SQL queries, R for data engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d93f1205-a70e-4fcf-8e1e-35a059ff490e",
   "metadata": {},
   "source": [
    "# Final Project (Group 3 Submission):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dafb0b9-2d30-4a3e-be4c-653bbaeaff15",
   "metadata": {},
   "source": [
    "## Ask 1 & Ask 2: Loading, surveying and wrangling the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b76972b-e4c8-4fe6-9afa-0fdd44f03c1f",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "81eb9598-2415-4a49-ab13-740e1a3a93b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "446d3c47-d3a8-4320-8c55-5326e5921bff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "createdb: error: database creation failed: ERROR:  database \"project_final\" already exists\n"
     ]
    }
   ],
   "source": [
    "!createdb -U student project_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3dea567f-c6e2-4790-ae7f-50b94c6243e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql postgresql://student@/project_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "20dee48d-7ee7-4c80-a23d-e13d1e437dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df1671f9-4ae0-4837-95ea-03570e0350c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['SPARK_HOME'] = '/usr/local/lib/spark'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb908bc0-d9d1-459f-88d9-5051f8141f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4eaed5f8-49f1-4b78-9463-6e5698287590",
   "metadata": {},
   "outputs": [],
   "source": [
    "findspark.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8ae61545-90b3-4ddb-880d-38346a0f080f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SparkContext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "34791835-0c3c-44c7-a3ed-192a9c88d49e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "21/12/11 21:14:51 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
      "Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n",
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n"
     ]
    }
   ],
   "source": [
    "spark = SparkContext(appName='project_final')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa6a4a55-0cc6-4d18-8319-8a49847dbce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://ip-172-31-17-87.ec2.internal:4040\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v3.1.2</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[*]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>project_final</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "<SparkContext master=local[*] appName=project_final>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bcd1d6db-4bf1-48f9-a2a9-dd84ee0534d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SQLContext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd9ddb91-8870-4bda-941e-c61bfb6af6a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sqlc = SQLContext(spark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b7382f3d-6249-4acb-aaf7-7b44fc589644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyspark.sql.context.SQLContext at 0x7fb2a99fbd60>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqlc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eb22e14-6957-4dce-951b-a2ed09481105",
   "metadata": {},
   "source": [
    "Lets also import all the packages which we'll need later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "17747621-5028-4f4d-ab3d-b000b2c8153c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm ## You may need to install this on to your PC first using a pip command\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd0fe8c-ee33-4e78-bcf8-ba422cc4b87f",
   "metadata": {},
   "source": [
    "### Cases Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49d517eb-ccfb-4950-9bb9-f393ea3f9846",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
      "FINISHED --2021-12-11 16:25:55--\n",
      "Total wall clock time: 38s\n",
      "Downloaded: 598 files, 5.2M in 0.03s (159 MB/s)\n"
     ]
    }
   ],
   "source": [
    "!wget -i cases.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fdaa8842-e566-433b-9f54-032bd7bb5d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "!csvstack -d \",\" *2021.csv > yearcases21.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f2391a13-4a60-43fd-bed5-754b99f3a103",
   "metadata": {},
   "outputs": [],
   "source": [
    "!csvstack -d \",\" *2020.csv > yearcases20.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e9a65834-8aac-4694-8f7e-887693f2ca44",
   "metadata": {},
   "outputs": [],
   "source": [
    "!csvstack -d \",\" yearcases*.csv > cases2.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5d602e56-aa87-4aca-88ca-2d9fc57237ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34703 cases2.csv\n"
     ]
    }
   ],
   "source": [
    "!wc -l cases2.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03f3a390-9abf-466f-8f41-169daf4c3425",
   "metadata": {},
   "source": [
    "Lets now remove the files which we no longer need, for simplicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "143e1280-f087-42ce-a909-72ee99a5eeea",
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm -f *20.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1610fae1-904d-4c6f-8457-f4e68ba33a3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm -f *21.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "12682bcb-dcc6-4ce8-98ad-c43930719692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1: Province_State\n",
      "  2: Country_Region\n",
      "  3: Last_Update\n",
      "  4: Lat\n",
      "  5: Long_\n",
      "  6: Confirmed\n",
      "  7: Deaths\n",
