Algorand_eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algorand Covid-19 Project\n",
    "## Demo: Basic Exploratory Analysis\n",
    "\n",
    "Source documentation: https://github.com/algorandfoundation/IReport-Covid/blob/master/js/retrieveData.js"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "date = str(datetime.today().strftime('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1272, 40)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tx_id</th>\n",
       "      <th>age_group</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state_code</th>\n",
       "      <th>region_code</th>\n",
       "      <th>gender</th>\n",
       "      <th>3_dig_zip</th>\n",
       "      <th>doctors_office</th>\n",
       "      <th>walk_in_clinic</th>\n",
       "      <th>virtual_care</th>\n",
       "      <th>hospital_or_ER</th>\n",
       "      <th>other</th>\n",
       "      <th>hospitalized</th>\n",
       "      <th>when_discharged</th>\n",
       "      <th>still_in_hospital</th>\n",
       "      <th>when_admitted</th>\n",
       "      <th>received_care</th>\n",
       "      <th>symptom_quarantine</th>\n",
       "      <th>voluntary_quarantine</th>\n",
       "      <th>personally_required_quarantine</th>\n",
       "      <th>general_quarantine</th>\n",
       "      <th>when_quarantine_ended</th>\n",
       "      <th>still_in_quarantine</th>\n",
       "      <th>when_quarantine_started</th>\n",
       "      <th>left_quarantine_temporarily</th>\n",
       "      <th>was_quarantined</th>\n",
       "      <th>fever</th>\n",
       "      <th>cough</th>\n",
       "      <th>difficulty_breathing</th>\n",
       "      <th>fatigue</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>when_symptoms_ended</th>\n",
       "      <th>still_symptomatic</th>\n",
       "      <th>when_symptoms_started</th>\n",
       "      <th>is_symptomatic</th>\n",
       "      <th>test_date</th>\n",
       "      <th>test_location</th>\n",
       "      <th>test_results</th>\n",
       "      <th>tried_to_get_tested</th>\n",
       "      <th>tested</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UGY5YWRRYLLCDC5SD2BIAUEBXKZROG4N56VT6QRZBZD566...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>US</td>\n",
       "      <td>NY</td>\n",
       "      <td>NY</td>\n",
       "      <td>f</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5USYC4OOGQYGSKRS3LLTRCSLYIX5CY6Y2HIDRY6S3MISED...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>AU</td>\n",
       "      <td>ACT</td>\n",
       "      <td>ACT</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSJEL2ZTRSNKJLA6G5H5ITPLY6ZFYBFVG47G3765XM7OQJ...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>US</td>\n",
       "      <td>CO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FJD33KOQXQ5E2SA5VWB5MAIHXZTKVZUM5NEK7FJN56UKGS...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>US</td>\n",
       "      <td>NY</td>\n",
       "      <td>NY</td>\n",
       "      <td>f</td>\n",
       "      <td>104.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>QTLPDQHRDHMD5NGGIZFSRZ4MQACT3MK76JZ6JMDXDNLIRZ...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>US</td>\n",
       "      <td>MD</td>\n",
       "      <td>MD</td>\n",
       "      <td>m</td>\n",
       "      <td>207.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                               tx_id  age_group country_code  \\\n",
       "0  UGY5YWRRYLLCDC5SD2BIAUEBXKZROG4N56VT6QRZBZD566...       55.0           US   \n",
       "1  5USYC4OOGQYGSKRS3LLTRCSLYIX5CY6Y2HIDRY6S3MISED...       40.0           AU   \n",
       "2  MSJEL2ZTRSNKJLA6G5H5ITPLY6ZFYBFVG47G3765XM7OQJ...       65.0           US   \n",
       "3  FJD33KOQXQ5E2SA5VWB5MAIHXZTKVZUM5NEK7FJN56UKGS...       40.0           US   \n",
       "4  QTLPDQHRDHMD5NGGIZFSRZ4MQACT3MK76JZ6JMDXDNLIRZ...       20.0           US   \n",
       "\n",
       "  state_code region_code gender  3_dig_zip  doctors_office  walk_in_clinic  \\\n",
       "0         NY          NY      f      100.0             NaN             NaN   \n",
       "1        ACT         ACT      m        NaN             NaN             NaN   \n",
       "2         CO         NaN      m      100.0             NaN             NaN   \n",
       "3         NY          NY      f      104.0             NaN             NaN   \n",
       "4         MD          MD      m      207.0             NaN             NaN   \n",
       "\n",
       "   virtual_care  hospital_or_ER  other  hospitalized when_discharged  \\\n",
       "0           NaN             NaN    NaN           NaN             NaN   \n",
       "1           NaN             NaN    NaN           NaN             NaN   \n",
       "2           NaN             NaN    NaN           NaN             NaN   \n",
       "3           NaN             NaN    NaN           NaN             NaN   \n",
