Health Insurance Coverage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from subprocess import check_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "health = pd.read_csv('states.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "health_data = health\n",
    "col_name = health_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_data = pd.DataFrame() # storing numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for r in health_data:\n",
    "    #print(type(raw_data[r][0]))\n",
    "    if type(health_data[r][0]) == str:\n",
    "        #print(raw_data[r][0])\n",
    "        if re.search('\\$', health_data[r][0]) is not None:\n",
    "            for i,row in enumerate(health_data[r]):\n",
    "                \n",
    "                temp = float(row.strip('$ ')) # strip '$' and accidental space ''\n",
    "                health_data[r][i] = temp\n",
    "        elif re.search('%', health_data[r][0]) is not None:\n",
    "            #print(r)\n",
    "            for i,row in enumerate(health_data[r]):\n",
    "                \n",
    "                temp = float(row.strip('% '))/100 # strip '%' and accidental space ''\n",
    "                health_data[r][i] = temp\n",
    "                #print(row)\n",
    "for r in health_data:\n",
    "    if type(health_data[r][0]) != str:\n",
    "        num_data[r] = health_data[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State', 'Uninsured Rate (2010)', 'Uninsured Rate (2015)',\n",
       "       'Uninsured Rate Change (2010-2015)',\n",
       "       'Health Insurance Coverage Change (2010-2015)',\n",
       "       'Employer Health Insurance Coverage (2015)',\n",
       "       'Marketplace Health Insurance Coverage (2016)',\n",
       "       'Marketplace Tax Credits (2016)', 'Average Monthly Tax Credit (2016)',\n",
       "       'State Medicaid Expansion (2016)', 'Medicaid Enrollment (2013)',\n",
       "       'Medicaid Enrollment (2016)', 'Medicaid Enrollment Change (2013-2016)',\n",
       "       'Medicare Enrollment (2016)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Uninsured Rate (2010)</th>\n",
       "      <th>Uninsured Rate (2015)</th>\n",
       "      <th>Uninsured Rate Change (2010-2015)</th>\n",
       "      <th>Health Insurance Coverage Change (2010-2015)</th>\n",
       "      <th>Employer Health Insurance Coverage (2015)</th>\n",
       "      <th>Marketplace Health Insurance Coverage (2016)</th>\n",
       "      <th>Marketplace Tax Credits (2016)</th>\n",
       "      <th>Average Monthly Tax Credit (2016)</th>\n",
       "      <th>State Medicaid Expansion (2016)</th>\n",
       "      <th>Medicaid Enrollment (2013)</th>\n",
       "      <th>Medicaid Enrollment (2016)</th>\n",
       "      <th>Medicaid Enrollment Change (2013-2016)</th>\n",
       "      <th>Medicare Enrollment (2016)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.101</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>215000</td>\n",
       "      <td>2545000</td>\n",
       "      <td>165534</td>\n",
       "      <td>152206</td>\n",
       "      <td>310</td>\n",
       "      <td>False</td>\n",
       "      <td>799176.0</td>\n",
       "      <td>910775</td>\n",
       "      <td>111599.0</td>\n",
       "      <td>989855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.149</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>36000</td>\n",
       "      <td>390000</td>\n",
       "      <td>17995</td>\n",
       "      <td>16205</td>\n",
       "      <td>750</td>\n",
       "      <td>True</td>\n",
       "      <td>122334.0</td>\n",
       "      <td>166625</td>\n",
       "      <td>44291.0</td>\n",
       "      <td>88966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.108</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>410000</td>\n",
       "      <td>3288000</td>\n",
       "      <td>179445</td>\n",
       "      <td>124346</td>\n",
       "      <td>230</td>\n",
       "      <td>True</td>\n",
       "      <td>1201770.0</td>\n",
       "      <td>1716198</td>\n",
       "      <td>514428.0</td>\n",
       "      <td>1175624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.095</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>234000</td>\n",
       "      <td>1365000</td>\n",