      "  8: Recovered\n",
      "  9: Active\n",
      " 10: FIPS\n",
      " 11: Incident_Rate\n",
      " 12: People_Tested\n",
      " 13: People_Hospitalized\n",
      " 14: Mortality_Rate\n",
      " 15: UID\n",
      " 16: ISO3\n",
      " 17: Testing_Rate\n",
      " 18: Hospitalization_Rate\n"
     ]
    }
   ],
   "source": [
    "!csvcut -n cases2.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "40374fbc-d771-483f-9b0e-2336df963fa4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    598 Alabama\n",
      "    598 Alaska\n",
      "    598 American Samoa\n",
      "    598 Arizona\n",
      "    598 Arkansas\n",
      "    598 California\n",
      "    598 Colorado\n",
      "    598 Connecticut\n",
      "    598 Delaware\n",
      "    598 Diamond Princess\n",
      "    598 District of Columbia\n",
      "    598 Florida\n",
      "    598 Georgia\n",
      "    598 Grand Princess\n",
      "    598 Guam\n",
      "    598 Hawaii\n",
      "    598 Idaho\n",
      "    598 Illinois\n",
      "    598 Indiana\n",
      "    598 Iowa\n",
      "    598 Kansas\n",
      "    598 Kentucky\n",
      "    598 Louisiana\n",
      "    598 Maine\n",
      "    598 Maryland\n",
      "    598 Massachusetts\n",
      "    598 Michigan\n",
      "    598 Minnesota\n",
      "    598 Mississippi\n",
      "    598 Missouri\n",
      "    598 Montana\n",
      "    598 Nebraska\n",
      "    598 Nevada\n",
      "    598 New Hampshire\n",
      "    598 New Jersey\n",
      "    598 New Mexico\n",
      "    598 New York\n",
      "    598 North Carolina\n",
      "    598 North Dakota\n",
      "    598 Northern Mariana Islands\n",
      "    598 Ohio\n",
      "    598 Oklahoma\n",
      "    598 Oregon\n",
      "    598 Pennsylvania\n",
      "    598 Puerto Rico\n",
      "     18 Recovered\n",
      "    598 Rhode Island\n",
      "    598 South Carolina\n",
      "    598 South Dakota\n",
      "    598 Tennessee\n",
      "    598 Texas\n",
      "    598 Utah\n",
      "    598 Vermont\n",
      "    598 Virgin Islands\n",
      "    598 Virginia\n",
      "    598 Washington\n",
      "    598 West Virginia\n",
      "    598 Wisconsin\n",
      "    598 Wyoming\n"
     ]
    }
   ],
   "source": [
    "!csvcut -c1 cases2.csv | sort | uniq -cd "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff8fc46-1fcd-4ae1-a56b-0ee34ed8f515",
   "metadata": {},
   "source": [
    "Lets analyse the data further before we build the tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "71a289d2-7b98-4ec2-b9d9-29fe46b92d2b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1. Province_State: Text\n",
      "  2. Country_Region: Text\n",
      "  3. Last_Update: DateTime\n",
      "  4. Lat: Number\n",
      "  5. Long_: Number\n",
      "  6. Confirmed: Number\n",
      "  7. Deaths: Number\n",
      "  8. Recovered: Number\n",
      "  9. Active: Number\n",
      " 10. FIPS: Number\n",
      " 11. Incident_Rate: Number\n",
      " 12. People_Tested: Number\n",
      " 13. People_Hospitalized: Number\n",
      " 14. Mortality_Rate: Number\n",
      " 15. UID: Number\n",
      " 16. ISO3: Text\n",
      " 17. Testing_Rate: Number\n",
      " 18. Hospitalization_Rate: Number\n"
     ]
    }
   ],
   "source": [
    "!csvstat --type cases2.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0509ed5c-a851-4493-b863-00762bcf9d7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "cases = sqlc.read.csv(\"cases2.csv\",  header=True, inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "82fc4bbb-56d1-4afb-839d-27083bf34886",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Province_State: string (nullable = true)\n",
      " |-- Country_Region: string (nullable = true)\n",
      " |-- Last_Update: string (nullable = true)\n",
      " |-- Lat: double (nullable = true)\n",
      " |-- Long_: double (nullable = true)\n",
      " |-- Confirmed: integer (nullable = true)\n",
      " |-- Deaths: integer (nullable = true)\n",
      " |-- Recovered: double (nullable = true)\n",
      " |-- Active: double (nullable = true)\n",
      " |-- FIPS: double (nullable = true)\n",
      " |-- Incident_Rate: double (nullable = true)\n",
      " |-- People_Tested: double (nullable = true)\n",
      " |-- People_Hospitalized: double (nullable = true)\n",
      " |-- Mortality_Rate: double (nullable = true)\n",
      " |-- UID: double (nullable = true)\n",
      " |-- ISO3: string (nullable = true)\n",
      " |-- Testing_Rate: double (nullable = true)\n",
      " |-- Hospitalization_Rate: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cases.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c65bbd20-5c81-4157-8742-2065a88f9599",
   "metadata": {},
   "source": [
    "Based on the above, we expect that we can work with the following table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "699bb8e4-41be-45c1-9199-de84fc2a2cc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP TABLE IF EXISTS cases;\n",
    "\n",
    "CREATE TABLE cases (\n",
    "    Province_State VARCHAR(30) NOT NULL,\n",
    "    Country_Region VARCHAR(30),\n",
    "    Last_Update DATE,\n",
    "    Lat NUMERIC (9,6),\n",
    "    Long_ NUMERIC (9,6),\n",
    "    Confirmed INTEGER,\n",
    "    Deaths INTEGER,\n",
    "    Recovered NUMERIC,\n",
    "    Active NUMERIC,\n",
    "    FIPS NUMERIC,\n",
    "    Incident_Rate NUMERIC,\n",
    "    People_Tested NUMERIC,\n",
    "    People_Hospitalized NUMERIC,\n",
    "    Mortality_Rate NUMERIC,\n",
    "    UID NUMERIC,\n",
    "    ISO3 VARCHAR(3),\n",
    "    Testing_Rate NUMERIC,\n",