       "4           NaN             NaN    NaN           NaN             NaN   \n",
       "\n",
       "   still_in_hospital when_admitted  received_care  symptom_quarantine  \\\n",
       "0                NaN           NaN           -1.0                 NaN   \n",
       "1                NaN           NaN           -1.0                 NaN   \n",
       "2                NaN           NaN           -1.0                 1.0   \n",
       "3                NaN           NaN           -1.0                 NaN   \n",
       "4                NaN           NaN           -1.0                 NaN   \n",
       "\n",
       "  voluntary_quarantine  personally_required_quarantine  general_quarantine  \\\n",
       "0                  1.0                             NaN                 NaN   \n",
       "1                  NaN                             NaN                 NaN   \n",
       "2                  NaN                             NaN                 NaN   \n",
       "3                  NaN                             NaN                 NaN   \n",
       "4                  NaN                             NaN                 NaN   \n",
       "\n",
       "  when_quarantine_ended still_in_quarantine when_quarantine_started  \\\n",
       "0                   NaN                 1.0              2020-03-10   \n",
       "1                   NaN                 NaN                     NaN   \n",
       "2            2020-03-20                 NaN              2020-03-13   \n",
       "3                   NaN                 NaN                     NaN   \n",
       "4                   NaN                 NaN                     NaN   \n",
       "\n",
       "   left_quarantine_temporarily  was_quarantined  fever  cough  \\\n",
       "0                          1.0              1.0    NaN    NaN   \n",
       "1                          NaN             -1.0    NaN    NaN   \n",
       "2                         -1.0              1.0    NaN    NaN   \n",
       "3                          NaN             -1.0    NaN    NaN   \n",
       "4                          NaN             -1.0    NaN    NaN   \n",
       "\n",
       "   difficulty_breathing  fatigue  sore_throat when_symptoms_ended  \\\n",
       "0                   NaN      NaN          NaN                 NaN   \n",
       "1                   NaN      NaN          NaN                 NaN   \n",
       "2                   NaN      NaN          NaN                 NaN   \n",
       "3                   NaN      NaN          NaN                 NaN   \n",
       "4                   NaN      NaN          NaN                 NaN   \n",
       "\n",
       "   still_symptomatic when_symptoms_started  is_symptomatic   test_date  \\\n",
       "0                NaN                   NaN            -1.0         NaN   \n",
       "1                NaN                   NaN            -1.0         NaN   \n",
       "2                NaN                   NaN            -1.0  2020-03-20   \n",
       "3                NaN                   NaN            -1.0         NaN   \n",
       "4                NaN                   NaN            -1.0         NaN   \n",
       "\n",
       "   test_location  test_results  tried_to_get_tested  tested  \n",
       "0            NaN           NaN                 -1.0    -1.0  \n",
       "1            NaN           NaN                 -1.0    -1.0  \n",
       "2            3.0          -1.0                  NaN     1.0  \n",
       "3            NaN           NaN                 -1.0    -1.0  \n",
       "4            NaN           NaN                 -1.0    -1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cleaner_data_df = pd.read_csv('data/cleaner_covidData'+date+'.csv')\n",
    "cleaner_data_df = pd.read_csv('data/cleaner_covidData2020-04-29.csv')\n",
    "print(cleaner_data_df.shape)\n",
    "cleaner_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_group</th>\n",
       "      <th>3_dig_zip</th>\n",
       "      <th>doctors_office</th>\n",
       "      <th>walk_in_clinic</th>\n",
       "      <th>virtual_care</th>\n",
       "      <th>hospital_or_ER</th>\n",
       "      <th>other</th>\n",
       "      <th>hospitalized</th>\n",
       "      <th>still_in_hospital</th>\n",
       "      <th>received_care</th>\n",
       "      <th>symptom_quarantine</th>\n",
       "      <th>personally_required_quarantine</th>\n",
       "      <th>general_quarantine</th>\n",
       "      <th>left_quarantine_temporarily</th>\n",
       "      <th>was_quarantined</th>\n",
       "      <th>fever</th>\n",
       "      <th>cough</th>\n",
       "      <th>difficulty_breathing</th>\n",
       "      <th>fatigue</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>still_symptomatic</th>\n",
       "      <th>is_symptomatic</th>\n",
       "      <th>test_location</th>\n",
       "      <th>test_results</th>\n",
