</th>\n",
       "      <th>Job Number</th>\n",
       "      <th>REQUIREMENT_SET_ID</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
       "      <td>1</td>\n",
       "      <td>An Arts Associate performs a broad range of en...</td>\n",
       "      <td>$48,797-$71,326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>Arts (design, literary, media/communication, p...</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>ARTS ASSOCIATE 2454</td>\n",
       "      <td>702</td>\n",
       "      <td>430</td>\n",
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       "      <td>ARTS ASSOCIATE</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>two-year</td>\n",
       "      <td>college with a</td>\n",
       "      <td>Arts (design, literary, media/communication, p...</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>ARTS ASSOCIATE 2454</td>\n",
       "      <td>702</td>\n",
       "      <td>430</td>\n",
       "      <td>240</td>\n",
       "      <td>32</td>\n",
       "      <td>0.612536</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARTS ASSOCIATE</td>\n",
       "      <td>2454</td>\n",
       "      <td>3</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.612536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN</td>\n",
       "      <td>3638</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Communications Electrician acts as a ...</td>\n",
       "      <td>$94,941 (flat-rated)</td>\n",
       "      <td>$114,568 (flat-rated)</td>\n",
       "      <td>03-03-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN (3638)</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PIPEFITTER</td>\n",
       "      <td>3433</td>\n",
       "      <td>1</td>\n",
       "      <td>A Pipefitter does skilled work in the installa...</td>\n",
       "      <td>$94,455 (flat-rated)</td>\n",
       "      <td>$101,622 (flat-rated)</td>\n",
       "      <td>08-03-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>apprenticeship</td>\n",
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       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>PIPEFITTER 3433 - ARCHIVE</td>\n",
       "      <td>223</td>\n",
       "      <td>3</td>\n",
       "      <td>215</td>\n",
       "      <td>5</td>\n",
       "      <td>0.013453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC</td>\n",
       "      <td>3812</td>\n",
       "      <td>2</td>\n",
       "      <td>An Underground Distribution Construction Mecha...</td>\n",
       "      <td>$70,783-$87,946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10-19-18</td>\n",
       "      <td>One year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...</td>\n",
       "      <td>591</td>\n",
       "      <td>7</td>\n",
       "      <td>573</td>\n",
       "      <td>11</td>\n",
       "      <td>0.011844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC</td>\n",
       "      <td>3812</td>\n",
       "      <td>3</td>\n",
       "      <td>An Underground Distribution Construction Mecha...</td>\n",
       "      <td>$70,783-$87,946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10-19-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...</td>\n",
       "      <td>591</td>\n",
       "      <td>7</td>\n",
       "      <td>573</td>\n",
       "      <td>11</td>\n",
       "      <td>0.011844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC</td>\n",
       "      <td>3812</td>\n",
       "      <td>4</td>\n",
       "      <td>An Underground Distribution Construction Mecha...</td>\n",
       "      <td>$70,783-$87,946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10-19-18</td>\n",
       "      <td>One year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...</td>\n",
       "      <td>591</td>\n",
       "      <td>7</td>\n",
       "      <td>573</td>\n",
       "      <td>11</td>\n",
       "      <td>0.011844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>SENIOR PLUMBING INSPECTOR</td>\n",
       "      <td>4233</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Plumbing Inspector supervises and tra...</td>\n",
       "      <td>$90,410-$109,306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-18-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR PLUMBING INSPECTOR 4233 REVISED 4-10-14</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>SENIOR PLUMBING INSPECTOR</td>\n",
       "      <td>4233</td>\n",
       "      <td>2</td>\n",
       "      <td>A Senior Plumbing Inspector supervises and tra...</td>\n",
       "      <td>$90,410-$109,306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-18-18</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>Industrial Technology, Industrial Science</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>neutral</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR PLUMBING INSPECTOR 4233 REVISED 4-10-14</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>293 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    JOB_CLASS_TITLE  Job Number  \\\n",
       "id                                                                \n",
       "0                                    ARTS ASSOCIATE        2454   \n",
       "1                                    ARTS ASSOCIATE        2454   \n",
       "2                                    ARTS ASSOCIATE        2454   \n",
       "3                 SENIOR COMMUNICATIONS ELECTRICIAN        3638   \n",
       "4                                        PIPEFITTER        3433   \n",
       "..                                              ...         ...   \n",
       "288  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC        3812   \n",
       "289  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC        3812   \n",
       "290  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC        3812   \n",
       "291                       SENIOR PLUMBING INSPECTOR        4233   \n",
       "292                       SENIOR PLUMBING INSPECTOR        4233   \n",
       "\n",
       "     REQUIREMENT_SET_ID                                         JOB_DUTIES  \\\n",
       "id                                                                           \n",
       "0                     1  An Arts Associate performs a broad range of en...   \n",
       "1                     2  An Arts Associate performs a broad range of en...   \n",