    "    Hospitalization_Rate NUMERIC\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8f16e7c3-b8e5-4092-ad60-f62d8c8fdb50",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "34702 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "COPY cases FROM '/home/ubuntu/notebooks/Final Group Project/Final Submission/cases2.csv'\n",
    "CSV\n",
    "HEADER;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "93e8d05e-16f4-407b-b3c7-4288b560e4a1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "5 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "            <th>country_region</th>\n",
       "            <th>last_update</th>\n",
       "            <th>lat</th>\n",
       "            <th>long_</th>\n",
       "            <th>confirmed</th>\n",
       "            <th>deaths</th>\n",
       "            <th>recovered</th>\n",
       "            <th>active</th>\n",
       "            <th>fips</th>\n",
       "            <th>incident_rate</th>\n",
       "            <th>people_tested</th>\n",
       "            <th>people_hospitalized</th>\n",
       "            <th>mortality_rate</th>\n",
       "            <th>uid</th>\n",
       "            <th>iso3</th>\n",
       "            <th>testing_rate</th>\n",
       "            <th>hospitalization_rate</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-04-12</td>\n",
       "            <td>32.318200</td>\n",
       "            <td>-86.902300</td>\n",
       "            <td>3667</td>\n",
       "            <td>93</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>1</td>\n",
       "            <td>74.78812241430825</td>\n",
       "            <td>62029</td>\n",
       "            <td>437</td>\n",
       "            <td>2.5361330788110172</td>\n",
       "            <td>84000001</td>\n",
       "            <td>USA</td>\n",
       "            <td>1265.0756600046705</td>\n",
       "            <td>12.26494527</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alaska</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-04-12</td>\n",
       "            <td>61.370700</td>\n",
       "            <td>-152.404400</td>\n",
       "            <td>272</td>\n",
       "            <td>8</td>\n",
       "            <td>66</td>\n",
       "            <td>198</td>\n",
       "            <td>2</td>\n",
       "            <td>37.18158144748443</td>\n",
       "            <td>8038</td>\n",
       "            <td>31</td>\n",
       "            <td>2.9411764705882355</td>\n",
       "            <td>84000002</td>\n",
       "            <td>USA</td>\n",
       "            <td>1344.711576</td>\n",
       "            <td>11.39705882</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arizona</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-04-12</td>\n",
       "            <td>33.729800</td>\n",
       "            <td>-111.431200</td>\n",
       "            <td>3542</td>\n",
       "            <td>115</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>4</td>\n",
       "            <td>48.662422237325615</td>\n",
       "            <td>52289</td>\n",
       "            <td>None</td>\n",
       "            <td>3.2467532467532467</td>\n",
       "            <td>84000004</td>\n",
       "            <td>USA</td>\n",
       "            <td>578.5222863</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arkansas</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-04-12</td>\n",
       "            <td>34.969700</td>\n",
       "            <td>-92.373100</td>\n",
       "            <td>1280</td>\n",
       "            <td>27</td>\n",
       "            <td>367</td>\n",
       "            <td>886</td>\n",
       "            <td>5</td>\n",
       "            <td>42.41494808807994</td>\n",
       "            <td>19722</td>\n",
       "            <td>130</td>\n",
       "            <td>2.109375</td>\n",
       "            <td>84000005</td>\n",
       "            <td>USA</td>\n",
       "            <td>761.7533537</td>\n",
       "            <td>10.15625</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>California</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-04-12</td>\n",
       "            <td>36.116200</td>\n",
       "            <td>-119.681600</td>\n",
       "            <td>22182</td>\n",
       "            <td>631</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>6</td>\n",
       "            <td>56.139590020030006</td>\n",
       "            <td>190328</td>\n",
       "            <td>5234</td>\n",
       "            <td>2.8446488143539805</td>\n",
       "            <td>84000006</td>\n",
       "            <td>USA</td>\n",
       "            <td>485.4238685</td>\n",
       "            <td>22.9611757</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Alabama', 'US', datetime.date(2020, 4, 12), Decimal('32.318200'), Decimal('-86.902300'), 3667, 93, None, None, Decimal('1'), Decimal('74.78812241430825'), Decimal('62029'), Decimal('437'), Decimal('2.5361330788110172'), Decimal('84000001'), 'USA', Decimal('1265.0756600046705'), Decimal('12.26494527')),\n",
       " ('Alaska', 'US', datetime.date(2020, 4, 12), Decimal('61.370700'), Decimal('-152.404400'), 272, 8, Decimal('66'), Decimal('198'), Decimal('2'), Decimal('37.18158144748443'), Decimal('8038'), Decimal('31'), Decimal('2.9411764705882355'), Decimal('84000002'), 'USA', Decimal('1344.711576'), Decimal('11.39705882')),\n",
       " ('Arizona', 'US', datetime.date(2020, 4, 12), Decimal('33.729800'), Decimal('-111.431200'), 3542, 115, None, None, Decimal('4'), Decimal('48.662422237325615'), Decimal('52289'), None, Decimal('3.2467532467532467'), Decimal('84000004'), 'USA', Decimal('578.5222863'), None),\n",