       "      <th>tried_to_get_tested</th>\n",
       "      <th>tested</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1265.000000</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1261.000000</td>\n",
       "      <td>93.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>720.000000</td>\n",
       "      <td>1259.000000</td>\n",
       "      <td>77.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>1268.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1008.000000</td>\n",
       "      <td>1264.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.752569</td>\n",
       "      <td>314.878738</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.090909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.947661</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.229547</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.741325</td>\n",
       "      <td>2.677419</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>-0.826389</td>\n",
       "      <td>-0.947785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.469998</td>\n",
       "      <td>309.112987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.044466</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.319406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.880598</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.671411</td>\n",
       "      <td>1.275071</td>\n",
       "      <td>1.121635</td>\n",
       "      <td>0.671906</td>\n",
       "      <td>0.319037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>986.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age_group   3_dig_zip  doctors_office  walk_in_clinic  virtual_care  \\\n",
       "count  1265.000000  602.000000            10.0             5.0          13.0   \n",
       "mean     36.752569  314.878738             1.0             1.0           1.0   \n",
       "std      15.469998  309.112987             0.0             0.0           0.0   \n",
       "min       1.000000   12.000000             1.0             1.0           1.0   \n",
       "25%      20.000000  100.000000             1.0             1.0           1.0   \n",
       "50%      30.000000  207.000000             1.0             1.0           1.0   \n",
       "75%      50.000000  372.000000             1.0             1.0           1.0   \n",
       "max      85.000000  986.000000             1.0             1.0           1.0   \n",
       "\n",
       "       hospital_or_ER  other  hospitalized  still_in_hospital  received_care  \\\n",
       "count            11.0    2.0     11.000000                2.0    1261.000000   \n",
       "mean              1.0    1.0     -0.090909                1.0      -0.947661   \n",
       "std               0.0    0.0      1.044466                0.0       0.319406   \n",
       "min               1.0    1.0     -1.000000                1.0      -1.000000   \n",
       "25%               1.0    1.0     -1.000000                1.0      -1.000000   \n",
       "50%               1.0    1.0     -1.000000                1.0      -1.000000   \n",
       "75%               1.0    1.0      1.000000                1.0      -1.000000   \n",
       "max               1.0    1.0      1.000000                1.0       1.000000   \n",
       "\n",
       "       symptom_quarantine  personally_required_quarantine  general_quarantine  \\\n",
       "count                93.0                            61.0               362.0   \n",
       "mean                  1.0                             1.0                 1.0   \n",
       "std                   0.0                             0.0                 0.0   \n",
       "min                   1.0                             1.0                 1.0   \n",
       "25%                   1.0                             1.0                 1.0   \n",
       "50%                   1.0                             1.0                 1.0   \n",
       "75%                   1.0                             1.0                 1.0   \n",
       "max                   1.0                             1.0                 1.0   \n",
       "\n",
       "       left_quarantine_temporarily  was_quarantined  fever  cough  \\\n",
       "count                   720.000000      1259.000000   77.0  110.0   \n",
       "mean                      0.475000         0.229547    1.0    1.0   \n",
       "std                       0.880598         0.973684    0.0    0.0   \n",
       "min                      -1.000000        -1.000000    1.0    1.0   \n",
       "25%                      -1.000000        -1.000000    1.0    1.0   \n",
       "50%                       1.000000         1.000000    1.0    1.0   \n",
       "75%                       1.000000         1.000000    1.0    1.0   \n",
       "max                       1.000000         1.000000    1.0    1.0   \n",
       "\n",
       "       difficulty_breathing  fatigue  sore_throat  still_symptomatic  \\\n",
