covidProjNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6b80b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "36799708",
   "metadata": {},
   "outputs": [],
   "source": [
    "import snowflake.connector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aad58c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ACCOUNT = 'bab48039'\n",
    "USER = 'nzaw96'\n",
    "PASSWORD = 'Abc123!@#'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22fabb7f",
   "metadata": {},
   "source": [
    "## Seems like there's some issue with connecting Jupyter notebook to Snowflake. The goal of this connection was to directly export the processed files into tables in Snowflake. For now, the processed will be first save to my local computer and then staged+loaded into Snowflake using Snowsql."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "994e9d7b",
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "Cannot allocate write+execute memory for ffi.callback(). You might be running on a system that prevents this. For more information, see https://cffi.readthedocs.io/en/latest/using.html#callbacks",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [5], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Connecting to Snowflake\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m con \u001b[38;5;241m=\u001b[39m \u001b[43msnowflake\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnector\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m  \u001b[49m\u001b[43muser\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mUSER\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m  \u001b[49m\u001b[43mpassword\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mPASSWORD\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m  \u001b[49m\u001b[43maccount\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mACCOUNT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/__init__.py:51\u001b[0m, in \u001b[0;36mConnect\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mConnect\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m SnowflakeConnection:\n\u001b[0;32m---> 51\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSnowflakeConnection\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/connection.py:297\u001b[0m, in \u001b[0;36mSnowflakeConnection.__init__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    295\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    296\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__set_error_attributes()\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_telemetry \u001b[38;5;241m=\u001b[39m TelemetryClient(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_rest)\n\u001b[1;32m    300\u001b[0m \u001b[38;5;66;03m# get the imported modules from sys.modules\u001b[39;00m\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/connection.py:550\u001b[0m, in \u001b[0;36mSnowflakeConnection.connect\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    548\u001b[0m         connection_diag\u001b[38;5;241m.\u001b[39mgenerate_report()\n\u001b[1;32m    549\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 550\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__open_connection\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/connection.py:789\u001b[0m, in \u001b[0;36mSnowflakeConnection.__open_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    787\u001b[0m auth \u001b[38;5;241m=\u001b[39m Auth(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrest)\n\u001b[1;32m    788\u001b[0m auth\u001b[38;5;241m.\u001b[39mread_temporary_credentials(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_session_parameters)\n\u001b[0;32m--> 789\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_authenticate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mauth_instance\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    791\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_password \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m  \u001b[38;5;66;03m# ensure password won't persist\u001b[39;00m\n\u001b[1;32m    793\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient_session_keep_alive:\n\u001b[1;32m    794\u001b[0m     \u001b[38;5;66;03m# This will be called after the heartbeat frequency has actually been set.\u001b[39;00m\n\u001b[1;32m    795\u001b[0m     \u001b[38;5;66;03m# By this point it should have been decided if the heartbeat has to be enabled\u001b[39;00m\n\u001b[1;32m    796\u001b[0m     \u001b[38;5;66;03m# and what would the heartbeat frequency be\u001b[39;00m\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/connection.py:1052\u001b[0m, in \u001b[0;36mSnowflakeConnection._authenticate\u001b[0;34m(self, auth_instance)\u001b[0m\n\u001b[1;32m   1049\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_authenticate\u001b[39m(\u001b[38;5;28mself\u001b[39m, auth_instance):\n\u001b[1;32m   1050\u001b[0m     \u001b[38;5;66;03m# make some changes if needed before real __authenticate\u001b[39;00m\n\u001b[1;32m   1051\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1052\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__authenticate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__preprocess_auth_instance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mauth_instance\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1053\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m ReauthenticationRequest \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[1;32m   1054\u001b[0m         \u001b[38;5;66;03m# cached id_token expiration error, we have cleaned id_token and try to authenticate again\u001b[39;00m\n\u001b[1;32m   1055\u001b[0m         logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mID token expired. Reauthenticating...: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, ex)\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/connection.py:1072\u001b[0m, in \u001b[0;36mSnowflakeConnection.__authenticate\u001b[0;34m(self, auth_instance)\u001b[0m\n\u001b[1;32m   1070\u001b[0m auth \u001b[38;5;241m=\u001b[39m Auth(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrest)\n\u001b[1;32m   1071\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1072\u001b[0m     \u001b[43mauth\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mauthenticate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1073\u001b[0m \u001b[43m        \u001b[49m\u001b[43mauth_instance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth_instance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1074\u001b[0m \u001b[43m        \u001b[49m\u001b[43maccount\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccount\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1075\u001b[0m \u001b[43m        \u001b[49m\u001b[43muser\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1076\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdatabase\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatabase\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1077\u001b[0m \u001b[43m        \u001b[49m\u001b[43mschema\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mschema\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1078\u001b[0m \u001b[43m        \u001b[49m\u001b[43mwarehouse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwarehouse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1079\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrole\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrole\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1080\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpasscode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_passcode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1081\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpasscode_in_password\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_passcode_in_password\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1082\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmfa_callback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mfa_callback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1083\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpassword_callback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_password_callback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1084\u001b[0m \u001b[43m        \u001b[49m\u001b[43msession_parameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_session_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1085\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1086\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m OperationalError:\n\u001b[1;32m   1087\u001b[0m     logger\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[1;32m   1088\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOperational Error raised at authentication\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1089\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor authenticator: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(auth_instance)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1090\u001b[0m     )\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/auth.py:257\u001b[0m, in \u001b[0;36mAuth.authenticate\u001b[0;34m(self, auth_instance, account, user, database, schema, warehouse, role, passcode, passcode_in_password, mfa_callback, password_callback, session_parameters, timeout)\u001b[0m\n\u001b[1;32m    254\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTimeout set to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mauth_timeout\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    256\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 257\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_rest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    259\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    260\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdumps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    261\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    262\u001b[0m \u001b[43m        \u001b[49m\u001b[43msocket_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    263\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    264\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ForbiddenError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    265\u001b[0m     \u001b[38;5;66;03m# HTTP 403\u001b[39;00m\n\u001b[1;32m    266\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m(\n\u001b[1;32m    267\u001b[0m         msg\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    268\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to connect to DB. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    275\u001b[0m         sqlstate\u001b[38;5;241m=\u001b[39mSQLSTATE_CONNECTION_WAS_NOT_ESTABLISHED,\n\u001b[1;32m    276\u001b[0m     )\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/network.py:704\u001b[0m, in \u001b[0;36mSnowflakeRestful._post_request\u001b[0;34m(self, url, headers, body, token, timeout, _no_results, no_retry, socket_timeout, _include_retry_params)\u001b[0m\n\u001b[1;32m    701\u001b[0m     ret \u001b[38;5;241m=\u001b[39m probe_connection(full_url)\n\u001b[1;32m    702\u001b[0m     pprint(ret)\n\u001b[0;32m--> 704\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpost\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    706\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfull_url\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    707\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    708\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    709\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    710\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    711\u001b[0m \u001b[43m    \u001b[49m\u001b[43mno_retry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mno_retry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    712\u001b[0m \u001b[43m    \u001b[49m\u001b[43msocket_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msocket_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    713\u001b[0m \u001b[43m    \u001b[49m\u001b[43m_include_retry_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_include_retry_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    714\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    715\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[1;32m    716\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mret[code] = \u001b[39m\u001b[38;5;132;01m{code}\u001b[39;00m\u001b[38;5;124m, after post request\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m    717\u001b[0m         code\u001b[38;5;241m=\u001b[39m(ret\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mN/A\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m    718\u001b[0m     )\n\u001b[1;32m    719\u001b[0m )\n\u001b[1;32m    721\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcode\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m==\u001b[39m SESSION_EXPIRED_GS_CODE:\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/network.py:794\u001b[0m, in \u001b[0;36mSnowflakeRestful.fetch\u001b[0;34m(self, method, full_url, headers, data, timeout, **kwargs)\u001b[0m\n\u001b[1;32m    792\u001b[0m retry_ctx \u001b[38;5;241m=\u001b[39m RetryCtx(timeout, include_retry_params)\n\u001b[1;32m    793\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 794\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request_exec_wrapper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m        \u001b[49m\u001b[43msession\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfull_url\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretry_ctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    797\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    798\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/network.py:917\u001b[0m, in \u001b[0;36mSnowflakeRestful._request_exec_wrapper\u001b[0;34m(self, session, method, full_url, headers, data, retry_ctx, no_retry, token, **kwargs)\u001b[0m\n\u001b[1;32m    915\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    916\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_retry:\n\u001b[0;32m--> 917\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    918\u001b[0m     logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIgnored error\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    919\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {}\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/network.py:837\u001b[0m, in \u001b[0;36mSnowflakeRestful._request_exec_wrapper\u001b[0;34m(self, session, method, full_url, headers, data, retry_ctx, no_retry, token, **kwargs)\u001b[0m\n\u001b[1;32m    835\u001b[0m full_url \u001b[38;5;241m=\u001b[39m SnowflakeRestful\u001b[38;5;241m.\u001b[39madd_request_guid(full_url)\n\u001b[1;32m    836\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 837\u001b[0m     return_object \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request_exec\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    838\u001b[0m \u001b[43m        \u001b[49m\u001b[43msession\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msession\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    839\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    840\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfull_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfull_url\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    841\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    842\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    843\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    844\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    845\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    846\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m return_object \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    847\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m return_object\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/network.py:1116\u001b[0m, in \u001b[0;36mSnowflakeRestful._request_exec\u001b[0;34m(self, session, method, full_url, headers, data, token, catch_okta_unauthorized_error, is_raw_text, is_raw_binary, binary_data_handler, socket_timeout)\u001b[0m\n\u001b[1;32m   1106\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m   1107\u001b[0m     TelemetryService\u001b[38;5;241m.\u001b[39mget_instance()\u001b[38;5;241m.\u001b[39mlog_http_request_error(\n\u001b[1;32m   1108\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHttpException\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mstr\u001b[39m(err),\n\u001b[1;32m   1109\u001b[0m         full_url,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1114\u001b[0m         stack_trace\u001b[38;5;241m=\u001b[39mtraceback\u001b[38;5;241m.\u001b[39mformat_exc(),\n\u001b[1;32m   1115\u001b[0m     )\n\u001b[0;32m-> 1116\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/network.py:1018\u001b[0m, in \u001b[0;36mSnowflakeRestful._request_exec\u001b[0;34m(self, session, method, full_url, headers, data, token, catch_okta_unauthorized_error, is_raw_text, is_raw_binary, binary_data_handler, socket_timeout)\u001b[0m\n\u001b[1;32m   1014\u001b[0m download_start_time \u001b[38;5;241m=\u001b[39m get_time_millis()\n\u001b[1;32m   1015\u001b[0m \u001b[38;5;66;03m# socket timeout is constant. You should be able to receive\u001b[39;00m\n\u001b[1;32m   1016\u001b[0m \u001b[38;5;66;03m# the response within the time. If not, ConnectReadTimeout or\u001b[39;00m\n\u001b[1;32m   1017\u001b[0m \u001b[38;5;66;03m# ReadTimeout is raised.\u001b[39;00m\n\u001b[0;32m-> 1018\u001b[0m raw_ret \u001b[38;5;241m=\u001b[39m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1019\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1020\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfull_url\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1021\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1022\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1023\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msocket_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1024\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1025\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_raw_binary\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1026\u001b[0m \u001b[43m    \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mSnowflakeAuth\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1027\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1028\u001b[0m download_end_time \u001b[38;5;241m=\u001b[39m get_time_millis()\n\u001b[1;32m   1030\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/vendored/requests/sessions.py:587\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    582\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    583\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    584\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    585\u001b[0m }\n\u001b[1;32m    586\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 587\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    589\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/vendored/requests/sessions.py:701\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    698\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    700\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 701\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    703\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    704\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/vendored/requests/adapters.py:489\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    487\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    488\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m chunked:\n\u001b[0;32m--> 489\u001b[0m         resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m            \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    492\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m            \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m            \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    495\u001b[0m \u001b[43m            \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m            \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    499\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    502\u001b[0m     \u001b[38;5;66;03m# Send the request.\u001b[39;00m\n\u001b[1;32m    503\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    504\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(conn, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproxy_pool\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/vendored/urllib3/connectionpool.py:703\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    700\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_proxy(conn)\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    704\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    706\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    707\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    708\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    709\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    710\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    711\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    713\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[1;32m    714\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[1;32m    715\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[1;32m    716\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n\u001b[1;32m    717\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/vendored/urllib3/connectionpool.py:386\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[38;5;66;03m# Trigger any extra validation we need to do.\u001b[39;00m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 386\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    387\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    388\u001b[0m     \u001b[38;5;66;03m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[39;00m\n\u001b[1;32m    389\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mconn\u001b[38;5;241m.\u001b[39mtimeout)\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/vendored/urllib3/connectionpool.py:1042\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m   1040\u001b[0m \u001b[38;5;66;03m# Force connect early to allow us to validate the connection.\u001b[39;00m\n\u001b[1;32m   1041\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(conn, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msock\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):  \u001b[38;5;66;03m# AppEngine might not have  `.sock`\u001b[39;00m\n\u001b[0;32m-> 1042\u001b[0m     \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1044\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_verified:\n\u001b[1;32m   1045\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m   1046\u001b[0m         (\n\u001b[1;32m   1047\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnverified HTTPS request is being made to host \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1052\u001b[0m         InsecureRequestWarning,\n\u001b[1;32m   1053\u001b[0m     )\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/vendored/urllib3/connection.py:395\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mssl_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    394\u001b[0m     default_ssl_context \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 395\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mssl_context \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_urllib3_context\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    396\u001b[0m \u001b[43m        \u001b[49m\u001b[43mssl_version\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolve_ssl_version\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mssl_version\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    397\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcert_reqs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolve_cert_reqs\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcert_reqs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    398\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    400\u001b[0m context \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mssl_context\n\u001b[1;32m    401\u001b[0m context\u001b[38;5;241m.\u001b[39mverify_mode \u001b[38;5;241m=\u001b[39m resolve_cert_reqs(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcert_reqs)\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/vendored/urllib3/util/ssl_.py:339\u001b[0m, in \u001b[0;36mcreate_urllib3_context\u001b[0;34m(ssl_version, cert_reqs, options, ciphers)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[38;5;66;03m# The order of the below lines setting verify_mode and check_hostname\u001b[39;00m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;66;03m# matter due to safe-guards SSLContext has to prevent an SSLContext with\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \u001b[38;5;66;03m# check_hostname=True, verify_mode=NONE/OPTIONAL. This is made even more\u001b[39;00m\n\u001b[1;32m    336\u001b[0m \u001b[38;5;66;03m# complex because we don't know whether PROTOCOL_TLS_CLIENT will be used\u001b[39;00m\n\u001b[1;32m    337\u001b[0m \u001b[38;5;66;03m# or not so we don't know the initial state of the freshly created SSLContext.\u001b[39;00m\n\u001b[1;32m    338\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cert_reqs \u001b[38;5;241m==\u001b[39m ssl\u001b[38;5;241m.\u001b[39mCERT_REQUIRED:\n\u001b[0;32m--> 339\u001b[0m     \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverify_mode\u001b[49m \u001b[38;5;241m=\u001b[39m cert_reqs\n\u001b[1;32m    340\u001b[0m     disable_check_hostname()\n\u001b[1;32m    341\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/snowflake/connector/vendored/urllib3/contrib/pyopenssl.py:443\u001b[0m, in \u001b[0;36mPyOpenSSLContext.verify_mode\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;129m@verify_mode\u001b[39m\u001b[38;5;241m.\u001b[39msetter\n\u001b[1;32m    442\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mverify_mode\u001b[39m(\u001b[38;5;28mself\u001b[39m, value):\n\u001b[0;32m--> 443\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_verify\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_stdlib_to_openssl_verify\u001b[49m\u001b[43m[\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_verify_callback\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/OpenSSL/SSL.py:1135\u001b[0m, in \u001b[0;36mContext.set_verify\u001b[0;34m(self, mode, callback)\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m callable(callback):\n\u001b[1;32m   1133\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallback must be callable\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1135\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verify_helper \u001b[38;5;241m=\u001b[39m \u001b[43m_VerifyHelper\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1136\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verify_callback \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verify_helper\u001b[38;5;241m.\u001b[39mcallback\n\u001b[1;32m   1137\u001b[0m _lib\u001b[38;5;241m.\u001b[39mSSL_CTX_set_verify(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_context, mode, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_verify_callback)\n",
      "File \u001b[0;32m~/envsAndProjs/covidProj/lib/python3.8/site-packages/OpenSSL/SSL.py:365\u001b[0m, in \u001b[0;36m_VerifyHelper.__init__\u001b[0;34m(self, callback)\u001b[0m\n\u001b[1;32m    362\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    363\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 365\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback \u001b[38;5;241m=\u001b[39m \u001b[43m_ffi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    366\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mint (*)(int, X509_STORE_CTX *)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwrapper\u001b[49m\n\u001b[1;32m    367\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mMemoryError\u001b[0m: Cannot allocate write+execute memory for ffi.callback(). You might be running on a system that prevents this. For more information, see https://cffi.readthedocs.io/en/latest/using.html#callbacks"
     ]
    }
   ],
   "source": [
    "# Seems like there's issues connecting Jupyter Notebook to Snowflake\n",
    "con = snowflake.connector.connect(\n",
    "  user=USER,\n",
    "  password=PASSWORD,\n",
    "  account=ACCOUNT,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2f795d",
   "metadata": {},
   "source": [
    "## 1. Data is (are?) imported from Google's Covid-19 Open Data source. Each file is imported into a separate dataframe. The file 'index.csv' contains the keys that can be used to join the tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "96bd9f46",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_index = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/index.csv\")\n",
    "df_epidem = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/epidemiology.csv\")\n",
    "df_demog = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/demographics.csv\")\n",
    "df_emergDeclare = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/lawatlas-emergency-declarations.csv\")\n",
    "df_hospitalized = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/hospitalizations.csv\")\n",
    "df_mobility = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/mobility.csv\")\n",
    "df_vaccinations = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/vaccinations.csv\")\n",
    "df_govResponse =pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/oxford-government-response.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef65ca55",
   "metadata": {},
   "source": [
    "## Now, we will clean all the dataframes in the order that they were imported in above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b86c35d6",
   "metadata": {},
   "source": [
    "### First up is df_index which is a dataframe that contains keys to join the tables together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7726192f",
   "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>location_key</th>\n",
       "      <th>place_id</th>\n",
       "      <th>wikidata_id</th>\n",
       "      <th>datacommons_id</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>subregion1_code</th>\n",
       "      <th>subregion1_name</th>\n",
       "      <th>subregion2_code</th>\n",
       "      <th>subregion2_name</th>\n",
       "      <th>locality_code</th>\n",
       "      <th>locality_name</th>\n",
       "      <th>iso_3166_1_alpha_2</th>\n",
       "      <th>iso_3166_1_alpha_3</th>\n",
       "      <th>aggregation_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AD</td>\n",
       "      <td>ChIJlfCemC71pRIRkn_qeNc-yQc</td>\n",
       "      <td>Q228</td>\n",
       "      <td>country/AND</td>\n",
       "      <td>AD</td>\n",
       "      <td>Andorra</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>AD</td>\n",
       "      <td>AND</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AE</td>\n",
       "      <td>ChIJvRKrsd9IXj4RpwoIwFYv0zM</td>\n",
       "      <td>Q878</td>\n",
       "      <td>country/ARE</td>\n",
       "      <td>AE</td>\n",
       "      <td>United Arab Emirates</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>AE</td>\n",
       "      <td>ARE</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF</td>\n",
       "      <td>ChIJbQL_-LZu0TgReNqWvg1GtfM</td>\n",
       "      <td>Q889</td>\n",
       "      <td>country/AFG</td>\n",
       "      <td>AF</td>\n",
       "      <td>Afghanistan</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>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF_BAL</td>\n",
       "      <td>ChIJWZOescNSyzgRkmVJ8fmfR3k</td>\n",
       "      <td>Q121104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AF</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Balkh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF_BAM</td>\n",
       "      <td>ChIJw-bKQwDr0jgRQiw8jHsQ-HI</td>\n",
       "      <td>Q171382</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AF</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>BAM</td>\n",
       "      <td>Bamyan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location_key                     place_id wikidata_id datacommons_id  \\\n",
       "0           AD  ChIJlfCemC71pRIRkn_qeNc-yQc        Q228    country/AND   \n",
       "1           AE  ChIJvRKrsd9IXj4RpwoIwFYv0zM        Q878    country/ARE   \n",
       "2           AF  ChIJbQL_-LZu0TgReNqWvg1GtfM        Q889    country/AFG   \n",
       "3       AF_BAL  ChIJWZOescNSyzgRkmVJ8fmfR3k     Q121104            NaN   \n",
       "4       AF_BAM  ChIJw-bKQwDr0jgRQiw8jHsQ-HI     Q171382            NaN   \n",
       "\n",
       "  country_code          country_name subregion1_code subregion1_name  \\\n",
       "0           AD               Andorra             NaN             NaN   \n",
       "1           AE  United Arab Emirates             NaN             NaN   \n",
       "2           AF           Afghanistan             NaN             NaN   \n",
       "3           AF           Afghanistan             BAL           Balkh   \n",
       "4           AF           Afghanistan             BAM          Bamyan   \n",
       "\n",
       "  subregion2_code subregion2_name locality_code locality_name  \\\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",
       "  iso_3166_1_alpha_2 iso_3166_1_alpha_3  aggregation_level  \n",
       "0                 AD                AND                  0  \n",
       "1                 AE                ARE                  0  \n",
       "2                 AF                AFG                  0  \n",
       "3                 AF                AFG                  1  \n",
       "4                 AF                AFG                  1  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_index.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195e7e14",
   "metadata": {},
   "source": [
    "### We will only keep rows where country_name = 'United States of America'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "73590316",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_index = df_index.loc[df_index['country_name']=='United States of America']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25269ccb",
