Ben.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Substance</th>\n",
       "      <th>Dependence</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Alcohol</th>\n",
       "      <th>No/Non-Veteran</th>\n",
       "      <td>98.437551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No/Veteran</th>\n",
       "      <td>98.143886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes/Non-Veteran</th>\n",
       "      <td>1.562449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes/Veteran</th>\n",
       "      <td>1.856114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cocaine</th>\n",
       "      <th>No/Non-Veteran</th>\n",
       "      <td>99.923352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Percentage\n",
       "Substance Dependence                 \n",
       "Alcohol   No/Non-Veteran    98.437551\n",
       "          No/Veteran        98.143886\n",
       "          Yes/Non-Veteran    1.562449\n",
       "          Yes/Veteran        1.856114\n",
       "Cocaine   No/Non-Veteran    99.923352"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Data from SAMHDA - National Survey on Drug Use and Health, 2018\n",
    "\n",
    "substancecsv = pd.read_csv(\"Data/Veteran Data/veteransubstance.csv\", index_col=[1,0], skipinitialspace=True).drop(['Weighted Count'],axis=1)\n",
    "substancecsv['Percentage'] = substancecsv['Percentage'].multiply(100)\n",
    "substancecsv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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></th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Substance</th>\n",
       "      <th>Dependence</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Alcohol</th>\n",
       "      <th>Yes/Non-Veteran</th>\n",
       "      <td>1.562449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes/Veteran</th>\n",
       "      <td>1.856114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Cocaine</th>\n",
       "      <th>Yes/Non-Veteran</th>\n",
       "      <td>0.076648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes/Veteran</th>\n",
       "      <td>0.037546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heroin</th>\n",
       "      <th>Yes/Non-Veteran</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Percentage\n",
       "Substance Dependence                 \n",
       "Alcohol   Yes/Non-Veteran    1.562449\n",
       "          Yes/Veteran        1.856114\n",
       "Cocaine   Yes/Non-Veteran    0.076648\n",
       "          Yes/Veteran        0.037546\n",
       "Heroin    Yes/Non-Veteran    0.000000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop \"No\" answers\n",
    "substance2csv = pd.read_csv(\"Data/Veteran Data/veteransubstance.csv\", skipinitialspace=True).drop(['Weighted Count'],axis=1)\n",
    "substance2csv = substance2csv.drop(substance2csv.index[substance2csv['Dependence']=='No/Non-Veteran'])\n",
    "substance2csv = substance2csv.drop(substance2csv.index[substance2csv['Dependence']=='No/Veteran'])\n",
    "substance2csv = substance2csv.set_index(['Substance','Dependence'])\n",
    "substance2csv['Percentage'] = substance2csv['Percentage'].multiply(100)\n",
    "substance2csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>State</th>\n",
       "      <th>Overall Homeless</th>\n",
       "      <th>Homeless Veterans</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>2016</td>\n",
       "      <td>132</td>\n",
       "      <td>6.145251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>3434</td>\n",
       "      <td>339</td>\n",
       "      <td>8.984893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>2712</td>\n",
       "      <td>251</td>\n",
       "      <td>8.471144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>9865</td>\n",
       "      <td>893</td>\n",
       "      <td>8.300799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>129972</td>\n",
       "      <td>10836</td>\n",
       "      <td>7.695585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Overall Homeless  Homeless Veterans  Percentage\n",
       "0    AK              2016                132    6.145251\n",
       "1    AL              3434                339    8.984893\n",
       "2    AR              2712                251    8.471144\n",
       "3    AZ              9865                893    8.300799\n",
       "4    CA            129972              10836    7.695585"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Data from HUD Exchange, removed all except \"Overall\" and \"Veterans\", added p\n",
    "\n",
    "vetcsv = pd.read_csv(\"Data/Veteran Data/2018-PIT-Counts Vets.csv\",skipinitialspace=True)\n",
    "vetcsv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "##code from https://stackoverflow.com/questions/25386870/pandas-plotting-with-multi-index\n",
    "substancecsv.unstack(level=0).plot(kind='bar')\n",
    "\n",
    "plt.title('Dependence on Substances of the US Population in 2018')\n",
    "plt.legend(title=None,loc='center right')\n",
    "plt.tick_params(axis='x',labelrotation=0)\n",
    "plt.savefig(\"Images/Dependence on Substances All.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "substance2csv.unstack(level=0).plot(kind='bar')\n",
    "\n",
    "plt.legend(loc='center')\n",
    "plt.tick_params(axis='x',labelrotation=0)\n",
    "plt.title('Dependence on Substances of the US Population in 2018')\n",
    "plt.savefig(\"Images/Dependence on Substances Yes.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "states = vetcsv['State']\n",
    "homelessvets = vetcsv['Percentage']\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(x=states,height=homelessvets)\n",
    "plt.tick_params(axis='x',labelrotation=90)\n",
    "plt.title('Percentage of Homeless Vets in 2018')\n",
    "plt.savefig(\"Images/Homeless Vets.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
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