Gender v Occupation Bar Graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Occupation</th>\n",
       "      <th>UR Total 2019</th>\n",
       "      <th>UR Total 2020</th>\n",
       "      <th>UR of Men 2019</th>\n",
       "      <th>UR of Men 2020</th>\n",
       "      <th>UR of Women 2019</th>\n",
       "      <th>UR of Women 2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Management, professional, and related occupations</td>\n",
       "      <td>2.4</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Service occupations</td>\n",
       "      <td>4.5</td>\n",
       "      <td>16.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>15.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sales and office occupations</td>\n",
       "      <td>3.6</td>\n",
       "      <td>10.3</td>\n",
       "      <td>3.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Natural resources, construction, and maintenan...</td>\n",
       "      <td>4.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Production, transportation, and material movin...</td>\n",
       "      <td>4.6</td>\n",
       "      <td>13.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Occupation  UR Total 2019  \\\n",
       "0  Management, professional, and related occupations            2.4   \n",
       "1                                Service occupations            4.5   \n",
       "2                       Sales and office occupations            3.6   \n",
       "3  Natural resources, construction, and maintenan...            4.2   \n",
       "4  Production, transportation, and material movin...            4.6   \n",
       "\n",
       "   UR Total 2020  UR of Men 2019  UR of Men 2020  UR of Women 2019   \\\n",
       "0            6.6             1.8             5.5                3.0   \n",
       "1           16.2             4.6            15.7                4.4   \n",
       "2           10.3             3.1             9.4                4.0   \n",
       "3            9.2             3.9             9.1                9.6   \n",
       "4           13.7             4.4            12.6                5.4   \n",
       "\n",
       "   UR of Women 2020  \n",
       "0               7.6  \n",
       "1              16.6  \n",
       "2              10.9  \n",
       "3              11.5  \n",
       "4              16.8  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('Gender v Occupation July - Sheet1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "men_2019 = df[\"UR of Men 2019\"]\n",
    "men_2020 = df[\"UR of Men 2020\"]\n",
    "women_2019 = df[\"UR of Women 2019 \"]\n",
    "women_2020 = df[\"UR of Women 2020\"]\n",
    "\n",
    "men_diff = []\n",
    "women_diff = []\n",
    "\n",
    "for x in range(len(men_2019)):\n",
    "    men_diff.append(men_2020[x]-men_2019[x])\n",
    "    women_diff.append(women_2020[x]-women_2019[x])\n",
    "    \n",
    "index = [\"Management, professional, and related occupations\", \"Service occupations\",\n",
    "         \"Sales and office occupations\", \"Natural resources, construction, and maintenance occupations\",\n",
    "        \"Production, transportation, and material moving occupations\"]\n",
    "\n",
    "df = pd.DataFrame({'Difference in Men UR 2019-2020': men_diff,\n",
    "                   'Difference in Women UR 2019-2020': women_diff}, index=index)\n",
    "\n",
    "ax = df.plot.bar(rot=0, color = [\"blue\", \"pink\"])\n",
    "plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right', fontsize='x-small')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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