   "metadata": {},
   "source": [
    "### We also see that the values in the location_key column are specific to the city/county level as zipcodes are appended to them. We only want to specify the state so we can unappend the zipcode and cities. For example, 'US_FL_12009' -> 'US_FL'. So, we will add a column called 'location_key_2' that stores the state-level granularity of the location keys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fbf93547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'US_WI_55119',\n",
       " 'US_CA_06103',\n",
       " 'US_CO_08113',\n",
       " 'US_VA_51005',\n",
       " 'US_MT_30031',\n",
       " 'US_UT_49031',\n",
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       " 'US_KY_21115',\n",
       " 'US_WV_54023',\n",
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       " 'US_OR_41015',\n",
       " 'US_IN_18095',\n",
       " 'US_TX_48265',\n",
       " 'US_AZ_04013',\n",
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       " 'US_GA_13283',\n",
       " 'US_MS_28127',\n",
       " 'US_ID_16065',\n",
       " 'US_PR_72001',\n",
       " 'US_TX_48127',\n",
       " 'US_OK_40063',\n",
       " 'US_IA_19013',\n",
       " 'US_TX_48317',\n",
       " 'US_ID_16037',\n",
       " 'US_MD_24013',\n",
       " 'US_WV_54017',\n",
       " 'US_ID_16011',\n",
       " 'US_IN_18107',\n",
       " 'US_AL_01097',\n",
       " 'US_MO_29203',\n",
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       " 'US_WV_54055',\n",
       " 'US_PR_72147',\n",
       " ...}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df_index['location_key'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "dfa40e62",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_index['location_key_2'] = df_index['location_key'].apply(lambda x: '_'.join(x.split('_')[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "id": "f56d8ca3",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>location_key</th>\n",
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       "      <td>US</td>\n",
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       "      <td>US_AK</td>\n",
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       "      <td>United States of America</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>geoId/02013</td>\n",
       "      <td>US</td>\n",
       "      <td>United States of America</td>\n",
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       "      <td>Alaska</td>\n",
       "      <td>02013</td>\n",
       "      <td>Aleutians East</td>\n",
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       "      <td>US_AK_02016</td>\n",
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       "      <td>Q185533</td>\n",
       "      <td>geoId/02016</td>\n",
       "      <td>US</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>02016</td>\n",
       "      <td>Aleutians West</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>2</td>\n",
       "      <td>US_AK</td>\n",
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       "    <tr>\n",
       "      <th>19561</th>\n",
       "      <td>US_AK_02020</td>\n",
       "      <td>ChIJQT-zBHaRyFYR42iEp1q6fSU</td>\n",
       "      <td>Q39450</td>\n",
       "      <td>geoId/02020</td>\n",
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       "      <td>United States of America</td>\n",
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       "      <td>Alaska</td>\n",
       "      <td>02020</td>\n",
       "      <td>Anchorage Municipality</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
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       "      location_key                     place_id wikidata_id datacommons_id  \\\n",
       "19557           US  ChIJCzYy5IS16lQRQrfeQ5K5Oxw         Q30    country/USA   \n",
       "19558        US_AK  ChIJG8CuwJzfAFQRNduKqSde27w        Q797       geoId/02   \n",
       "19559  US_AK_02013  ChIJ7a-UgVuW_lYRjTetzbIWL48     Q504371    geoId/02013   \n",
       "19560  US_AK_02016  ChIJI9ivbI1Pg1cRg8d4nlGjGVU     Q185533    geoId/02016   \n",
       "19561  US_AK_02020  ChIJQT-zBHaRyFYR42iEp1q6fSU      Q39450    geoId/02020   \n",
       "\n",
       "      country_code              country_name subregion1_code subregion1_name  \\\n",
       "19557           US  United States of America             NaN             NaN   \n",
       "19558           US  United States of America              AK          Alaska   \n",
       "19559           US  United States of America              AK          Alaska   \n",
       "19560           US  United States of America              AK          Alaska   \n",
       "19561           US  United States of America              AK          Alaska   \n",
       "\n",
       "      subregion2_code         subregion2_name locality_code locality_name  \\\n",
       "19557             NaN                     NaN           NaN           NaN   \n",
       "19558             NaN                     NaN           NaN           NaN   \n",
       "19559           02013          Aleutians East           NaN           NaN   \n",
       "19560           02016          Aleutians West           NaN           NaN   \n",
       "19561           02020  Anchorage Municipality           NaN           NaN   \n",
       "\n",
       "      iso_3166_1_alpha_2 iso_3166_1_alpha_3  aggregation_level location_key_2  \n",
       "19557                 US                USA                  0             US  \n",
       "19558                 US                USA                  1          US_AK  \n",
       "19559                 US                USA                  2          US_AK  \n",
       "19560                 US                USA                  2          US_AK  \n",
       "19561                 US                USA                  2          US_AK  "
      ]
     },
     "execution_count": 597,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_index.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ef4045b",
   "metadata": {},
   "source": [
    "### We will leave the df_index dataframe just as it is, at least for now. Let's now move on to df_epidem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "e7024fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_epidem = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/epidemiology.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "04276f84",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "         date location_key  new_confirmed  new_deceased  new_recovered  \\\n",
       "0  2020-01-01           AD            0.0           0.0            NaN   \n",
       "1  2020-01-02           AD            0.0           0.0            NaN   \n",
       "2  2020-01-03           AD            0.0           0.0            NaN   \n",
       "3  2020-01-04           AD            0.0           0.0            NaN   \n",
       "4  2020-01-05           AD            0.0           0.0            NaN   \n",
       "\n",
       "   new_tested  cumulative_confirmed  cumulative_deceased  \\\n",
       "0         NaN                   0.0                  0.0   \n",
       "1         NaN                   0.0                  0.0   \n",
       "2         NaN                   0.0                  0.0   \n",
       "3         NaN                   0.0                  0.0   \n",
       "4         NaN                   0.0                  0.0   \n",
       "\n",
       "   cumulative_recovered  cumulative_tested  \n",
       "0                   NaN                NaN  \n",
       "1                   NaN                NaN  \n",
       "2                   NaN                NaN  \n",
       "3                   NaN                NaN  \n",
       "4                   NaN                NaN  "
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epidem.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b44801a4",
   "metadata": {},
   "source": [
    "### Just like with df_index, we will only keep the rows in df_epidem that correspond to the US. We will use location_key column to filter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "244cb9b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_key_US = set(df_index['location_key'].tolist()) # this will be used later on as well for reference\n",
    "df_epidem = df_epidem[df_epidem['location_key'].isin(location_key_US)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "f179cc6a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>location_key</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_deceased</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_tested</th>\n",
       "      <th>cumulative_confirmed</th>\n",
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       "      <th>cumulative_recovered</th>\n",
       "      <th>cumulative_tested</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9895200</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9895201</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9895202</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9895203</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9895204</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date location_key  new_confirmed  new_deceased  new_recovered  \\\n",
       "9895200  2020-01-01           US            0.0           0.0            NaN   \n",
       "9895201  2020-01-02           US            0.0           0.0            NaN   \n",
       "9895202  2020-01-03           US            0.0           0.0            NaN   \n",
       "9895203  2020-01-04           US            0.0           0.0            NaN   \n",
       "9895204  2020-01-05           US            0.0           0.0            NaN   \n",
       "\n",
       "         new_tested  cumulative_confirmed  cumulative_deceased  \\\n",
       "9895200         NaN                   0.0                  0.0   \n",
       "9895201         NaN                   0.0                  0.0   \n",
       "9895202         NaN                   0.0                  0.0   \n",
       "9895203         NaN                   0.0                  0.0   \n",
       "9895204         NaN                   0.0                  0.0   \n",
       "\n",
       "         cumulative_recovered  cumulative_tested  \n",
       "9895200                   NaN                NaN  \n",
       "9895201                   NaN                NaN  \n",
       "9895202                   NaN                NaN  \n",
       "9895203                   NaN                NaN  \n",
       "9895204                   NaN                NaN  "
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epidem.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "082ac09a",
   "metadata": {},
   "source": [
    "### Just like what we did with df_index, let's add an additional column to the dataframe called 'location_key_2' which will display the state-level granularity of the location. For example, 'US_FL_12009' in 'location_key' will be 'US_FL' in 'location_key_2'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "28c0f7c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_epidem['location_key_2'] = df_epidem['location_key'].apply(lambda x: '_'.join(x.split('_')[:2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb8a775",
   "metadata": {},
   "source": [
    "### We see that location_key_2 contains both just 'US' and 'US_stateX'. So, we will split df_epidem into two dataframes, df_epidem_US and df_epidem_states. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "83769b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_epidem_2 = df_epidem.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "1d2c00b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_epidem_2['date_month'] = df_epidem_2['date'].apply(lambda x: '-'.join(x.split('-')[:2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5752f7f9",
   "metadata": {},
   "source": [
    "### Here, we will try to keep the rows where location_key is at the state_level. In other words, rows where location_key == location_key_2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "3bf89497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2551031, 12)"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epidem_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "2369dd69",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_epidem_2 = df_epidem_2.loc[df_epidem_2['location_key'] == df_epidem_2['location_key_2']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545a0d1e",
   "metadata": {},
   "source": [
    "### Here's we will do groupby + fillna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "b284f3cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>location_key</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_deceased</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_tested</th>\n",
       "      <th>cumulative_confirmed</th>\n",
       "      <th>cumulative_deceased</th>\n",
       "      <th>cumulative_recovered</th>\n",
       "      <th>cumulative_tested</th>\n",
       "      <th>location_key_2</th>\n",
       "      <th>date_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9895200</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>US</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9895202</th>\n",
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       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9895203</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9895204</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date location_key  new_confirmed  new_deceased  new_recovered  \\\n",
       "9895200  2020-01-01           US            0.0           0.0            NaN   \n",
       "9895201  2020-01-02           US            0.0           0.0            NaN   \n",
       "9895202  2020-01-03           US            0.0           0.0            NaN   \n",
       "9895203  2020-01-04           US            0.0           0.0            NaN   \n",
       "9895204  2020-01-05           US            0.0           0.0            NaN   \n",
       "\n",
       "         new_tested  cumulative_confirmed  cumulative_deceased  \\\n",
       "9895200         NaN                   0.0                  0.0   \n",
       "9895201         NaN                   0.0                  0.0   \n",
       "9895202         NaN                   0.0                  0.0   \n",
       "9895203         NaN                   0.0                  0.0   \n",
       "9895204         NaN                   0.0                  0.0   \n",
       "\n",
       "         cumulative_recovered  cumulative_tested location_key_2 date_month  \n",
       "9895200                   NaN                NaN             US    2020-01  \n",
       "9895201                   NaN                NaN             US    2020-01  \n",
       "9895202                   NaN                NaN             US    2020-01  \n",
       "9895203                   NaN                NaN             US    2020-01  \n",
       "9895204                   NaN                NaN             US    2020-01  "
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epidem_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "27fc29a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_epidem_2[['new_recovered', 'new_tested', 'new_deceased']] = df_epidem_2[['new_recovered', 'new_tested', 'new_deceased']].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "d66b84df",
   "metadata": {},
   "outputs": [],
   "source": [
    "colsForGroupbyFillna = ['cumulative_confirmed', 'cumulative_deceased', 'cumulative_recovered', 'cumulative_tested']\n",
    "df_epidem_2[colsForGroupbyFillna] = df_epidem_2.groupby(['location_key'])[colsForGroupbyFillna].apply(lambda x: x.ffill())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "id": "d7d84ff0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 55206 entries, 9895200 to 12428419\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   date                  55206 non-null  object \n",
      " 1   location_key          55206 non-null  object \n",
      " 2   new_confirmed         55206 non-null  float64\n",
      " 3   new_deceased          55206 non-null  float64\n",
      " 4   new_recovered         55206 non-null  float64\n",
      " 5   new_tested            55206 non-null  float64\n",
      " 6   cumulative_confirmed  55206 non-null  float64\n",
      " 7   cumulative_deceased   55206 non-null  float64\n",
      " 8   cumulative_recovered  55206 non-null  float64\n",
      " 9   cumulative_tested     55206 non-null  float64\n",
      " 10  location_key_2        55206 non-null  object \n",
      " 11  date_month            55206 non-null  object \n",
      "dtypes: float64(8), object(4)\n",
      "memory usage: 5.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_epidem_2 = df_epidem_2.fillna(0)\n",
    "df_epidem_2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71faaf03",
   "metadata": {},
   "source": [
    "### Since location_key and location_key_2 are exactly the same, we can drop location_key_2 since we don't need it anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "ccaa9edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>US</td>\n",
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       "      <th>9895203</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>US</td>\n",
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       "    <tr>\n",
       "      <th>9895204</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>US</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date location_key  new_confirmed  new_deceased  new_recovered  \\\n",
       "9895200  2020-01-01           US            0.0           0.0            0.0   \n",
       "9895201  2020-01-02           US            0.0           0.0            0.0   \n",
       "9895202  2020-01-03           US            0.0           0.0            0.0   \n",
       "9895203  2020-01-04           US            0.0           0.0            0.0   \n",
       "9895204  2020-01-05           US            0.0           0.0            0.0   \n",
       "\n",
       "         new_tested  cumulative_confirmed  cumulative_deceased  \\\n",
       "9895200         0.0                   0.0                  0.0   \n",
       "9895201         0.0                   0.0                  0.0   \n",
       "9895202         0.0                   0.0                  0.0   \n",
       "9895203         0.0                   0.0                  0.0   \n",
       "9895204         0.0                   0.0                  0.0   \n",
       "\n",
       "         cumulative_recovered  cumulative_tested date_month  \n",
       "9895200                   0.0                0.0    2020-01  \n",
       "9895201                   0.0                0.0    2020-01  \n",
       "9895202                   0.0                0.0    2020-01  \n",
       "9895203                   0.0                0.0    2020-01  \n",
       "9895204                   0.0                0.0    2020-01  "
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epidem_2.drop(columns=['location_key_2'], inplace=True)\n",
    "df_epidem_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "ed2d6aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_epidem_US = df_epidem_2.loc[df_epidem_2['location_key'] == 'US']\n",
    "df_epidem_US.reset_index(drop=True, inplace=True)\n",
    "df_epidem_states = df_epidem_2.loc[df_epidem_2['location_key'] != 'US']\n",
    "df_epidem_states.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "356d1cc1",
   "metadata": {},
   "source": [
    "### We see that there is one row in the dataframe where data_month is '2022-12' (and date is '2022-12-30') which is most likely a mistake and should be '2022-08', because the data source states the data was last actively added to the repo on Sep 15, 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "5a43048d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>location_key</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_deceased</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_tested</th>\n",
       "      <th>cumulative_confirmed</th>\n",
       "      <th>cumulative_deceased</th>\n",
       "      <th>cumulative_recovered</th>\n",
       "      <th>cumulative_tested</th>\n",
       "      <th>date_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8712</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>US_DC</td>\n",
       "      <td>-31136.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103487.0</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>34985.0</td>\n",
       "      <td>1261363.0</td>\n",
       "      <td>2022-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date location_key  new_confirmed  new_deceased  new_recovered  \\\n",
       "8712  2022-12-30        US_DC       -31136.0           0.0            0.0   \n",
       "\n",
       "      new_tested  cumulative_confirmed  cumulative_deceased  \\\n",
       "8712         0.0              103487.0               1382.0   \n",
       "\n",
       "      cumulative_recovered  cumulative_tested date_month  \n",
       "8712               34985.0          1261363.0    2022-12  "
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_epidem_states.loc[df_epidem_states['date_month'] == '2022-12']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "261b6941",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/3135985351.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_epidem_states['date'] = df_epidem_states['date'].replace(['2022-12-30'], '2022-08-30')\n",
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/3135985351.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_epidem_states['date_month'] = df_epidem_states['date_month'].replace(['2022-12'], '2022-08')\n"
     ]
    }
   ],
   "source": [
    "df_epidem_states['date'] = df_epidem_states['date'].replace(['2022-12-30'], '2022-08-30')\n",
    "df_epidem_states['date_month'] = df_epidem_states['date_month'].replace(['2022-12'], '2022-08')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd5c5953",
   "metadata": {},
   "source": [
    "### Another thing we see is that columns that should be int are currently float. We need to cast them back to the correct data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "c473f5ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fixDataTypesOfColumns(df, colsToChange, typeFrom, typeTo):\n",
    "    for col in colsToChange:\n",
    "        if df[col].dtypes == typeFrom:\n",
    "            df[col] = df[col].astype(typeTo)\n",
    "        else:\n",
    "            print('Datatypes not matching!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "ac61b23d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 988 entries, 0 to 987\n",
      "Data columns (total 11 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   date                  988 non-null    object \n",
      " 1   location_key          988 non-null    object \n",
      " 2   new_confirmed         988 non-null    float64\n",
      " 3   new_deceased          988 non-null    float64\n",
      " 4   new_recovered         988 non-null    float64\n",
      " 5   new_tested            988 non-null    float64\n",
      " 6   cumulative_confirmed  988 non-null    float64\n",
      " 7   cumulative_deceased   988 non-null    float64\n",
      " 8   cumulative_recovered  988 non-null    float64\n",
      " 9   cumulative_tested     988 non-null    float64\n",
      " 10  date_month            988 non-null    object \n",
      "dtypes: float64(8), object(3)\n",
      "memory usage: 85.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_epidem_US.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "90055b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/4103920634.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[col] = df[col].astype(typeTo)\n"
     ]
    }
   ],
   "source": [
    "fixDataTypesOfColumns(df_epidem_states, df_epidem_states.columns.tolist()[2:10], 'float64', 'int')\n",
    "fixDataTypesOfColumns(df_epidem_US, df_epidem_US.columns.tolist()[2:10], 'float64', 'int')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8845fd78",
   "metadata": {},
   "source": [
    "### Now, moving on to the demographics data (df_demog)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "a1f086f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demog = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/demographics.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "904bfac9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_key</th>\n",
       "      <th>population</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>population_rural</th>\n",
       "      <th>population_urban</th>\n",
       "      <th>population_largest_city</th>\n",
       "      <th>population_clustered</th>\n",
       "      <th>population_density</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>population_age_00_09</th>\n",
       "      <th>population_age_10_19</th>\n",
       "      <th>population_age_20_29</th>\n",
       "      <th>population_age_30_39</th>\n",
       "      <th>population_age_40_49</th>\n",
       "      <th>population_age_50_59</th>\n",
       "      <th>population_age_60_69</th>\n",
       "      <th>population_age_70_79</th>\n",
       "      <th>population_age_80_and_older</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AD</td>\n",
       "      <td>77265.0</td>\n",
       "      <td>58625.0</td>\n",
       "      <td>55581.0</td>\n",
       "      <td>9269.0</td>\n",
       "      <td>67873.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164.394</td>\n",
       "      <td>0.858</td>\n",
       "      <td>9370.0</td>\n",
       "      <td>12022.0</td>\n",
       "      <td>10727.0</td>\n",
       "      <td>12394.0</td>\n",
       "      <td>21001.0</td>\n",
       "      <td>20720.0</td>\n",
       "      <td>14433.0</td>\n",
       "      <td>8657.0</td>\n",
       "      <td>4881.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AE</td>\n",
       "      <td>9890400.0</td>\n",
       "      <td>6836349.0</td>\n",
       "      <td>3054051.0</td>\n",
       "      <td>1290785.0</td>\n",
       "      <td>8479744.0</td>\n",
       "      <td>2833079.0</td>\n",
       "      <td>5914068.0</td>\n",
       "      <td>118.306</td>\n",
       "      <td>0.863</td>\n",
       "      <td>1011713.0</td>\n",
       "      <td>842991.0</td>\n",
       "      <td>2149343.0</td>\n",
       "      <td>3169314.0</td>\n",
       "      <td>1608109.0</td>\n",
       "      <td>797913.0</td>\n",
       "      <td>242707.0</td>\n",
       "      <td>55884.0</td>\n",
       "      <td>12426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>19976265.0</td>\n",
       "      <td>18952076.0</td>\n",
       "      <td>28244481.0</td>\n",
       "      <td>9797273.0</td>\n",
       "      <td>4114030.0</td>\n",
       "      <td>4114030.0</td>\n",
       "      <td>59.627</td>\n",
       "      <td>0.498</td>\n",
       "      <td>11088732.0</td>\n",
       "      <td>9821559.0</td>\n",
       "      <td>7035871.0</td>\n",
       "      <td>4534646.0</td>\n",
       "      <td>2963459.0</td>\n",
       "      <td>1840198.0</td>\n",
       "      <td>1057496.0</td>\n",
       "      <td>480455.0</td>\n",
       "      <td>105925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF_BAL</td>\n",
       "      <td>1509183.0</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF_BAM</td>\n",
       "      <td>495557.0</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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location_key  population  population_male  population_female  \\\n",
       "0           AD     77265.0          58625.0            55581.0   \n",
       "1           AE   9890400.0        6836349.0          3054051.0   \n",
       "2           AF  38928341.0       19976265.0         18952076.0   \n",
       "3       AF_BAL   1509183.0              NaN                NaN   \n",
       "4       AF_BAM    495557.0              NaN                NaN   \n",
       "\n",
       "   population_rural  population_urban  population_largest_city  \\\n",
       "0            9269.0           67873.0                      NaN   \n",
       "1         1290785.0         8479744.0                2833079.0   \n",
       "2        28244481.0         9797273.0                4114030.0   \n",
       "3               NaN               NaN                      NaN   \n",
       "4               NaN               NaN                      NaN   \n",
       "\n",
       "   population_clustered  population_density  human_development_index  \\\n",
       "0                   NaN             164.394                    0.858   \n",
       "1             5914068.0             118.306                    0.863   \n",
       "2             4114030.0              59.627                    0.498   \n",
       "3                   NaN                 NaN                      NaN   \n",
       "4                   NaN                 NaN                      NaN   \n",
       "\n",
       "   population_age_00_09  population_age_10_19  population_age_20_29  \\\n",
       "0                9370.0               12022.0               10727.0   \n",
       "1             1011713.0              842991.0             2149343.0   \n",
       "2            11088732.0             9821559.0             7035871.0   \n",
       "3                   NaN                   NaN                   NaN   \n",
       "4                   NaN                   NaN                   NaN   \n",
       "\n",
       "   population_age_30_39  population_age_40_49  population_age_50_59  \\\n",
       "0               12394.0               21001.0               20720.0   \n",
       "1             3169314.0             1608109.0              797913.0   \n",
       "2             4534646.0             2963459.0             1840198.0   \n",
       "3                   NaN                   NaN                   NaN   \n",
       "4                   NaN                   NaN                   NaN   \n",
       "\n",
       "   population_age_60_69  population_age_70_79  population_age_80_and_older  \n",
       "0               14433.0                8657.0                       4881.0  \n",
       "1              242707.0               55884.0                      12426.0  \n",
       "2             1057496.0              480455.0                     105925.0  \n",
       "3                   NaN                   NaN                          NaN  \n",
       "4                   NaN                   NaN                          NaN  "
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demog.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c67cb0d",
   "metadata": {},
   "source": [
    "### Again, here we will only keep the rows that correspond to the US."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "429d922c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demog = df_demog[df_demog.location_key.isin(location_key_US)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "b256a961",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demog['location_key_2'] = df_demog['location_key'].apply(lambda x: '_'.join(x.split('_')[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "a26c22a0",
   "metadata": {},
   "outputs": [
    {
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       "      <th>population_age_20_29</th>\n",
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       "      <th>population_age_40_49</th>\n",
       "      <th>population_age_50_59</th>\n",
       "      <th>population_age_60_69</th>\n",
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       "      <th>population_age_80_and_older</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>331002647.0</td>\n",
       "      <td>163786016.0</td>\n",
       "      <td>167216631.0</td>\n",
       "      <td>57576495.0</td>\n",
       "      <td>270663028.0</td>\n",
       "      <td>18804722.0</td>\n",
       "      <td>152587187.0</td>\n",
       "      <td>36.185</td>\n",
       "      <td>0.92</td>\n",
       "      <td>39721484.0</td>\n",
       "      <td>42332395.0</td>\n",
       "      <td>46094075.0</td>\n",
       "      <td>44668270.0</td>\n",
       "      <td>40348397.0</td>\n",
       "      <td>42120077.0</td>\n",
       "      <td>38488170.0</td>\n",
       "      <td>24082597.0</td>\n",
       "      <td>13147182.0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US_AK</td>\n",
       "      <td>733391.0</td>\n",
       "      <td>424916.0</td>\n",
       "      <td>391925.0</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>115198.0</td>\n",
       "      <td>111045.0</td>\n",
       "      <td>128022.0</td>\n",
       "      <td>113254.0</td>\n",
       "      <td>102310.0</td>\n",
       "      <td>116061.0</td>\n",
       "      <td>82608.0</td>\n",
       "      <td>35021.0</td>\n",
       "      <td>13322.0</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US_AK_02013</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>2345.0</td>\n",
       "      <td>1110.0</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>178.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US_AK_02016</td>\n",
       "      <td>5729.0</td>\n",
       "      <td>3795.0</td>\n",
       "      <td>1934.0</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>359.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>957.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>1146.0</td>\n",
       "      <td>622.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US_AK_02020</td>\n",
       "      <td>294338.0</td>\n",
       "      <td>149817.0</td>\n",
       "      <td>144521.0</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>41514.0</td>\n",
       "      <td>37461.0</td>\n",
       "      <td>49984.0</td>\n",
       "      <td>45570.0</td>\n",
       "      <td>35093.0</td>\n",
       "      <td>37143.0</td>\n",
       "      <td>28931.0</td>\n",
       "      <td>12887.0</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location_key   population  population_male  population_female  \\\n",
       "0           US  331002647.0      163786016.0        167216631.0   \n",
