Q1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1: Binary Computation\n",
    "\n",
    "How does this dataset treat people with non binary gender identity?\n",
    "\n",
    "How can you test that?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000026A3F7B71F0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# source = 'data/population.csv'\n",
    "source = 'https://www.opendatani.gov.uk/dataset/62e7073f-e924-4d3f-81a5-ad45b5127682/resource/67c25586-b9aa-4717-9a4b-42de21a403f2/download/parliamentary-constituencies-by-single-year-of-age-and-gender-mid-2001-to-mid-2019.csv'\n",
    "\n",
    "df = pd.read_csv(source) # `read_csv` can read from URL's or from local files aswell\n",
    "\n",
    "df_2019 = df[\n",
    "    (df['Mid_Year_Ending'] == 2019)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "All persons    1893667\n",
       "Females         960950\n",
       "Males           932717\n",
       "Name: Population_Estimate, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_pop = df_2019.groupby('Gender')['Population_Estimate'].sum()\n",
    "gender_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1893667, 1893667)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_pop['Males']+gender_pop['Females'], gender_pop['All persons']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26a4400f9d0>"
      ]
     },
     "execution_count": 11,
     "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": [
    "pd.DataFrame.from_dict({\n",
    "    'All persons': {'All':gender_pop['All persons']},\n",
    "    'Gendered': {'Males':gender_pop['Males'],\n",
    "                 'Females':gender_pop['Females']}\n",
    "}).T.plot.bar(stacked=True, width=1)"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}