homeless_la.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "homeless_2017 = pd.read_csv('Data/LA Data/homeless_la_2017.csv')\n",
    "homeless_2017\n",
    "\n",
    "homeless_2018 = pd.read_csv('Data/LA Data/homeless_la_2018.csv')\n",
    "# # # # homeless_2018\n",
    "# \n",
    "homeless_2019 = pd.read_csv('Data/LA Data/homeless_la_2019.csv')\n",
    "# # homeless_2019\n",
    "\n",
    "# # homeless_2019.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_2017 = homeless_2017[\"total_homeless\"].sum()\n",
    "# total_2017\n",
    "\n",
    "sud_2017 = homeless_2017[\"SUD\"].sum()\n",
    "# sud_2017\n",
    "\n",
    "sud_perc_2017 = sud_2017 / total_2017*100\n",
    "# sud_perc_2017\n",
    "\n",
    "chronic_2017 = homeless_2017[\"Chronic_HL\"].sum()\n",
    "# chronic_2017\n",
    "\n",
    "chronic_perc_2017 = chronic_2017 / total_2017*100\n",
    "# chronic_perc_2017\n",
    "\n",
    "mental_2017 = homeless_2017[\"Serious Mental Illness\"].sum()\n",
    "# mental_2017\n",
    "\n",
    "mental_perc_2017 = mental_2017 / total_2017*100\n",
    "# mental_perc_2017\n",
    "\n",
    "dv_2017 = homeless_2017[\"DV_Exp\"].sum()\n",
    "# dv_2017\n",
    "\n",
    "dv_perc_2017 = dv_2017 / total_2017*100\n",
    "# dv_perc_2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsheltered_2017 = homeless_2017.loc[homeless_2017[\"Shelter Status\"] == \"Unsheltered\"]\n",
    "# unsheltered_2017.head()\n",
    "\n",
    "unsheltered_2018 = homeless_2018.loc[homeless_2018[\"Shelter Status\"] == \"Unsheltered\"]\n",
    "# unsheltered_2018.head()\n",
    "\n",
    "unsheltered_2019 = homeless_2019.loc[homeless_2019[\"Shelter Status\"] == \"Unsheltered\"]\n",
    "# unsheltered_2019.head()\n",
    "\n",
    "\n",
    "sheltered_2017 = homeless_2017.loc[homeless_2017[\"Shelter Status\"] == \"Sheltered\"]\n",
    "# sheltered_2017.head()\n",
    "\n",
    "sheltered_2018 = homeless_2018.loc[homeless_2018[\"Shelter Status\"] == \"Sheltered\"]\n",
    "# sheltered_2018.head()\n",
    "\n",
    "sheltered_2019 = homeless_2019.loc[homeless_2019[\"Shelter Status\"] == \"Sheltered\"]\n",
    "# sheltered_2019.head()\n",
    "\n",
    "# # # sheltered_2017.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uns_total_2017 = unsheltered_2017[\"total_homeless\"].sum()\n",
    "# uns_total_2017\n",
    "\n",
    "uns_total_perc_2017 = uns_total_2017 / total_2017*100\n",
    "uns_total_perc_2017\n",
    "\n",
    "uns_sud_2017 = unsheltered_2017[\"SUD\"].sum()\n",
    "# uns_sud_2017\n",
    "\n",
    "uns_sud_perc_2017 = uns_sud_2017 / uns_total_2017*100\n",
    "# uns_sud_perc_2017\n",
    "\n",
    "uns_chronic_2017 = unsheltered_2017[\"Chronic_HL\"].sum()\n",
    "# uns_chronic_2017\n",
    "\n",
    "uns_chronic_perc_2017 = uns_chronic_2017 / uns_total_2017*100\n",
    "# uns_chronic_perc_2017\n",
    "\n",
    "uns_mental_2017 = unsheltered_2017[\"Serious Mental Illness\"].sum()\n",
    "# uns_mental_2017\n",
    "\n",
    "uns_mental_perc_2017 = uns_mental_2017 / uns_total_2017*100\n",
    "# uns_mental_perc_2017\n",
    "\n",
    "uns_dv_2017 = unsheltered_2017[\"DV_Exp\"].sum()\n",
    "# uns_dv_2017\n",
    "\n",
    "uns_dv_perc_2017 = uns_dv_2017 / uns_total_2017*100\n",
    "# uns_dv_perc_2017\n",
    "\n",