       " ('Arkansas', 'US', datetime.date(2020, 4, 12), Decimal('34.969700'), Decimal('-92.373100'), 1280, 27, Decimal('367'), Decimal('886'), Decimal('5'), Decimal('42.41494808807994'), Decimal('19722'), Decimal('130'), Decimal('2.109375'), Decimal('84000005'), 'USA', Decimal('761.7533537'), Decimal('10.15625')),\n",
       " ('California', 'US', datetime.date(2020, 4, 12), Decimal('36.116200'), Decimal('-119.681600'), 22182, 631, None, None, Decimal('6'), Decimal('56.139590020030006'), Decimal('190328'), Decimal('5234'), Decimal('2.8446488143539805'), Decimal('84000006'), 'USA', Decimal('485.4238685'), Decimal('22.9611757'))]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT * from cases limit 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c168ef-4c4c-419a-8248-04a69a64ece9",
   "metadata": {},
   "source": [
    "Cleaning the cases table:\n",
    "* We noticed that there were some null values for lat and long. We also realized that this corresponded to 'states' which weren't really states - \"Diamond Princess\" and \"Grand Princess\" and \"Recovered\". The first 2 were ships that were affected by COVID outbreaks. Since they are not relevant to our analysis, we deleted them the tables. \"Recovered\" seems to be some kind of data error. We will remove all of these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9c6e1fde-1e64-41ff-a883-0455616752a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1214</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1214,)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(*) FROM cases\n",
    "WHERE lat is null or long_ is null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "809c85be-9c7a-4106-a512-a508d7da1b81",
   "metadata": {},
   "source": [
    "As we can see below, all 1214 rows of data with null values for latitude or longitude are from diamond princess, grand princess or 'recovered'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f2737b97-e841-44b1-b097-244a88e715fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "0 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "            <th>country_region</th>\n",
       "            <th>last_update</th>\n",
       "            <th>lat</th>\n",
       "            <th>long_</th>\n",
       "            <th>confirmed</th>\n",
       "            <th>deaths</th>\n",
       "            <th>recovered</th>\n",
       "            <th>active</th>\n",
       "            <th>fips</th>\n",
       "            <th>incident_rate</th>\n",
       "            <th>people_tested</th>\n",
       "            <th>people_hospitalized</th>\n",
       "            <th>mortality_rate</th>\n",
       "            <th>uid</th>\n",
       "            <th>iso3</th>\n",
       "            <th>testing_rate</th>\n",
       "            <th>hospitalization_rate</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT * FROM cases\n",
    "WHERE (lat is null or long_ is null) and (province_state != 'Diamond Princess' and  province_state != 'Grand Princess' and province_state != 'Recovered' )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ee3566-745b-4d20-b56f-d987216ab679",
   "metadata": {},
   "source": [
    "Now let's delete those rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d2b26fd9-4f43-48e4-82fd-ff157f56af6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1214 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DELETE FROM cases\n",
    "WHERE lat is null or long_ is null;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "461837fa-5c93-40e6-9a09-a62ef617bc2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(0,)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(*) FROM cases\n",
    "WHERE lat is null or long_ is null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce54cc4-7e1f-45b5-8de0-0ab5b92e1d7e",
   "metadata": {},
   "source": [
    "Cleaning the cases table:\n",
    "\n",
    "* We then also decided to remove all US territories or any 'province_state' entries that were not either one of the 50 states or the District of Columbia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "796b715a-2299-47ca-b3ad-cc39f50efe6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "56 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Virgin Islands</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Nevada</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>West Virginia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Carolina</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Mexico</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arkansas</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Dakota</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>American Samoa</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Washington</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Kentucky</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Nebraska</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Tennessee</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Oregon</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Idaho</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Colorado</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Mississippi</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Louisiana</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Guam</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Jersey</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indiana</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Michigan</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Georgia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Maine</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>California</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Kansas</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alaska</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Oklahoma</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Vermont</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New