       "2                     3  An Arts Associate performs a broad range of en...   \n",
       "3                     1  A Senior Communications Electrician acts as a ...   \n",
       "4                     1  A Pipefitter does skilled work in the installa...   \n",
       "..                  ...                                                ...   \n",
       "288                   2  An Underground Distribution Construction Mecha...   \n",
       "289                   3  An Underground Distribution Construction Mecha...   \n",
       "290                   4  An Underground Distribution Construction Mecha...   \n",
       "291                   1  A Senior Plumbing Inspector supervises and tra...   \n",
       "292                   2  A Senior Plumbing Inspector supervises and tra...   \n",
       "\n",
       "         ENTRY_SALARY_GEN       ENTRY_SALARY_DWP OPEN_DATE EDUCATION_YEARS  \\\n",
       "id                                                                           \n",
       "0         $48,797-$71,326                    NaN  07-21-17       four-year   \n",
       "1         $48,797-$71,326                    NaN  07-21-17        two-year   \n",
       "2         $48,797-$71,326                    NaN  07-21-17             NaN   \n",
       "3    $94,941 (flat-rated)  $114,568 (flat-rated)  03-03-17             NaN   \n",
       "4    $94,455 (flat-rated)  $101,622 (flat-rated)  08-03-18             NaN   \n",
       "..                    ...                    ...       ...             ...   \n",
       "288       $70,783-$87,946                    NaN  10-19-18        One year   \n",
       "289       $70,783-$87,946                    NaN  10-19-18             NaN   \n",
       "290       $70,783-$87,946                    NaN  10-19-18        One year   \n",
       "291      $90,410-$109,306                    NaN  05-18-18             NaN   \n",
       "292      $90,410-$109,306                    NaN  05-18-18       four-year   \n",
       "\n",
       "               SCHOOL_TYPE                                    EDUCATION_MAJOR  \\\n",
       "id                                                                              \n",
       "0    college or university  Arts (design, literary, media/communication, p...   \n",
       "1           college with a  Arts (design, literary, media/communication, p...   \n",
       "2                      NaN                                                NaN   \n",
       "3                      NaN                                                NaN   \n",
       "4           apprenticeship                                         pipefitter   \n",
       "..                     ...                                                ...   \n",
       "288                    NaN                                                NaN   \n",
       "289                    NaN                                                NaN   \n",
       "290                    NaN                                                NaN   \n",
       "291                    NaN                                                NaN   \n",
       "292  college or university          Industrial Technology, Industrial Science   \n",
       "\n",
       "     ... amount_masc amount_fem genderbias Fiscal Year  \\\n",
       "id   ...                                                 \n",
       "0    ...           2          2    neutral   2013-2014   \n",
       "1    ...           2          2    neutral   2013-2014   \n",
       "2    ...           2          2    neutral   2013-2014   \n",
       "3    ...           1          0       male   2013-2014   \n",
       "4    ...           1          0       male   2014-2015   \n",
       "..   ...         ...        ...        ...         ...   \n",
       "288  ...           2          2    neutral   2014-2015   \n",
       "289  ...           2          2    neutral   2014-2015   \n",
       "290  ...           2          2    neutral   2014-2015   \n",
       "291  ...           3          3    neutral   2013-2014   \n",
       "292  ...           3          3    neutral   2013-2014   \n",
       "\n",
       "                                       Job Description Apps Received Female  \\\n",
       "id                                                                            \n",
       "0                                  ARTS ASSOCIATE 2454           702    430   \n",
       "1                                  ARTS ASSOCIATE 2454           702    430   \n",
       "2                                  ARTS ASSOCIATE 2454           702    430   \n",
       "3             SENIOR COMMUNICATIONS ELECTRICIAN (3638)            77      1   \n",
       "4                            PIPEFITTER 3433 - ARCHIVE           223      3   \n",
       "..                                                 ...           ...    ...   \n",
       "288  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...           591      7   \n",
       "289  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...           591      7   \n",
       "290  UNDERGROUND DISTRIBUTION CONSTRUCTION MECHANIC...           591      7   \n",
       "291     SENIOR PLUMBING INSPECTOR 4233 REVISED 4-10-14            25      1   \n",
       "292     SENIOR PLUMBING INSPECTOR 4233 REVISED 4-10-14            25      1   \n",
       "\n",
       "    Male Unknown_Gender     %_Fem  \n",
       "id                                 \n",
       "0    240             32  0.612536  \n",
       "1    240             32  0.612536  \n",
       "2    240             32  0.612536  \n",
       "3     76              0  0.012987  \n",
       "4    215              5  0.013453  \n",
       "..   ...            ...       ...  \n",
       "288  573             11  0.011844  \n",
       "289  573             11  0.011844  \n",
       "290  573             11  0.011844  \n",
       "291   24              0  0.040000  \n",
       "292   24              0  0.040000  \n",
       "\n",
       "[293 rows x 41 columns]"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of the amounts of women applied\n",
    "resulting_df['%_Fem'] = resulting_df['Female']/resulting_df['Apps Received']\n",
    "resulting_df.index.name = 'id'\n",
