gender v occupation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\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": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 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({'Point Change in Men Unemployment Rate': men_diff,\n",
    "                   'Point Change in Women Unemployment Rate': women_diff}, index=index)\n",
    "\n",
    "ax = df.plot.bar(rot=0, color = [\"blue\", \"pink\"], figsize=(10,8))\n",
    "labels = [item.get_text() for item in ax.get_xticklabels()]\n",
    "labels[0] = 'Management and Professional'\n",
    "labels[1] = 'Service'\n",
    "labels[2] = 'Sales'\n",
    "labels[3] = 'Construction and Maintenance'\n",
    "labels[4] = 'Production and Transportation'\n",
    "\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), shadow=True, ncol=1)\n",
    "plt.setp(ax.set_xticklabels(labels), rotation=20, horizontalalignment='center', fontsize='medium')\n",
    "plt.ylabel(\"Point Change in Unemployment Rate\")\n",
    "plt.xlabel(\"Occupation\", weight = \"bold\")\n",
    "plt.savefig(\"gender_occupation.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}