       "1        US_AK     733391.0         424916.0           391925.0   \n",
       "2  US_AK_02013       3455.0           2345.0             1110.0   \n",
       "3  US_AK_02016       5729.0           3795.0             1934.0   \n",
       "4  US_AK_02020     294338.0         149817.0           144521.0   \n",
       "\n",
       "   population_rural  population_urban  population_largest_city  \\\n",
       "0        57576495.0       270663028.0               18804722.0   \n",
       "1               NaN               NaN                      NaN   \n",
       "2               NaN               NaN                      NaN   \n",
       "3               NaN               NaN                      NaN   \n",
       "4               NaN               NaN                      NaN   \n",
       "\n",
       "   population_clustered  population_density  human_development_index  \\\n",
       "0           152587187.0              36.185                     0.92   \n",
       "1                   NaN                 NaN                      NaN   \n",
       "2                   NaN                 NaN                      NaN   \n",
       "3                   NaN                 NaN                      NaN   \n",
       "4                   NaN                 NaN                      NaN   \n",
       "\n",
       "   population_age_00_09  population_age_10_19  population_age_20_29  \\\n",
       "0            39721484.0            42332395.0            46094075.0   \n",
       "1              115198.0              111045.0              128022.0   \n",
       "2                 178.0                 165.0                 571.0   \n",
       "3                 359.0                 400.0                 951.0   \n",
       "4               41514.0               37461.0               49984.0   \n",
       "\n",
       "   population_age_30_39  population_age_40_49  population_age_50_59  \\\n",
       "0            44668270.0            40348397.0            42120077.0   \n",
       "1              113254.0              102310.0              116061.0   \n",
       "2                 532.0                 792.0                 696.0   \n",
       "3                 957.0                1149.0                1146.0   \n",
       "4               45570.0               35093.0               37143.0   \n",
       "\n",
       "   population_age_60_69  population_age_70_79  population_age_80_and_older  \\\n",
       "0            38488170.0            24082597.0                   13147182.0   \n",
       "1               82608.0               35021.0                      13322.0   \n",
       "2                 321.0                 156.0                         44.0   \n",
       "3                 622.0                 118.0                         27.0   \n",
       "4               28931.0               12887.0                       5755.0   \n",
       "\n",
       "  location_key_2  \n",
       "0             US  \n",
       "1          US_AK  \n",
       "2          US_AK  \n",
       "3          US_AK  \n",
       "4          US_AK  "
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demog.reset_index(drop=True, inplace=True)\n",
    "df_demog.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "7d2a4116",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demog_US = df_demog.loc[df_demog['location_key'] == 'US']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "6491822c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demog_states = df_demog.loc[df_demog['location_key'] != 'US']\n",
    "df_demog_states.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "f6543d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_key</th>\n",
       "      <th>population</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>population_rural</th>\n",
       "      <th>population_urban</th>\n",
       "      <th>population_largest_city</th>\n",
       "      <th>population_clustered</th>\n",
       "      <th>population_density</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>population_age_00_09</th>\n",
       "      <th>population_age_10_19</th>\n",
       "      <th>population_age_20_29</th>\n",
       "      <th>population_age_30_39</th>\n",
       "      <th>population_age_40_49</th>\n",
       "      <th>population_age_50_59</th>\n",
       "      <th>population_age_60_69</th>\n",
       "      <th>population_age_70_79</th>\n",
       "      <th>population_age_80_and_older</th>\n",
       "      <th>location_key_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US_AK</td>\n",
       "      <td>733391.0</td>\n",
       "      <td>424916.0</td>\n",
       "      <td>391925.0</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>115198.0</td>\n",
       "      <td>111045.0</td>\n",
       "      <td>128022.0</td>\n",
       "      <td>113254.0</td>\n",
       "      <td>102310.0</td>\n",
       "      <td>116061.0</td>\n",
       "      <td>82608.0</td>\n",
       "      <td>35021.0</td>\n",
       "      <td>13322.0</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US_AK_02013</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>2345.0</td>\n",
       "      <td>1110.0</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>178.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US_AK_02016</td>\n",
       "      <td>5729.0</td>\n",
       "      <td>3795.0</td>\n",
       "      <td>1934.0</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>359.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>957.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>1146.0</td>\n",
       "      <td>622.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US_AK_02020</td>\n",
       "      <td>294338.0</td>\n",
       "      <td>149817.0</td>\n",
       "      <td>144521.0</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>41514.0</td>\n",
       "      <td>37461.0</td>\n",
       "      <td>49984.0</td>\n",
       "      <td>45570.0</td>\n",
       "      <td>35093.0</td>\n",
       "      <td>37143.0</td>\n",
       "      <td>28931.0</td>\n",
       "      <td>12887.0</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US_AK_02050</td>\n",
       "      <td>18090.0</td>\n",
       "      <td>9369.0</td>\n",
       "      <td>8721.0</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>3888.0</td>\n",
       "      <td>3211.0</td>\n",
       "      <td>2833.0</td>\n",
       "      <td>2294.0</td>\n",
       "      <td>1682.0</td>\n",
       "      <td>2053.0</td>\n",
       "      <td>1378.0</td>\n",
       "      <td>568.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location_key  population  population_male  population_female  \\\n",
       "0        US_AK    733391.0         424916.0           391925.0   \n",
       "1  US_AK_02013      3455.0           2345.0             1110.0   \n",
       "2  US_AK_02016      5729.0           3795.0             1934.0   \n",
       "3  US_AK_02020    294338.0         149817.0           144521.0   \n",
       "4  US_AK_02050     18090.0           9369.0             8721.0   \n",
       "\n",
       "   population_rural  population_urban  population_largest_city  \\\n",
       "0               NaN               NaN                      NaN   \n",
       "1               NaN               NaN                      NaN   \n",
       "2               NaN               NaN                      NaN   \n",
       "3               NaN               NaN                      NaN   \n",
       "4               NaN               NaN                      NaN   \n",
       "\n",
       "   population_clustered  population_density  human_development_index  \\\n",
       "0                   NaN                 NaN                      NaN   \n",
       "1                   NaN                 NaN                      NaN   \n",
       "2                   NaN                 NaN                      NaN   \n",
       "3                   NaN                 NaN                      NaN   \n",
       "4                   NaN                 NaN                      NaN   \n",
       "\n",
       "   population_age_00_09  population_age_10_19  population_age_20_29  \\\n",
       "0              115198.0              111045.0              128022.0   \n",
       "1                 178.0                 165.0                 571.0   \n",
       "2                 359.0                 400.0                 951.0   \n",
       "3               41514.0               37461.0               49984.0   \n",
       "4                3888.0                3211.0                2833.0   \n",
       "\n",
       "   population_age_30_39  population_age_40_49  population_age_50_59  \\\n",
       "0              113254.0              102310.0              116061.0   \n",
       "1                 532.0                 792.0                 696.0   \n",
       "2                 957.0                1149.0                1146.0   \n",
       "3               45570.0               35093.0               37143.0   \n",
       "4                2294.0                1682.0                2053.0   \n",
       "\n",
       "   population_age_60_69  population_age_70_79  population_age_80_and_older  \\\n",
       "0               82608.0               35021.0                      13322.0   \n",
       "1                 321.0                 156.0                         44.0   \n",
       "2                 622.0                 118.0                         27.0   \n",
       "3               28931.0               12887.0                       5755.0   \n",
       "4                1378.0                 568.0                        183.0   \n",
       "\n",
       "  location_key_2  \n",
       "0          US_AK  \n",
       "1          US_AK  \n",
       "2          US_AK  \n",
       "3          US_AK  \n",
       "4          US_AK  "
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demog_states.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3d444e",
   "metadata": {},
   "source": [
    "### We see that the six of the columns in df_demog_states, namely population_rural, population_urban, population_largest_city, population_clustered, population_density  and human_development_index, have NaN values in all the rows. This means that these are the columns only useful in comparing different countries. Since we are limiting our analyses to the US, we can drop all six columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "3048bb50",
   "metadata": {},
   "outputs": [],
   "source": [
    "colsDrop_dfDemogStates = ['population_rural', 'population_urban', 'population_largest_city', 'population_clustered',\n",
    "                           'population_density', 'human_development_index']\n",
    "df_demog_states = df_demog_states.drop(columns=colsDrop_dfDemogStates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "b6504f6e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3284 entries, 0 to 3283\n",
      "Data columns (total 14 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   location_key                 3284 non-null   object \n",
      " 1   population                   3284 non-null   float64\n",
      " 2   population_male              3202 non-null   float64\n",
      " 3   population_female            3202 non-null   float64\n",
      " 4   population_age_00_09         3202 non-null   float64\n",
      " 5   population_age_10_19         3202 non-null   float64\n",
      " 6   population_age_20_29         3202 non-null   float64\n",
      " 7   population_age_30_39         3202 non-null   float64\n",
      " 8   population_age_40_49         3202 non-null   float64\n",
      " 9   population_age_50_59         3202 non-null   float64\n",
      " 10  population_age_60_69         3202 non-null   float64\n",
      " 11  population_age_70_79         3202 non-null   float64\n",
      " 12  population_age_80_and_older  3202 non-null   float64\n",
      " 13  location_key_2               3284 non-null   object \n",
      "dtypes: float64(12), object(2)\n",
      "memory usage: 359.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_demog_states.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79b4342",
   "metadata": {},
   "source": [
    "### There seems to be 82 rows where all the columns, except for location_key and location_key_2, are null. We will go ahead and drop those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "ce033585",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>population_age_80_and_older</th>\n",
       "      <th>location_key_2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>US_AS</td>\n",
       "      <td>49710.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1546</th>\n",
       "      <td>US_MP_69110</td>\n",
       "      <td>48220.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_MP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>US_MP_69120</td>\n",
       "      <td>3136.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_MP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2359</th>\n",
       "      <td>US_PR_72001</td>\n",
       "      <td>18020.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2360</th>\n",
       "      <td>US_PR_72003</td>\n",
       "      <td>38136.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2361</th>\n",
       "      <td>US_PR_72005</td>\n",
       "      <td>55101.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2362</th>\n",
       "      <td>US_PR_72007</td>\n",
       "      <td>24223.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2363</th>\n",
       "      <td>US_PR_72009</td>\n",
       "      <td>24637.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2364</th>\n",
       "      <td>US_PR_72011</td>\n",
       "      <td>25596.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2365</th>\n",
       "      <td>US_PR_72013</td>\n",
       "      <td>87754.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2366</th>\n",
       "      <td>US_PR_72015</td>\n",
       "      <td>15843.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2367</th>\n",
       "      <td>US_PR_72017</td>\n",
       "      <td>22657.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2368</th>\n",
       "      <td>US_PR_72019</td>\n",
       "      <td>28983.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2369</th>\n",
       "      <td>US_PR_72021</td>\n",
       "      <td>185187.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>US_PR_72023</td>\n",
       "      <td>47158.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>US_PR_72025</td>\n",
       "      <td>127244.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>US_PR_72027</td>\n",
       "      <td>32827.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>US_PR_72029</td>\n",
       "      <td>42337.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>US_PR_72031</td>\n",
       "      <td>154815.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375</th>\n",
       "      <td>US_PR_72033</td>\n",
       "      <td>23155.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2376</th>\n",
       "      <td>US_PR_72035</td>\n",
       "      <td>41652.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2377</th>\n",
       "      <td>US_PR_72037</td>\n",
       "      <td>11307.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2378</th>\n",
       "      <td>US_PR_72039</td>\n",
       "      <td>18782.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2379</th>\n",
       "      <td>US_PR_72041</td>\n",
       "      <td>43480.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2380</th>\n",
       "      <td>US_PR_72043</td>\n",
       "      <td>40512.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2381</th>\n",
       "      <td>US_PR_72045</td>\n",
       "      <td>20778.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2382</th>\n",
       "      <td>US_PR_72047</td>\n",
       "      <td>37142.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2384</th>\n",
       "      <td>US_PR_72051</td>\n",
       "      <td>38165.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2385</th>\n",
       "      <td>US_PR_72053</td>\n",
       "      <td>32124.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2386</th>\n",
       "      <td>US_PR_72054</td>\n",
       "      <td>11692.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2387</th>\n",
       "      <td>US_PR_72055</td>\n",
       "      <td>19427.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2388</th>\n",
       "      <td>US_PR_72057</td>\n",
       "      <td>36614.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>US_PR_72059</td>\n",
       "      <td>17784.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>US_PR_72061</td>\n",
       "      <td>89780.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>US_PR_72063</td>\n",
       "      <td>40622.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2392</th>\n",
       "      <td>US_PR_72065</td>\n",
       "      <td>38486.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>US_PR_72067</td>\n",
       "      <td>17250.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2394</th>\n",
       "      <td>US_PR_72069</td>\n",
       "      <td>58466.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>US_PR_72071</td>\n",
       "      <td>42943.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2396</th>\n",
       "      <td>US_PR_72073</td>\n",
       "      <td>16642.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>US_PR_72075</td>\n",
       "      <td>50747.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398</th>\n",
       "      <td>US_PR_72077</td>\n",
       "      <td>40290.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2399</th>\n",
       "      <td>US_PR_72079</td>\n",
       "      <td>23334.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>US_PR_72081</td>\n",
       "      <td>28105.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401</th>\n",
       "      <td>US_PR_72083</td>\n",
       "      <td>9881.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402</th>\n",
       "      <td>US_PR_72085</td>\n",
       "      <td>38675.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2403</th>\n",
       "      <td>US_PR_72087</td>\n",
       "      <td>30060.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2404</th>\n",
       "      <td>US_PR_72089</td>\n",
       "      <td>20068.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>US_PR_72091</td>\n",
       "      <td>44113.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2406</th>\n",
       "      <td>US_PR_72093</td>\n",
       "      <td>6276.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2407</th>\n",
       "      <td>US_PR_72095</td>\n",
       "      <td>12225.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2408</th>\n",
       "      <td>US_PR_72097</td>\n",
       "      <td>73077.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2409</th>\n",
       "      <td>US_PR_72099</td>\n",
       "      <td>40109.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2410</th>\n",
       "      <td>US_PR_72101</td>\n",
       "      <td>32610.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>US_PR_72103</td>\n",
       "      <td>26720.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>US_PR_72105</td>\n",
       "      <td>30402.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>US_PR_72107</td>\n",
       "      <td>23423.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>US_PR_72109</td>\n",
       "      <td>19277.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>US_PR_72111</td>\n",
       "      <td>24282.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2416</th>\n",
       "      <td>US_PR_72113</td>\n",
       "      <td>137491.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2417</th>\n",
       "      <td>US_PR_72115</td>\n",
       "      <td>23638.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2418</th>\n",
       "      <td>US_PR_72117</td>\n",
       "      <td>15200.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2419</th>\n",
       "      <td>US_PR_72119</td>\n",
       "      <td>54304.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2420</th>\n",
       "      <td>US_PR_72121</td>\n",
       "      <td>25265.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2421</th>\n",
       "      <td>US_PR_72123</td>\n",
       "      <td>25789.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2422</th>\n",
       "      <td>US_PR_72125</td>\n",
       "      <td>35527.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2424</th>\n",
       "      <td>US_PR_72129</td>\n",
       "      <td>41058.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2425</th>\n",
       "      <td>US_PR_72131</td>\n",
       "      <td>42430.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2426</th>\n",
       "      <td>US_PR_72133</td>\n",
       "      <td>23274.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2427</th>\n",
       "      <td>US_PR_72135</td>\n",
       "      <td>74066.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2428</th>\n",
       "      <td>US_PR_72137</td>\n",
       "      <td>89609.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2429</th>\n",
       "      <td>US_PR_72139</td>\n",
       "      <td>67740.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430</th>\n",
       "      <td>US_PR_72141</td>\n",
       "      <td>33149.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>US_PR_72143</td>\n",
       "      <td>39951.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2432</th>\n",
       "      <td>US_PR_72145</td>\n",
       "      <td>59662.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2434</th>\n",
       "      <td>US_PR_72149</td>\n",
       "      <td>26073.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2435</th>\n",
       "      <td>US_PR_72151</td>\n",
       "      <td>30426.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2436</th>\n",
       "      <td>US_PR_72153</td>\n",
       "      <td>34172.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2437</th>\n",
       "      <td>US_RI</td>\n",
       "      <td>1097379.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_RI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3073</th>\n",
       "      <td>US_VI_78010</td>\n",
       "      <td>50601.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_VI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3074</th>\n",
       "      <td>US_VI_78020</td>\n",
       "      <td>4170.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_VI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3075</th>\n",
       "      <td>US_VI_78030</td>\n",
       "      <td>42261.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US_VI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     location_key  population  population_male  population_female  \\\n",
       "174         US_AS     49710.0              NaN                NaN   \n",
       "1546  US_MP_69110     48220.0              NaN                NaN   \n",
       "1547  US_MP_69120      3136.0              NaN                NaN   \n",
       "2359  US_PR_72001     18020.0              NaN                NaN   \n",
       "2360  US_PR_72003     38136.0              NaN                NaN   \n",
       "2361  US_PR_72005     55101.0              NaN                NaN   \n",
       "2362  US_PR_72007     24223.0              NaN                NaN   \n",
       "2363  US_PR_72009     24637.0              NaN                NaN   \n",
       "2364  US_PR_72011     25596.0              NaN                NaN   \n",
       "2365  US_PR_72013     87754.0              NaN                NaN   \n",
       "2366  US_PR_72015     15843.0              NaN                NaN   \n",
       "2367  US_PR_72017     22657.0              NaN                NaN   \n",
       "2368  US_PR_72019     28983.0              NaN                NaN   \n",
       "2369  US_PR_72021    185187.0              NaN                NaN   \n",
       "2370  US_PR_72023     47158.0              NaN                NaN   \n",
       "2371  US_PR_72025    127244.0              NaN                NaN   \n",
       "2372  US_PR_72027     32827.0              NaN                NaN   \n",
       "2373  US_PR_72029     42337.0              NaN                NaN   \n",
       "2374  US_PR_72031    154815.0              NaN                NaN   \n",
       "2375  US_PR_72033     23155.0              NaN                NaN   \n",
       "2376  US_PR_72035     41652.0              NaN                NaN   \n",
       "2377  US_PR_72037     11307.0              NaN                NaN   \n",
       "2378  US_PR_72039     18782.0              NaN                NaN   \n",
       "2379  US_PR_72041     43480.0              NaN                NaN   \n",
       "2380  US_PR_72043     40512.0              NaN                NaN   \n",
       "2381  US_PR_72045     20778.0              NaN                NaN   \n",
       "2382  US_PR_72047     37142.0              NaN                NaN   \n",
       "2384  US_PR_72051     38165.0              NaN                NaN   \n",
       "2385  US_PR_72053     32124.0              NaN                NaN   \n",
       "2386  US_PR_72054     11692.0              NaN                NaN   \n",
       "2387  US_PR_72055     19427.0              NaN                NaN   \n",
       "2388  US_PR_72057     36614.0              NaN                NaN   \n",
       "2389  US_PR_72059     17784.0              NaN                NaN   \n",
       "2390  US_PR_72061     89780.0              NaN                NaN   \n",
       "2391  US_PR_72063     40622.0              NaN                NaN   \n",
       "2392  US_PR_72065     38486.0              NaN                NaN   \n",
       "2393  US_PR_72067     17250.0              NaN                NaN   \n",
       "2394  US_PR_72069     58466.0              NaN                NaN   \n",
       "2395  US_PR_72071     42943.0              NaN                NaN   \n",
       "2396  US_PR_72073     16642.0              NaN                NaN   \n",
       "2397  US_PR_72075     50747.0              NaN                NaN   \n",
       "2398  US_PR_72077     40290.0              NaN                NaN   \n",
       "2399  US_PR_72079     23334.0              NaN                NaN   \n",
       "2400  US_PR_72081     28105.0              NaN                NaN   \n",
       "2401  US_PR_72083      9881.0              NaN                NaN   \n",
       "2402  US_PR_72085     38675.0              NaN                NaN   \n",
       "2403  US_PR_72087     30060.0              NaN                NaN   \n",
       "2404  US_PR_72089     20068.0              NaN                NaN   \n",
       "2405  US_PR_72091     44113.0              NaN                NaN   \n",
       "2406  US_PR_72093      6276.0              NaN                NaN   \n",
       "2407  US_PR_72095     12225.0              NaN                NaN   \n",
       "2408  US_PR_72097     73077.0              NaN                NaN   \n",
       "2409  US_PR_72099     40109.0              NaN                NaN   \n",
       "2410  US_PR_72101     32610.0              NaN                NaN   \n",
       "2411  US_PR_72103     26720.0              NaN                NaN   \n",
       "2412  US_PR_72105     30402.0              NaN                NaN   \n",
       "2413  US_PR_72107     23423.0              NaN                NaN   \n",
       "2414  US_PR_72109     19277.0              NaN                NaN   \n",
       "2415  US_PR_72111     24282.0              NaN                NaN   \n",
       "2416  US_PR_72113    137491.0              NaN                NaN   \n",
       "2417  US_PR_72115     23638.0              NaN                NaN   \n",
       "2418  US_PR_72117     15200.0              NaN                NaN   \n",
       "2419  US_PR_72119     54304.0              NaN                NaN   \n",
       "2420  US_PR_72121     25265.0              NaN                NaN   \n",
       "2421  US_PR_72123     25789.0              NaN                NaN   \n",
       "2422  US_PR_72125     35527.0              NaN                NaN   \n",
       "2424  US_PR_72129     41058.0              NaN                NaN   \n",
       "2425  US_PR_72131     42430.0              NaN                NaN   \n",
       "2426  US_PR_72133     23274.0              NaN                NaN   \n",
       "2427  US_PR_72135     74066.0              NaN                NaN   \n",
       "2428  US_PR_72137     89609.0              NaN                NaN   \n",
       "2429  US_PR_72139     67740.0              NaN                NaN   \n",
       "2430  US_PR_72141     33149.0              NaN                NaN   \n",
       "2431  US_PR_72143     39951.0              NaN                NaN   \n",
       "2432  US_PR_72145     59662.0              NaN                NaN   \n",
       "2434  US_PR_72149     26073.0              NaN                NaN   \n",
       "2435  US_PR_72151     30426.0              NaN                NaN   \n",
       "2436  US_PR_72153     34172.0              NaN                NaN   \n",
       "2437        US_RI   1097379.0              NaN                NaN   \n",
       "3073  US_VI_78010     50601.0              NaN                NaN   \n",
       "3074  US_VI_78020      4170.0              NaN                NaN   \n",
       "3075  US_VI_78030     42261.0              NaN                NaN   \n",
       "\n",
       "      population_age_00_09  population_age_10_19  population_age_20_29  \\\n",
       "174                    NaN                   NaN                   NaN   \n",
       "1546                   NaN                   NaN                   NaN   \n",
       "1547                   NaN                   NaN                   NaN   \n",
       "2359                   NaN                   NaN                   NaN   \n",
       "2360                   NaN                   NaN                   NaN   \n",
       "2361                   NaN                   NaN                   NaN   \n",
       "2362                   NaN                   NaN                   NaN   \n",
       "2363                   NaN                   NaN                   NaN   \n",
       "2364                   NaN                   NaN                   NaN   \n",
       "2365                   NaN                   NaN                   NaN   \n",
       "2366                   NaN                   NaN                   NaN   \n",
       "2367                   NaN                   NaN                   NaN   \n",
       "2368                   NaN                   NaN                   NaN   \n",
       "2369                   NaN                   NaN                   NaN   \n",
       "2370                   NaN                   NaN                   NaN   \n",
       "2371                   NaN                   NaN                   NaN   \n",
       "2372                   NaN                   NaN                   NaN   \n",
       "2373                   NaN                   NaN                   NaN   \n",
       "2374                   NaN                   NaN                   NaN   \n",
       "2375                   NaN                   NaN                   NaN   \n",
       "2376                   NaN                   NaN                   NaN   \n",
       "2377                   NaN                   NaN                   NaN   \n",
       "2378                   NaN                   NaN                   NaN   \n",
       "2379                   NaN                   NaN                   NaN   \n",
       "2380                   NaN                   NaN                   NaN   \n",
       "2381                   NaN                   NaN                   NaN   \n",
       "2382                   NaN                   NaN                   NaN   \n",
       "2384                   NaN                   NaN                   NaN   \n",
       "2385                   NaN                   NaN                   NaN   \n",
       "2386                   NaN                   NaN                   NaN   \n",
       "2387                   NaN                   NaN                   NaN   \n",
       "2388                   NaN                   NaN                   NaN   \n",
       "2389                   NaN                   NaN                   NaN   \n",
       "2390                   NaN                   NaN                   NaN   \n",
       "2391                   NaN                   NaN                   NaN   \n",
       "2392                   NaN                   NaN                   NaN   \n",
       "2393                   NaN                   NaN                   NaN   \n",
       "2394                   NaN                   NaN                   NaN   \n",
       "2395                   NaN                   NaN                   NaN   \n",
       "2396                   NaN                   NaN                   NaN   \n",
       "2397                   NaN                   NaN                   NaN   \n",
       "2398                   NaN                   NaN                   NaN   \n",
       "2399                   NaN                   NaN                   NaN   \n",
       "2400                   NaN                   NaN                   NaN   \n",
       "2401                   NaN                   NaN                   NaN   \n",
       "2402                   NaN                   NaN                   NaN   \n",
       "2403                   NaN                   NaN                   NaN   \n",
       "2404                   NaN                   NaN                   NaN   \n",
       "2405                   NaN                   NaN                   NaN   \n",
       "2406                   NaN                   NaN                   NaN   \n",
       "2407                   NaN                   NaN                   NaN   \n",
       "2408                   NaN                   NaN                   NaN   \n",
       "2409                   NaN                   NaN                   NaN   \n",
       "2410                   NaN                   NaN                   NaN   \n",
       "2411                   NaN                   NaN                   NaN   \n",