York</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Hawaii</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Missouri</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>District of Columbia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Ohio</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Minnesota</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arizona</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Maryland</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Puerto Rico</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Wyoming</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Hampshire</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>North Dakota</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>North Carolina</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Florida</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Delaware</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Northern Mariana Islands</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Connecticut</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Iowa</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Massachusetts</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Rhode Island</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Pennsylvania</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Wisconsin</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Montana</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Illinois</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Virginia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Utah</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Texas</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Virgin Islands',),\n",
       " ('Nevada',),\n",
       " ('West Virginia',),\n",
       " ('South Carolina',),\n",
       " ('New Mexico',),\n",
       " ('Arkansas',),\n",
       " ('South Dakota',),\n",
       " ('American Samoa',),\n",
       " ('Washington',),\n",
       " ('Kentucky',),\n",
       " ('Nebraska',),\n",
       " ('Tennessee',),\n",
       " ('Oregon',),\n",
       " ('Idaho',),\n",
       " ('Alabama',),\n",
       " ('Colorado',),\n",
       " ('Mississippi',),\n",
       " ('Louisiana',),\n",
       " ('Guam',),\n",
       " ('New Jersey',),\n",
       " ('Indiana',),\n",
       " ('Michigan',),\n",
       " ('Georgia',),\n",
       " ('Maine',),\n",
       " ('California',),\n",
       " ('Kansas',),\n",
       " ('Alaska',),\n",
       " ('Oklahoma',),\n",
       " ('Vermont',),\n",
       " ('New York',),\n",
       " ('Hawaii',),\n",
       " ('Missouri',),\n",
       " ('District of Columbia',),\n",
       " ('Ohio',),\n",
       " ('Minnesota',),\n",
       " ('Arizona',),\n",
       " ('Maryland',),\n",
       " ('Puerto Rico',),\n",
       " ('Wyoming',),\n",
       " ('New Hampshire',),\n",
       " ('North Dakota',),\n",
       " ('North Carolina',),\n",
       " ('Florida',),\n",
       " ('Delaware',),\n",
       " ('Northern Mariana Islands',),\n",
       " ('Connecticut',),\n",
       " ('Iowa',),\n",
       " ('Massachusetts',),\n",
       " ('Rhode Island',),\n",
       " ('Pennsylvania',),\n",
       " ('Wisconsin',),\n",
       " ('Montana',),\n",
       " ('Illinois',),\n",
       " ('Virginia',),\n",
       " ('Utah',),\n",
       " ('Texas',)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT DISTINCT province_state\n",
    "FROM cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f5308b5a-9b52-43b8-a122-7c7cce18e0ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "2990 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DELETE FROM cases\n",
    "WHERE province_state = 'Virgin Islands' or province_state = 'American Samoa' or province_state = 'Puerto Rico' or province_state = 'Northern Mariana Islands' or province_state = 'Guam'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "689cba80-d92c-413b-b066-589e23a48164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>51</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(51,)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(DISTINCT province_state)\n",
    "FROM cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0f4a99a-131f-40e6-976b-b803120e21f4",
   "metadata": {},
   "source": [
    "Now we have only 51 states (which includes the 50 states plus District of Columbia), great!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a18d86a-68eb-457c-8c32-fb7acbfdb1dd",
   "metadata": {},
   "source": [
    "We now look at the prevalence of other null values in the table. For example, people_hospitalized and hospitalization_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "30d1b138-0878-4718-92ba-a49ab0eaf2bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>25436</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(25436,)]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(*)\n",
    "FROM cases\n",
    "where people_hospitalized is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "46fe0e91-b861-424d-8290-6cc14e778dd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>25436</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(25436,)]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(*)\n",
    "FROM cases\n",