    "resulting_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the jobs women apply most to (and what do they apply least to)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>JOB_CLASS_TITLE</th>\n",
       "      <th>Job Number</th>\n",
       "      <th>REQUIREMENT_SET_ID</th>\n",
       "      <th>JOB_DUTIES</th>\n",
       "      <th>ENTRY_SALARY_GEN</th>\n",
       "      <th>ENTRY_SALARY_DWP</th>\n",
       "      <th>OPEN_DATE</th>\n",
       "      <th>EDUCATION_YEARS</th>\n",
       "      <th>SCHOOL_TYPE</th>\n",
       "      <th>EDUCATION_MAJOR</th>\n",
       "      <th>...</th>\n",
       "      <th>amount_masc</th>\n",
       "      <th>amount_fem</th>\n",
       "      <th>genderbias</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Job Description</th>\n",
       "      <th>Apps Received</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Unknown_Gender</th>\n",
       "      <th>%_Fem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN</td>\n",
       "      <td>3638</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Communications Electrician acts as a ...</td>\n",
       "      <td>$94,941 (flat-rated)</td>\n",
       "      <td>$114,568 (flat-rated)</td>\n",
       "      <td>03-03-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN (3638)</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FIRE PROTECTION ENGINEERING ASSOCIATE</td>\n",
       "      <td>7978</td>\n",
       "      <td>1</td>\n",
       "      <td>A Fire Protection Engineering Associate perfor...</td>\n",
       "      <td>$66,231-$96,841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-13-18</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>engineering</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>FIRE PROTECTION ENGINEERING ASSOCIATE</td>\n",
       "      <td>107</td>\n",
       "      <td>16</td>\n",
       "      <td>89</td>\n",
       "      <td>2</td>\n",
       "      <td>0.149533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ARCHIVIST</td>\n",
       "      <td>1191</td>\n",
       "      <td>1</td>\n",
       "      <td>An Archivist performs professional archival wo...</td>\n",
       "      <td>$57,273-$83,728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02-09-18</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>ARCHIVIST1191</td>\n",
       "      <td>161</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>6</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SENIOR UTILITY BUYER</td>\n",
       "      <td>1862</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Utility Buyer performs, plans, organi...</td>\n",
       "      <td>$107,364-$113,357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-25-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR UTILITY BUYER 1862</td>\n",
       "      <td>65</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>0.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>UTILITY SERVICES SPECIALIST</td>\n",
       "      <td>3755</td>\n",
       "      <td>1</td>\n",
       "      <td>Promotes through extensive customer contacts t...</td>\n",
       "      <td>$74,750-$92,895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>UTILITY SERVICES SPECIALIST 3755 - ARCHIVE</td>\n",
       "      <td>735</td>\n",
       "      <td>409</td>\n",
       "      <td>282</td>\n",
       "      <td>44</td>\n",
       "      <td>0.556463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>DIRECTOR OF HOUSING</td>\n",
       "      <td>1568</td>\n",
       "      <td>1</td>\n",
       "      <td>A Director of Housing plans, directs and coord...</td>\n",
       "      <td>$120,644-$171,654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>06-23-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>DIRECTOR OF HOUSING (1568) - ARCHIVE</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>0.576271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>SENIOR ELECTRICAL INSPECTOR</td>\n",
       "      <td>4223</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Electrical Inspector supervises the w...</td>\n",
       "      <td>$90,410-$109,306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-27-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR ELECTRICAL INSPECTOR 4223</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>INFORMATION SYSTEMS MANAGER</td>\n",
       "      <td>1409</td>\n",
       "      <td>1</td>\n",
       "      <td>An Information Systems Manager plans, organize...</td>\n",
       "      <td>$111,373-$158,500</td>\n",
       "      <td>$121,500-$150,962</td>\n",
       "      <td>09-01-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>International Information Systems Security Cer...</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>INFORMATION SYSTEMS MANAGER 1409</td>\n",
       "      <td>111</td>\n",
       "      <td>27</td>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "      <td>0.243243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>INTERNAL AUDITOR</td>\n",
       "      <td>1625</td>\n",
       "      <td>1</td>\n",
       "      <td>An Internal Auditor performs independent perfo...</td>\n",
       "      <td>$58,171-$85,044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-19-18</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>business, accounting, economics, finance, publ...</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>INTERNAL AUDITOR 1625</td>\n",
       "      <td>247</td>\n",
       "      <td>114</td>\n",
       "      <td>127</td>\n",
       "      <td>6</td>\n",
       "      <td>0.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>HEAVY DUTY TRUCK OPERATOR</td>\n",
       "      <td>3584</td>\n",
       "      <td>1</td>\n",
       "      <td>A Heavy Duty Truck Operator drives a heavy dut...</td>\n",
       "      <td>$54,037-$65,333</td>\n",
       "      <td>$67,985-$71,785</td>\n",
       "      <td>12-23-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>HEAVY DUTY TRUCK OPERATOR 3584</td>\n",
       "      <td>1319</td>\n",
       "      <td>24</td>\n",
       "      <td>1291</td>\n",
       "      <td>4</td>\n",
       "      <td>0.018196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           JOB_CLASS_TITLE  Job Number  REQUIREMENT_SET_ID  \\\n",
       "id                                                                           \n",
       "3        SENIOR COMMUNICATIONS ELECTRICIAN        3638                   1   \n",
