{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair\n",
    "import pandas"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "equal_pay = pandas.read_csv(\"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-03-05/jobs_gender.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>occupation</th>\n",
       "      <th>major_category</th>\n",
       "      <th>minor_category</th>\n",
       "      <th>total_workers</th>\n",
       "      <th>workers_male</th>\n",
       "      <th>workers_female</th>\n",
       "      <th>percent_female</th>\n",
       "      <th>total_earnings</th>\n",
       "      <th>total_earnings_male</th>\n",
       "      <th>total_earnings_female</th>\n",
       "      <th>wage_percent_of_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>Chief executives</td>\n",
       "      <td>Management, Business, and Financial</td>\n",
       "      <td>Management</td>\n",
       "      <td>1024259</td>\n",
       "      <td>782400</td>\n",
       "      <td>241859</td>\n",
       "      <td>23.6</td>\n",
       "      <td>120254</td>\n",
       "      <td>126142.0</td>\n",
       "      <td>95921.0</td>\n",
       "      <td>76.042080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>General and operations managers</td>\n",
       "      <td>Management, Business, and Financial</td>\n",
       "      <td>Management</td>\n",
       "      <td>977284</td>\n",
       "      <td>681627</td>\n",
       "      <td>295657</td>\n",
       "      <td>30.3</td>\n",
       "      <td>73557</td>\n",
       "      <td>81041.0</td>\n",
       "      <td>60759.0</td>\n",
       "      <td>74.973162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>Legislators</td>\n",
       "      <td>Management, Business, and Financial</td>\n",
       "      <td>Management</td>\n",
       "      <td>14815</td>\n",
       "      <td>8375</td>\n",
       "      <td>6440</td>\n",
       "      <td>43.5</td>\n",
       "      <td>67155</td>\n",
       "      <td>71530.0</td>\n",
       "      <td>65325.0</td>\n",
       "      <td>91.325318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>Advertising and promotions managers</td>\n",
       "      <td>Management, Business, and Financial</td>\n",
       "      <td>Management</td>\n",
       "      <td>43015</td>\n",
       "      <td>17775</td>\n",
       "      <td>25240</td>\n",
       "      <td>58.7</td>\n",
       "      <td>61371</td>\n",
       "      <td>75190.0</td>\n",
       "      <td>55860.0</td>\n",
       "      <td>74.291794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>Marketing and sales managers</td>\n",
       "      <td>Management, Business, and Financial</td>\n",
       "      <td>Management</td>\n",
       "      <td>754514</td>\n",
       "      <td>440078</td>\n",
       "      <td>314436</td>\n",
       "      <td>41.7</td>\n",
       "      <td>78455</td>\n",
       "      <td>91998.0</td>\n",
       "      <td>65040.0</td>\n",
       "      <td>70.697189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year                           occupation  \\\n",
       "0  2013                     Chief executives   \n",
       "1  2013      General and operations managers   \n",
       "2  2013                          Legislators   \n",
       "3  2013  Advertising and promotions managers   \n",
       "4  2013         Marketing and sales managers   \n",
       "\n",
       "                        major_category minor_category  total_workers  \\\n",
       "0  Management, Business, and Financial     Management        1024259   \n",
       "1  Management, Business, and Financial     Management         977284   \n",
       "2  Management, Business, and Financial     Management          14815   \n",
       "3  Management, Business, and Financial     Management          43015   \n",
       "4  Management, Business, and Financial     Management         754514   \n",
       "\n",
       "   workers_male  workers_female  percent_female  total_earnings  \\\n",
       "0        782400          241859            23.6          120254   \n",
       "1        681627          295657            30.3           73557   \n",
       "2          8375            6440            43.5           67155   \n",
       "3         17775           25240            58.7           61371   \n",
       "4        440078          314436            41.7           78455   \n",
       "\n",
       "   total_earnings_male  total_earnings_female  wage_percent_of_male  \n",
       "0             126142.0                95921.0             76.042080  \n",
       "1              81041.0                60759.0             74.973162  \n",
       "2              71530.0                65325.0             91.325318  \n",
       "3              75190.0                55860.0             74.291794  \n",
       "4              91998.0                65040.0             70.697189  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "equal_pay.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013    522\n",
       "2014    522\n",
       "2015    522\n",
       "2016    522\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "equal_pay[\"year\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chief executives                      4\n",
       "Helpers, construction trades          4\n",
       "Sheet metal workers                   4\n",
       "Roofers                               4\n",
       "Reinforcing iron and rebar workers    4\n",
       "                                     ..\n",
       "Physical therapists                   4\n",
       "Occupational therapists               4\n",
       "Audiologists                          4\n",
       "Podiatrists                           4\n",
       "Material moving workers, all other    4\n",
       "Name: occupation, Length: 522, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "equal_pay[\"occupation\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Production, Transportation, and Material Moving         444\n",