       "count                  56.0    110.0         94.0               83.0   \n",
       "mean                    1.0      1.0          1.0                1.0   \n",
       "std                     0.0      0.0          0.0                0.0   \n",
       "min                     1.0      1.0          1.0                1.0   \n",
       "25%                     1.0      1.0          1.0                1.0   \n",
       "50%                     1.0      1.0          1.0                1.0   \n",
       "75%                     1.0      1.0          1.0                1.0   \n",
       "max                     1.0      1.0          1.0                1.0   \n",
       "\n",
       "       is_symptomatic  test_location  test_results  tried_to_get_tested  \\\n",
       "count     1268.000000      31.000000     31.000000          1008.000000   \n",
       "mean        -0.741325       2.677419      0.516129            -0.826389   \n",
       "std          0.671411       1.275071      1.121635             0.671906   \n",
       "min         -1.000000       1.000000     -1.000000            -1.000000   \n",
       "25%         -1.000000       2.000000     -1.000000            -1.000000   \n",
       "50%         -1.000000       3.000000      1.000000            -1.000000   \n",
       "75%         -1.000000       4.000000      1.000000            -1.000000   \n",
       "max          1.000000       5.000000      2.000000             2.000000   \n",
       "\n",
       "            tested  \n",
       "count  1264.000000  \n",
       "mean     -0.947785  \n",
       "std       0.319037  \n",
       "min      -1.000000  \n",
       "25%      -1.000000  \n",
       "50%      -1.000000  \n",
       "75%      -1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaner_data_df.describe() # time columns: when_discharged, when_admitted, when_quarantine_ended, when_quarantine_started"
   ]
  },
  {
   "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": [
    "cleaner_data_df.age_group.hist()\n",
    "plt.title(\"Age Group Distribution in Surveys\")\n",
    "plt.ylabel(\"# individuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stats(df):\n",
    "    print(\"{} survey responses with data\".format(len(df)))\n",
    "    print('\\n')\n",
    "    num_was_wasnot_quar = len(df[(df.was_quarantined==1.0)|(df.was_quarantined==-1.0)])\n",
    "    num_was_quar = len(df[df.was_quarantined==1.0])\n",
    "    print(\"From {} responses : {}% reported being quarantined\".format(num_was_wasnot_quar,round(100*num_was_quar/num_was_wasnot_quar,2)))\n",
    "    num_left_stayed_quar = len(df[(df.left_quarantine_temporarily==1.0)|(df.left_quarantine_temporarily==-1.0)])\n",
    "    num_left_quar = len(df[(df.left_quarantine_temporarily==1.0)])\n",
    "    \n",
    "    num_quar_left_quar = len(df[(df.was_quarantined==1.0)&(df.left_quarantine_temporarily==1.0)])\n",
    "    print(\"From those that reported being quarantined {}% left temporarily\".format(round(100*num_quar_left_quar/num_was_quar,2)))\n",
    "\n",
    "    print('\\n')\n",
    "    symptomatic_df = df[df.is_symptomatic==1.0]\n",
    "    print(\"From {} people who are symptomatic\".format(len(symptomatic_df)))\n",
    "    print('\\n')\n",
    "    print(\"{}% got care at the doctor's office\".format(round(100*len(symptomatic_df[symptomatic_df.doctors_office==1.0])/len(symptomatic_df),2)))\n",
    "    print(\"{}% got care at a walk in clinic\".format(round(100*len(symptomatic_df[symptomatic_df.walk_in_clinic==1.0])/len(symptomatic_df),2)))\n",
    "    print(\"{}% got virtual care\".format(round(100*len(symptomatic_df[symptomatic_df.virtual_care==1.0])/len(symptomatic_df),2)))\n",
    "    print(\"{}% got care at the hospital/ER\".format(round(100*len(symptomatic_df[symptomatic_df.hospital_or_ER==1.0])/len(symptomatic_df),2)))\n",
    "    print('\\n')\n",
    "    print(\"{}% reported a fever\".format(round(100*len(symptomatic_df[symptomatic_df.fever==1.0])/len(symptomatic_df),2)))\n",
    "    print(\"{}% reported a cough\".format(round(100*len(symptomatic_df[symptomatic_df.cough==1.0])/len(symptomatic_df),2)))\n",
    "    print(\"{}% reported difficulty breathing\".format(round(100*len(symptomatic_df[symptomatic_df.difficulty_breathing==1.0])/len(symptomatic_df),2)))\n",
    "    print(\"{}% reported fatigue\".format(round(100*len(symptomatic_df[symptomatic_df.fatigue==1.0])/len(symptomatic_df),2)))\n",
    "    print(\"{}% reported sore throat\".format(round(100*len(symptomatic_df[symptomatic_df.sore_throat==1.0])/len(symptomatic_df),2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1272 survey responses with data\n",
      "\n",
      "\n",
      "From 1259 responses : 61.48% reported being quarantined\n",
      "From those that reported being quarantined 68.6% left temporarily\n",
      "\n",
      "\n",