    "where hospitalization_rate is null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c1c7757-74ee-4f53-8299-6b01921a4e20",
   "metadata": {},
   "source": [
    "This is a huge number of missing values! In fact 83% of the rows are null for these 2 columns. Based on our 4 analytical questions, we also see note that these 2 columns will not be required to answer any of the questions. We will certainly not delete the rows assosciated with these null values, as they contain alot of valuable information elsewhere. Instead, we will drop these columns from our table as they add little value to us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7c293b66-27d3-464f-86e5-73328623ace4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "ALTER table cases\n",
    "drop column people_hospitalized, drop column hospitalization_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2fee7e-15c1-41ad-bb15-ad1cdf5a285b",
   "metadata": {},
   "source": [
    "We will also remove the 'people_tested' and 'testing_rate' columns. The reason for this is because we will be getting this information from the testing table, so having 2 records of similar information in 2 different tables would be redundant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "26b3d13f-acb6-48cf-a255-0de001804292",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "ALTER table cases\n",
    "drop column people_tested, drop column testing_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d1e3e2b-7b2d-47ca-98c9-362e13afc01d",
   "metadata": {},
   "source": [
    "We see below that we have a lot of null values for 'recovered' and 'active' (almost 50% of all rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f3848333-c941-40c3-bf7c-6ba7c36d0d12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>16582</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(16582,)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(*)\n",
    "FROM cases\n",
    "where recovered is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "22bc7ca4-9209-44a5-bb62-2bc0f653e7d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>16582</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(16582,)]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(*)\n",
    "FROM cases\n",
    "where active is null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0463647f-36a5-4b52-b193-6fdd9d80fef8",
   "metadata": {},
   "source": [
    "We note with the code below that the null values for recovered are the same as the null values for active."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1231de19-20da-4c87-be12-7d0b51f6d0cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>16582</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(16582,)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(*)\n",
    "FROM cases\n",
    "where recovered is null and active is null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27d8725-0f98-4dc7-baea-0c366a309aa4",
   "metadata": {},
   "source": [
    "However, we will not delete this columns. This is because there is still alot of useful information available here that others can analyze (even if they are not relevant to our analytical questions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7525f97e-5875-4f89-86b2-7c4c310e45fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "            <th>country_region</th>\n",
       "            <th>last_update</th>\n",
       "            <th>lat</th>\n",
       "            <th>long_</th>\n",
       "            <th>confirmed</th>\n",
       "            <th>deaths</th>\n",
       "            <th>recovered</th>\n",
       "            <th>active</th>\n",
       "            <th>fips</th>\n",
       "            <th>incident_rate</th>\n",
       "            <th>mortality_rate</th>\n",
       "            <th>uid</th>\n",
       "            <th>iso3</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-04-12</td>\n",
       "            <td>32.318200</td>\n",
       "            <td>-86.902300</td>\n",
       "            <td>3667</td>\n",
       "            <td>93</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>1</td>\n",
       "            <td>74.78812241430825</td>\n",
       "            <td>2.5361330788110172</td>\n",
       "            <td>84000001</td>\n",
       "            <td>USA</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Alabama', 'US', datetime.date(2020, 4, 12), Decimal('32.318200'), Decimal('-86.902300'), 3667, 93, None, None, Decimal('1'), Decimal('74.78812241430825'), Decimal('2.5361330788110172'), Decimal('84000001'), 'USA')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT *\n",
    "FROM cases\n",
    "limit 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f83eead-99bf-48df-a235-53ad2871fb4f",
   "metadata": {},
   "source": [
    "### Testing (Time Series) Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "349f21ab-8b96-47ab-821e-84af2e00c591",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-12-11 16:28:09--  https://raw.githubusercontent.com/govex/COVID-19/master/data_tables/testing_data/time_series_covid19_US.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2328413 (2.2M) [text/plain]\n",
      "Saving to: ‘time_series_covid19_US.csv’\n",
      "\n",
      "time_series_covid19 100%[===================>]   2.22M  --.-KB/s    in 0.01s   \n",
      "\n",
      "2021-12-11 16:28:09 (216 MB/s) - ‘time_series_covid19_US.csv’ saved [2328413/2328413]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/govex/COVID-19/master/data_tables/testing_data/time_series_covid19_US.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "72864828-96ef-41eb-906a-18e37b9ca8ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1: date\n",