       "      <td>63357</td>\n",
       "      <td>56843</td>\n",
       "      <td>306</td>\n",
       "      <td>True</td>\n",
       "      <td>556851.0</td>\n",
       "      <td>920194</td>\n",
       "      <td>363343.0</td>\n",
       "      <td>606146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.086</td>\n",
       "      <td>-0.099</td>\n",
       "      <td>3826000</td>\n",
       "      <td>19552000</td>\n",
       "      <td>1415428</td>\n",
       "      <td>1239893</td>\n",
       "      <td>309</td>\n",
       "      <td>True</td>\n",
       "      <td>7755381.0</td>\n",
       "      <td>11843081</td>\n",
       "      <td>4087700.0</td>\n",
       "      <td>5829777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Uninsured Rate (2010) Uninsured Rate (2015)  \\\n",
       "0    Alabama                  0.146                 0.101   \n",
       "1     Alaska                  0.199                 0.149   \n",
       "2    Arizona                  0.169                 0.108   \n",
       "3   Arkansas                  0.175                 0.095   \n",
       "4  California                 0.185                 0.086   \n",
       "\n",
       "  Uninsured Rate Change (2010-2015)  \\\n",
       "0                            -0.045   \n",
       "1                             -0.05   \n",
       "2                            -0.061   \n",
       "3                             -0.08   \n",
       "4                            -0.099   \n",
       "\n",
       "   Health Insurance Coverage Change (2010-2015)  \\\n",
       "0                                        215000   \n",
       "1                                         36000   \n",
       "2                                        410000   \n",
       "3                                        234000   \n",
       "4                                       3826000   \n",
       "\n",
       "   Employer Health Insurance Coverage (2015)  \\\n",
       "0                                    2545000   \n",
       "1                                     390000   \n",
       "2                                    3288000   \n",
       "3                                    1365000   \n",
       "4                                   19552000   \n",
       "\n",
       "   Marketplace Health Insurance Coverage (2016)  \\\n",
       "0                                        165534   \n",
       "1                                         17995   \n",
       "2                                        179445   \n",
       "3                                         63357   \n",
       "4                                       1415428   \n",
       "\n",
       "   Marketplace Tax Credits (2016) Average Monthly Tax Credit (2016)  \\\n",
       "0                          152206                               310   \n",
       "1                           16205                               750   \n",
       "2                          124346                               230   \n",
       "3                           56843                               306   \n",
       "4                         1239893                               309   \n",
       "\n",
       "  State Medicaid Expansion (2016)  Medicaid Enrollment (2013)  \\\n",
       "0                           False                    799176.0   \n",
       "1                            True                    122334.0   \n",
       "2                            True                   1201770.0   \n",
       "3                            True                    556851.0   \n",
       "4                            True                   7755381.0   \n",
       "\n",
       "   Medicaid Enrollment (2016)  Medicaid Enrollment Change (2013-2016)  \\\n",
       "0                      910775                                111599.0   \n",
       "1                      166625                                 44291.0   \n",
       "2                     1716198                                514428.0   \n",
       "3                      920194                                363343.0   \n",
       "4                    11843081                               4087700.0   \n",
       "\n",
       "   Medicare Enrollment (2016)  \n",
       "0                      989855  \n",