      "From 164 people who are symptomatic\n",
      "\n",
      "\n",
      "6.1% got care at the doctor's office\n",
      "3.05% got care at a walk in clinic\n",
      "7.32% got virtual care\n",
      "4.27% got care at the hospital/ER\n",
      "\n",
      "\n",
      "46.95% reported a fever\n",
      "67.07% reported a cough\n",
      "34.15% reported difficulty breathing\n",
      "67.07% reported fatigue\n",
      "57.32% reported sore throat\n"
     ]
    }
   ],
   "source": [
    "get_stats(cleaner_data_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def encode_cols(df):\n",
    "    cols_to_encode = ['hospitalized','received_care','left_quarantine_temporarily',\n",
    "                      'was_quarantined','is_symptomatic','test_location','test_results',\n",
    "                      'tried_to_get_tested','tested','gender','age_group']\n",
    "\n",
    "    cols_not_encoding = ['country_code','state_code','doctors_office','walk_in_clinic','virtual_care','hospital_or_ER',\n",
    "                         'other','symptom_quarantine','voluntary_quarantine','personally_required_quarantine',\n",
    "                         'general_quarantine','still_in_quarantine','still_symptomatic',\n",
    "                          'fever','cough','difficulty_breathing','fatigue','sore_throat']\n",
    "\n",
    "    adjusted_df = pd.get_dummies(data=df[cols_to_encode], \n",
    "                                 columns=cols_to_encode)\n",
    "\n",
    "    adjusted_cols = ['not_hospitalized','hospitalized',\n",
    "                     'no_care_received','care_received',\n",
    "                     'stayed_in_quarantine','left_quarantine_temporarily',\n",
    "                     'not_quarantined','was_quarantined',\n",
    "                     'not_symptomatic','is_symptomatic',\n",
    "                     'tested_at_dr_office','tested_at_hospital','tested_at_urgent_care','tested_at_ad_hoc',\n",
    "                     'tested_at_other','tested_negative','tested_positive','tested_still_waiting',\n",
    "                     'testing_not_attempted','testing_attempted','testing_attempted_and_denied',\n",
    "                     'not_tested','tested',\n",
    "                     'female','male']\n",
    "    \n",
    "    for age_value in sorted(list(set(df.age_group.dropna()))):\n",
    "        adjusted_cols.append('age_group_'+str(int(age_value)))\n",
    "\n",
    "    adjusted_df.columns = adjusted_cols\n",
    "    new_df_counts = df[cols_not_encoding].join(adjusted_df)\n",
    "    #print(new_df_counts.shape)\n",
    "\n",
    "    country_df_counts = new_df_counts.dropna(axis=0, subset=['country_code']).drop('state_code',axis=1).fillna(0).reset_index(drop=True)\n",
    "    country_df_counts[['voluntary_quarantine', 'still_in_quarantine']] = country_df_counts[['voluntary_quarantine', 'still_in_quarantine']].astype(bool).astype(float)\n",
    "    country_state_df_counts = new_df_counts.dropna(axis=0, subset=['country_code','state_code']).fillna(0).reset_index(drop=True)\n",
    "    country_state_df_counts[['voluntary_quarantine', 'still_in_quarantine']] = country_state_df_counts[['voluntary_quarantine', 'still_in_quarantine']].astype(bool).astype(float)\n",
    "    \n",
    "    return country_df_counts, country_state_df_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df_counts, country_state_df_counts = encode_cols(cleaner_data_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupbys\n",
    "country_state_df_counts = country_state_df_counts.groupby(['country_code','state_code']).sum().reset_index()\n",
    "country_only_df_counts = country_df_counts.groupby(['country_code']).sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_state_df_counts.to_csv('data/global_country_state_counts'+date+'.csv',index=False)\n",
    "#country_only_df_counts.to_csv('data/global_country_counts'+date+'.csv',index=False)\n",
    "country_state_df_counts.to_csv('data/global_country_state_counts2020-04-29.csv',index=False)\n",
    "country_only_df_counts.to_csv('data/global_country_counts2020-04-29.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "US_grouped_df = country_state_df_counts[country_state_df_counts.country_code == 'US'].reset_index(drop=True)\n",
    "#US_grouped_df.to_csv('data/US_state_sums'+date+'.csv',index=False)\n",
    "US_grouped_df.to_csv('data/US_state_sums2020-04-29.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_df_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_state_df_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_only_df_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_state_df_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_hist(df,size):\n",
    "    plt.figure(figsize=size)\n",
    "    plt.bar(df.columns,list(df.loc[0].values),color=list('rgbkymc'))\n",
    "    plt.ylabel(\"# individuals\")\n",
    "    plt.show()\n",
    "\n",