      "  2: state\n",
      "  3: cases_conf_probable\n",
      "  4: cases_confirmed\n",
      "  5: cases_probable\n",
      "  6: tests_viral_positive\n",
      "  7: tests_viral_negative\n",
      "  8: tests_viral_total\n",
      "  9: tests_antigen_positive\n",
      " 10: tests_antigen_total\n",
      " 11: people_viral_positive\n",
      " 12: people_viral_total\n",
      " 13: people_antigen_positive\n",
      " 14: people_antigen_total\n",
      " 15: encounters_viral_total\n",
      " 16: tests_combined_total\n"
     ]
    }
   ],
   "source": [
    "!csvcut -n time_series_covid19_US.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7fa5a569-309c-4cec-8baf-564a9ead0215",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1. date: Date\n",
      "  2. state: Text\n",
      "  3. cases_conf_probable: Number\n",
      "  4. cases_confirmed: Number\n",
      "  5. cases_probable: Number\n",
      "  6. tests_viral_positive: Number\n",
      "  7. tests_viral_negative: Number\n",
      "  8. tests_viral_total: Number\n",
      "  9. tests_antigen_positive: Number\n",
      " 10. tests_antigen_total: Number\n",
      " 11. people_viral_positive: Number\n",
      " 12. people_viral_total: Number\n",
      " 13. people_antigen_positive: Number\n",
      " 14. people_antigen_total: Number\n",
      " 15. encounters_viral_total: Number\n",
      " 16. tests_combined_total: Number\n"
     ]
    }
   ],
   "source": [
    "!csvstat --type time_series_covid19_US.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d158a5ab-f778-4a66-9463-1e026df9dade",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_series = sqlc.read.csv(\"time_series_covid19_US.csv\",  header=True, inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ff5abaa8-a82a-4114-8bfd-34a3f563afd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- date: string (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- cases_conf_probable: integer (nullable = true)\n",
      " |-- cases_confirmed: integer (nullable = true)\n",
      " |-- cases_probable: integer (nullable = true)\n",
      " |-- tests_viral_positive: integer (nullable = true)\n",
      " |-- tests_viral_negative: integer (nullable = true)\n",
      " |-- tests_viral_total: integer (nullable = true)\n",
      " |-- tests_antigen_positive: integer (nullable = true)\n",
      " |-- tests_antigen_total: integer (nullable = true)\n",
      " |-- people_viral_positive: integer (nullable = true)\n",
      " |-- people_viral_total: integer (nullable = true)\n",
      " |-- people_antigen_positive: integer (nullable = true)\n",
      " |-- people_antigen_total: integer (nullable = true)\n",
      " |-- encounters_viral_total: integer (nullable = true)\n",
      " |-- tests_combined_total: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "time_series.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db5eee6-0364-47b3-aeb5-f8cd5a18e151",
   "metadata": {},
   "source": [
    "Based on the above, we expect that we can work with the following table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d2b1c7aa-2633-4317-b29f-2474a8c416d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP TABLE IF EXISTS testing;\n",
    "\n",
    "CREATE TABLE testing (\n",
    "    date DATE NOT NULL,\n",
    "    state VARCHAR(64),\n",
    "    cases_conf_probable INTEGER,\n",
    "    cases_confirmed INTEGER,\n",
    "    cases_probable INTEGER,\n",
    "    tests_viral_positive INTEGER,\n",
    "    tests_viral_negative INTEGER,\n",
    "    tests_viral_total INTEGER,\n",
    "    tests_antigen_positive INTEGER,\n",
    "    tests_antigen_total INTEGER,\n",
    "    people_viral_positive INTEGER,\n",
    "    people_viral_total INTEGER,\n",
    "    people_antigen_positive INTEGER,\n",
    "    people_antigen_total INTEGER,\n",
    "    encounters_viral_total INTEGER,\n",
    "    tests_combined_total INTEGER\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "39630c42-299e-459e-8880-f7fb43b5e3c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "36348 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "COPY testing FROM '/home/ubuntu/notebooks/Final Group Project/Final Submission/time_series_covid19_US.csv'\n",
    "CSV\n",
    "HEADER;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0f3b04e2-8728-48cc-af4d-c1e2f035592a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "5 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>date</th>\n",
       "            <th>state</th>\n",
       "            <th>cases_conf_probable</th>\n",
       "            <th>cases_confirmed</th>\n",
       "            <th>cases_probable</th>\n",
       "            <th>tests_viral_positive</th>\n",
       "            <th>tests_viral_negative</th>\n",
       "            <th>tests_viral_total</th>\n",
       "            <th>tests_antigen_positive</th>\n",
       "            <th>tests_antigen_total</th>\n",
       "            <th>people_viral_positive</th>\n",
       "            <th>people_viral_total</th>\n",
       "            <th>people_antigen_positive</th>\n",
       "            <th>people_antigen_total</th>\n",
       "            <th>encounters_viral_total</th>\n",
       "            <th>tests_combined_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2020-03-06</td>\n",
       "            <td>AK</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>8</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>8</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2020-03-07</td>\n",