       "2412                   NaN                   NaN                   NaN   \n",
       "2413                   NaN                   NaN                   NaN   \n",
       "2414                   NaN                   NaN                   NaN   \n",
       "2415                   NaN                   NaN                   NaN   \n",
       "2416                   NaN                   NaN                   NaN   \n",
       "2417                   NaN                   NaN                   NaN   \n",
       "2418                   NaN                   NaN                   NaN   \n",
       "2419                   NaN                   NaN                   NaN   \n",
       "2420                   NaN                   NaN                   NaN   \n",
       "2421                   NaN                   NaN                   NaN   \n",
       "2422                   NaN                   NaN                   NaN   \n",
       "2424                   NaN                   NaN                   NaN   \n",
       "2425                   NaN                   NaN                   NaN   \n",
       "2426                   NaN                   NaN                   NaN   \n",
       "2427                   NaN                   NaN                   NaN   \n",
       "2428                   NaN                   NaN                   NaN   \n",
       "2429                   NaN                   NaN                   NaN   \n",
       "2430                   NaN                   NaN                   NaN   \n",
       "2431                   NaN                   NaN                   NaN   \n",
       "2432                   NaN                   NaN                   NaN   \n",
       "2434                   NaN                   NaN                   NaN   \n",
       "2435                   NaN                   NaN                   NaN   \n",
       "2436                   NaN                   NaN                   NaN   \n",
       "2437                   NaN                   NaN                   NaN   \n",
       "3073                   NaN                   NaN                   NaN   \n",
       "3074                   NaN                   NaN                   NaN   \n",
       "3075                   NaN                   NaN                   NaN   \n",
       "\n",
       "      population_age_30_39  population_age_40_49  population_age_50_59  \\\n",
       "174                    NaN                   NaN                   NaN   \n",
       "1546                   NaN                   NaN                   NaN   \n",
       "1547                   NaN                   NaN                   NaN   \n",
       "2359                   NaN                   NaN                   NaN   \n",
       "2360                   NaN                   NaN                   NaN   \n",
       "2361                   NaN                   NaN                   NaN   \n",
       "2362                   NaN                   NaN                   NaN   \n",
       "2363                   NaN                   NaN                   NaN   \n",
       "2364                   NaN                   NaN                   NaN   \n",
       "2365                   NaN                   NaN                   NaN   \n",
       "2366                   NaN                   NaN                   NaN   \n",
       "2367                   NaN                   NaN                   NaN   \n",
       "2368                   NaN                   NaN                   NaN   \n",
       "2369                   NaN                   NaN                   NaN   \n",
       "2370                   NaN                   NaN                   NaN   \n",
       "2371                   NaN                   NaN                   NaN   \n",
       "2372                   NaN                   NaN                   NaN   \n",
       "2373                   NaN                   NaN                   NaN   \n",
       "2374                   NaN                   NaN                   NaN   \n",
       "2375                   NaN                   NaN                   NaN   \n",
       "2376                   NaN                   NaN                   NaN   \n",
       "2377                   NaN                   NaN                   NaN   \n",
       "2378                   NaN                   NaN                   NaN   \n",
       "2379                   NaN                   NaN                   NaN   \n",
       "2380                   NaN                   NaN                   NaN   \n",
       "2381                   NaN                   NaN                   NaN   \n",
       "2382                   NaN                   NaN                   NaN   \n",
       "2384                   NaN                   NaN                   NaN   \n",
       "2385                   NaN                   NaN                   NaN   \n",
       "2386                   NaN                   NaN                   NaN   \n",
       "2387                   NaN                   NaN                   NaN   \n",
       "2388                   NaN                   NaN                   NaN   \n",
       "2389                   NaN                   NaN                   NaN   \n",
       "2390                   NaN                   NaN                   NaN   \n",
       "2391                   NaN                   NaN                   NaN   \n",
       "2392                   NaN                   NaN                   NaN   \n",
       "2393                   NaN                   NaN                   NaN   \n",
       "2394                   NaN                   NaN                   NaN   \n",
       "2395                   NaN                   NaN                   NaN   \n",
       "2396                   NaN                   NaN                   NaN   \n",
       "2397                   NaN                   NaN                   NaN   \n",
       "2398                   NaN                   NaN                   NaN   \n",
       "2399                   NaN                   NaN                   NaN   \n",
       "2400                   NaN                   NaN                   NaN   \n",
       "2401                   NaN                   NaN                   NaN   \n",
       "2402                   NaN                   NaN                   NaN   \n",
       "2403                   NaN                   NaN                   NaN   \n",
       "2404                   NaN                   NaN                   NaN   \n",
       "2405                   NaN                   NaN                   NaN   \n",
       "2406                   NaN                   NaN                   NaN   \n",
       "2407                   NaN                   NaN                   NaN   \n",
       "2408                   NaN                   NaN                   NaN   \n",
       "2409                   NaN                   NaN                   NaN   \n",
       "2410                   NaN                   NaN                   NaN   \n",
       "2411                   NaN                   NaN                   NaN   \n",
       "2412                   NaN                   NaN                   NaN   \n",
       "2413                   NaN                   NaN                   NaN   \n",
       "2414                   NaN                   NaN                   NaN   \n",
       "2415                   NaN                   NaN                   NaN   \n",
       "2416                   NaN                   NaN                   NaN   \n",
       "2417                   NaN                   NaN                   NaN   \n",
       "2418                   NaN                   NaN                   NaN   \n",
       "2419                   NaN                   NaN                   NaN   \n",
       "2420                   NaN                   NaN                   NaN   \n",
       "2421                   NaN                   NaN                   NaN   \n",
       "2422                   NaN                   NaN                   NaN   \n",
       "2424                   NaN                   NaN                   NaN   \n",
       "2425                   NaN                   NaN                   NaN   \n",
       "2426                   NaN                   NaN                   NaN   \n",
       "2427                   NaN                   NaN                   NaN   \n",
       "2428                   NaN                   NaN                   NaN   \n",
       "2429                   NaN                   NaN                   NaN   \n",
       "2430                   NaN                   NaN                   NaN   \n",
       "2431                   NaN                   NaN                   NaN   \n",
       "2432                   NaN                   NaN                   NaN   \n",
       "2434                   NaN                   NaN                   NaN   \n",
       "2435                   NaN                   NaN                   NaN   \n",
       "2436                   NaN                   NaN                   NaN   \n",
       "2437                   NaN                   NaN                   NaN   \n",
       "3073                   NaN                   NaN                   NaN   \n",
       "3074                   NaN                   NaN                   NaN   \n",
       "3075                   NaN                   NaN                   NaN   \n",
       "\n",
       "      population_age_60_69  population_age_70_79  population_age_80_and_older  \\\n",
       "174                    NaN                   NaN                          NaN   \n",
       "1546                   NaN                   NaN                          NaN   \n",
       "1547                   NaN                   NaN                          NaN   \n",
       "2359                   NaN                   NaN                          NaN   \n",
       "2360                   NaN                   NaN                          NaN   \n",
       "2361                   NaN                   NaN                          NaN   \n",
       "2362                   NaN                   NaN                          NaN   \n",
       "2363                   NaN                   NaN                          NaN   \n",
       "2364                   NaN                   NaN                          NaN   \n",
       "2365                   NaN                   NaN                          NaN   \n",
       "2366                   NaN                   NaN                          NaN   \n",
       "2367                   NaN                   NaN                          NaN   \n",
       "2368                   NaN                   NaN                          NaN   \n",
       "2369                   NaN                   NaN                          NaN   \n",
       "2370                   NaN                   NaN                          NaN   \n",
       "2371                   NaN                   NaN                          NaN   \n",
       "2372                   NaN                   NaN                          NaN   \n",
       "2373                   NaN                   NaN                          NaN   \n",
       "2374                   NaN                   NaN                          NaN   \n",
       "2375                   NaN                   NaN                          NaN   \n",
       "2376                   NaN                   NaN                          NaN   \n",
       "2377                   NaN                   NaN                          NaN   \n",
       "2378                   NaN                   NaN                          NaN   \n",
       "2379                   NaN                   NaN                          NaN   \n",
       "2380                   NaN                   NaN                          NaN   \n",
       "2381                   NaN                   NaN                          NaN   \n",
       "2382                   NaN                   NaN                          NaN   \n",
       "2384                   NaN                   NaN                          NaN   \n",
       "2385                   NaN                   NaN                          NaN   \n",
       "2386                   NaN                   NaN                          NaN   \n",
       "2387                   NaN                   NaN                          NaN   \n",
       "2388                   NaN                   NaN                          NaN   \n",
       "2389                   NaN                   NaN                          NaN   \n",
       "2390                   NaN                   NaN                          NaN   \n",
       "2391                   NaN                   NaN                          NaN   \n",
       "2392                   NaN                   NaN                          NaN   \n",
       "2393                   NaN                   NaN                          NaN   \n",
       "2394                   NaN                   NaN                          NaN   \n",
       "2395                   NaN                   NaN                          NaN   \n",
       "2396                   NaN                   NaN                          NaN   \n",
       "2397                   NaN                   NaN                          NaN   \n",
       "2398                   NaN                   NaN                          NaN   \n",
       "2399                   NaN                   NaN                          NaN   \n",
       "2400                   NaN                   NaN                          NaN   \n",
       "2401                   NaN                   NaN                          NaN   \n",
       "2402                   NaN                   NaN                          NaN   \n",
       "2403                   NaN                   NaN                          NaN   \n",
       "2404                   NaN                   NaN                          NaN   \n",
       "2405                   NaN                   NaN                          NaN   \n",
       "2406                   NaN                   NaN                          NaN   \n",
       "2407                   NaN                   NaN                          NaN   \n",
       "2408                   NaN                   NaN                          NaN   \n",
       "2409                   NaN                   NaN                          NaN   \n",
       "2410                   NaN                   NaN                          NaN   \n",
       "2411                   NaN                   NaN                          NaN   \n",
       "2412                   NaN                   NaN                          NaN   \n",
       "2413                   NaN                   NaN                          NaN   \n",
       "2414                   NaN                   NaN                          NaN   \n",
       "2415                   NaN                   NaN                          NaN   \n",
       "2416                   NaN                   NaN                          NaN   \n",
       "2417                   NaN                   NaN                          NaN   \n",
       "2418                   NaN                   NaN                          NaN   \n",
       "2419                   NaN                   NaN                          NaN   \n",
       "2420                   NaN                   NaN                          NaN   \n",
       "2421                   NaN                   NaN                          NaN   \n",
       "2422                   NaN                   NaN                          NaN   \n",
       "2424                   NaN                   NaN                          NaN   \n",
       "2425                   NaN                   NaN                          NaN   \n",
       "2426                   NaN                   NaN                          NaN   \n",
       "2427                   NaN                   NaN                          NaN   \n",
       "2428                   NaN                   NaN                          NaN   \n",
       "2429                   NaN                   NaN                          NaN   \n",
       "2430                   NaN                   NaN                          NaN   \n",
       "2431                   NaN                   NaN                          NaN   \n",
       "2432                   NaN                   NaN                          NaN   \n",
       "2434                   NaN                   NaN                          NaN   \n",
       "2435                   NaN                   NaN                          NaN   \n",
       "2436                   NaN                   NaN                          NaN   \n",
       "2437                   NaN                   NaN                          NaN   \n",
       "3073                   NaN                   NaN                          NaN   \n",
       "3074                   NaN                   NaN                          NaN   \n",
       "3075                   NaN                   NaN                          NaN   \n",
       "\n",
       "     location_key_2  \n",
       "174           US_AS  \n",
       "1546          US_MP  \n",
       "1547          US_MP  \n",
       "2359          US_PR  \n",
       "2360          US_PR  \n",
       "2361          US_PR  \n",
       "2362          US_PR  \n",
       "2363          US_PR  \n",
       "2364          US_PR  \n",
       "2365          US_PR  \n",
       "2366          US_PR  \n",
       "2367          US_PR  \n",
       "2368          US_PR  \n",
       "2369          US_PR  \n",
       "2370          US_PR  \n",
       "2371          US_PR  \n",
       "2372          US_PR  \n",
       "2373          US_PR  \n",
       "2374          US_PR  \n",
       "2375          US_PR  \n",
       "2376          US_PR  \n",
       "2377          US_PR  \n",
       "2378          US_PR  \n",
       "2379          US_PR  \n",
       "2380          US_PR  \n",
       "2381          US_PR  \n",
       "2382          US_PR  \n",
       "2384          US_PR  \n",
       "2385          US_PR  \n",
       "2386          US_PR  \n",
       "2387          US_PR  \n",
       "2388          US_PR  \n",
       "2389          US_PR  \n",
       "2390          US_PR  \n",
       "2391          US_PR  \n",
       "2392          US_PR  \n",
       "2393          US_PR  \n",
       "2394          US_PR  \n",
       "2395          US_PR  \n",
       "2396          US_PR  \n",
       "2397          US_PR  \n",
       "2398          US_PR  \n",
       "2399          US_PR  \n",
       "2400          US_PR  \n",
       "2401          US_PR  \n",
       "2402          US_PR  \n",
       "2403          US_PR  \n",
       "2404          US_PR  \n",
       "2405          US_PR  \n",
       "2406          US_PR  \n",
       "2407          US_PR  \n",
       "2408          US_PR  \n",
       "2409          US_PR  \n",
       "2410          US_PR  \n",
       "2411          US_PR  \n",
       "2412          US_PR  \n",
       "2413          US_PR  \n",
       "2414          US_PR  \n",
       "2415          US_PR  \n",
       "2416          US_PR  \n",
       "2417          US_PR  \n",
       "2418          US_PR  \n",
       "2419          US_PR  \n",
       "2420          US_PR  \n",
       "2421          US_PR  \n",
       "2422          US_PR  \n",
       "2424          US_PR  \n",
       "2425          US_PR  \n",
       "2426          US_PR  \n",
       "2427          US_PR  \n",
       "2428          US_PR  \n",
       "2429          US_PR  \n",
       "2430          US_PR  \n",
       "2431          US_PR  \n",
       "2432          US_PR  \n",
       "2434          US_PR  \n",
       "2435          US_PR  \n",
       "2436          US_PR  \n",
       "2437          US_RI  \n",
       "3073          US_VI  \n",
       "3074          US_VI  \n",
       "3075          US_VI  "
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demog_states.loc[df_demog_states['population_male'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "81c14dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demog_states = df_demog_states.dropna()\n",
    "df_demog_states.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "aad56bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixDataTypesOfColumns(df_demog_states, df_demog_states.columns.tolist()[1:-1], 'float64', 'int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "22430cd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/4103920634.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[col] = df[col].astype(typeTo)\n"
     ]
    }
   ],
   "source": [
    "fixDataTypesOfColumns(df_demog_US, df_demog_US.columns.tolist()[1:-1], 'float64', 'int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "id": "8d5da92c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>location_key</th>\n",
       "      <th>population</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>population_age_00_09</th>\n",
       "      <th>population_age_10_19</th>\n",
       "      <th>population_age_20_29</th>\n",
       "      <th>population_age_30_39</th>\n",
       "      <th>population_age_40_49</th>\n",
       "      <th>population_age_50_59</th>\n",
       "      <th>population_age_60_69</th>\n",
       "      <th>population_age_70_79</th>\n",
       "      <th>population_age_80_and_older</th>\n",
       "      <th>location_key_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US_AK</td>\n",
       "      <td>733391</td>\n",
       "      <td>424916</td>\n",
       "      <td>391925</td>\n",
       "      <td>115198</td>\n",
       "      <td>111045</td>\n",
       "      <td>128022</td>\n",
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       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US_AK_02013</td>\n",
       "      <td>3455</td>\n",
       "      <td>2345</td>\n",
       "      <td>1110</td>\n",
       "      <td>178</td>\n",
       "      <td>165</td>\n",
       "      <td>571</td>\n",
       "      <td>532</td>\n",
       "      <td>792</td>\n",
       "      <td>696</td>\n",
       "      <td>321</td>\n",
       "      <td>156</td>\n",
       "      <td>44</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US_AK_02016</td>\n",
       "      <td>5729</td>\n",
       "      <td>3795</td>\n",
       "      <td>1934</td>\n",
       "      <td>359</td>\n",
       "      <td>400</td>\n",
       "      <td>951</td>\n",
       "      <td>957</td>\n",
       "      <td>1149</td>\n",
       "      <td>1146</td>\n",
       "      <td>622</td>\n",
       "      <td>118</td>\n",
       "      <td>27</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US_AK_02020</td>\n",
       "      <td>294338</td>\n",
       "      <td>149817</td>\n",
       "      <td>144521</td>\n",
       "      <td>41514</td>\n",
       "      <td>37461</td>\n",
       "      <td>49984</td>\n",
       "      <td>45570</td>\n",
       "      <td>35093</td>\n",
       "      <td>37143</td>\n",
       "      <td>28931</td>\n",
       "      <td>12887</td>\n",
       "      <td>5755</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US_AK_02050</td>\n",
       "      <td>18090</td>\n",
       "      <td>9369</td>\n",
       "      <td>8721</td>\n",
       "      <td>3888</td>\n",
       "      <td>3211</td>\n",
       "      <td>2833</td>\n",
       "      <td>2294</td>\n",
       "      <td>1682</td>\n",
       "      <td>2053</td>\n",
       "      <td>1378</td>\n",
       "      <td>568</td>\n",
       "      <td>183</td>\n",
       "      <td>US_AK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location_key  population  population_male  population_female  \\\n",
       "0        US_AK      733391           424916             391925   \n",
       "1  US_AK_02013        3455             2345               1110   \n",
       "2  US_AK_02016        5729             3795               1934   \n",
       "3  US_AK_02020      294338           149817             144521   \n",
       "4  US_AK_02050       18090             9369               8721   \n",
       "\n",
       "   population_age_00_09  population_age_10_19  population_age_20_29  \\\n",
       "0                115198                111045                128022   \n",
       "1                   178                   165                   571   \n",
       "2                   359                   400                   951   \n",
       "3                 41514                 37461                 49984   \n",
       "4                  3888                  3211                  2833   \n",
       "\n",
       "   population_age_30_39  population_age_40_49  population_age_50_59  \\\n",
       "0                113254                102310                116061   \n",
       "1                   532                   792                   696   \n",
       "2                   957                  1149                  1146   \n",
       "3                 45570                 35093                 37143   \n",
       "4                  2294                  1682                  2053   \n",
       "\n",
       "   population_age_60_69  population_age_70_79  population_age_80_and_older  \\\n",
       "0                 82608                 35021                        13322   \n",
       "1                   321                   156                           44   \n",
       "2                   622                   118                           27   \n",
       "3                 28931                 12887                         5755   \n",
       "4                  1378                   568                          183   \n",
       "\n",
       "  location_key_2  \n",
       "0          US_AK  \n",
       "1          US_AK  \n",
       "2          US_AK  \n",
       "3          US_AK  \n",
       "4          US_AK  "
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demog_states.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3926a15c",
   "metadata": {},
   "source": [
    "### As with df_epidem, we will only keep the rows where location_key == location_key_2 since we only want to retain data at the state level. Then, we will drop location_key_2 column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "edf3b052",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demog_states = df_demog_states.loc[df_demog_states['location_key'] == df_demog_states['location_key_2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "6257ffa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/1539207519.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_demog_US.drop(columns=['location_key_2'], inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df_demog_US.drop(columns=['location_key_2'], inplace=True)\n",
    "df_demog_states.drop(columns=['location_key_2'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9356761c",
   "metadata": {},
   "source": [
    "### Moving onto df_emergDeclare, we see that there are 104 rows in df_emergDeclare. We will only hand-pick some and retain them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "bed45cf2",
   "metadata": {},
   "outputs": [
    {
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       "      <th>lawatlas_essential_def_pet_stores</th>\n",
       "      <th>lawatlas_essential_def_type_of_essential_retail_business_is_not_specified</th>\n",
       "      <th>lawatlas_essential_def_no_restriction_on_retail_businesses</th>\n",
       "      <th>lawatlas_business_statewide</th>\n",
       "      <th>lawatlas_rest_restrict</th>\n",
       "      <th>lawatlas_service_type_takeout</th>\n",
       "      <th>lawatlas_service_type_delivery</th>\n",
       "      <th>lawatlas_service_type_limited_on_site_service</th>\n",
       "      <th>lawatlas_rest_statewide</th>\n",
       "      <th>lawatlas_schools_requirement</th>\n",
       "      <th>lawatlas_schools_type_private_elementary_schools</th>\n",
       "      <th>lawatlas_schools_type_private_secondary_schools</th>\n",
       "      <th>lawatlas_schools_type_public_elementary_schools</th>\n",
       "      <th>lawatlas_schools_type_public_secondary_schools</th>\n",
       "      <th>lawatlas_schools_type_colleges_and_universities</th>\n",
       "      <th>lawatlas_schools_type_technical_schools</th>\n",
       "      <th>lawatlas_schools_type_type_of_school_not_specified</th>\n",
       "      <th>lawatlas_schools_statewide</th>\n",
       "      <th>lawatlas_gathering_ban</th>\n",
       "      <th>lawatlas_gathering_type</th>\n",
       "      <th>lawatlas_gathering_statewide</th>\n",
       "      <th>lawatlas_med_restrict</th>\n",
       "      <th>lawatlas_med_except_delay_would_threaten_patients_health</th>\n",
       "      <th>lawatlas_med_except_delay_would_threaten_patients_life</th>\n",
       "      <th>lawatlas_med_except_procedure_needed_to_treat_emergency</th>\n",
       "      <th>lawatlas_med_except_procedure_does_not_deplete_hospital_capacity</th>\n",
       "      <th>lawatlas_med_except_procedure_does_not_deplete_personal_protective_equipment</th>\n",
       "      <th>lawatlas_med_except_family_planning_services</th>\n",
       "      <th>lawatlas_med_except_no_exception_specified</th>\n",
       "      <th>lawatlas_abortion_essential_new</th>\n",
       "      <th>lawatlas_med_statewide</th>\n",
       "      <th>lawatlas_correct_requirement</th>\n",
       "      <th>lawatlas_correct_facility_all_state_facilities</th>\n",
       "      <th>lawatlas_correct_facility_all_department_of_corrections_facilities</th>\n",
       "      <th>lawatlas_correct_facility_all_county_jails</th>\n",
       "      <th>lawatlas_correct_facility_juvenile_detention_centers</th>\n",
       "      <th>lawatlas_correct_type_intakes_suspended</th>\n",
       "      <th>lawatlas_correct_type_duty_to_receive_prisoners_suspended</th>\n",
       "      <th>lawatlas_correct_type_transfers_to_custody_suspended</th>\n",
       "      <th>lawatlas_correct_type_release_of_inmates</th>\n",
       "      <th>lawatlas_correct_type_rules_regarding_inmate_release_suspended</th>\n",
       "      <th>lawatlas_correct_type_release_notice_suspended</th>\n",
       "      <th>lawatlas_correct_type_cease_in_person_parole_hearings</th>\n",
       "      <th>lawatlas_correct_type_develop_process_for_virtual_parole_hearings</th>\n",
       "      <th>lawatlas_correct_type_visitation_suspended</th>\n",
       "      <th>lawatlas_correct_statewide</th>\n",
       "      <th>lawatlas_state_preempt</th>\n",
       "      <th>lawatlas_action_preempt_imposing_additional_social_distancing_limitations_on_essential_business</th>\n",
       "      <th>lawatlas_action_preempt_imposing_additional_restrictions_on_public_spaces</th>\n",
       "      <th>lawatlas_action_preempt_restricting_scope_of_services_of_an_essential_business</th>\n",
       "      <th>lawatlas_action_preempt_expanding_the_definition_of_non-essential_business</th>\n",
       "      <th>lawatlas_action_preempt_restricting_the_hours_of_operation_of_an_essential_business</th>\n",
       "      <th>lawatlas_action_preempt_imposing_restrictions_that_conflict_with_state_order</th>\n",
       "      <th>lawatlas_action_preempt_restricting_the_performance_of_an_essential_function</th>\n",
       "      <th>lawatlas_action_preempt_restricting_people_from_leaving_home</th>\n",
       "      <th>lawatlas_action_preempt_restricting_the_operations_of_schools</th>\n",
       "      <th>lawatlas_action_preempt_imposing_gathering_bans</th>\n",
       "      <th>lawatlas_local_allow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8354</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>US_WY</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>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>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</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>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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8355</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>US_WY</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>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>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</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>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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8356</th>\n",
       "      <td>2020-06-24</td>\n",
       "      <td>US_WY</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>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>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</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>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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8357</th>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>US_WY</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>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>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</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>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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8358</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>US_WY</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>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>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</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>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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8359</th>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>US_WY</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>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>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</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>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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8360</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>US_WY</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>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>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</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>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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8361</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>US_WY</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>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>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</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>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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8362</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>US_WY</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>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>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</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>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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8363</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>US_WY</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>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>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</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>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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date location_key  lawatlas_mitigation_policy  \\\n",