    "def country_stat_display(df,country):\n",
    "    num_surveys = len(country_df_counts[country_df_counts.country_code==country])\n",
    "    print(\"From {} surveys in {}...\\n\".format(num_surveys,country))\n",
    "    for col in df.columns[1:6]:\n",
    "        print(\"{} got care from {} [{}%]\".format(int(sum(df[col])),col,round((100*sum(df[col]))/num_surveys,2)))\n",
    "    get_hist(df[df.columns[1:6]].reset_index(drop=True),(12,5))\n",
    "    print('\\n')\n",
    "    for col in df.columns[6:10]:\n",
    "        print(\"{} underwent {} [{}%]\".format(int(sum(df[col])),col.replace(\"_\",\" \"),round((100*sum(df[col]))/num_surveys,2)))\n",
    "    get_hist(df[df.columns[6:10]].reset_index(drop=True),(12,5))\n",
    "    print('\\n')\n",
    "    for col in df.columns[12:17]:\n",
    "        print(\"{} reported {} [{}%]\".format(int(sum(df[col])),col,round((100*sum(df[col]))/num_surveys,2)))\n",
    "    get_hist(df[df.columns[12:17]].reset_index(drop=True),(12,5))\n",
    "    print('\\n')\n",
    "    for col in df.columns[19:21]:\n",
    "        print(\"{} received {} [{}%]\".format(int(sum(df[col])),col.replace(\"_\",\" \").replace(\"received\",\"\"),round((100*sum(df[col]))/num_surveys,2)))\n",
    "    get_hist(df[df.columns[19:21]].reset_index(drop=True),(6,5))\n",
    "    print('\\n')\n",
    "    \n",
    "    other_col_ranges = [df.columns[10:12],df.columns[21:23],df.columns[25:27],df.columns[27:32],df.columns[32:35],\n",
    "                        df.columns[35:38],df.columns[38:40],df.columns[40:42],df.columns[42:]]\n",
    "    other_col_plt_sizes = [(6,5),(6,5),(6,5),(12,5),(12,5),(12,5),(6,5),(6,5),(17,5)]\n",
    "    \n",
    "    for col_subset,plt_size in zip(other_col_ranges,other_col_plt_sizes):\n",
    "        for col in col_subset:\n",
    "            print(\"{} {} [{}%]\".format(int(sum(df[col])),col.replace(\"_\",\" \"),round((100*sum(df[col]))/num_surveys,2)))\n",
    "        get_hist(df[col_subset].reset_index(drop=True),plt_size)\n",
    "        print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### US level analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>country_code</th>\n",
       "      <th>state_code</th>\n",
       "      <th>doctors_office</th>\n",
       "      <th>walk_in_clinic</th>\n",
       "      <th>virtual_care</th>\n",
       "      <th>hospital_or_ER</th>\n",
       "      <th>other</th>\n",
       "      <th>symptom_quarantine</th>\n",
       "      <th>voluntary_quarantine</th>\n",
       "      <th>personally_required_quarantine</th>\n",
       "      <th>general_quarantine</th>\n",
       "      <th>still_in_quarantine</th>\n",
       "      <th>still_symptomatic</th>\n",
       "      <th>fever</th>\n",
       "      <th>cough</th>\n",
       "      <th>difficulty_breathing</th>\n",
       "      <th>fatigue</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>not_hospitalized</th>\n",
       "      <th>hospitalized</th>\n",
       "      <th>no_care_received</th>\n",
       "      <th>care_received</th>\n",
       "      <th>stayed_in_quarantine</th>\n",
       "      <th>left_quarantine_temporarily</th>\n",
       "      <th>not_quarantined</th>\n",
       "      <th>was_quarantined</th>\n",
       "      <th>not_symptomatic</th>\n",
       "      <th>is_symptomatic</th>\n",
       "      <th>tested_at_dr_office</th>\n",
       "      <th>tested_at_hospital</th>\n",
       "      <th>tested_at_urgent_care</th>\n",
       "      <th>tested_at_ad_hoc</th>\n",
       "      <th>tested_at_other</th>\n",
       "      <th>tested_negative</th>\n",
       "      <th>tested_positive</th>\n",
       "      <th>tested_still_waiting</th>\n",
       "      <th>testing_not_attempted</th>\n",
       "      <th>testing_attempted</th>\n",
       "      <th>testing_attempted_and_denied</th>\n",
       "      <th>not_tested</th>\n",
       "      <th>tested</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>age_group_1</th>\n",
       "      <th>age_group_10</th>\n",
       "      <th>age_group_20</th>\n",
       "      <th>age_group_30</th>\n",
       "      <th>age_group_40</th>\n",
       "      <th>age_group_50</th>\n",
       "      <th>age_group_55</th>\n",
       "      <th>age_group_60</th>\n",
       "      <th>age_group_65</th>\n",
       "      <th>age_group_70</th>\n",
       "      <th>age_group_75</th>\n",
       "      <th>age_group_85</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>AL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>AR</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>AZ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>CO</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code state_code  doctors_office  walk_in_clinic  virtual_care  \\\n",
       "0           US         AL             0.0             0.0           0.0   \n",
       "1           US         AR             2.0             0.0           0.0   \n",