       "7    FIRE PROTECTION ENGINEERING ASSOCIATE        7978                   1   \n",
       "8                                ARCHIVIST        1191                   1   \n",
       "14                    SENIOR UTILITY BUYER        1862                   1   \n",
       "15             UTILITY SERVICES SPECIALIST        3755                   1   \n",
       "..                                     ...         ...                 ...   \n",
       "272                    DIRECTOR OF HOUSING        1568                   1   \n",
       "273            SENIOR ELECTRICAL INSPECTOR        4223                   1   \n",
       "279            INFORMATION SYSTEMS MANAGER        1409                   1   \n",
       "280                       INTERNAL AUDITOR        1625                   1   \n",
       "283              HEAVY DUTY TRUCK OPERATOR        3584                   1   \n",
       "\n",
       "                                            JOB_DUTIES      ENTRY_SALARY_GEN  \\\n",
       "id                                                                             \n",
       "3    A Senior Communications Electrician acts as a ...  $94,941 (flat-rated)   \n",
       "7    A Fire Protection Engineering Associate perfor...       $66,231-$96,841   \n",
       "8    An Archivist performs professional archival wo...       $57,273-$83,728   \n",
       "14   A Senior Utility Buyer performs, plans, organi...     $107,364-$113,357   \n",
       "15   Promotes through extensive customer contacts t...       $74,750-$92,895   \n",
       "..                                                 ...                   ...   \n",
       "272  A Director of Housing plans, directs and coord...     $120,644-$171,654   \n",
       "273  A Senior Electrical Inspector supervises the w...      $90,410-$109,306   \n",
       "279  An Information Systems Manager plans, organize...     $111,373-$158,500   \n",
       "280  An Internal Auditor performs independent perfo...       $58,171-$85,044   \n",
       "283  A Heavy Duty Truck Operator drives a heavy dut...       $54,037-$65,333   \n",
       "\n",
       "          ENTRY_SALARY_DWP OPEN_DATE EDUCATION_YEARS            SCHOOL_TYPE  \\\n",
       "id                                                                            \n",
       "3    $114,568 (flat-rated)  03-03-17             NaN                    NaN   \n",
       "7                      NaN  04-13-18       four-year  college or university   \n",
       "8                      NaN  02-09-18       four-year  college or university   \n",
       "14                     NaN  05-25-18             NaN                    NaN   \n",
       "15                     NaN  07-21-17       four-year  college or university   \n",
       "..                     ...       ...             ...                    ...   \n",
       "272                    NaN  06-23-17             NaN                    NaN   \n",
       "273                    NaN  04-27-18             NaN                    NaN   \n",
       "279      $121,500-$150,962  09-01-17             NaN                    NaN   \n",
       "280                    NaN  01-19-18       four-year  college or university   \n",
       "283        $67,985-$71,785  12-23-16             NaN                    NaN   \n",
       "\n",
       "                                       EDUCATION_MAJOR  ... amount_masc  \\\n",
       "id                                                      ...               \n",
       "3                                                  NaN  ...           1   \n",
       "7                                          engineering  ...           3   \n",
       "8                                                  NaN  ...           3   \n",
       "14                                                 NaN  ...           3   \n",
       "15                                                 NaN  ...           4   \n",
       "..                                                 ...  ...         ...   \n",
       "272                                                NaN  ...           4   \n",
       "273                                                NaN  ...           1   \n",
       "279  International Information Systems Security Cer...  ...           5   \n",
       "280  business, accounting, economics, finance, publ...  ...           7   \n",
       "283                                                NaN  ...           2   \n",
       "\n",
       "    amount_fem genderbias Fiscal Year  \\\n",
       "id                                      \n",
       "3            0       male   2013-2014   \n",
       "7            0       male   2013-2014   \n",
       "8            1       male   2013-2014   \n",
       "14           1       male   2013-2014   \n",
       "15           3       male   2014-2015   \n",
       "..         ...        ...         ...   \n",
       "272          3       male   2014-2015   \n",
       "273          2     female   2013-2014   \n",
       "279          3       male   2013-2014   \n",
       "280          2       male   2013-2014   \n",
       "283          0       male   2013-2014   \n",
       "\n",
       "                                Job Description Apps Received Female  Male  \\\n",
       "id                                                                           \n",
       "3      SENIOR COMMUNICATIONS ELECTRICIAN (3638)            77      1    76   \n",
       "7         FIRE PROTECTION ENGINEERING ASSOCIATE           107     16    89   \n",
       "8                                 ARCHIVIST1191           161     89    66   \n",
       "14                    SENIOR UTILITY BUYER 1862            65     30    32   \n",
       "15   UTILITY SERVICES SPECIALIST 3755 - ARCHIVE           735    409   282   \n",
       "..                                          ...           ...    ...   ...   \n",
       "272        DIRECTOR OF HOUSING (1568) - ARCHIVE            59     34    23   \n",
       "273            SENIOR ELECTRICAL INSPECTOR 4223            16      2    14   \n",
       "279            INFORMATION SYSTEMS MANAGER 1409           111     27    78   \n",
       "280                       INTERNAL AUDITOR 1625           247    114   127   \n",
       "283              HEAVY DUTY TRUCK OPERATOR 3584          1319     24  1291   \n",
       "\n",
       "    Unknown_Gender     %_Fem  \n",
