Data/SHED/SHED Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survey of Household Economics and Decisionmaking Data\n",
    "\n",
    "Author: Dhruval Bhatt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About this data: it has 11,316 respondants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_18 = pd.read_csv('2018_data.csv', encoding = \"ISO-8859-1\")\n",
    "df_17 = pd.read_csv('2017_data.csv', encoding = \"ISO-8859-1\")\n",
    "df_16 = pd.read_csv('2016_data.csv', encoding = \"ISO-8859-1\")\n",
    "df_15 = pd.read_csv('2015_data.csv', encoding = \"ISO-8859-1\")\n",
    "df_14 = pd.read_csv('2014_data.csv', encoding = \"ISO-8859-1\")\n",
    "df_13 = pd.read_csv('2013_data.csv', encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26af711a518>"
      ]
     },
     "execution_count": 14,
     "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": [
    "# Where do people work? \n",
    "\n",
    "survey18_df['D3A'].value_counts().plot('bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_17 = pd.read_csv('Data/SHED/2017_data.csv', encoding = \"ISO-8859-1\")\n",
    "df_16 = pd.read_csv('Data/SHED/2016_data.csv', encoding = \"ISO-8859-1\")\n",
    "df_15 = pd.read_csv('Data/SHED/2015_data.csv', encoding = \"ISO-8859-1\")\n",
    "df_14 = pd.read_csv('Data/SHED/2014_data.csv', encoding = \"ISO-8859-1\")\n",
    "df_13 = pd.read_csv('Data/SHED/2013_data.csv', encoding = \"ISO-8859-1\")"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}