       "2           US         AZ             0.0             0.0           0.0   \n",
       "3           US         CA             0.0             0.0           0.0   \n",
       "4           US         CO             0.0             0.0           1.0   \n",
       "\n",
       "   hospital_or_ER  other  symptom_quarantine  voluntary_quarantine  \\\n",
       "0             0.0    0.0                 0.0                   2.0   \n",
       "1             0.0    0.0                 3.0                   5.0   \n",
       "2             0.0    0.0                 1.0                   3.0   \n",
       "3             0.0    0.0                 3.0                  26.0   \n",
       "4             0.0    0.0                 4.0                  12.0   \n",
       "\n",
       "   personally_required_quarantine  general_quarantine  still_in_quarantine  \\\n",
       "0                             0.0                 0.0                  1.0   \n",
       "1                             1.0                 3.0                  7.0   \n",
       "2                             0.0                 1.0                  4.0   \n",
       "3                             3.0                25.0                 37.0   \n",
       "4                             1.0                 8.0                 19.0   \n",
       "\n",
       "   still_symptomatic  fever  cough  difficulty_breathing  fatigue  \\\n",
       "0                0.0    0.0    0.0                   0.0      0.0   \n",
       "1                2.0    2.0    2.0                   1.0      2.0   \n",
       "2                2.0    1.0    2.0                   0.0      0.0   \n",
       "3                4.0    3.0    8.0                   1.0      6.0   \n",
       "4                5.0    1.0    2.0                   2.0      4.0   \n",
       "\n",
       "   sore_throat  not_hospitalized  hospitalized  no_care_received  \\\n",
       "0          0.0                 0             0                 2   \n",
       "1          0.0                 0             0                 8   \n",
       "2          1.0                 0             0                 6   \n",
       "3          8.0                 0             0                60   \n",
       "4          4.0                 0             0                26   \n",
       "\n",
       "   care_received  stayed_in_quarantine  left_quarantine_temporarily  \\\n",
       "0              0                     0                            2   \n",
       "1              2                     2                            5   \n",
       "2              0                     3                            1   \n",
       "3              0                     5                           28   \n",
       "4              1                     5                           12   \n",
       "\n",
       "   not_quarantined  was_quarantined  not_symptomatic  is_symptomatic  \\\n",
       "0                0                2                2               0   \n",
       "1                3                7                7               3   \n",
       "2                2                4                4               2   \n",
       "3               20               39               51               9   \n",
       "4                7               20               20               7   \n",
       "\n",
       "   tested_at_dr_office  tested_at_hospital  tested_at_urgent_care  \\\n",
       "0                    0                   0                      0   \n",
       "1                    2                   0                      0   \n",
       "2                    0                   0                      0   \n",
       "3                    0                   0                      0   \n",
       "4                    0                   0                      1   \n",
       "\n",
       "   tested_at_ad_hoc  tested_at_other  tested_negative  tested_positive  \\\n",
       "0                 0                0                0                0   \n",
       "1                 0                0                0                2   \n",
       "2                 0                0                0                0   \n",
       "3                 1                0                1                0   \n",
       "4                 0                0                1                0   \n",
       "\n",
       "   tested_still_waiting  testing_not_attempted  testing_attempted  \\\n",
       "0                     0                      2                  0   \n",
       "1                     0                      5                  1   \n",
       "2                     0                      6                  0   \n",
       "3                     0                     45                  1   \n",
       "4                     0                     19                  1   \n",