       "8354  2020-06-22        US_WY                           1   \n",
       "8355  2020-06-23        US_WY                           1   \n",
       "8356  2020-06-24        US_WY                           1   \n",
       "8357  2020-06-25        US_WY                           1   \n",
       "8358  2020-06-26        US_WY                           1   \n",
       "8359  2020-06-27        US_WY                           1   \n",
       "8360  2020-06-28        US_WY                           1   \n",
       "8361  2020-06-29        US_WY                           1   \n",
       "8362  2020-06-30        US_WY                           1   \n",
       "8363  2020-07-01        US_WY                           1   \n",
       "\n",
       "      lawatlas_state_emergency  lawatlas_emerg_statewide  \\\n",
       "8354                       1.0                       1.0   \n",
       "8355                       1.0                       1.0   \n",
       "8356                       1.0                       1.0   \n",
       "8357                       1.0                       1.0   \n",
       "8358                       1.0                       1.0   \n",
       "8359                       1.0                       1.0   \n",
       "8360                       1.0                       1.0   \n",
       "8361                       1.0                       1.0   \n",
       "8362                       1.0                       1.0   \n",
       "8363                       1.0                       1.0   \n",
       "\n",
       "      lawatlas_travel_requirement  \\\n",
       "8354                          0.0   \n",
       "8355                          0.0   \n",
       "8356                          0.0   \n",
       "8357                          0.0   \n",
       "8358                          0.0   \n",
       "8359                          0.0   \n",
       "8360                          0.0   \n",
       "8361                          0.0   \n",
       "8362                          0.0   \n",
       "8363                          0.0   \n",
       "\n",
       "      lawatlas_traveler_type_all_people_entering_the_state  \\\n",
       "8354                                                NaN      \n",
       "8355                                                NaN      \n",
       "8356                                                NaN      \n",
       "8357                                                NaN      \n",
       "8358                                                NaN      \n",
       "8359                                                NaN      \n",
       "8360                                                NaN      \n",
       "8361                                                NaN      \n",
       "8362                                                NaN      \n",
       "8363                                                NaN      \n",
       "\n",
       "      lawatlas_traveler_type_travelers_from_specified_states  \\\n",
       "8354                                                NaN        \n",
       "8355                                                NaN        \n",
       "8356                                                NaN        \n",
       "8357                                                NaN        \n",
       "8358                                                NaN        \n",
       "8359                                                NaN        \n",
       "8360                                                NaN        \n",
       "8361                                                NaN        \n",
       "8362                                                NaN        \n",
       "8363                                                NaN        \n",
       "\n",
       "      lawatlas_traveler_type_travelers_from_specified_countries  \\\n",
       "8354                                                NaN           \n",
       "8355                                                NaN           \n",
       "8356                                                NaN           \n",
       "8357                                                NaN           \n",
       "8358                                                NaN           \n",
       "8359                                                NaN           \n",
       "8360                                                NaN           \n",
       "8361                                                NaN           \n",
       "8362                                                NaN           \n",
       "8363                                                NaN           \n",
       "\n",
       "      lawatlas_traveler_type_general_international_travelers  \\\n",
       "8354                                                NaN        \n",
       "8355                                                NaN        \n",
       "8356                                                NaN        \n",
       "8357                                                NaN        \n",
       "8358                                                NaN        \n",
       "8359                                                NaN        \n",
       "8360                                                NaN        \n",
       "8361                                                NaN        \n",
       "8362                                                NaN        \n",
       "8363                                                NaN        \n",
       "\n",
       "      lawatlas_traveler_type_all_air_travelers  \\\n",
       "8354                                       NaN   \n",
       "8355                                       NaN   \n",
       "8356                                       NaN   \n",
       "8357                                       NaN   \n",
       "8358                                       NaN   \n",
       "8359                                       NaN   \n",
       "8360                                       NaN   \n",
       "8361                                       NaN   \n",
       "8362                                       NaN   \n",
       "8363                                       NaN   \n",
       "\n",
       "      lawatlas_requirement_type_traveler_must_self_quarantine  \\\n",
       "8354                                                NaN         \n",
       "8355                                                NaN         \n",
       "8356                                                NaN         \n",
       "8357                                                NaN         \n",
       "8358                                                NaN         \n",
       "8359                                                NaN         \n",
       "8360                                                NaN         \n",
       "8361                                                NaN         \n",
       "8362                                                NaN         \n",
       "8363                                                NaN         \n",
       "\n",
       "      lawatlas_requirement_type_traveler_must_inform_others_of_travel  \\\n",
       "8354                                                NaN                 \n",
       "8355                                                NaN                 \n",
       "8356                                                NaN                 \n",
       "8357                                                NaN                 \n",
       "8358                                                NaN                 \n",
       "8359                                                NaN                 \n",
       "8360                                                NaN                 \n",
       "8361                                                NaN                 \n",
       "8362                                                NaN                 \n",
       "8363                                                NaN                 \n",
       "\n",
       "      lawatlas_requirement_type_checkpoints_must_be_established  \\\n",
       "8354                                                NaN           \n",
       "8355                                                NaN           \n",
       "8356                                                NaN           \n",
       "8357                                                NaN           \n",
       "8358                                                NaN           \n",
       "8359                                                NaN           \n",
       "8360                                                NaN           \n",
       "8361                                                NaN           \n",
       "8362                                                NaN           \n",
       "8363                                                NaN           \n",
       "\n",
       "      lawatlas_requirement_type_travel_requirement_must_be_posted  \\\n",
       "8354                                                NaN             \n",
       "8355                                                NaN             \n",
       "8356                                                NaN             \n",
       "8357                                                NaN             \n",
       "8358                                                NaN             \n",
       "8359                                                NaN             \n",
       "8360                                                NaN             \n",
       "8361                                                NaN             \n",
       "8362                                                NaN             \n",
       "8363                                                NaN             \n",
       "\n",
       "      lawatlas_travel_statewide  lawatlas_home_requirement  \\\n",
       "8354                        NaN                        0.0   \n",
       "8355                        NaN                        0.0   \n",
       "8356                        NaN                        0.0   \n",
       "8357                        NaN                        0.0   \n",
       "8358                        NaN                        0.0   \n",
       "8359                        NaN                        0.0   \n",
       "8360                        NaN                        0.0   \n",
       "8361                        NaN                        0.0   \n",
       "8362                        NaN                        0.0   \n",
       "8363                        NaN                        0.0   \n",
       "\n",
       "      lawatlas_home_except_engaging_in_essential_business_activities  \\\n",
       "8354                                                NaN                \n",
       "8355                                                NaN                \n",
       "8356                                                NaN                \n",
       "8357                                                NaN                \n",
       "8358                                                NaN                \n",
       "8359                                                NaN                \n",
       "8360                                                NaN                \n",
       "8361                                                NaN                \n",
       "8362                                                NaN                \n",
       "8363                                                NaN                \n",
       "\n",
       "      lawatlas_home_except_obtaining_necessary_supplies  \\\n",
       "8354                                                NaN   \n",
       "8355                                                NaN   \n",
       "8356                                                NaN   \n",
       "8357                                                NaN   \n",
       "8358                                                NaN   \n",
       "8359                                                NaN   \n",
       "8360                                                NaN   \n",
       "8361                                                NaN   \n",
       "8362                                                NaN   \n",
       "8363                                                NaN   \n",
       "\n",
       "      lawatlas_home_except_accessing_emergency_services  \\\n",
       "8354                                                NaN   \n",
       "8355                                                NaN   \n",
       "8356                                                NaN   \n",
       "8357                                                NaN   \n",
       "8358                                                NaN   \n",
       "8359                                                NaN   \n",
       "8360                                                NaN   \n",
       "8361                                                NaN   \n",
       "8362                                                NaN   \n",
       "8363                                                NaN   \n",
       "\n",
       "      lawatlas_home_except_caring_for_a_person_outside_the_home  \\\n",
       "8354                                                NaN           \n",
       "8355                                                NaN           \n",
       "8356                                                NaN           \n",
       "8357                                                NaN           \n",
       "8358                                                NaN           \n",
       "8359                                                NaN           \n",
       "8360                                                NaN           \n",
       "8361                                                NaN           \n",
       "8362                                                NaN           \n",
       "8363                                                NaN           \n",
       "\n",
       "      lawatlas_home_except_caring_for_a_pet_outside_the_home  \\\n",
       "8354                                                NaN        \n",
       "8355                                                NaN        \n",
       "8356                                                NaN        \n",
       "8357                                                NaN        \n",
       "8358                                                NaN        \n",
       "8359                                                NaN        \n",
       "8360                                                NaN        \n",
       "8361                                                NaN        \n",
       "8362                                                NaN        \n",
       "8363                                                NaN        \n",
       "\n",
       "      lawatlas_home_except_engaging_in_outdoor_activities  \\\n",
       "8354                                                NaN     \n",
       "8355                                                NaN     \n",
       "8356                                                NaN     \n",
       "8357                                                NaN     \n",
       "8358                                                NaN     \n",
       "8359                                                NaN     \n",
       "8360                                                NaN     \n",
       "8361                                                NaN     \n",
       "8362                                                NaN     \n",
       "8363                                                NaN     \n",
       "\n",
       "      lawatlas_home_except_attending_religious_services  \\\n",
       "8354                                                NaN   \n",
       "8355                                                NaN   \n",
       "8356                                                NaN   \n",
       "8357                                                NaN   \n",
       "8358                                                NaN   \n",
       "8359                                                NaN   \n",
       "8360                                                NaN   \n",
       "8361                                                NaN   \n",
       "8362                                                NaN   \n",
       "8363                                                NaN   \n",
       "\n",
       "      lawatlas_home_except_engaging_in_essential_health_care_services  \\\n",
       "8354                                                NaN                 \n",
       "8355                                                NaN                 \n",
       "8356                                                NaN                 \n",
       "8357                                                NaN                 \n",
       "8358                                                NaN                 \n",
       "8359                                                NaN                 \n",
       "8360                                                NaN                 \n",
       "8361                                                NaN                 \n",
       "8362                                                NaN                 \n",
       "8363                                                NaN                 \n",
       "\n",
       "      lawatlas_home_statewide  lawatlas_curfew_reg  lawatlas_mask_requirement  \\\n",
       "8354                      NaN                  0.0                        1.0   \n",
       "8355                      NaN                  0.0                        1.0   \n",
       "8356                      NaN                  0.0                        1.0   \n",
       "8357                      NaN                  0.0                        1.0   \n",
       "8358                      NaN                  0.0                        1.0   \n",
       "8359                      NaN                  0.0                        1.0   \n",
       "8360                      NaN                  0.0                        1.0   \n",
       "8361                      NaN                  0.0                        1.0   \n",
       "8362                      NaN                  0.0                        1.0   \n",
       "8363                      NaN                  0.0                        1.0   \n",
       "\n",
       "      lawatlas_mask_statewide  lawatlas_business_close  \\\n",
       "8354                      1.0                      0.0   \n",
       "8355                      1.0                      0.0   \n",
       "8356                      1.0                      0.0   \n",
       "8357                      1.0                      0.0   \n",
       "8358                      1.0                      0.0   \n",
       "8359                      1.0                      0.0   \n",
       "8360                      1.0                      0.0   \n",
       "8361                      1.0                      0.0   \n",
       "8362                      1.0                      0.0   \n",
       "8363                      1.0                      0.0   \n",
       "\n",
       "      lawatlas_business_type_all_non_essential_businesses  \\\n",
       "8354                                                NaN     \n",
       "8355                                                NaN     \n",
       "8356                                                NaN     \n",
       "8357                                                NaN     \n",
       "8358                                                NaN     \n",
       "8359                                                NaN     \n",
       "8360                                                NaN     \n",
       "8361                                                NaN     \n",
       "8362                                                NaN     \n",
       "8363                                                NaN     \n",
       "\n",
       "      lawatlas_business_type_non_essential_retail_businesses  \\\n",
       "8354                                                NaN        \n",
       "8355                                                NaN        \n",
       "8356                                                NaN        \n",
       "8357                                                NaN        \n",
       "8358                                                NaN        \n",
       "8359                                                NaN        \n",
       "8360                                                NaN        \n",
       "8361                                                NaN        \n",
       "8362                                                NaN        \n",
       "8363                                                NaN        \n",
       "\n",
       "      lawatlas_business_type_entertainment_businesses  \\\n",
       "8354                                              NaN   \n",
       "8355                                              NaN   \n",
       "8356                                              NaN   \n",
       "8357                                              NaN   \n",
       "8358                                              NaN   \n",
       "8359                                              NaN   \n",
       "8360                                              NaN   \n",
       "8361                                              NaN   \n",
       "8362                                              NaN   \n",
       "8363                                              NaN   \n",
       "\n",
       "      lawatlas_business_type_personal_service_businesses  \\\n",
       "8354                                                NaN    \n",
       "8355                                                NaN    \n",
       "8356                                                NaN    \n",
       "8357                                                NaN    \n",
       "8358                                                NaN    \n",
       "8359                                                NaN    \n",
       "8360                                                NaN    \n",
       "8361                                                NaN    \n",
       "8362                                                NaN    \n",
       "8363                                                NaN    \n",
       "\n",
       "      lawatlas_business_type_restaurants  lawatlas_business_type_bars  \\\n",
       "8354                                 NaN                          NaN   \n",
       "8355                                 NaN                          NaN   \n",
       "8356                                 NaN                          NaN   \n",
       "8357                                 NaN                          NaN   \n",
       "8358                                 NaN                          NaN   \n",
       "8359                                 NaN                          NaN   \n",
       "8360                                 NaN                          NaN   \n",
       "8361                                 NaN                          NaN   \n",
       "8362                                 NaN                          NaN   \n",
       "8363                                 NaN                          NaN   \n",
       "\n",
       "      lawatlas_business_type_fitness_centers  \\\n",
       "8354                                     NaN   \n",
       "8355                                     NaN   \n",
       "8356                                     NaN   \n",
       "8357                                     NaN   \n",
       "8358                                     NaN   \n",
       "8359                                     NaN   \n",
       "8360                                     NaN   \n",
       "8361                                     NaN   \n",
       "8362                                     NaN   \n",
       "8363                                     NaN   \n",
       "\n",
       "      lawatlas_essential_def_appliance_stores  \\\n",
       "8354                                      NaN   \n",
       "8355                                      NaN   \n",
       "8356                                      NaN   \n",
       "8357                                      NaN   \n",
       "8358                                      NaN   \n",
       "8359                                      NaN   \n",
       "8360                                      NaN   \n",
       "8361                                      NaN   \n",
       "8362                                      NaN   \n",
       "8363                                      NaN   \n",
       "\n",
       "      lawatlas_essential_def_convenience_stores  \\\n",
       "8354                                        NaN   \n",
       "8355                                        NaN   \n",
       "8356                                        NaN   \n",
       "8357                                        NaN   \n",
       "8358                                        NaN   \n",
       "8359                                        NaN   \n",
       "8360                                        NaN   \n",
       "8361                                        NaN   \n",
       "8362                                        NaN   \n",
       "8363                                        NaN   \n",
       "\n",
       "      lawatlas_essential_def_gas_stations  \\\n",
       "8354                                  NaN   \n",
       "8355                                  NaN   \n",
       "8356                                  NaN   \n",
       "8357                                  NaN   \n",
       "8358                                  NaN   \n",
       "8359                                  NaN   \n",
       "8360                                  NaN   \n",
       "8361                                  NaN   \n",
       "8362                                  NaN   \n",
       "8363                                  NaN   \n",
       "\n",
       "      lawatlas_essential_def_grocery_stores  \\\n",
       "8354                                    NaN   \n",
       "8355                                    NaN   \n",
       "8356                                    NaN   \n",
       "8357                                    NaN   \n",
       "8358                                    NaN   \n",
       "8359                                    NaN   \n",
       "8360                                    NaN   \n",
       "8361                                    NaN   \n",
       "8362                                    NaN   \n",
       "8363                                    NaN   \n",
       "\n",
       "      lawatlas_essential_def_gun_stores  \\\n",
       "8354                                NaN   \n",
       "8355                                NaN   \n",
       "8356                                NaN   \n",
       "8357                                NaN   \n",
       "8358                                NaN   \n",
       "8359                                NaN   \n",
       "8360                                NaN   \n",
       "8361                                NaN   \n",
       "8362                                NaN   \n",
       "8363                                NaN   \n",
       "\n",
       "      lawatlas_essential_def_hardware_stores  \\\n",
       "8354                                     NaN   \n",
       "8355                                     NaN   \n",
       "8356                                     NaN   \n",
       "8357                                     NaN   \n",
       "8358                                     NaN   \n",
       "8359                                     NaN   \n",
       "8360                                     NaN   \n",
       "8361                                     NaN   \n",
       "8362                                     NaN   \n",
       "8363                                     NaN   \n",
       "\n",
       "      lawatlas_essential_def_liquor_stores  lawatlas_essential_def_pharmacies  \\\n",
       "8354                                   NaN                                NaN   \n",
       "8355                                   NaN                                NaN   \n",
       "8356                                   NaN                                NaN   \n",
       "8357                                   NaN                                NaN   \n",
       "8358                                   NaN                                NaN   \n",
       "8359                                   NaN                                NaN   \n",
       "8360                                   NaN                                NaN   \n",
       "8361                                   NaN                                NaN   \n",
       "8362                                   NaN                                NaN   \n",
       "8363                                   NaN                                NaN   \n",
       "\n",
       "      lawatlas_essential_def_marijuana_dispensaries  \\\n",
       "8354                                            NaN   \n",
       "8355                                            NaN   \n",
       "8356                                            NaN   \n",
       "8357                                            NaN   \n",
       "8358                                            NaN   \n",
       "8359                                            NaN   \n",
       "8360                                            NaN   \n",
       "8361                                            NaN   \n",
       "8362                                            NaN   \n",
       "8363                                            NaN   \n",
       "\n",
       "      lawatlas_essential_def_pet_stores  \\\n",
       "8354                                NaN   \n",
       "8355                                NaN   \n",
       "8356                                NaN   \n",
       "8357                                NaN   \n",
       "8358                                NaN   \n",
       "8359                                NaN   \n",
       "8360                                NaN   \n",
       "8361                                NaN   \n",
       "8362                                NaN   \n",
       "8363                                NaN   \n",
       "\n",
       "      lawatlas_essential_def_type_of_essential_retail_business_is_not_specified  \\\n",
       "8354                                                NaN                           \n",
       "8355                                                NaN                           \n",
       "8356                                                NaN                           \n",
       "8357                                                NaN                           \n",
       "8358                                                NaN                           \n",
       "8359                                                NaN                           \n",
       "8360                                                NaN                           \n",
       "8361                                                NaN                           \n",
       "8362                                                NaN                           \n",
       "8363                                                NaN                           \n",
       "\n",
       "      lawatlas_essential_def_no_restriction_on_retail_businesses  \\\n",
       "8354                                                NaN            \n",
       "8355                                                NaN            \n",
       "8356                                                NaN            \n",
       "8357                                                NaN            \n",
       "8358                                                NaN            \n",
       "8359                                                NaN            \n",
       "8360                                                NaN            \n",
       "8361                                                NaN            \n",
       "8362                                                NaN            \n",
       "8363                                                NaN            \n",
       "\n",
       "      lawatlas_business_statewide  lawatlas_rest_restrict  \\\n",
       "8354                          NaN                     1.0   \n",
       "8355                          NaN                     1.0   \n",
       "8356                          NaN                     1.0   \n",
       "8357                          NaN                     1.0   \n",
       "8358                          NaN                     1.0   \n",
       "8359                          NaN                     1.0   \n",
       "8360                          NaN                     1.0   \n",
       "8361                          NaN                     1.0   \n",