       "id                            \n",
       "3                0  0.012987  \n",
       "7                2  0.149533  \n",
       "8                6  0.552795  \n",
       "14               3  0.461538  \n",
       "15              44  0.556463  \n",
       "..             ...       ...  \n",
       "272              2  0.576271  \n",
       "273              0  0.125000  \n",
       "279              6  0.243243  \n",
       "280              6  0.461538  \n",
       "283              4  0.018196  \n",
       "\n",
       "[93 rows x 41 columns]"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset = resulting_df.drop_duplicates(subset=['Job Number'], keep=False) \n",
    "df_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 50))\n",
    "\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(x=\"Apps Received\", y=\"JOB_CLASS_TITLE\", data=df_subset,\n",
    "            label=\"Total applicants\", color=\"b\")\n",
    "\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x=\"Female\", y=\"JOB_CLASS_TITLE\", data=df_subset,\n",
    "            label=\"Female applicants\", color=\"b\")\n",
    "\n",
    "ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n",
    "ax.set(xlim=(0, 24), ylabel=\"\",\n",
    "       xlabel=\"Applicants of job types\")\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do the male notated jobs also earn more? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nienke/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/nienke/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/Users/nienke/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:966: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\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>JOB_CLASS_TITLE</th>\n",
       "      <th>Job Number</th>\n",
       "      <th>REQUIREMENT_SET_ID</th>\n",
       "      <th>JOB_DUTIES</th>\n",
       "      <th>ENTRY_SALARY_GEN</th>\n",
       "      <th>ENTRY_SALARY_DWP</th>\n",
       "      <th>OPEN_DATE</th>\n",
       "      <th>EDUCATION_YEARS</th>\n",
       "      <th>SCHOOL_TYPE</th>\n",
       "      <th>EDUCATION_MAJOR</th>\n",
       "      <th>...</th>\n",
       "      <th>genderbias</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Job Description</th>\n",
       "      <th>Apps Received</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Unknown_Gender</th>\n",
       "      <th>%_Fem</th>\n",
       "      <th>calculated_salary</th>\n",
       "      <th>difference_words</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN</td>\n",
       "      <td>3638</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Communications Electrician acts as a ...</td>\n",
       "      <td>$94,941 (flat-rated)</td>\n",
       "      <td>$114,568 (flat-rated)</td>\n",
       "      <td>03-03-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR COMMUNICATIONS ELECTRICIAN (3638)</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012987</td>\n",
       "      <td>94941.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FIRE PROTECTION ENGINEERING ASSOCIATE</td>\n",
       "      <td>7978</td>\n",
       "      <td>1</td>\n",
       "      <td>A Fire Protection Engineering Associate perfor...</td>\n",
       "      <td>$66,231-$96,841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-13-18</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>engineering</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>FIRE PROTECTION ENGINEERING ASSOCIATE</td>\n",
       "      <td>107</td>\n",
       "      <td>16</td>\n",
       "      <td>89</td>\n",
       "      <td>2</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>30610.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ARCHIVIST</td>\n",
       "      <td>1191</td>\n",
       "      <td>1</td>\n",
       "      <td>An Archivist performs professional archival wo...</td>\n",
       "      <td>$57,273-$83,728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02-09-18</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>ARCHIVIST1191</td>\n",
       "      <td>161</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>6</td>\n",
       "      <td>0.552795</td>\n",
       "      <td>26455.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SENIOR UTILITY BUYER</td>\n",
       "      <td>1862</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Utility Buyer performs, plans, organi...</td>\n",
       "      <td>$107,364-$113,357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-25-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR UTILITY BUYER 1862</td>\n",
       "      <td>65</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>5993.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>UTILITY SERVICES SPECIALIST</td>\n",
       "      <td>3755</td>\n",
       "      <td>1</td>\n",
       "      <td>Promotes through extensive customer contacts t...</td>\n",
       "      <td>$74,750-$92,895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-21-17</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>UTILITY SERVICES SPECIALIST 3755 - ARCHIVE</td>\n",
       "      <td>735</td>\n",
       "      <td>409</td>\n",
       "      <td>282</td>\n",
       "      <td>44</td>\n",
       "      <td>0.556463</td>\n",
       "      <td>18145.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>DIRECTOR OF HOUSING</td>\n",
       "      <td>1568</td>\n",
       "      <td>1</td>\n",
       "      <td>A Director of Housing plans, directs and coord...</td>\n",
       "      <td>$120,644-$171,654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>06-23-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>2014-2015</td>\n",
       "      <td>DIRECTOR OF HOUSING (1568) - ARCHIVE</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>0.576271</td>\n",
       "      <td>51010.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>SENIOR ELECTRICAL INSPECTOR</td>\n",
       "      <td>4223</td>\n",
       "      <td>1</td>\n",
       "      <td>A Senior Electrical Inspector supervises the w...</td>\n",
       "      <td>$90,410-$109,306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-27-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>female</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>SENIOR ELECTRICAL INSPECTOR 4223</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>18896.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>INFORMATION SYSTEMS MANAGER</td>\n",