       "\n",
       "   testing_attempted_and_denied  not_tested  tested  female  male  \\\n",
       "0                             0           2       0       1     1   \n",
       "1                             0           8       2       6     4   \n",
       "2                             0           6       0       4     2   \n",
       "3                             3          59       1      22    36   \n",
       "4                             1          26       1      14    12   \n",
       "\n",
       "   age_group_1  age_group_10  age_group_20  age_group_30  age_group_40  \\\n",
       "0            0             0             1             1             0   \n",
       "1            0             0             3             2             1   \n",
       "2            0             0             2             3             0   \n",
       "3            0             1            11            20            12   \n",
       "4            0             0             3             9             6   \n",
       "\n",
       "   age_group_50  age_group_55  age_group_60  age_group_65  age_group_70  \\\n",
       "0             0             0             0             0             0   \n",
       "1             2             0             0             1             0   \n",
       "2             0             1             0             0             0   \n",
       "3             7             5             2             0             2   \n",
       "4             0             2             2             3             2   \n",
       "\n",
       "   age_group_75  age_group_85  \n",
       "0             0             0  \n",
       "1             0             1  \n",
       "2             0             0  \n",
       "3             0             0  \n",
       "4             0             0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_grouped_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data from 41 states\n"
     ]
    }
   ],
   "source": [
    "unq_states = list(set(US_grouped_df.state_code))\n",
    "print(\"Data from {} states\".format(len(unq_states)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From 618 surveys in US...\n",
      "\n",
      "4 got care from doctors_office [0.65%]\n",
      "3 got care from walk_in_clinic [0.49%]\n",
      "9 got care from virtual_care [1.46%]\n",
      "5 got care from hospital_or_ER [0.81%]\n",
      "1 got care from other [0.16%]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "53 underwent symptom quarantine [8.58%]\n",
      "309 underwent voluntary quarantine [50.0%]\n",
      "22 underwent personally required quarantine [3.56%]\n",
      "156 underwent general quarantine [25.24%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "43 reported fever [6.96%]\n",
      "63 reported cough [10.19%]\n",
      "30 reported difficulty_breathing [4.85%]\n",
      "65 reported fatigue [10.52%]\n",
      "52 reported sore_throat [8.41%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "599 received no care  [96.93%]\n",
      "17 received care  [2.75%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "373 still in quarantine [60.36%]\n",
      "46 still symptomatic [7.44%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "74 stayed in quarantine [11.97%]\n",
      "309 left quarantine temporarily [50.0%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "527 not symptomatic [85.28%]\n",
      "89 is symptomatic [14.4%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "5 tested at dr office [0.81%]\n",
      "5 tested at hospital [0.81%]\n",
      "4 tested at urgent care [0.65%]\n",
      "6 tested at ad hoc [0.97%]\n",
      "1 tested at other [0.16%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "6 tested negative [0.97%]\n",
      "13 tested positive [2.1%]\n",
      "2 tested still waiting [0.32%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "468 testing not attempted [75.73%]\n",
      "7 testing attempted [1.13%]\n",
      "24 testing attempted and denied [3.88%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "593 not tested [95.95%]\n",
      "21 tested [3.4%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "319 female [51.62%]\n",
      "287 male [46.44%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "0 age group 1 [0.0%]\n",
      "13 age group 10 [2.1%]\n",
      "146 age group 20 [23.62%]\n",
      "185 age group 30 [29.94%]\n",
      "97 age group 40 [15.7%]\n",
      "42 age group 50 [6.8%]\n",
      "41 age group 55 [6.63%]\n",
      "25 age group 60 [4.05%]\n",
      "23 age group 65 [3.72%]\n",
      "38 age group 70 [6.15%]\n",
      "5 age group 75 [0.81%]\n",
      "1 age group 85 [0.16%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "country_stat_display(country_only_df_counts[country_only_df_counts.country_code == 'US'],'US')"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}