    "sh_total_2017 = sheltered_2017[\"total_homeless\"].sum()\n",
    "# sh_total_2017\n",
    "\n",
    "sh_total_perc_2017 = sh_total_2017 / total_2017*100\n",
    "# sh_total_perc_2017\n",
    "\n",
    "sh_sud_2017 = sheltered_2017[\"SUD\"].sum()\n",
    "# sh_sud_2017\n",
    "\n",
    "sh_sud_perc_2017 = sh_sud_2017 / sh_total_2017*100\n",
    "# sh_sud_perc_2017\n",
    "\n",
    "sh_chronic_2017 = sheltered_2017[\"Chronic_HL\"].sum()\n",
    "# sh_chronic_2017\n",
    "\n",
    "sh_chronic_perc_2017 = sh_chronic_2017 / sh_total_2017*100\n",
    "# sh_chronic_perc_2017\n",
    "\n",
    "sh_mental_2017 = sheltered_2017[\"Serious Mental Illness\"].sum()\n",
    "# sh_mental_2017\n",
    "\n",
    "sh_mental_perc_2017 = sh_mental_2017 / sh_total_2017*100\n",
    "# sh_mental_perc_2017\n",
    "\n",
    "sh_dv_2017 = sheltered_2017[\"DV_Exp\"].sum()\n",
    "# sh_dv_2017\n",
    "\n",
    "sh_dv_perc_2017 = sh_dv_2017 / sh_total_2017*100\n",
    "# sh_dv_perc_2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_2018 = homeless_2018[\"total_homeless\"].sum()\n",
    "# total_2018\n",
    "\n",
    "sud_2018 = homeless_2018[\"SUD\"].sum()\n",
    "# sud_2018\n",
    "\n",
    "sud_perc_2018 = sud_2018 / total_2018*100\n",
    "# sud_perc_2018\n",
    "\n",
    "chronic_2018 = homeless_2018[\"Chronic_HL\"].sum()\n",
    "# # chronic_2018\n",
    "\n",
    "chronic_perc_2018 = chronic_2018 / total_2018*100\n",
    "# chronic_perc_2018\n",
    "\n",
    "mental_2018 = homeless_2018[\"Serious Mental Illness\"].sum()\n",
    "# mental_2017\n",
    "\n",
    "mental_perc_2018 = mental_2018 / total_2018*100\n",
    "# mental_perc_2017\n",
    "\n",
    "dv_2018 = homeless_2018[\"DV_Exp\"].sum()\n",
    "# dv_2018\n",
    "\n",
    "dv_perc_2018 = dv_2018 / total_2018*100\n",
    "# dv_perc_2018\n",
    "\n",
    "uns_total_2018 = unsheltered_2018[\"total_homeless\"].sum()\n",
    "# uns_total_2018\n",
    "\n",
    "uns_total_perc_2018 = uns_total_2018 / total_2018*100\n",
    "# uns_total_perc_2018\n",
    "\n",
    "uns_sud_2018 = unsheltered_2018[\"SUD\"].sum()\n",
    "uns_sud_2018\n",
    "\n",
    "uns_sud_perc_2018 = uns_sud_2018 / uns_total_2018*100\n",
    "# uns_sud_perc_2018\n",
    "\n",
    "uns_chronic_2018 = unsheltered_2018[\"Chronic_HL\"].sum()\n",
    "# uns_chronic_2018\n",
    "\n",
    "uns_chronic_perc_2018 = uns_chronic_2018 / uns_total_2018*100\n",
    "# uns_chronic_perc_2018\n",
    "\n",
    "uns_mental_2018 = unsheltered_2018[\"Serious Mental Illness\"].sum()\n",
    "# uns_mental_2018\n",
    "\n",
    "uns_mental_perc_2018 = uns_mental_2018 / uns_total_2018*100\n",
    "# uns_mental_perc_2018\n",
    "\n",
    "uns_dv_2018 = unsheltered_2018[\"DV_Exp\"].sum()\n",
    "# uns_dv_2018\n",
    "\n",
    "uns_dv_perc_2018 = uns_dv_2018 / uns_total_2018*100\n",
    "# uns_dv_perc_2018\n",
    "\n",
    "sh_total_2018 = sheltered_2018[\"total_homeless\"].sum()\n",
    "# sh_total_2018\n",
    "\n",
    "sh_total_perc_2018 = sh_total_2018 / total_2018*100\n",
    "# sh_total_perc_2018\n",
    "\n",
    "sh_sud_2018 = sheltered_2018[\"SUD\"].sum()\n",
    "# sh_sud_2018\n",
    "\n",
    "sh_sud_perc_2018 = sh_sud_2018 / sh_total_2018*100\n",
    "# sh_sud_perc_2018\n",
    "\n",
    "sh_chronic_2018 = sheltered_2018[\"Chronic_HL\"].sum()\n",
    "# sh_chronic_2018\n",