       "Natural Resources, Construction, and Maintenance        328\n",
       "Sales and Office                                        280\n",
       "Service                                                 272\n",
       "Computer, Engineering, and Science                      236\n",
       "Management, Business, and Financial                     232\n",
       "Education, Legal, Community Service, Arts, and Media    168\n",
       "Healthcare Practitioners and Technical                  128\n",
       "Name: major_category, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "equal_pay[\"major_category\"].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean by group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_female_per_category = equal_pay.groupby(\"major_category\").agg(\n",
    "    mean = pandas.NamedAgg(column=\"percent_female\", aggfunc=\"mean\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major_category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Computer, Engineering, and Science</th>\n",
       "      <td>26.966418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education, Legal, Community Service, Arts, and Media</th>\n",
       "      <td>54.402564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare Practitioners and Technical</th>\n",
       "      <td>65.218243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Management, Business, and Financial</th>\n",
       "      <td>46.480240</td>\n",
       "    </tr>\n",
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       "      <th>Natural Resources, Construction, and Maintenance</th>\n",
       "      <td>5.671603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         mean\n",
       "major_category                                               \n",
       "Computer, Engineering, and Science                  26.966418\n",
       "Education, Legal, Community Service, Arts, and ...  54.402564\n",
       "Healthcare Practitioners and Technical              65.218243\n",
       "Management, Business, and Financial                 46.480240\n",
       "Natural Resources, Construction, and Maintenance     5.671603"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_female_per_category.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_female_per_category[\"category\"] = mean_female_per_category.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>category</th>\n",
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       "    <tr>\n",
       "      <th>major_category</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Computer, Engineering, and Science</th>\n",
       "      <td>26.966418</td>\n",
       "      <td>Computer, Engineering, and Science</td>\n",
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       "      <th>Education, Legal, Community Service, Arts, and Media</th>\n",
       "      <td>54.402564</td>\n",
       "      <td>Education, Legal, Community Service, Arts, and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare Practitioners and Technical</th>\n",
       "      <td>65.218243</td>\n",
       "      <td>Healthcare Practitioners and Technical</td>\n",
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       "    <tr>\n",
       "      <th>Management, Business, and Financial</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         mean  \\\n",
       "major_category                                                  \n",
       "Computer, Engineering, and Science                  26.966418   \n",
       "Education, Legal, Community Service, Arts, and ...  54.402564   \n",
       "Healthcare Practitioners and Technical              65.218243   \n",
       "Management, Business, and Financial                 46.480240   \n",
       "Natural Resources, Construction, and Maintenance     5.671603   \n",
       "\n",
       "                                                                                             category  \n",
       "major_category                                                                                         \n",
       "Computer, Engineering, and Science                                 Computer, Engineering, and Science  \n",
       "Education, Legal, Community Service, Arts, and ...  Education, Legal, Community Service, Arts, and...  \n",
       "Healthcare Practitioners and Technical                         Healthcare Practitioners and Technical  \n",
       "Management, Business, and Financial                               Management, Business, and Financial  \n",
       "Natural Resources, Construction, and Maintenance     Natural Resources, Construction, and Maintenance  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_female_per_category.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_female_per_category[\"rounded_mean\"] = round(mean_female_per_category[\"mean\"], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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       "alt.Chart(...)"
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     "execution_count": 21,
     "metadata": {},
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   ],
   "source": [
    "altair.Chart(mean_female_per_category).mark_bar().encode(\n",
    "    altair.Y(\"category:O\", sort=\"-x\"),\n",
    "    x = \"mean:Q\",\n",
    "    tooltip = \"rounded_mean:Q\"\n",
    ").interactive()"
   ]
  },
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   "execution_count": 26,
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      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "altair.Chart(equal_pay).mark_point().encode(\n",
    "    y = \"percent_female\",\n",
    "    x = \"wage_percent_of_male\",\n",
    "    tooltip = \"occupation\",\n",
    "    color = \"major_category\"\n",
    ").interactive()"
   ]
  }
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