       "1                       88966  \n",
       "2                     1175624  \n",
       "3                      606146  \n",
       "4                     5829777  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for NA Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18c13051a20>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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KyhJHjx6V9rMne/xlflvJwoUL8frrr8PFxQWVKlXCkiVLyjriudLS0jB69Gg0b94cDRo0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18c13056630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(health.isnull(),yticklabels=False,cbar=False,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x18c12f01b70>,\n",
       " <matplotlib.text.Text at 0x18c12f04470>,\n",
       " <matplotlib.text.Text at 0x18c12fd4b38>,\n",
       " <matplotlib.text.Text at 0x18c12fda630>,\n",
       " <matplotlib.text.Text at 0x18c12fe0128>,\n",
       " <matplotlib.text.Text at 0x18c12fe0be0>,\n",
       " <matplotlib.text.Text at 0x18c12fe36d8>,\n",
       " <matplotlib.text.Text at 0x18c12fe71d0>,\n",
       " <matplotlib.text.Text at 0x18c12fe7c88>,\n",
       " <matplotlib.text.Text at 0x18c12fec780>,\n",
       " <matplotlib.text.Text at 0x18c12fef278>,\n",
       " <matplotlib.text.Text at 0x18c12fefd30>,\n",
       " <matplotlib.text.Text at 0x18c12ff3828>,\n",
       " <matplotlib.text.Text at 0x18c12ff8320>,\n",
       " <matplotlib.text.Text at 0x18c12ff8dd8>,\n",
       " <matplotlib.text.Text at 0x18c12ffc8d0>,\n",
       " <matplotlib.text.Text at 0x18c130013c8>,\n",
       " <matplotlib.text.Text at 0x18c13001e80>,\n",
       " <matplotlib.text.Text at 0x18c13004978>,\n",
       " <matplotlib.text.Text at 0x18c13008470>,\n",
       " <matplotlib.text.Text at 0x18c13008f28>,\n",
       " <matplotlib.text.Text at 0x18c1300ca20>,\n",
       " <matplotlib.text.Text at 0x18c13011518>,\n",
       " <matplotlib.text.Text at 0x18c13011fd0>,\n",
       " <matplotlib.text.Text at 0x18c13015ac8>,\n",
       " <matplotlib.text.Text at 0x18c1301b5c0>,\n",
       " <matplotlib.text.Text at 0x18c1301d0b8>,\n",
       " <matplotlib.text.Text at 0x18c1301db70>,\n",
       " <matplotlib.text.Text at 0x18c13023668>,\n",
       " <matplotlib.text.Text at 0x18c13025160>,\n",
       " <matplotlib.text.Text at 0x18c13025c18>,\n",
       " <matplotlib.text.Text at 0x18c1302a710>,\n",
       " <matplotlib.text.Text at 0x18c1302f208>,\n",
       " <matplotlib.text.Text at 0x18c1302fcc0>,\n",
       " <matplotlib.text.Text at 0x18c130337b8>,\n",
       " <matplotlib.text.Text at 0x18c130372b0>,\n",
       " <matplotlib.text.Text at 0x18c13037d68>,\n",
       " <matplotlib.text.Text at 0x18c1303b860>,\n",
       " <matplotlib.text.Text at 0x18c1303f358>,\n",
       " <matplotlib.text.Text at 0x18c1303fe10>,\n",
       " <matplotlib.text.Text at 0x18c13045908>,\n",
       " <matplotlib.text.Text at 0x18c13049400>,\n",
       " <matplotlib.text.Text at 0x18c13049eb8>,\n",
       " <matplotlib.text.Text at 0x18c1304d9b0>,\n",
       " <matplotlib.text.Text at 0x18c130514a8>,\n",
       " <matplotlib.text.Text at 0x18c13051f60>,\n",
       " <matplotlib.text.Text at 0x18c13056a58>,\n",
       " <matplotlib.text.Text at 0x18c1305a550>,\n",
       " <matplotlib.text.Text at 0x18c1305f048>,\n",
       " <matplotlib.text.Text at 0x18c1305fb00>,\n",
       " <matplotlib.text.Text at 0x18c130625f8>,\n",
       " <matplotlib.text.Text at 0x18c130670f0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rNfMSFxenGTNm6NSpU+6A73Q6VbVqVVuP9/U9OGTIEHXp0sXW52DK6tWrlZ6e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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18c12bb1860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(10, 10))\n",
    "#ax.matshow(num_data.corr())\n",
    "\n",
    "\n",
    "#plt.xticks(range(len(num_data.columns)), num_data.columns, rotation='vertical');\n",
    "#plt.yticks(range(len(num_data.columns)), num_data.columns);\n",
    "\n",
    "y = health_data['Uninsured Rate (2010)']\n",
    "#print(y)\n",
    "x = range(len(y))\n",
    "plt.bar(x, y)\n",
    "ax1.set_xticks(x)\n",
    "ax1.set_xticklabels(health_data['State'], rotation='vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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