       "8362                          NaN                     1.0   \n",
       "8363                          NaN                     1.0   \n",
       "\n",
       "      lawatlas_service_type_takeout  lawatlas_service_type_delivery  \\\n",
       "8354                            1.0                             1.0   \n",
       "8355                            1.0                             1.0   \n",
       "8356                            1.0                             1.0   \n",
       "8357                            1.0                             1.0   \n",
       "8358                            1.0                             1.0   \n",
       "8359                            1.0                             1.0   \n",
       "8360                            1.0                             1.0   \n",
       "8361                            1.0                             1.0   \n",
       "8362                            1.0                             1.0   \n",
       "8363                            1.0                             1.0   \n",
       "\n",
       "      lawatlas_service_type_limited_on_site_service  lawatlas_rest_statewide  \\\n",
       "8354                                            NaN                      1.0   \n",
       "8355                                            NaN                      1.0   \n",
       "8356                                            NaN                      1.0   \n",
       "8357                                            NaN                      1.0   \n",
       "8358                                            NaN                      1.0   \n",
       "8359                                            NaN                      1.0   \n",
       "8360                                            NaN                      1.0   \n",
       "8361                                            NaN                      1.0   \n",
       "8362                                            NaN                      1.0   \n",
       "8363                                            NaN                      1.0   \n",
       "\n",
       "      lawatlas_schools_requirement  \\\n",
       "8354                           0.0   \n",
       "8355                           0.0   \n",
       "8356                           0.0   \n",
       "8357                           0.0   \n",
       "8358                           0.0   \n",
       "8359                           0.0   \n",
       "8360                           0.0   \n",
       "8361                           0.0   \n",
       "8362                           0.0   \n",
       "8363                           0.0   \n",
       "\n",
       "      lawatlas_schools_type_private_elementary_schools  \\\n",
       "8354                                               NaN   \n",
       "8355                                               NaN   \n",
       "8356                                               NaN   \n",
       "8357                                               NaN   \n",
       "8358                                               NaN   \n",
       "8359                                               NaN   \n",
       "8360                                               NaN   \n",
       "8361                                               NaN   \n",
       "8362                                               NaN   \n",
       "8363                                               NaN   \n",
       "\n",
       "      lawatlas_schools_type_private_secondary_schools  \\\n",
       "8354                                              NaN   \n",
       "8355                                              NaN   \n",
       "8356                                              NaN   \n",
       "8357                                              NaN   \n",
       "8358                                              NaN   \n",
       "8359                                              NaN   \n",
       "8360                                              NaN   \n",
       "8361                                              NaN   \n",
       "8362                                              NaN   \n",
       "8363                                              NaN   \n",
       "\n",
       "      lawatlas_schools_type_public_elementary_schools  \\\n",
       "8354                                              NaN   \n",
       "8355                                              NaN   \n",
       "8356                                              NaN   \n",
       "8357                                              NaN   \n",
       "8358                                              NaN   \n",
       "8359                                              NaN   \n",
       "8360                                              NaN   \n",
       "8361                                              NaN   \n",
       "8362                                              NaN   \n",
       "8363                                              NaN   \n",
       "\n",
       "      lawatlas_schools_type_public_secondary_schools  \\\n",
       "8354                                             NaN   \n",
       "8355                                             NaN   \n",
       "8356                                             NaN   \n",
       "8357                                             NaN   \n",
       "8358                                             NaN   \n",
       "8359                                             NaN   \n",
       "8360                                             NaN   \n",
       "8361                                             NaN   \n",
       "8362                                             NaN   \n",
       "8363                                             NaN   \n",
       "\n",
       "      lawatlas_schools_type_colleges_and_universities  \\\n",
       "8354                                              NaN   \n",
       "8355                                              NaN   \n",
       "8356                                              NaN   \n",
       "8357                                              NaN   \n",
       "8358                                              NaN   \n",
       "8359                                              NaN   \n",
       "8360                                              NaN   \n",
       "8361                                              NaN   \n",
       "8362                                              NaN   \n",
       "8363                                              NaN   \n",
       "\n",
       "      lawatlas_schools_type_technical_schools  \\\n",
       "8354                                      NaN   \n",
       "8355                                      NaN   \n",
       "8356                                      NaN   \n",
       "8357                                      NaN   \n",
       "8358                                      NaN   \n",
       "8359                                      NaN   \n",
       "8360                                      NaN   \n",
       "8361                                      NaN   \n",
       "8362                                      NaN   \n",
       "8363                                      NaN   \n",
       "\n",
       "      lawatlas_schools_type_type_of_school_not_specified  \\\n",
       "8354                                                NaN    \n",
       "8355                                                NaN    \n",
       "8356                                                NaN    \n",
       "8357                                                NaN    \n",
       "8358                                                NaN    \n",
       "8359                                                NaN    \n",
       "8360                                                NaN    \n",
       "8361                                                NaN    \n",
       "8362                                                NaN    \n",
       "8363                                                NaN    \n",
       "\n",
       "      lawatlas_schools_statewide  lawatlas_gathering_ban  \\\n",
       "8354                         NaN                     1.0   \n",
       "8355                         NaN                     1.0   \n",
       "8356                         NaN                     1.0   \n",
       "8357                         NaN                     1.0   \n",
       "8358                         NaN                     1.0   \n",
       "8359                         NaN                     1.0   \n",
       "8360                         NaN                     1.0   \n",
       "8361                         NaN                     1.0   \n",
       "8362                         NaN                     1.0   \n",
       "8363                         NaN                     1.0   \n",
       "\n",
       "      lawatlas_gathering_type  lawatlas_gathering_statewide  \\\n",
       "8354                     10.0                           1.0   \n",
       "8355                     10.0                           1.0   \n",
       "8356                     10.0                           1.0   \n",
       "8357                     10.0                           1.0   \n",
       "8358                     10.0                           1.0   \n",
       "8359                     10.0                           1.0   \n",
       "8360                     10.0                           1.0   \n",
       "8361                     10.0                           1.0   \n",
       "8362                     10.0                           1.0   \n",
       "8363                     10.0                           1.0   \n",
       "\n",
       "      lawatlas_med_restrict  \\\n",
       "8354                    0.0   \n",
       "8355                    0.0   \n",
       "8356                    0.0   \n",
       "8357                    0.0   \n",
       "8358                    0.0   \n",
       "8359                    0.0   \n",
       "8360                    0.0   \n",
       "8361                    0.0   \n",
       "8362                    0.0   \n",
       "8363                    0.0   \n",
       "\n",
       "      lawatlas_med_except_delay_would_threaten_patients_health  \\\n",
       "8354                                                NaN          \n",
       "8355                                                NaN          \n",
       "8356                                                NaN          \n",
       "8357                                                NaN          \n",
       "8358                                                NaN          \n",
       "8359                                                NaN          \n",
       "8360                                                NaN          \n",
       "8361                                                NaN          \n",
       "8362                                                NaN          \n",
       "8363                                                NaN          \n",
       "\n",
       "      lawatlas_med_except_delay_would_threaten_patients_life  \\\n",
       "8354                                                NaN        \n",
       "8355                                                NaN        \n",
       "8356                                                NaN        \n",
       "8357                                                NaN        \n",
       "8358                                                NaN        \n",
       "8359                                                NaN        \n",
       "8360                                                NaN        \n",
       "8361                                                NaN        \n",
       "8362                                                NaN        \n",
       "8363                                                NaN        \n",
       "\n",
       "      lawatlas_med_except_procedure_needed_to_treat_emergency  \\\n",
       "8354                                                NaN         \n",
       "8355                                                NaN         \n",
       "8356                                                NaN         \n",
       "8357                                                NaN         \n",
       "8358                                                NaN         \n",
       "8359                                                NaN         \n",
       "8360                                                NaN         \n",
       "8361                                                NaN         \n",
       "8362                                                NaN         \n",
       "8363                                                NaN         \n",
       "\n",
       "      lawatlas_med_except_procedure_does_not_deplete_hospital_capacity  \\\n",
       "8354                                                NaN                  \n",
       "8355                                                NaN                  \n",
       "8356                                                NaN                  \n",
       "8357                                                NaN                  \n",
       "8358                                                NaN                  \n",
       "8359                                                NaN                  \n",
       "8360                                                NaN                  \n",
       "8361                                                NaN                  \n",
       "8362                                                NaN                  \n",
       "8363                                                NaN                  \n",
       "\n",
       "      lawatlas_med_except_procedure_does_not_deplete_personal_protective_equipment  \\\n",
       "8354                                                NaN                              \n",
       "8355                                                NaN                              \n",
       "8356                                                NaN                              \n",
       "8357                                                NaN                              \n",
       "8358                                                NaN                              \n",
       "8359                                                NaN                              \n",
       "8360                                                NaN                              \n",
       "8361                                                NaN                              \n",
       "8362                                                NaN                              \n",
       "8363                                                NaN                              \n",
       "\n",
       "      lawatlas_med_except_family_planning_services  \\\n",
       "8354                                           NaN   \n",
       "8355                                           NaN   \n",
       "8356                                           NaN   \n",
       "8357                                           NaN   \n",
       "8358                                           NaN   \n",
       "8359                                           NaN   \n",
       "8360                                           NaN   \n",
       "8361                                           NaN   \n",
       "8362                                           NaN   \n",
       "8363                                           NaN   \n",
       "\n",
       "      lawatlas_med_except_no_exception_specified  \\\n",
       "8354                                         NaN   \n",
       "8355                                         NaN   \n",
       "8356                                         NaN   \n",
       "8357                                         NaN   \n",
       "8358                                         NaN   \n",
       "8359                                         NaN   \n",
       "8360                                         NaN   \n",
       "8361                                         NaN   \n",
       "8362                                         NaN   \n",
       "8363                                         NaN   \n",
       "\n",
       "      lawatlas_abortion_essential_new  lawatlas_med_statewide  \\\n",
       "8354                              NaN                     NaN   \n",
       "8355                              NaN                     NaN   \n",
       "8356                              NaN                     NaN   \n",
       "8357                              NaN                     NaN   \n",
       "8358                              NaN                     NaN   \n",
       "8359                              NaN                     NaN   \n",
       "8360                              NaN                     NaN   \n",
       "8361                              NaN                     NaN   \n",
       "8362                              NaN                     NaN   \n",
       "8363                              NaN                     NaN   \n",
       "\n",
       "      lawatlas_correct_requirement  \\\n",
       "8354                           0.0   \n",
       "8355                           0.0   \n",
       "8356                           0.0   \n",
       "8357                           0.0   \n",
       "8358                           0.0   \n",
       "8359                           0.0   \n",
       "8360                           0.0   \n",
       "8361                           0.0   \n",
       "8362                           0.0   \n",
       "8363                           0.0   \n",
       "\n",
       "      lawatlas_correct_facility_all_state_facilities  \\\n",
       "8354                                             NaN   \n",
       "8355                                             NaN   \n",
       "8356                                             NaN   \n",
       "8357                                             NaN   \n",
       "8358                                             NaN   \n",
       "8359                                             NaN   \n",
       "8360                                             NaN   \n",
       "8361                                             NaN   \n",
       "8362                                             NaN   \n",
       "8363                                             NaN   \n",
       "\n",
       "      lawatlas_correct_facility_all_department_of_corrections_facilities  \\\n",
       "8354                                                NaN                    \n",
       "8355                                                NaN                    \n",
       "8356                                                NaN                    \n",
       "8357                                                NaN                    \n",
       "8358                                                NaN                    \n",
       "8359                                                NaN                    \n",
       "8360                                                NaN                    \n",
       "8361                                                NaN                    \n",
       "8362                                                NaN                    \n",
       "8363                                                NaN                    \n",
       "\n",
       "      lawatlas_correct_facility_all_county_jails  \\\n",
       "8354                                         NaN   \n",
       "8355                                         NaN   \n",
       "8356                                         NaN   \n",
       "8357                                         NaN   \n",
       "8358                                         NaN   \n",
       "8359                                         NaN   \n",
       "8360                                         NaN   \n",
       "8361                                         NaN   \n",
       "8362                                         NaN   \n",
       "8363                                         NaN   \n",
       "\n",
       "      lawatlas_correct_facility_juvenile_detention_centers  \\\n",
       "8354                                                NaN      \n",
       "8355                                                NaN      \n",
       "8356                                                NaN      \n",
       "8357                                                NaN      \n",
       "8358                                                NaN      \n",
       "8359                                                NaN      \n",
       "8360                                                NaN      \n",
       "8361                                                NaN      \n",
       "8362                                                NaN      \n",
       "8363                                                NaN      \n",
       "\n",
       "      lawatlas_correct_type_intakes_suspended  \\\n",
       "8354                                      NaN   \n",
       "8355                                      NaN   \n",
       "8356                                      NaN   \n",
       "8357                                      NaN   \n",
       "8358                                      NaN   \n",
       "8359                                      NaN   \n",
       "8360                                      NaN   \n",
       "8361                                      NaN   \n",
       "8362                                      NaN   \n",
       "8363                                      NaN   \n",
       "\n",
       "      lawatlas_correct_type_duty_to_receive_prisoners_suspended  \\\n",
       "8354                                                NaN           \n",
       "8355                                                NaN           \n",
       "8356                                                NaN           \n",
       "8357                                                NaN           \n",
       "8358                                                NaN           \n",
       "8359                                                NaN           \n",
       "8360                                                NaN           \n",
       "8361                                                NaN           \n",
       "8362                                                NaN           \n",
       "8363                                                NaN           \n",
       "\n",
       "      lawatlas_correct_type_transfers_to_custody_suspended  \\\n",
       "8354                                                NaN      \n",
       "8355                                                NaN      \n",
       "8356                                                NaN      \n",
       "8357                                                NaN      \n",
       "8358                                                NaN      \n",
       "8359                                                NaN      \n",
       "8360                                                NaN      \n",
       "8361                                                NaN      \n",
       "8362                                                NaN      \n",
       "8363                                                NaN      \n",
       "\n",
       "      lawatlas_correct_type_release_of_inmates  \\\n",
       "8354                                       NaN   \n",
       "8355                                       NaN   \n",
       "8356                                       NaN   \n",
       "8357                                       NaN   \n",
       "8358                                       NaN   \n",
       "8359                                       NaN   \n",
       "8360                                       NaN   \n",
       "8361                                       NaN   \n",
       "8362                                       NaN   \n",
       "8363                                       NaN   \n",
       "\n",
       "      lawatlas_correct_type_rules_regarding_inmate_release_suspended  \\\n",
       "8354                                                NaN                \n",
       "8355                                                NaN                \n",
       "8356                                                NaN                \n",
       "8357                                                NaN                \n",
       "8358                                                NaN                \n",
       "8359                                                NaN                \n",
       "8360                                                NaN                \n",
       "8361                                                NaN                \n",
       "8362                                                NaN                \n",
       "8363                                                NaN                \n",
       "\n",
       "      lawatlas_correct_type_release_notice_suspended  \\\n",
       "8354                                             NaN   \n",
       "8355                                             NaN   \n",
       "8356                                             NaN   \n",
       "8357                                             NaN   \n",
       "8358                                             NaN   \n",
       "8359                                             NaN   \n",
       "8360                                             NaN   \n",
       "8361                                             NaN   \n",
       "8362                                             NaN   \n",
       "8363                                             NaN   \n",
       "\n",
       "      lawatlas_correct_type_cease_in_person_parole_hearings  \\\n",
       "8354                                                NaN       \n",
       "8355                                                NaN       \n",
       "8356                                                NaN       \n",
       "8357                                                NaN       \n",
       "8358                                                NaN       \n",
       "8359                                                NaN       \n",
       "8360                                                NaN       \n",
       "8361                                                NaN       \n",
       "8362                                                NaN       \n",
       "8363                                                NaN       \n",
       "\n",
       "      lawatlas_correct_type_develop_process_for_virtual_parole_hearings  \\\n",
       "8354                                                NaN                   \n",
       "8355                                                NaN                   \n",
       "8356                                                NaN                   \n",
       "8357                                                NaN                   \n",
       "8358                                                NaN                   \n",
       "8359                                                NaN                   \n",
       "8360                                                NaN                   \n",
       "8361                                                NaN                   \n",
       "8362                                                NaN                   \n",
       "8363                                                NaN                   \n",
       "\n",
       "      lawatlas_correct_type_visitation_suspended  lawatlas_correct_statewide  \\\n",
       "8354                                         NaN                         NaN   \n",
       "8355                                         NaN                         NaN   \n",
       "8356                                         NaN                         NaN   \n",
       "8357                                         NaN                         NaN   \n",
       "8358                                         NaN                         NaN   \n",
       "8359                                         NaN                         NaN   \n",
       "8360                                         NaN                         NaN   \n",
       "8361                                         NaN                         NaN   \n",
       "8362                                         NaN                         NaN   \n",
       "8363                                         NaN                         NaN   \n",
       "\n",
       "      lawatlas_state_preempt  \\\n",
       "8354                     1.0   \n",
       "8355                     1.0   \n",
       "8356                     1.0   \n",
       "8357                     1.0   \n",
       "8358                     1.0   \n",
       "8359                     1.0   \n",
       "8360                     1.0   \n",
       "8361                     1.0   \n",
       "8362                     1.0   \n",
       "8363                     1.0   \n",
       "\n",
       "      lawatlas_action_preempt_imposing_additional_social_distancing_limitations_on_essential_business  \\\n",
       "8354                                                0.0                                                 \n",
       "8355                                                0.0                                                 \n",
       "8356                                                0.0                                                 \n",
       "8357                                                0.0                                                 \n",
       "8358                                                0.0                                                 \n",
       "8359                                                0.0                                                 \n",
       "8360                                                0.0                                                 \n",
       "8361                                                0.0                                                 \n",
       "8362                                                0.0                                                 \n",
       "8363                                                0.0                                                 \n",
       "\n",
       "      lawatlas_action_preempt_imposing_additional_restrictions_on_public_spaces  \\\n",
       "8354                                                0.0                           \n",
       "8355                                                0.0                           \n",
       "8356                                                0.0                           \n",
       "8357                                                0.0                           \n",
       "8358                                                0.0                           \n",
       "8359                                                0.0                           \n",
       "8360                                                0.0                           \n",
       "8361                                                0.0                           \n",
       "8362                                                0.0                           \n",
       "8363                                                0.0                           \n",
       "\n",
       "      lawatlas_action_preempt_restricting_scope_of_services_of_an_essential_business  \\\n",
       "8354                                                0.0                                \n",
       "8355                                                0.0                                \n",
       "8356                                                0.0                                \n",
       "8357                                                0.0                                \n",
       "8358                                                0.0                                \n",
       "8359                                                0.0                                \n",
       "8360                                                0.0                                \n",
       "8361                                                0.0                                \n",
       "8362                                                0.0                                \n",
       "8363                                                0.0                                \n",
       "\n",
       "      lawatlas_action_preempt_expanding_the_definition_of_non-essential_business  \\\n",
       "8354                                                0.0                            \n",
       "8355                                                0.0                            \n",
       "8356                                                0.0                            \n",
       "8357                                                0.0                            \n",
       "8358                                                0.0                            \n",
       "8359                                                0.0                            \n",
       "8360                                                0.0                            \n",
       "8361                                                0.0                            \n",
       "8362                                                0.0                            \n",
       "8363                                                0.0                            \n",
       "\n",
       "      lawatlas_action_preempt_restricting_the_hours_of_operation_of_an_essential_business  \\\n",
       "8354                                                0.0                                     \n",
       "8355                                                0.0                                     \n",
       "8356                                                0.0                                     \n",
       "8357                                                0.0                                     \n",
       "8358                                                0.0                                     \n",
       "8359                                                0.0                                     \n",
       "8360                                                0.0                                     \n",