    "\n",
    "sh_chronic_perc_2018 = sh_chronic_2018 / sh_total_2018*100\n",
    "# sh_chronic_perc_2018\n",
    "\n",
    "sh_mental_2018 = sheltered_2018[\"Serious Mental Illness\"].sum()\n",
    "# sh_mental_2018\n",
    "\n",
    "sh_mental_perc_2018 = sh_mental_2018 / sh_total_2018*100\n",
    "# sh_mental_perc_2018\n",
    "\n",
    "sh_dv_2018 = sheltered_2018[\"DV_Exp\"].sum()\n",
    "# sh_dv_2018\n",
    "\n",
    "sh_dv_perc_2018 = sh_dv_2018 / sh_total_2018*100\n",
    "# sh_dv_perc_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_2019 = homeless_2019[\"total_homeless\"].sum()\n",
    "# total_2019\n",
    "\n",
    "sud_2019 = homeless_2019[\"SUD\"].sum()\n",
    "# sud_2019\n",
    "\n",
    "sud_perc_2019 = sud_2019 / total_2019*100\n",
    "# # sud_perc_2019\n",
    "\n",
    "chronic_2019 = homeless_2019[\"Chronic_HL\"].sum()\n",
    "# # chronic_2019\n",
    "\n",
    "chronic_perc_2019 = chronic_2019 / total_2019*100\n",
    "# chronic_perc_2019\n",
    "\n",
    "mental_2019 = homeless_2019[\"Serious Mental Illness\"].sum()\n",
    "# mental_2017\n",
    "\n",
    "mental_perc_2019 = mental_2019 / total_2019*100\n",
    "# mental_perc_2017\n",
    "\n",
    "dv_2019 = homeless_2019[\"DV_Exp\"].sum()\n",
    "# dv_2018\n",
    "\n",
    "dv_perc_2019 = dv_2019 / total_2018*100\n",
    "# dv_perc_2019\n",
    "\n",
    "uns_total_2019 = unsheltered_2019[\"total_homeless\"].sum()\n",
    "# uns_total_2019\n",
    "\n",
    "uns_total_perc_2019 = uns_total_2019 / total_2019*100\n",
    "uns_total_perc_2019\n",
    "\n",
    "uns_sud_2019 = unsheltered_2019[\"SUD\"].sum()\n",
    "# uns_sud_2019\n",
    "\n",
    "uns_sud_perc_2019 = uns_sud_2019 / uns_total_2019*100\n",
    "# uns_sud_perc_2019\n",
    "\n",
    "uns_chronic_2019 = unsheltered_2019[\"Chronic_HL\"].sum()\n",
    "# uns_chronic_2019\n",
    "\n",
    "uns_chronic_perc_2019 = uns_chronic_2019 / uns_total_2019*100\n",
    "# uns_chronic_perc_2019\n",
    "\n",
    "uns_mental_2019 = unsheltered_2019[\"Serious Mental Illness\"].sum()\n",
    "# uns_mental_2019\n",
    "\n",
    "uns_mental_perc_2019 = uns_mental_2019 / uns_total_2019*100\n",
    "# uns_mental_perc_2019\n",
    "\n",
    "uns_dv_2019 = unsheltered_2019[\"DV_Exp\"].sum()\n",
    "# uns_dv_2019\n",
    "\n",
    "uns_dv_perc_2019 = uns_dv_2019 / uns_total_2019*100\n",
    "# uns_dv_perc_2019\n",
    "\n",
    "sh_total_2019 = sheltered_2019[\"total_homeless\"].sum()\n",
    "# sh_total_2019\n",
    "\n",
    "sh_total_perc_2019 = sh_total_2019 / total_2019*100\n",
    "# sh_total_perc_2019\n",
    "\n",
    "sh_sud_2019 = sheltered_2019[\"SUD\"].sum()\n",
    "# sh_sud_2019\n",
    "\n",
    "sh_sud_perc_2019 = sh_sud_2019 / sh_total_2019*100\n",
    "# sh_sud_perc_2019\n",
    "\n",
    "sh_chronic_2019 = sheltered_2019[\"Chronic_HL\"].sum()\n",
    "# sh_chronic_2019\n",
    "\n",
    "sh_chronic_perc_2019 = sh_chronic_2019 / sh_total_2019*100\n",
    "# sh_chronic_perc_2019\n",
    "\n",
    "sh_mental_2019 = sheltered_2019[\"Serious Mental Illness\"].sum()\n",
    "# sh_mental_2019\n",
    "\n",
    "sh_mental_perc_2019 = sh_mental_2019 / sh_total_2019*100\n",
    "# sh_mental_perc_2019\n",
    "\n",
    "sh_dv_2019 = sheltered_2019[\"DV_Exp\"].sum()\n",
    "# sh_dv_2019\n",