       "      <td>1409</td>\n",
       "      <td>1</td>\n",
       "      <td>An Information Systems Manager plans, organize...</td>\n",
       "      <td>$111,373-$158,500</td>\n",
       "      <td>$121,500-$150,962</td>\n",
       "      <td>09-01-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>International Information Systems Security Cer...</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>INFORMATION SYSTEMS MANAGER 1409</td>\n",
       "      <td>111</td>\n",
       "      <td>27</td>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>47127.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>INTERNAL AUDITOR</td>\n",
       "      <td>1625</td>\n",
       "      <td>1</td>\n",
       "      <td>An Internal Auditor performs independent perfo...</td>\n",
       "      <td>$58,171-$85,044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-19-18</td>\n",
       "      <td>four-year</td>\n",
       "      <td>college or university</td>\n",
       "      <td>business, accounting, economics, finance, publ...</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>INTERNAL AUDITOR 1625</td>\n",
       "      <td>247</td>\n",
       "      <td>114</td>\n",
       "      <td>127</td>\n",
       "      <td>6</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>26873.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>HEAVY DUTY TRUCK OPERATOR</td>\n",
       "      <td>3584</td>\n",
       "      <td>1</td>\n",
       "      <td>A Heavy Duty Truck Operator drives a heavy dut...</td>\n",
       "      <td>$54,037-$65,333</td>\n",
       "      <td>$67,985-$71,785</td>\n",
       "      <td>12-23-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>male</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>HEAVY DUTY TRUCK OPERATOR 3584</td>\n",
       "      <td>1319</td>\n",
       "      <td>24</td>\n",
       "      <td>1291</td>\n",
       "      <td>4</td>\n",
       "      <td>0.018196</td>\n",
       "      <td>11296.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           JOB_CLASS_TITLE  Job Number  REQUIREMENT_SET_ID  \\\n",
       "id                                                                           \n",
       "3        SENIOR COMMUNICATIONS ELECTRICIAN        3638                   1   \n",
       "7    FIRE PROTECTION ENGINEERING ASSOCIATE        7978                   1   \n",
       "8                                ARCHIVIST        1191                   1   \n",
       "14                    SENIOR UTILITY BUYER        1862                   1   \n",
       "15             UTILITY SERVICES SPECIALIST        3755                   1   \n",
       "..                                     ...         ...                 ...   \n",
       "272                    DIRECTOR OF HOUSING        1568                   1   \n",
       "273            SENIOR ELECTRICAL INSPECTOR        4223                   1   \n",
       "279            INFORMATION SYSTEMS MANAGER        1409                   1   \n",
       "280                       INTERNAL AUDITOR        1625                   1   \n",
       "283              HEAVY DUTY TRUCK OPERATOR        3584                   1   \n",
       "\n",
       "                                            JOB_DUTIES      ENTRY_SALARY_GEN  \\\n",
       "id                                                                             \n",
       "3    A Senior Communications Electrician acts as a ...  $94,941 (flat-rated)   \n",
       "7    A Fire Protection Engineering Associate perfor...       $66,231-$96,841   \n",
       "8    An Archivist performs professional archival wo...       $57,273-$83,728   \n",
       "14   A Senior Utility Buyer performs, plans, organi...     $107,364-$113,357   \n",
       "15   Promotes through extensive customer contacts t...       $74,750-$92,895   \n",
       "..                                                 ...                   ...   \n",
       "272  A Director of Housing plans, directs and coord...     $120,644-$171,654   \n",
       "273  A Senior Electrical Inspector supervises the w...      $90,410-$109,306   \n",
       "279  An Information Systems Manager plans, organize...     $111,373-$158,500   \n",
       "280  An Internal Auditor performs independent perfo...       $58,171-$85,044   \n",
       "283  A Heavy Duty Truck Operator drives a heavy dut...       $54,037-$65,333   \n",
       "\n",
       "          ENTRY_SALARY_DWP OPEN_DATE EDUCATION_YEARS            SCHOOL_TYPE  \\\n",
       "id                                                                            \n",
       "3    $114,568 (flat-rated)  03-03-17             NaN                    NaN   \n",
       "7                      NaN  04-13-18       four-year  college or university   \n",
       "8                      NaN  02-09-18       four-year  college or university   \n",
       "14                     NaN  05-25-18             NaN                    NaN   \n",
       "15                     NaN  07-21-17       four-year  college or university   \n",
       "..                     ...       ...             ...                    ...   \n",
       "272                    NaN  06-23-17             NaN                    NaN   \n",
       "273                    NaN  04-27-18             NaN                    NaN   \n",
       "279      $121,500-$150,962  09-01-17             NaN                    NaN   \n",
       "280                    NaN  01-19-18       four-year  college or university   \n",
       "283        $67,985-$71,785  12-23-16             NaN                    NaN   \n",
       "\n",
       "                                       EDUCATION_MAJOR  ... genderbias  \\\n",
       "id                                                      ...              \n",
       "3                                                  NaN  ...       male   \n",
       "7                                          engineering  ...       male   \n",
       "8                                                  NaN  ...       male   \n",
       "14                                                 NaN  ...       male   \n",
       "15                                                 NaN  ...       male   \n",
       "..                                                 ...  ...        ...   \n",