       "8361                                                0.0                                     \n",
       "8362                                                0.0                                     \n",
       "8363                                                0.0                                     \n",
       "\n",
       "      lawatlas_action_preempt_imposing_restrictions_that_conflict_with_state_order  \\\n",
       "8354                                                1.0                              \n",
       "8355                                                1.0                              \n",
       "8356                                                1.0                              \n",
       "8357                                                1.0                              \n",
       "8358                                                1.0                              \n",
       "8359                                                1.0                              \n",
       "8360                                                1.0                              \n",
       "8361                                                1.0                              \n",
       "8362                                                1.0                              \n",
       "8363                                                1.0                              \n",
       "\n",
       "      lawatlas_action_preempt_restricting_the_performance_of_an_essential_function  \\\n",
       "8354                                                0.0                              \n",
       "8355                                                0.0                              \n",
       "8356                                                0.0                              \n",
       "8357                                                0.0                              \n",
       "8358                                                0.0                              \n",
       "8359                                                0.0                              \n",
       "8360                                                0.0                              \n",
       "8361                                                0.0                              \n",
       "8362                                                0.0                              \n",
       "8363                                                0.0                              \n",
       "\n",
       "      lawatlas_action_preempt_restricting_people_from_leaving_home  \\\n",
       "8354                                                0.0              \n",
       "8355                                                0.0              \n",
       "8356                                                0.0              \n",
       "8357                                                0.0              \n",
       "8358                                                0.0              \n",
       "8359                                                0.0              \n",
       "8360                                                0.0              \n",
       "8361                                                0.0              \n",
       "8362                                                0.0              \n",
       "8363                                                0.0              \n",
       "\n",
       "      lawatlas_action_preempt_restricting_the_operations_of_schools  \\\n",
       "8354                                                0.0               \n",
       "8355                                                0.0               \n",
       "8356                                                0.0               \n",
       "8357                                                0.0               \n",
       "8358                                                0.0               \n",
       "8359                                                0.0               \n",
       "8360                                                0.0               \n",
       "8361                                                0.0               \n",
       "8362                                                0.0               \n",
       "8363                                                0.0               \n",
       "\n",
       "      lawatlas_action_preempt_imposing_gathering_bans  lawatlas_local_allow  \n",
       "8354                                              0.0                   0.0  \n",
       "8355                                              0.0                   0.0  \n",
       "8356                                              0.0                   0.0  \n",
       "8357                                              0.0                   0.0  \n",
       "8358                                              0.0                   0.0  \n",
       "8359                                              0.0                   0.0  \n",
       "8360                                              0.0                   0.0  \n",
       "8361                                              0.0                   0.0  \n",
       "8362                                              0.0                   0.0  \n",
       "8363                                              0.0                   0.0  "
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "df_emergDeclare.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "id": "46d7ce3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "la = 'lawatlas_' # this appears in every column name\n",
    "df_emergD2 = df_emergDeclare[['date', 'location_key', la+'mitigation_policy', la+'emerg_statewide', \n",
    "                              la+'requirement_type_traveler_must_self_quarantine',\n",
    "                             la+'home_statewide', la+'mask_statewide',\n",
    "                             la+'schools_statewide', la+'gathering_statewide']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8729b4b",
   "metadata": {},
   "source": [
    "### Next, we see that the values in all the columns except for the data and location_key columns are either integers 0 or 1. We need to change it from float to int type. For that, first we will replace all the NaN values with 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "795a1dbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8364 entries, 0 to 8363\n",
      "Data columns (total 9 columns):\n",
      " #   Column                                                   Non-Null Count  Dtype  \n",
      "---  ------                                                   --------------  -----  \n",
      " 0   date                                                     8364 non-null   object \n",
      " 1   location_key                                             8364 non-null   object \n",
      " 2   lawatlas_mitigation_policy                               8364 non-null   int64  \n",
      " 3   lawatlas_emerg_statewide                                 5794 non-null   float64\n",
      " 4   lawatlas_requirement_type_traveler_must_self_quarantine  0 non-null      float64\n",
      " 5   lawatlas_home_statewide                                  2157 non-null   float64\n",
      " 6   lawatlas_mask_statewide                                  2325 non-null   float64\n",
      " 7   lawatlas_schools_statewide                               4602 non-null   float64\n",
      " 8   lawatlas_gathering_statewide                             4391 non-null   float64\n",
      "dtypes: float64(6), int64(1), object(2)\n",
      "memory usage: 588.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_emergD2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f40f6df",
   "metadata": {},
   "source": [
    "### Seeing that all the entries in '...traveler_must_self_quarantine' column are NaN, we can drop that column entirely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "9464f126",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/4259409994.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_emergD2.drop(columns=[la+'requirement_type_traveler_must_self_quarantine'], inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df_emergD2.drop(columns=[la+'requirement_type_traveler_must_self_quarantine'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "bb28b1f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_emergD2.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fae9796",
   "metadata": {},
   "source": [
    "### Looks like we don't need to create location_key_2 column since entries in location_key are already at the state-level granularity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "adb1d688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'US_AK',\n",
       " 'US_AL',\n",
       " 'US_AR',\n",
       " 'US_AZ',\n",
       " 'US_CA',\n",
       " 'US_CO',\n",
       " 'US_CT',\n",
       " 'US_DC',\n",
       " 'US_DE',\n",
       " 'US_FL',\n",
       " 'US_GA',\n",
       " 'US_HI',\n",
       " 'US_IA',\n",
       " 'US_ID',\n",
       " 'US_IL',\n",
       " 'US_IN',\n",
       " 'US_KS',\n",
       " 'US_KY',\n",
       " 'US_LA',\n",
       " 'US_MA',\n",
       " 'US_MD',\n",
       " 'US_ME',\n",
       " 'US_MI',\n",
       " 'US_MN',\n",
       " 'US_MO',\n",
       " 'US_MS',\n",
       " 'US_MT',\n",
       " 'US_NC',\n",
       " 'US_ND',\n",
       " 'US_NE',\n",
       " 'US_NH',\n",
       " 'US_NJ',\n",
       " 'US_NM',\n",
       " 'US_NV',\n",
       " 'US_NY',\n",
       " 'US_OH',\n",
       " 'US_OK',\n",
       " 'US_OR',\n",
       " 'US_PA',\n",
       " 'US_RI',\n",
       " 'US_SC',\n",
       " 'US_SD',\n",
       " 'US_TN',\n",
       " 'US_TX',\n",
       " 'US_UT',\n",
       " 'US_VA',\n",
       " 'US_VT',\n",
       " 'US_WA',\n",
       " 'US_WI',\n",
       " 'US_WV',\n",
       " 'US_WY'}"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df_emergD2['location_key'].tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6ae3e3d",
   "metadata": {},
   "source": [
    "### Now, let's fill the NaN values using ffill first. Then we will fill the rest of the NaN values with 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "5b74c76c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8364 entries, 0 to 8363\n",
      "Data columns (total 8 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   date                          8364 non-null   object \n",
      " 1   location_key                  8364 non-null   object \n",
      " 2   lawatlas_mitigation_policy    8364 non-null   int64  \n",
      " 3   lawatlas_emerg_statewide      5794 non-null   float64\n",
      " 4   lawatlas_home_statewide       3766 non-null   float64\n",
      " 5   lawatlas_mask_statewide       2383 non-null   float64\n",
      " 6   lawatlas_schools_statewide    4952 non-null   float64\n",
      " 7   lawatlas_gathering_statewide  5008 non-null   float64\n",
      "dtypes: float64(5), int64(1), object(2)\n",
      "memory usage: 588.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_emergD2 = df_emergD2.groupby(['location_key']).apply(lambda x: x.ffill())\n",
    "df_emergD2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "76ccbbcd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8364 entries, 0 to 8363\n",
      "Data columns (total 8 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   date                          8364 non-null   object \n",
      " 1   location_key                  8364 non-null   object \n",
      " 2   lawatlas_mitigation_policy    8364 non-null   int64  \n",
      " 3   lawatlas_emerg_statewide      8364 non-null   float64\n",
      " 4   lawatlas_home_statewide       8364 non-null   float64\n",
      " 5   lawatlas_mask_statewide       8364 non-null   float64\n",
      " 6   lawatlas_schools_statewide    8364 non-null   float64\n",
      " 7   lawatlas_gathering_statewide  8364 non-null   float64\n",
      "dtypes: float64(5), int64(1), object(2)\n",
      "memory usage: 588.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_emergD2 = df_emergD2.fillna(0)\n",
    "df_emergD2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e77ab8cb",
   "metadata": {},
   "source": [
    "### Looks like all the rows in df_emergD2 are at the state-level granularity and there's none at the country-level granularity. Therefore, we DON'T need to split df_emergD2 into df_emergD2_US and df_emergD2_states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "1406d16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 8)"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_emergD2.loc[df_emergD2['location_key'] == 'US'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "2c404f09",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixDataTypesOfColumns(df_emergD2, df_emergD2.columns.tolist()[3:], 'float64', 'int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "id": "87063395",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_emergD2['date_month'] = df_emergD2['date'].apply(lambda x: '-'.join(x.split('-')[:2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f535d923",
   "metadata": {},
   "source": [
    "### Moving onto df_hospitalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "7c590fbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hospitalized = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/hospitalizations.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "9e36f678",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1768485 entries, 0 to 1768484\n",
      "Data columns (total 11 columns):\n",
      " #   Column                              Dtype  \n",
      "---  ------                              -----  \n",
      " 0   date                                object \n",
      " 1   location_key                        object \n",
      " 2   new_hospitalized_patients           float64\n",
      " 3   cumulative_hospitalized_patients    float64\n",
      " 4   current_hospitalized_patients       float64\n",
      " 5   new_intensive_care_patients         float64\n",
      " 6   cumulative_intensive_care_patients  float64\n",
      " 7   current_intensive_care_patients     float64\n",
      " 8   new_ventilator_patients             float64\n",
      " 9   cumulative_ventilator_patients      float64\n",
      " 10  current_ventilator_patients         float64\n",
      "dtypes: float64(9), object(2)\n",
      "memory usage: 148.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_hospitalized.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "id": "f1815ca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hospitalized = df_hospitalized[df_hospitalized['location_key'].isin(location_key_US)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "9b50d98b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 58141 entries, 1710344 to 1768484\n",
      "Data columns (total 11 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   date                                58141 non-null  object \n",
      " 1   location_key                        58141 non-null  object \n",
      " 2   new_hospitalized_patients           57238 non-null  float64\n",
      " 3   cumulative_hospitalized_patients    53093 non-null  float64\n",
      " 4   current_hospitalized_patients       49380 non-null  float64\n",
      " 5   new_intensive_care_patients         4521 non-null   float64\n",
      " 6   cumulative_intensive_care_patients  4209 non-null   float64\n",
      " 7   current_intensive_care_patients     47186 non-null  float64\n",
      " 8   new_ventilator_patients             1705 non-null   float64\n",
      " 9   cumulative_ventilator_patients      1710 non-null   float64\n",
      " 10  current_ventilator_patients         9546 non-null   float64\n",
      "dtypes: float64(9), object(2)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_hospitalized.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "id": "f56f1972",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hospitalized['location_key_2'] = df_hospitalized['location_key'].apply(lambda x: '_'.join(x.split('_')[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "id": "defe6296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51676, 11)"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hospitalized = df_hospitalized.loc[df_hospitalized['location_key'] == df_hospitalized['location_key_2']]\n",
    "df_hospitalized.drop(columns=['location_key_2'], inplace=True)\n",
    "df_hospitalized.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e92e9e",
   "metadata": {},
   "source": [
    "### Now, we need to do fillna(0) for the 'new_...' columns and groupby + fillna(ffill) for the 'cumulative_...' columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "d88a6bca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 51676 entries, 1710344 to 1768484\n",
      "Data columns (total 11 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   date                                51676 non-null  object \n",
      " 1   location_key                        51676 non-null  object \n",
      " 2   new_hospitalized_patients           51676 non-null  float64\n",
      " 3   cumulative_hospitalized_patients    47531 non-null  float64\n",
      " 4   current_hospitalized_patients       48477 non-null  float64\n",
      " 5   new_intensive_care_patients         4521 non-null   float64\n",
      " 6   cumulative_intensive_care_patients  4209 non-null   float64\n",
      " 7   current_intensive_care_patients     46283 non-null  float64\n",
      " 8   new_ventilator_patients             1705 non-null   float64\n",
      " 9   cumulative_ventilator_patients      1710 non-null   float64\n",
      " 10  current_ventilator_patients         9546 non-null   float64\n",
      "dtypes: float64(9), object(2)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_hospitalized.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "id": "81520d22",
   "metadata": {},
   "outputs": [],
   "source": [
    "colsForFillZero = ['new_hospitalized_patients', 'new_intensive_care_patients', 'new_ventilator_patients']\n",
    "colsForGroupbyFfill = ['cumulative_hospitalized_patients', 'cumulative_intensive_care_patients', \n",
    "                      'cumulative_ventilator_patients', 'current_hospitalized_patients', \n",
    "                       'current_intensive_care_patients', 'current_ventilator_patients']\n",
    "df_hospitalized[colsForFillZero] = df_hospitalized[colsForFillZero].fillna(0)\n",
    "df_hospitalized[colsForGroupbyFfill] = df_hospitalized.groupby(['location_key'])[colsForGroupbyFfill].apply(lambda x: x.ffill())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "id": "d26560cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hospitalized = df_hospitalized.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "1a843809",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hospitalized['date_month'] = df_hospitalized['date'].apply(lambda x: '-'.join(x.split('-')[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "id": "bbfd0af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hosp_US = df_hospitalized.loc[df_hospitalized['location_key'] == 'US']\n",
    "df_hosp_states = df_hospitalized.loc[df_hospitalized['location_key'] != 'US']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "d9085d0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50699 entries, 1711321 to 1768484\n",
      "Data columns (total 12 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   date                                50699 non-null  object \n",
      " 1   location_key                        50699 non-null  object \n",
      " 2   new_hospitalized_patients           50699 non-null  float64\n",
      " 3   cumulative_hospitalized_patients    50699 non-null  float64\n",
      " 4   current_hospitalized_patients       50699 non-null  float64\n",
      " 5   new_intensive_care_patients         50699 non-null  float64\n",
      " 6   cumulative_intensive_care_patients  50699 non-null  float64\n",
      " 7   current_intensive_care_patients     50699 non-null  float64\n",
      " 8   new_ventilator_patients             50699 non-null  float64\n",
      " 9   cumulative_ventilator_patients      50699 non-null  float64\n",
      " 10  current_ventilator_patients         50699 non-null  float64\n",
      " 11  date_month                          50699 non-null  object \n",
      "dtypes: float64(9), object(3)\n",
      "memory usage: 5.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_hosp_states.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "24691f87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/4103920634.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[col] = df[col].astype(typeTo)\n"
     ]
    }
   ],
   "source": [
    "df_hosp_US.reset_index(drop=True, inplace=True)\n",
    "df_hosp_states.reset_index(drop=True, inplace=True)\n",
    "fixDataTypesOfColumns(df_hosp_US, df_hosp_US.columns.tolist()[2:11], 'float64', 'int64')\n",
    "fixDataTypesOfColumns(df_hosp_states, df_hosp_states.columns.tolist()[2:11], 'float64', 'int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec842353",
   "metadata": {},
   "source": [
    "### Next is df_mobility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "92e9f13a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mobility = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/mobility.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "415b5475",
   "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>date</th>\n",
       "      <th>location_key</th>\n",
       "      <th>mobility_retail_and_recreation</th>\n",
       "      <th>mobility_grocery_and_pharmacy</th>\n",
       "      <th>mobility_parks</th>\n",
       "      <th>mobility_transit_stations</th>\n",
       "      <th>mobility_workplaces</th>\n",
       "      <th>mobility_residential</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>AE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>AE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>AE</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>AE</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>AE</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date location_key  mobility_retail_and_recreation  \\\n",
       "0  2020-02-15           AE                             0.0   \n",
       "1  2020-02-16           AE                             1.0   \n",
       "2  2020-02-17           AE                            -1.0   \n",
       "3  2020-02-18           AE                            -2.0   \n",
       "4  2020-02-19           AE                            -2.0   \n",
       "\n",
       "   mobility_grocery_and_pharmacy  mobility_parks  mobility_transit_stations  \\\n",
       "0                            4.0             5.0                        0.0   \n",
       "1                            4.0             4.0                        1.0   \n",
       "2                            1.0             5.0                        1.0   \n",
       "3                            1.0             5.0                        0.0   \n",
       "4                            0.0             4.0                       -1.0   \n",
       "\n",
       "   mobility_workplaces  mobility_residential  \n",
       "0                  2.0                   1.0  \n",
       "1                  2.0                   1.0  \n",
       "2                  2.0                   1.0  \n",
       "3                  2.0                   1.0  \n",
       "4                  2.0                   1.0  "
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mobility.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "id": "62db70a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mobility = df_mobility[df_mobility['location_key'].isin(location_key_US)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "id": "dd138770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                    0\n",
       "location_key                            0\n",
       "mobility_retail_and_recreation     860673\n",
       "mobility_grocery_and_pharmacy     1024790\n",
       "mobility_parks                    1846480\n",
       "mobility_transit_stations         1522028\n",
       "mobility_workplaces                 44522\n",
       "mobility_residential              1009563\n",
       "dtype: int64"
      ]
     },
     "execution_count": 523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mobility.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "a080e033",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mobility['location_key_2'] = df_mobility['location_key'].apply(lambda x: '_'.join(x.split('_')[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "0fad2030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2445945, 9)"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mobility.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "id": "0ef4aa06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48932, 8)"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mobility = df_mobility.loc[df_mobility['location_key'] == df_mobility['location_key_2']]\n",
    "df_mobility.drop(columns=['location_key_2'], inplace=True)\n",
    "df_mobility.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "add9d124",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48932 entries, 3845350 to 6274306\n",
      "Data columns (total 8 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   date                            48932 non-null  object \n",
      " 1   location_key                    48932 non-null  object \n",
      " 2   mobility_retail_and_recreation  48932 non-null  float64\n",
      " 3   mobility_grocery_and_pharmacy   48932 non-null  float64\n",
      " 4   mobility_parks                  48392 non-null  float64\n",
      " 5   mobility_transit_stations       48797 non-null  float64\n",
      " 6   mobility_workplaces             48932 non-null  float64\n",
      " 7   mobility_residential            48932 non-null  float64\n",
      "dtypes: float64(6), object(2)\n",
      "memory usage: 3.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_mobility.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "id": "b119c2a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mobility = df_mobility.groupby(['location_key']).apply(lambda x: x.ffill())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "b598cd0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mob_US = df_mobility.loc[df_mobility['location_key'] == 'US']\n",
    "df_mob_states = df_mobility.loc[df_mobility['location_key'] != 'US']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add3d5cf",
   "metadata": {},
   "source": [
    "### Next we look at df_vaccinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "38ed532e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vaccinations = pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/vaccinations.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "id": "753d561e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vaccinations = df_vaccinations[df_vaccinations['location_key'].isin(location_key_US)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "6710f758",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vaccinations['location_key_2'] = df_vaccinations['location_key'].apply(lambda x: '_'.join(x.split('_')[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "id": "5ccb36e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 34122 entries, 916955 to 2531126\n",
      "Data columns (total 33 columns):\n",
      " #   Column                                         Non-Null Count  Dtype  \n",
      "---  ------                                         --------------  -----  \n",
      " 0   date                                           34122 non-null  object \n",
      " 1   location_key                                   34122 non-null  object \n",
      " 2   new_persons_vaccinated                         33968 non-null  float64\n",
      " 3   cumulative_persons_vaccinated                  34025 non-null  float64\n",
      " 4   new_persons_fully_vaccinated                   33947 non-null  float64\n",
      " 5   cumulative_persons_fully_vaccinated            33955 non-null  float64\n",
      " 6   new_vaccine_doses_administered                 34065 non-null  float64\n",
      " 7   cumulative_vaccine_doses_administered          34122 non-null  float64\n",
      " 8   new_persons_vaccinated_pfizer                  0 non-null      float64\n",
      " 9   cumulative_persons_vaccinated_pfizer           0 non-null      float64\n",
      " 10  new_persons_fully_vaccinated_pfizer            30894 non-null  float64\n",
      " 11  cumulative_persons_fully_vaccinated_pfizer     30951 non-null  float64\n",
      " 12  new_vaccine_doses_administered_pfizer          30894 non-null  float64\n",
      " 13  cumulative_vaccine_doses_administered_pfizer   30951 non-null  float64\n",
      " 14  new_persons_vaccinated_moderna                 0 non-null      float64\n",
      " 15  cumulative_persons_vaccinated_moderna          0 non-null      float64\n",
      " 16  new_persons_fully_vaccinated_moderna           30894 non-null  float64\n",
      " 17  cumulative_persons_fully_vaccinated_moderna    30951 non-null  float64\n",
      " 18  new_vaccine_doses_administered_moderna         30894 non-null  float64\n",
      " 19  cumulative_vaccine_doses_administered_moderna  30951 non-null  float64\n",
      " 20  new_persons_vaccinated_janssen                 0 non-null      float64\n",
      " 21  cumulative_persons_vaccinated_janssen          0 non-null      float64\n",
      " 22  new_persons_fully_vaccinated_janssen           30894 non-null  float64\n",
      " 23  cumulative_persons_fully_vaccinated_janssen    30951 non-null  float64\n",
      " 24  new_vaccine_doses_administered_janssen         30894 non-null  float64\n",
      " 25  cumulative_vaccine_doses_administered_janssen  30951 non-null  float64\n",
      " 26  new_persons_vaccinated_sinovac                 0 non-null      float64\n",
      " 27  total_persons_vaccinated_sinovac               0 non-null      float64\n",
      " 28  new_persons_fully_vaccinated_sinovac           0 non-null      float64\n",
      " 29  total_persons_fully_vaccinated_sinovac         0 non-null      float64\n",
      " 30  new_vaccine_doses_administered_sinovac         0 non-null      float64\n",
      " 31  total_vaccine_doses_administered_sinovac       0 non-null      float64\n",
      " 32  location_key_2                                 34122 non-null  object \n",
      "dtypes: float64(30), object(3)\n",
      "memory usage: 8.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_vaccinations = df_vaccinations.loc[df_vaccinations['location_key'] == df_vaccinations['location_key_2']]\n",
    "df_vaccinations.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c0e827",
   "metadata": {},
   "source": [
    "### Next, we see that some of the columns are specific to the a brand of vaccine. For our purporses, we won't differentiate between pfizer, moderna and janssen so we will not keep those columns that have the name of a covid vaccine brand in their names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "id": "8706efd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vacc = df_vaccinations[['date', 'location_key', \n",
    "                          'new_persons_vaccinated',\n",
    "                          'cumulative_persons_vaccinated',\n",
    "                          'new_persons_fully_vaccinated',\n",
    "                          'cumulative_persons_fully_vaccinated',\n",
    "                          'new_vaccine_doses_administered',\n",
    "                          'cumulative_vaccine_doses_administered']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "id": "3c086a4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/13640423.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vacc['date_month'] = df_vacc['date'].apply(lambda x: '-'.join(x.split('-')[:2]))\n"
     ]
    },
    {
     "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>date</th>\n",
       "      <th>location_key</th>\n",
       "      <th>new_persons_vaccinated</th>\n",
       "      <th>cumulative_persons_vaccinated</th>\n",
       "      <th>new_persons_fully_vaccinated</th>\n",
       "      <th>cumulative_persons_fully_vaccinated</th>\n",
       "      <th>new_vaccine_doses_administered</th>\n",
       "      <th>cumulative_vaccine_doses_administered</th>\n",
       "      <th>date_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>916955</th>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>US</td>\n",
       "      <td>30817.0</td>\n",
       "      <td>30817.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7882.0</td>\n",
       "      <td>37821.0</td>\n",
       "      <td>37821.0</td>\n",
       "      <td>2020-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916956</th>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>US</td>\n",
       "      <td>4538.0</td>\n",
       "      <td>35355.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>8025.0</td>\n",
       "      <td>4709.0</td>\n",
       "      <td>42530.0</td>\n",
       "      <td>2020-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916957</th>\n",
       "      <td>2020-12-15</td>\n",
       "      <td>US</td>\n",
       "      <td>45876.0</td>\n",
       "      <td>81231.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>8315.0</td>\n",
       "      <td>47301.0</td>\n",
       "      <td>89831.0</td>\n",
       "      <td>2020-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916958</th>\n",
       "      <td>2020-12-16</td>\n",
       "      <td>US</td>\n",
       "      <td>154818.0</td>\n",
       "      <td>236049.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>8803.0</td>\n",
       "      <td>159341.0</td>\n",
       "      <td>249172.0</td>\n",
       "      <td>2020-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916959</th>\n",
       "      <td>2020-12-17</td>\n",
       "      <td>US</td>\n",
       "      <td>266883.0</td>\n",
       "      <td>502932.0</td>\n",
       "      <td>789.0</td>\n",
       "      <td>9592.0</td>\n",
       "      <td>273552.0</td>\n",
       "      <td>522724.0</td>\n",
       "      <td>2020-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date location_key  new_persons_vaccinated  \\\n",