    "\n",
    "sh_dv_perc_2019 = sh_dv_2019 / sh_total_2019*100\n",
    "# sh_dv_perc_2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hl_dv_2019 = homeless_2019[\"Homeless Due to Fleeing DV/IPV\"].sum()\n",
    "# hl_dv_2019\n",
    "\n",
    "hl_dv_perc_2019 = hl_dv_2019 / total_2019*100\n",
    "# hl_dv_perc_2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_2017 = pd.DataFrame({\"2017 - Total Homeless Persons\":[total_2017],\"Substance Use\":[sud_2017], \"% Substance Use\":[sud_perc_2017], \"Chronically Homeless\":[chronic_2017], \"% Chronically Homeless\":[chronic_perc_2017], \"Serious Mental Illness\":[mental_2017], \"% Serious Mental Illness\":[mental_perc_2017], \"Experiencing Domestic Violence\":[dv_2017], \"% Experiencing Domestic Violence\":[dv_perc_2017]})\n",
    "summary_2017\n",
    "\n",
    "uns_summary_2017 = pd.DataFrame({\"2017 - Total Unsheltered\":[uns_total_2017], \"% Unsheltered\":[uns_total_perc_2017], \"Substance Use\":[uns_sud_2017], \"% Substance Use\":[uns_sud_perc_2017], \"Chronically Homeless\":[uns_chronic_2017], \"% Chronically Homeless\":[uns_chronic_perc_2017], \"Serious Mental Illness\":[uns_mental_2017], \"% Serious Mental Illness\":[uns_mental_perc_2017], \"Experiencing Domestic Violence\":[uns_dv_2017], \"% Experiencing Domestic Violence\":[uns_dv_perc_2017]})\n",
    "uns_summary_2017\n",
    "\n",
    "sh_summary_2017 = pd.DataFrame({\"2017 - Total Sheltered\":[sh_total_2017], \"% Sheltered\":[sh_total_perc_2017], \"Substance Use\":[sh_sud_2017], \"% Substance Use\":[sh_sud_perc_2017], \"Chronically Homeless\":[sh_chronic_2017], \"% Chronically Homeless\":[sh_chronic_perc_2017], \"Serious Mental Illness\":[sh_mental_2017], \"% Serious Mental Illness\":[sh_mental_perc_2017], \"Experiencing Domestic Violence\":[sh_dv_2017], \"% Experiencing Domestic Violence\":[sh_dv_perc_2017]})\n",
    "sh_summary_2017\n",
    "\n",
    "summary_2018 = pd.DataFrame({\"2018 - Total Homeless Persons\":[total_2018],\"Substance Use\":[sud_2018], \"% Substance Use\":[sud_perc_2018], \"Chronically Homeless\":[chronic_2018], \"% Chronically Homeless\":[chronic_perc_2018], \"Serious Mental Illness\":[mental_2018], \"% Serious Mental Illness\":[mental_perc_2018], \"Experiencing Domestic Violence\":[dv_2018], \"% Experiencing Domestic Violence\":[dv_perc_2018]})\n",
    "summary_2018\n",
    "\n",
    "uns_summary_2018 = pd.DataFrame({\"2018 - Total Unsheltered\":[uns_total_2018], \"% Unsheltered\":[uns_total_perc_2018], \"Substance Use\":[uns_sud_2018], \"% Substance Use\":[uns_sud_perc_2018], \"Chronically Homeless\":[uns_chronic_2018], \"% Chronically Homeless\":[uns_chronic_perc_2018], \"Serious Mental Illness\":[uns_mental_2018], \"% Serious Mental Illness\":[uns_mental_perc_2018], \"Experiencing Domestic Violence\":[uns_dv_2018], \"% Experiencing Domestic Violence\":[uns_dv_perc_2018]})\n",
    "uns_summary_2018\n",
    "\n",
    "sh_summary_2018 = pd.DataFrame({\"2018 - Total Sheltered\":[sh_total_2018], \"% Sheltered\":[sh_total_perc_2018], \"Substance Use\":[sh_sud_2018], \"% Substance Use\":[sh_sud_perc_2018], \"Chronically Homeless\":[sh_chronic_2018], \"% Chronically Homeless\":[sh_chronic_perc_2018], \"Serious Mental Illness\":[sh_mental_2018], \"% Serious Mental Illness\":[sh_mental_perc_2018], \"Experiencing Domestic Violence\":[sh_dv_2018], \"% Experiencing Domestic Violence\":[sh_dv_perc_2018]})\n",