       "272                                                NaN  ...       male   \n",
       "273                                                NaN  ...     female   \n",
       "279  International Information Systems Security Cer...  ...       male   \n",
       "280  business, accounting, economics, finance, publ...  ...       male   \n",
       "283                                                NaN  ...       male   \n",
       "\n",
       "    Fiscal Year                             Job Description Apps Received  \\\n",
       "id                                                                          \n",
       "3     2013-2014    SENIOR COMMUNICATIONS ELECTRICIAN (3638)            77   \n",
       "7     2013-2014       FIRE PROTECTION ENGINEERING ASSOCIATE           107   \n",
       "8     2013-2014                               ARCHIVIST1191           161   \n",
       "14    2013-2014                   SENIOR UTILITY BUYER 1862            65   \n",
       "15    2014-2015  UTILITY SERVICES SPECIALIST 3755 - ARCHIVE           735   \n",
       "..          ...                                         ...           ...   \n",
       "272   2014-2015        DIRECTOR OF HOUSING (1568) - ARCHIVE            59   \n",
       "273   2013-2014            SENIOR ELECTRICAL INSPECTOR 4223            16   \n",
       "279   2013-2014            INFORMATION SYSTEMS MANAGER 1409           111   \n",
       "280   2013-2014                       INTERNAL AUDITOR 1625           247   \n",
       "283   2013-2014              HEAVY DUTY TRUCK OPERATOR 3584          1319   \n",
       "\n",
       "    Female  Male Unknown_Gender     %_Fem calculated_salary difference_words  \n",
       "id                                                                            \n",
       "3        1    76              0  0.012987           94941.0              1.0  \n",
       "7       16    89              2  0.149533           30610.0              3.0  \n",
       "8       89    66              6  0.552795           26455.0              2.0  \n",
       "14      30    32              3  0.461538            5993.0              2.0  \n",
       "15     409   282             44  0.556463           18145.0              1.0  \n",
       "..     ...   ...            ...       ...               ...              ...  \n",
       "272     34    23              2  0.576271           51010.0              1.0  \n",
       "273      2    14              0  0.125000           18896.0             -1.0  \n",
       "279     27    78              6  0.243243           47127.0              2.0  \n",
       "280    114   127              6  0.461538           26873.0              5.0  \n",
       "283     24  1291              4  0.018196           11296.0              2.0  \n",
       "\n",
       "[93 rows x 43 columns]"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df_subset['ENTRY_SALARY_GEN'].astype(str)\n",
    "df_subset[\"calculated_salary\"] = np.nan\n",
    "df_subset[\"difference_words\"] = np.nan\n",
    "for index, row in df_subset.iterrows(): \n",
    "    if('flat-rated' in row['ENTRY_SALARY_GEN']):\n",
    "        calculated_entry_sal = row['ENTRY_SALARY_GEN'].replace('(flat-rated)', '').replace('$', '').replace(',','')\n",
    "        calculated_entry_sal = int(calculated_entry_sal)\n",
    "        df_subset.loc[index,'calculated_salary'] = calculated_entry_sal\n",
    "    else:\n",
    "        before, sep, after = row['ENTRY_SALARY_GEN'].rpartition(\"-\")\n",
    "        before = before.replace('$', '').replace(',','')\n",
    "        before = int(before)\n",
    "        after = after.replace('$', '').replace(',','')\n",
    "        after = int(after)\n",
    "        #get median of salary given\n",
    "        calculated_entry_sal = after - before\n",
    "        df_subset.loc[index,'calculated_salary'] = calculated_entry_sal\n",
    "    df_subset.loc[index,'difference_words'] = row['amount_masc'] - row['amount_fem']\n",
    "\n",
    "df_subset\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = sns.scatterplot(x=\"difference_words\", y=\"calculated_salary\", hue=\"genderbias\", data=df_subset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above does show that feminine classified texts offer lower paying salary than the masculine classified texts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = sns.scatterplot(x=\"difference_words\", y=\"%_Fem\", data=df_subset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My prediction would have been this would be an upgoing line (as a positive difference in words means it is male biased, a higher difference in words would mean a more male biased job description which would make it less appealing for women to apply). The graph however shows that there is no specific correlation between the two values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearsons correlation: 0.069\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "corr, _ = pearsonr(df_subset[\"difference_words\"],df_subset[\"%_Fem\"])\n",
    "\n",
    "print('Pearsons correlation: %.3f' % corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pearson's R also shows there is very little correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1922 words in the vocabulary. A selection: ['defines', 'delinquent', 'deliveries', 'demanding', 'demographics', 'demonstrates', 'department', 'departmental', 'departments', 'deposit', 'deputy', 'describing', 'descriptions', 'design', 'designated', 'designer', 'designs', 'desired', 'detailed', 'details']\n",
      "  (0, 47)\t3\n",
      "  (0, 55)\t1\n",
      "  (0, 122)\t1\n",
      "  (0, 125)\t2\n",
      "  (0, 133)\t3\n",
      "  (0, 134)\t4\n",
      "  (0, 135)\t1\n",
      "  (0, 156)\t1\n",
      "  (0, 231)\t1\n",
      "  (0, 262)\t1\n",
      "  (0, 345)\t1\n",
      "  (0, 355)\t1\n",
      "  (0, 364)\t1\n",
      "  (0, 412)\t1\n",
      "  (0, 446)\t1\n",
      "  (0, 478)\t1\n",
      "  (1, 47)\t3\n",
      "  (1, 55)\t1\n",
      "  (1, 122)\t1\n",
      "  (1, 125)\t2\n",
      "  (1, 133)\t3\n",
      "  (1, 134)\t4\n",
      "  (1, 135)\t1\n",
      "  (1, 156)\t1\n",
      "  (1, 231)\t1\n",
      "  :\t:\n",
      "  (288, 417)\t1\n",
      "  (289, 416)\t1\n",
      "  (289, 417)\t1\n",
      "  (290, 416)\t1\n",
      "  (290, 417)\t1\n",