       "916955  2020-12-13           US                 30817.0   \n",
       "916956  2020-12-14           US                  4538.0   \n",
       "916957  2020-12-15           US                 45876.0   \n",
       "916958  2020-12-16           US                154818.0   \n",
       "916959  2020-12-17           US                266883.0   \n",
       "\n",
       "        cumulative_persons_vaccinated  new_persons_fully_vaccinated  \\\n",
       "916955                        30817.0                           NaN   \n",
       "916956                        35355.0                         143.0   \n",
       "916957                        81231.0                         290.0   \n",
       "916958                       236049.0                         488.0   \n",
       "916959                       502932.0                         789.0   \n",
       "\n",
       "        cumulative_persons_fully_vaccinated  new_vaccine_doses_administered  \\\n",
       "916955                               7882.0                         37821.0   \n",
       "916956                               8025.0                          4709.0   \n",
       "916957                               8315.0                         47301.0   \n",
       "916958                               8803.0                        159341.0   \n",
       "916959                               9592.0                        273552.0   \n",
       "\n",
       "        cumulative_vaccine_doses_administered date_month  \n",
       "916955                                37821.0    2020-12  \n",
       "916956                                42530.0    2020-12  \n",
       "916957                                89831.0    2020-12  \n",
       "916958                               249172.0    2020-12  \n",
       "916959                               522724.0    2020-12  "
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vacc['date_month'] = df_vacc['date'].apply(lambda x: '-'.join(x.split('-')[:2]))\n",
    "df_vacc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "849ee27f",
   "metadata": {},
   "source": [
    "### Now, we will perform fillna(0) on the 'new_...' columns and groupby + ffill() on the 'cumulative_...' columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "id": "39739eba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/733079642.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vacc[colsForFillZero] = df_vacc[colsForFillZero].fillna(0)\n",
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/733079642.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_vacc[colsForGroupbyFfill] = df_vacc.groupby(['location_key'])[colsForGroupbyFfill].apply(lambda x: x.ffill())\n"
     ]
    }
   ],
   "source": [
    "colsForFillZero = ['new_persons_vaccinated', 'new_persons_fully_vaccinated', 'new_vaccine_doses_administered']\n",
    "colsForGroupbyFfill = ['cumulative_persons_vaccinated', 'cumulative_persons_fully_vaccinated',\n",
    "                      'cumulative_vaccine_doses_administered']\n",
    "df_vacc[colsForFillZero] = df_vacc[colsForFillZero].fillna(0)\n",
    "df_vacc[colsForGroupbyFfill] = df_vacc.groupby(['location_key'])[colsForGroupbyFfill].apply(lambda x: x.ffill())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "id": "abfc1f6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datatypes not matching!\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>location_key</th>\n",
       "      <th>new_persons_vaccinated</th>\n",
       "      <th>cumulative_persons_vaccinated</th>\n",
       "      <th>new_persons_fully_vaccinated</th>\n",
       "      <th>cumulative_persons_fully_vaccinated</th>\n",
       "      <th>new_vaccine_doses_administered</th>\n",
       "      <th>cumulative_vaccine_doses_administered</th>\n",
       "      <th>date_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>916955</th>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>US</td>\n",
       "      <td>30817</td>\n",
       "      <td>30817</td>\n",
       "      <td>0</td>\n",
       "      <td>7882</td>\n",
       "      <td>37821</td>\n",
       "      <td>37821</td>\n",
       "      <td>2020-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916956</th>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>US</td>\n",
       "      <td>4538</td>\n",
       "      <td>35355</td>\n",
       "      <td>143</td>\n",
       "      <td>8025</td>\n",
       "      <td>4709</td>\n",
       "      <td>42530</td>\n",
       "      <td>2020-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916957</th>\n",
       "      <td>2020-12-15</td>\n",
       "      <td>US</td>\n",
       "      <td>45876</td>\n",
       "      <td>81231</td>\n",
       "      <td>290</td>\n",
       "      <td>8315</td>\n",
       "      <td>47301</td>\n",
       "      <td>89831</td>\n",
       "      <td>2020-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916958</th>\n",
       "      <td>2020-12-16</td>\n",
       "      <td>US</td>\n",
       "      <td>154818</td>\n",
       "      <td>236049</td>\n",
       "      <td>488</td>\n",
       "      <td>8803</td>\n",
       "      <td>159341</td>\n",
       "      <td>249172</td>\n",
       "      <td>2020-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916959</th>\n",
       "      <td>2020-12-17</td>\n",
       "      <td>US</td>\n",
       "      <td>266883</td>\n",
       "      <td>502932</td>\n",
       "      <td>789</td>\n",
       "      <td>9592</td>\n",
       "      <td>273552</td>\n",
       "      <td>522724</td>\n",
       "      <td>2020-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date location_key  new_persons_vaccinated  \\\n",
       "916955  2020-12-13           US                   30817   \n",
       "916956  2020-12-14           US                    4538   \n",
       "916957  2020-12-15           US                   45876   \n",
       "916958  2020-12-16           US                  154818   \n",
       "916959  2020-12-17           US                  266883   \n",
       "\n",
       "        cumulative_persons_vaccinated  new_persons_fully_vaccinated  \\\n",
       "916955                          30817                             0   \n",
       "916956                          35355                           143   \n",
       "916957                          81231                           290   \n",
       "916958                         236049                           488   \n",
       "916959                         502932                           789   \n",
       "\n",
       "        cumulative_persons_fully_vaccinated  new_vaccine_doses_administered  \\\n",
       "916955                                 7882                           37821   \n",
       "916956                                 8025                            4709   \n",
       "916957                                 8315                           47301   \n",
       "916958                                 8803                          159341   \n",
       "916959                                 9592                          273552   \n",
       "\n",
       "        cumulative_vaccine_doses_administered date_month  \n",
       "916955                                  37821    2020-12  \n",
       "916956                                  42530    2020-12  \n",
       "916957                                  89831    2020-12  \n",
       "916958                                 249172    2020-12  \n",
       "916959                                 522724    2020-12  "
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vacc = df_vacc.fillna(0)\n",
    "fixDataTypesOfColumns(df_vacc, df_vacc.columns.tolist()[2:8], 'float64', 'int64')\n",
    "df_vacc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "id": "07309f71",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vacc_US = df_vacc.loc[df_vacc['location_key'] == 'US']\n",
    "df_vacc_states = df_vacc.loc[df_vacc['location_key'] != 'US']\n",
    "df_vacc_US.reset_index(drop=True, inplace=True)\n",
    "df_vacc_states.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5875bbd3",
   "metadata": {},
   "source": [
    "### Next, we take a look at df_govResponse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "17d9d4cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_govResponse =pd.read_csv(\"https://storage.googleapis.com/covid19-open-data/v3/oxford-government-response.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "id": "94fc8414",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_govResponse = df_govResponse.loc[df_govResponse['location_key'].isin(location_key_US)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "id": "d25ac8c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48218 entries, 245543 to 293760\n",
      "Data columns (total 22 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   date                                48218 non-null  object \n",
      " 1   location_key                        48218 non-null  object \n",
      " 2   school_closing                      48217 non-null  float64\n",
      " 3   workplace_closing                   48218 non-null  float64\n",
      " 4   cancel_public_events                48218 non-null  float64\n",
      " 5   restrictions_on_gatherings          48218 non-null  float64\n",
      " 6   public_transport_closing            48210 non-null  float64\n",
      " 7   stay_at_home_requirements           48218 non-null  float64\n",
      " 8   restrictions_on_internal_movement   48212 non-null  float64\n",
      " 9   international_travel_controls       48203 non-null  float64\n",
      " 10  income_support                      48213 non-null  float64\n",
      " 11  debt_relief                         48168 non-null  float64\n",
      " 12  fiscal_measures                     12583 non-null  float64\n",
      " 13  international_support               12456 non-null  float64\n",
      " 14  public_information_campaigns        48215 non-null  float64\n",
      " 15  testing_policy                      48215 non-null  float64\n",
      " 16  contact_tracing                     48203 non-null  float64\n",
      " 17  emergency_investment_in_healthcare  12363 non-null  float64\n",
      " 18  investment_in_vaccines              14417 non-null  float64\n",
      " 19  facial_coverings                    48212 non-null  float64\n",
      " 20  vaccination_policy                  48208 non-null  float64\n",
      " 21  stringency_index                    48212 non-null  float64\n",
      "dtypes: float64(20), object(2)\n",
      "memory usage: 8.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_govResponse.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4290551b",
   "metadata": {},
   "source": [
    "### We will filter a few of the columns from df_govResponse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "id": "84116483",
   "metadata": {},
   "outputs": [
    {
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       "      <td>US</td>\n",
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      "text/plain": [
       "              date location_key  school_closing  workplace_closing  \\\n",
       "245543  2020-01-01           US             0.0                0.0   \n",
       "245544  2020-01-02           US             0.0                0.0   \n",
       "245545  2020-01-03           US             0.0                0.0   \n",
       "245546  2020-01-04           US             0.0                0.0   \n",
       "245547  2020-01-05           US             0.0                0.0   \n",
       "\n",
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       "245543                   0.0                         0.0   \n",
       "245544                   0.0                         0.0   \n",
       "245545                   0.0                         0.0   \n",
       "245546                   0.0                         0.0   \n",
       "245547                   0.0                         0.0   \n",
       "\n",
       "        public_transport_closing  stay_at_home_requirements  \\\n",
       "245543                       0.0                        0.0   \n",
       "245544                       0.0                        0.0   \n",
       "245545                       0.0                        0.0   \n",
       "245546                       0.0                        0.0   \n",
       "245547                       0.0                        0.0   \n",
       "\n",
       "        restrictions_on_internal_movement  international_travel_controls  \\\n",
       "245543                                0.0                            0.0   \n",
       "245544                                0.0                            0.0   \n",
       "245545                                0.0                            0.0   \n",
       "245546                                0.0                            0.0   \n",
       "245547                                0.0                            0.0   \n",
       "\n",
       "        income_support  public_information_campaigns  testing_policy  \\\n",
       "245543             0.0                           0.0             0.0   \n",
       "245544             0.0                           0.0             0.0   \n",
       "245545             0.0                           0.0             0.0   \n",
       "245546             0.0                           0.0             0.0   \n",
       "245547             0.0                           0.0             0.0   \n",
       "\n",
       "        contact_tracing  emergency_investment_in_healthcare  \\\n",
       "245543              0.0                                 0.0   \n",
       "245544              0.0                                 0.0   \n",
       "245545              0.0                                 0.0   \n",
       "245546              0.0                                 0.0   \n",
       "245547              0.0                                 0.0   \n",
       "\n",
       "        investment_in_vaccines  facial_coverings  vaccination_policy  \\\n",
       "245543                     0.0               0.0                 0.0   \n",
       "245544                     0.0               0.0                 0.0   \n",
       "245545                     0.0               0.0                 0.0   \n",
       "245546                     0.0               0.0                 0.0   \n",
       "245547                     0.0               0.0                 0.0   \n",
       "\n",
       "        stringency_index  \n",
       "245543               0.0  \n",
       "245544               0.0  \n",
       "245545               0.0  \n",
       "245546               0.0  \n",
       "245547               0.0  "
      ]
     },
     "execution_count": 570,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colsToLeaveOut = ['debt_relief', 'fiscal_measures', 'international_support']\n",
    "df_govResp = df_govResponse.drop(columns=colsToLeaveOut)\n",
    "df_govResp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "id": "b7b9db91",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_govResp = df_govResp.groupby(['location_key']).apply(lambda x: x.ffill())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "id": "545249e3",
   "metadata": {},
   "outputs": [
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       "      <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>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245546</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>US</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>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245547</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>US</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>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date location_key  school_closing  workplace_closing  \\\n",
       "245543  2020-01-01           US             0.0                0.0   \n",
       "245544  2020-01-02           US             0.0                0.0   \n",
       "245545  2020-01-03           US             0.0                0.0   \n",
       "245546  2020-01-04           US             0.0                0.0   \n",
       "245547  2020-01-05           US             0.0                0.0   \n",
       "\n",
       "        cancel_public_events  restrictions_on_gatherings  \\\n",
       "245543                   0.0                         0.0   \n",
       "245544                   0.0                         0.0   \n",
       "245545                   0.0                         0.0   \n",
       "245546                   0.0                         0.0   \n",
       "245547                   0.0                         0.0   \n",
       "\n",
       "        public_transport_closing  stay_at_home_requirements  \\\n",
       "245543                       0.0                        0.0   \n",
       "245544                       0.0                        0.0   \n",
       "245545                       0.0                        0.0   \n",
       "245546                       0.0                        0.0   \n",
       "245547                       0.0                        0.0   \n",
       "\n",
       "        restrictions_on_internal_movement  international_travel_controls  \\\n",
       "245543                                0.0                            0.0   \n",
       "245544                                0.0                            0.0   \n",
       "245545                                0.0                            0.0   \n",
       "245546                                0.0                            0.0   \n",
       "245547                                0.0                            0.0   \n",
       "\n",
       "        income_support  public_information_campaigns  testing_policy  \\\n",
       "245543             0.0                           0.0             0.0   \n",
       "245544             0.0                           0.0             0.0   \n",
       "245545             0.0                           0.0             0.0   \n",
       "245546             0.0                           0.0             0.0   \n",
       "245547             0.0                           0.0             0.0   \n",
       "\n",
       "        contact_tracing  emergency_investment_in_healthcare  \\\n",
       "245543              0.0                                 0.0   \n",
       "245544              0.0                                 0.0   \n",
       "245545              0.0                                 0.0   \n",
       "245546              0.0                                 0.0   \n",
       "245547              0.0                                 0.0   \n",
       "\n",
       "        investment_in_vaccines  facial_coverings  vaccination_policy  \\\n",
       "245543                     0.0               0.0                 0.0   \n",
       "245544                     0.0               0.0                 0.0   \n",
       "245545                     0.0               0.0                 0.0   \n",
       "245546                     0.0               0.0                 0.0   \n",
       "245547                     0.0               0.0                 0.0   \n",
       "\n",
       "        stringency_index  \n",
       "245543               0.0  \n",
       "245544               0.0  \n",
       "245545               0.0  \n",
       "245546               0.0  \n",
       "245547               0.0  "
      ]
     },
     "execution_count": 575,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_govResp = df_govResp.fillna(0)\n",
    "df_govResp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "id": "669d3c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_govResp['date_month'] = df_govResp['date'].apply(lambda x: '-'.join(x.split('-')[:2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48646bd5",
   "metadata": {},
   "source": [
    "### Now, casting columns to correct data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "id": "8fcdaea7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48218 entries, 245543 to 293760\n",
      "Data columns (total 20 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   date                                48218 non-null  object \n",
      " 1   location_key                        48218 non-null  object \n",
      " 2   school_closing                      48218 non-null  float64\n",
      " 3   workplace_closing                   48218 non-null  float64\n",
      " 4   cancel_public_events                48218 non-null  float64\n",
      " 5   restrictions_on_gatherings          48218 non-null  float64\n",
      " 6   public_transport_closing            48218 non-null  float64\n",
      " 7   stay_at_home_requirements           48218 non-null  float64\n",
      " 8   restrictions_on_internal_movement   48218 non-null  float64\n",
      " 9   international_travel_controls       48218 non-null  float64\n",
      " 10  income_support                      48218 non-null  float64\n",
      " 11  public_information_campaigns        48218 non-null  float64\n",
      " 12  testing_policy                      48218 non-null  float64\n",
      " 13  contact_tracing                     48218 non-null  float64\n",
      " 14  emergency_investment_in_healthcare  48218 non-null  float64\n",
      " 15  investment_in_vaccines              48218 non-null  float64\n",
      " 16  facial_coverings                    48218 non-null  float64\n",
      " 17  vaccination_policy                  48218 non-null  float64\n",
      " 18  stringency_index                    48218 non-null  float64\n",
      " 19  date_month                          48218 non-null  object \n",
      "dtypes: float64(17), object(3)\n",
      "memory usage: 7.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_govResp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "id": "76e9d1c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "fixDataTypesOfColumns(df_govResp, df_govResp.columns.tolist()[2:18], 'float64', 'int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "id": "82e1f312",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_govResp_US = df_govResp.loc[df_govResp['location_key'] == 'US']\n",
    "df_govResp_states = df_govResp.loc[df_govResp['location_key'] != 'US']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "id": "f8c83e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_govResp_US.reset_index(drop=True, inplace=True)\n",
    "df_govResp_states.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f1dff3",
   "metadata": {},
   "source": [
    "# Finally, we will export the clean dataframes as csv files to CovidProj folder on Desktop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "id": "4a1eaeaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '~/Desktop/CovidProj/'\n",
    "df_epidem_US.to_csv(path+'epidem_US.csv', sep=',', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "id": "1e208e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_epidem_states.to_csv(path+'epidem_states.csv', sep=',', index=False)\n",
    "df_demog_US.to_csv(path+'demog_US.csv', sep=',', index=False)\n",
    "df_demog_states.to_csv(path+'demog_states.csv', sep=',', index=False)\n",
    "df_emergD2.to_csv(path+'emerg_declarations.csv', sep=',', index=False)\n",
    "df_hosp_US.to_csv(path+'hospitalized_US.csv', sep=',', index=False)\n",
    "df_hosp_states.to_csv(path+'hospitalized_states.csv', sep=',', index=False)\n",
    "df_mob_US.to_csv(path+'mobility_US.csv', sep=',', index=False)\n",
    "df_mob_states.to_csv(path+'mobility_states.csv', sep=',', index=False)\n",
    "df_vacc_US.to_csv(path+'vaccination_US.csv', sep=',', index=False)\n",
    "df_vacc_states.to_csv(path+'vaccination_states.csv', sep=',', index=False)\n",
    "df_govResp_US.to_csv(path+'govResp_US.csv', sep=',', index=False)\n",
    "df_govResp_states.to_csv(path+'govResp_states.csv', sep=',', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "id": "9be8a356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'US_AK',\n",
       " 'US_AL',\n",
       " 'US_AR',\n",
       " 'US_AZ',\n",
       " 'US_CA',\n",
       " 'US_CO',\n",
       " 'US_CT',\n",
       " 'US_DC',\n",
       " 'US_DE',\n",
       " 'US_FL',\n",
       " 'US_GA',\n",
       " 'US_GU',\n",
       " 'US_HI',\n",
       " 'US_IA',\n",
       " 'US_ID',\n",
       " 'US_IL',\n",
       " 'US_IN',\n",
       " 'US_KS',\n",
       " 'US_KY',\n",
       " 'US_LA',\n",
       " 'US_MA',\n",
       " 'US_MD',\n",
       " 'US_ME',\n",
       " 'US_MI',\n",
       " 'US_MN',\n",
       " 'US_MO',\n",
       " 'US_MP',\n",
       " 'US_MS',\n",
       " 'US_MT',\n",
       " 'US_NC',\n",
       " 'US_ND',\n",
       " 'US_NE',\n",
       " 'US_NH',\n",
       " 'US_NJ',\n",
       " 'US_NM',\n",
       " 'US_NV',\n",
       " 'US_NY',\n",
       " 'US_OH',\n",
       " 'US_OK',\n",
       " 'US_OR',\n",
       " 'US_PA',\n",
       " 'US_PR',\n",
       " 'US_SC',\n",
       " 'US_SD',\n",
       " 'US_TN',\n",
       " 'US_TX',\n",
       " 'US_UT',\n",
       " 'US_VA',\n",
       " 'US_VI',\n",
       " 'US_VT',\n",
       " 'US_WA',\n",
       " 'US_WI',\n",
       " 'US_WV',\n",
       " 'US_WY'}"
      ]
     },
     "execution_count": 604,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df_demog_states.location_key.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "id": "af230841",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/3801502853.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_mob_states['date_month'] = df_mob_states['date'].apply(lambda x: '-'.join(x.split('-')[:2]))\n",
      "/var/folders/8_/94pckqjd52dgrgd_fxmw31280000gp/T/ipykernel_3674/3801502853.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_mob_US['date_month'] = df_mob_US['date'].apply(lambda x: '-'.join(x.split('-')[:2]))\n"
     ]
    }
   ],
   "source": [
    "df_mob_states['date_month'] = df_mob_states['date'].apply(lambda x: '-'.join(x.split('-')[:2]))\n",
    "df_mob_US['date_month'] = df_mob_US['date'].apply(lambda x: '-'.join(x.split('-')[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "id": "81f83411",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>location_key</th>\n",
       "      <th>mobility_retail_and_recreation</th>\n",
       "      <th>mobility_grocery_and_pharmacy</th>\n",
       "      <th>mobility_parks</th>\n",
       "      <th>mobility_transit_stations</th>\n",
       "      <th>mobility_workplaces</th>\n",
       "      <th>mobility_residential</th>\n",
       "      <th>date_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3846291</th>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>US_AK</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2020-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3846292</th>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>US_AK</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2020-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3846293</th>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>US_AK</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3846294</th>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>US_AK</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2020-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3846295</th>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>US_AK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2020-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date location_key  mobility_retail_and_recreation  \\\n",
       "3846291  2020-02-15        US_AK                             5.0   \n",
       "3846292  2020-02-16        US_AK                            15.0   \n",
       "3846293  2020-02-17        US_AK                             3.0   \n",
       "3846294  2020-02-18        US_AK                            -2.0   \n",
       "3846295  2020-02-19        US_AK                             0.0   \n",
       "\n",
       "         mobility_grocery_and_pharmacy  mobility_parks  \\\n",
       "3846291                            3.0            14.0   \n",
       "3846292                            5.0            29.0   \n",
       "3846293                           -2.0             9.0   \n",
       "3846294                           -2.0             1.0   \n",
       "3846295                           -2.0             1.0   \n",
       "\n",
       "         mobility_transit_stations  mobility_workplaces  mobility_residential  \\\n",
       "3846291                        2.0                  1.0                   0.0   \n",
       "3846292                        6.0                  1.0                  -1.0   \n",
       "3846293                       -6.0                -33.0                   8.0   \n",
       "3846294                        4.0                 -2.0                   2.0   \n",
       "3846295                        4.0                 -3.0                   2.0   \n",
       "\n",
       "        date_month  \n",
       "3846291    2020-02  \n",
       "3846292    2020-02  \n",
       "3846293    2020-02  \n",
       "3846294    2020-02  \n",
       "3846295    2020-02  "
      ]
     },
     "execution_count": 609,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mob_states.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60d32dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-importing the saved files for analysis\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('~/Desktop/CovidProj/epidem_states.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2bd0a04d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>location_key</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_deceased</th>\n",
       "      <th>new_recovered</th>\n",
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       "      <td>2020-01-22</td>\n",
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       "      <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>2020-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>US_AK</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>2020-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>US_AK</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>2020-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>US_AK</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>2020-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>US_AK</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>2020-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date location_key  new_confirmed  new_deceased  new_recovered  \\\n",
       "0  2020-01-22        US_AK              0             0              0   \n",
       "1  2020-01-23        US_AK              0             0              0   \n",
       "2  2020-01-24        US_AK              0             0              0   \n",
       "3  2020-01-25        US_AK              0             0              0   \n",
       "4  2020-01-26        US_AK              0             0              0   \n",
       "\n",
       "   new_tested  cumulative_confirmed  cumulative_deceased  \\\n",
       "0           0                     0                    0   \n",
       "1           0                     0                    0   \n",
       "2           0                     0                    0   \n",
       "3           0                     0                    0   \n",
       "4           0                     0                    0   \n",
       "\n",
       "   cumulative_recovered  cumulative_tested date_month  \n",
       "0                     0                  0    2020-01  \n",
       "1                     0                  0    2020-01  \n",
       "2                     0                  0    2020-01  \n",
       "3                     0                  0    2020-01  \n",
       "4                     0                  0    2020-01  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce5b9578",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['location_key'] = df['location_key'].apply(lambda x: x.split('_')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7eab00e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['date', 'location_key', 'new_confirmed', 'new_deceased', 'new_tested']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "632b51b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             object\n",
       "location_key     object\n",
       "new_confirmed     int64\n",
       "new_deceased      int64\n",
       "new_tested        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "07b3b314",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('~/Desktop/CovidProj/epidem_states_daily.csv', sep=',', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b769845b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54218, 5)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ba85be45",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('~/Desktop/CovidProj/epidem_US_daily.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0d28ea72",
   "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>date</th>\n",
       "      <th>location_key</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_deceased</th>\n",
       "      <th>new_tested</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date location_key  new_confirmed  new_deceased  new_tested\n",
       "0  2020-01-01           US              0             0           0\n",
       "1  2020-01-02           US              0             0           0\n",
       "2  2020-01-03           US              0             0           0\n",
       "3  2020-01-04           US              0             0           0\n",
       "4  2020-01-05           US              0             0           0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "32886fd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             0\n",
       "location_key     0\n",
       "new_confirmed    0\n",
       "new_deceased     0\n",
       "new_tested       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7e9dfc40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "988028"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['new_deceased'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d24dc37e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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