    "sh_summary_2018\n",
    "\n",
    "summary_2019 = pd.DataFrame({\"2019 - Total Homeless Persons\":[total_2019],\"Substance Use\":[sud_2019], \"% Substance Use\":[sud_perc_2019], \"Chronically Homeless\":[chronic_2019], \"% Chronically Homeless\":[chronic_perc_2019], \"Serious Mental Illness\":[mental_2019], \"% Serious Mental Illness\":[mental_perc_2019], \"Experiencing Domestic Violence\":[dv_2019], \"% Experiencing Domestic Violence\":[dv_perc_2019]})\n",
    "# summary_2019\n",
    "\n",
    "uns_summary_2019 = pd.DataFrame({\"2019 - Total Unsheltered\":[uns_total_2019], \"% Unsheltered\":[uns_total_perc_2019], \"Substance Use\":[uns_sud_2019], \"% Substance Use\":[uns_sud_perc_2019], \"Chronically Homeless\":[uns_chronic_2019], \"% Chronically Homeless\":[uns_chronic_perc_2019], \"Serious Mental Illness\":[uns_mental_2019], \"% Serious Mental Illness\":[uns_mental_perc_2019], \"Experiencing Domestic Violence\":[uns_dv_2019], \"% Experiencing Domestic Violence\":[uns_dv_perc_2019]})\n",
    "# uns_summary_2019\n",
    "\n",
    "sh_summary_2019 = pd.DataFrame({\"2019 - Total Sheltered\":[sh_total_2019], \"% Sheltered\":[sh_total_perc_2019], \"Substance Use\":[sh_sud_2019], \"% Substance Use\":[sh_sud_perc_2019], \"Chronically Homeless\":[sh_chronic_2019], \"% Chronically Homeless\":[sh_chronic_perc_2019], \"Serious Mental Illness\":[sh_mental_2019], \"% Serious Mental Illness\":[sh_mental_perc_2019], \"Experiencing Domestic Violence\":[sh_dv_2019], \"% Experiencing Domestic Violence\":[sh_dv_perc_2019]})\n",
    "# sh_summary_2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = [\"2007\", \"2018\", \"2019\"]\n",
    "totals = [52442, 49955, 56257]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x_axis = np.arange(0, len(years))\n",
    "tick_locations = []\n",
    "for x in x_axis:\n",
    "    tick_locations.append(x)\n",
    "\n",
    "plt.title(\"Total Homeless Population in Los Angeles County per Year\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Total Homeless Persons\")\n",
    "\n",
    "plt.xlim(-0.75, len(years)-.25)\n",
    "plt.ylim(0, max(totals) + 5)\n",
    "\n",
    "plt.bar(x_axis, totals, facecolor=\"blue\", alpha=0.75, align=\"center\")\n",
    "plt.xticks(tick_locations, years)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(homeless_2017.iloc[:,3],homeless_2017.iloc[:,14])\n",
    "plt.title(\"Substance Use by Homeless Population in Los Angeles County\")\n",
    "plt.xlabel('Homeless Population')\n",
    "plt.ylabel('Substance Use Disorder Rates')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(homeless_2018.iloc[:,3],homeless_2018.iloc[:,14])\n",
    "plt.title(\"Substance Use by Homeless Population in Los Angeles County\")\n",
    "plt.xlabel('Homeless Population')\n",
    "plt.ylabel('Substance Use Disorder Rates')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(homeless_2019.iloc[:,3],homeless_2019.iloc[:,14])\n",
    "plt.title(\"Substance Use by Homeless Population in Los Angeles County\")\n",
    "plt.xlabel('Homeless Population')\n",
    "plt.ylabel('Substance Use Disorder Rates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(homeless_2017.iloc[:,13],homeless_2017.iloc[:,14])\n",