      "  (291, 98)\t1\n",
      "  (291, 161)\t1\n",
      "  (291, 237)\t1\n",
      "  (291, 238)\t1\n",
      "  (291, 308)\t1\n",
      "  (291, 340)\t2\n",
      "  (291, 376)\t1\n",
      "  (291, 406)\t2\n",
      "  (291, 416)\t1\n",
      "  (291, 430)\t1\n",
      "  (292, 98)\t1\n",
      "  (292, 161)\t1\n",
      "  (292, 237)\t1\n",
      "  (292, 238)\t1\n",
      "  (292, 308)\t1\n",
      "  (292, 340)\t2\n",
      "  (292, 376)\t1\n",
      "  (292, 406)\t2\n",
      "  (292, 416)\t1\n",
      "  (292, 430)\t1\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer #The CountVectorizer object\n",
    "\n",
    "text = resulting_df['JOB_DUTIES'].values.astype('U') #Taking the text from the df. We need to convert it to Unicode\n",
    "vect = CountVectorizer(stop_words='english') #Create the CV object, with English stop words\n",
    "vect = vect.fit(text) #We fit the model with the words from the review text\n",
    "vect\n",
    "feature_names = vect.get_feature_names() #Get the words from the vocabulary\n",
    "print(f\"There are {len(feature_names)} words in the vocabulary. A selection: {feature_names[500:520]}\")\n",
    "docu_feat = vect.transform(text) #The transform method from the CountVectorizer object creates the matrix\n",
    "print(docu_feat[0:500,0:500]) #Let's print a little part of the matrix: the first 50 words & documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting if job ad is either female, male or neutrally biased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = resulting_df['genderbias'] #We need to take out the price as our Y-variable\n",
    "X = docu_feat  #this slices the dataframe to include all rows I need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) #split the data, store it into different variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<205x1922 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 8684 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "clf = MultinomialNB() #clf = classifier\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  1,  0],\n",
       "       [ 4, 65,  1],\n",
       "       [ 0,  2, 12]])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_test_pred = clf.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_test_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['female', 'male', 'neutral'], dtype='<U7')"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y_test.value_counts() works both\n",
    "clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>predicted female</th>\n",
       "      <th>predicted male</th>\n",
       "      <th>predicted neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutral</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         predicted female  predicted male  predicted neutral\n",
       "female                  3               1                  0\n",
       "male                    4              65                  1\n",
       "neutral                 0               2                 12"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In order to read it easily , let's make a dataframe out of it, and add labels to it.\n",
    "conf_matrix = pd.DataFrame(cm, index=['female', 'male', 'neutral' ], columns = ['predicted female', 'predicted male', 'predicted neutral']) \n",
    "conf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      female       0.43      0.75      0.55         4\n",
      "        male       0.96      0.93      0.94        70\n",
      "     neutral       0.92      0.86      0.89        14\n",
      "\n",
      "    accuracy                           0.91        88\n",
      "   macro avg       0.77      0.85      0.79        88\n",
      "weighted avg       0.93      0.91      0.92        88\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9090909090909091"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_pred_valid = clf.predict(X_test)\n",
    "acc = accuracy_score(y_test, y_pred_valid)\n",
    "acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As acurracy is 0.91 it is pretty height. If the algorithm had to guess the chance would be 1/3 (33%) of getting it right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Bernoulli NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "clf_b = BernoulliNB()\n",
    "clf_b.fit(X, y)\n",
    "BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  4,  0],\n",
       "       [ 1, 69,  0],\n",
       "       [ 0,  1, 13]])"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred_b = clf_b.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_test_pred_b)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      female       0.00      0.00      0.00         4\n",
      "        male       0.93      0.99      0.96        70\n",
      "     neutral       1.00      0.93      0.96        14\n",
      "\n",
      "    accuracy                           0.93        88\n",
      "   macro avg       0.64      0.64      0.64        88\n",
      "weighted avg       0.90      0.93      0.92        88\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_test_pred_b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy has improved, however precision & recall have not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Multinomial NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_m = MultinomialNB()\n",
    "clf_m.fit(X, y)\n",
    "MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  1,  0],\n",
       "       [ 3, 67,  0],\n",
       "       [ 0,  0, 14]])"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred_m = clf_m.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_test_pred_m)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      female       0.50      0.75      0.60         4\n",
      "        male       0.99      0.96      0.97        70\n",
      "     neutral       1.00      1.00      1.00        14\n",
      "\n",
      "    accuracy                           0.95        88\n",
      "   macro avg       0.83      0.90      0.86        88\n",
      "weighted avg       0.97      0.95      0.96        88\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_test_pred_m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is better than regular NB and Bernoulli NB. So are the precision & recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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