    "plt.title(\"Substance Use by Homeless Population in Los Angeles County\")\n",
    "plt.xlabel('Homeless Population')\n",
    "plt.ylabel('Substance Use Disorder Rates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(homeless_2018.iloc[:,13],homeless_2018.iloc[:,14])\n",
    "plt.title(\"Substance Use by Homeless Population in Los Angeles County\")\n",
    "plt.xlabel('Homeless Population')\n",
    "plt.ylabel('Substance Use Disorder Rates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(homeless_2019.iloc[:,13],homeless_2019.iloc[:,14])\n",
    "plt.title(\"Substance Use by Homeless Population in Los Angeles County\")\n",
    "plt.xlabel('Homeless Population')\n",
    "plt.ylabel('Substance Use Disorder Rates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "homeless = homeless_2017.iloc[:,3]\n",
    "sud = homeless_2017.iloc[:,14]\n",
    "correlation = st.pearsonr(homeless,sud)\n",
    "print(f\"The correlation between both factors is {round(correlation[0],2)}\")\n",
    "\n",
    "homeless = homeless_2018.iloc[:,3]\n",
    "sud = homeless_2018.iloc[:,14]\n",
    "correlation = st.pearsonr(homeless,sud)\n",
    "print(f\"The correlation between both factors is {round(correlation[0],2)}\")\n",
    "\n",
    "homeless = homeless_2019.iloc[:,3]\n",
    "sud = homeless_2019.iloc[:,14]\n",
    "correlation = st.pearsonr(homeless,sud)\n",
    "print(f\"The correlation between both factors is {round(correlation[0],2)}\")\n",
    "\n",
    "chr_homeless = homeless_2017.iloc[:,13]\n",
    "sud = homeless_2017.iloc[:,14]\n",
    "correlation = st.pearsonr(chr_homeless,sud)\n",
    "print(f\"The correlation between both factors is {round(correlation[0],2)}\")\n",
    "\n",
    "chr_homeless = homeless_2018.iloc[:,13]\n",
    "sud = homeless_2018.iloc[:,14]\n",
    "correlation = st.pearsonr(chr_homeless,sud)\n",
    "print(f\"The correlation between both factors is {round(correlation[0],2)}\")\n",
    "\n",
    "chr_homeless = homeless_2019.iloc[:,13]\n",
    "sud = homeless_2019.iloc[:,14]\n",
    "correlation = st.pearsonr(chr_homeless,sud)\n",
    "print(f\"The correlation between both factors is {round(correlation[0],2)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x_axis = np.arange(1,4,1)\n",
    "# x_axis = \n",
    "plt.xticks(np.arange(0,3,1),['2017','2018','2019'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Rates')"
      ]
     },
     "execution_count": 55,
     "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": [
    "total_sud = [16.03, 13.36, 12.91]\n",
    "uns_sud = [19.71, 17.05, 15.49]\n",
    "sh_sud = [5.88, 2.18, 4.93]\n",
    "plt.xticks(np.arange(0,3,1),['2017','2018','2019'])\n",
    "\n",
    "hmless_pop, = plt.plot(x_axis, total_sud, marker=\"+\",color=\"blue\", linewidth=1, label=\"Homeless Population\")\n",
    "hmless_uns, = plt.plot(x_axis, uns_sud, marker=\"s\", color=\"Red\", linewidth=1, label=\"Unsheltered\")\n",
    "hmless_sh, = plt.plot(x_axis, sh_sud, marker=\"s\", color=\"green\", linewidth=1, label=\"Sheltered\")\n",
    "plt.legend(handles=[hmless_pop, hmless_uns, hmless_sh], loc=\"best\")\n",
    "plt.title(\"Substance Use Rates by Shelter Status in Los Angeles County\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Rates\")"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}