       "            <td>AK</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>12</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>12</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2020-03-08</td>\n",
       "            <td>AK</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>14</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>14</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2020-03-09</td>\n",
       "            <td>AK</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>23</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>23</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2020-03-10</td>\n",
       "            <td>AK</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>23</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>23</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(datetime.date(2020, 3, 6), 'AK', None, None, None, None, None, 8, None, None, None, None, None, None, None, 8),\n",
       " (datetime.date(2020, 3, 7), 'AK', None, None, None, None, None, 12, None, None, None, None, None, None, None, 12),\n",
       " (datetime.date(2020, 3, 8), 'AK', None, None, None, None, None, 14, None, None, None, None, None, None, None, 14),\n",
       " (datetime.date(2020, 3, 9), 'AK', None, None, None, None, None, 23, None, None, None, None, None, None, None, 23),\n",
       " (datetime.date(2020, 3, 10), 'AK', None, None, None, None, None, 23, None, None, None, None, None, None, None, 23)]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT * from testing limit 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1176112a-eb89-4975-b677-806451956152",
   "metadata": {},
   "source": [
    "Let's limit only to 51 states (50 US states + DC) like we did in the cases table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c3b83d53-f3ff-4c52-ac37-42203a651396",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "56 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>state</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>AK</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AL</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AR</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AS</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AZ</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CO</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CT</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>DC</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>DE</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>FL</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>GA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>GU</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>HI</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>IA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ID</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>IL</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>IN</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>KS</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>KY</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>LA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MD</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ME</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MI</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MN</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MO</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MP</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MS</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MT</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NC</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ND</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NE</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NH</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NJ</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NM</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NV</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NY</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>OH</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>OK</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>OR</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>PA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>PR</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>RI</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SC</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SD</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>TN</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>TX</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>UT</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>VA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>VI</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>VT</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>WA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>WI</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>WV</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>WY</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('AK',),\n",
       " ('AL',),\n",
       " ('AR',),\n",
       " ('AS',),\n",
       " ('AZ',),\n",
       " ('CA',),\n",
       " ('CO',),\n",
       " ('CT',),\n",
       " ('DC',),\n",
       " ('DE',),\n",
       " ('FL',),\n",
       " ('GA',),\n",
       " ('GU',),\n",
       " ('HI',),\n",
       " ('IA',),\n",
       " ('ID',),\n",
       " ('IL',),\n",
       " ('IN',),\n",
       " ('KS',),\n",
       " ('KY',),\n",
       " ('LA',),\n",
       " ('MA',),\n",
       " ('MD',),\n",
       " ('ME',),\n",
       " ('MI',),\n",
       " ('MN',),\n",
       " ('MO',),\n",
       " ('MP',),\n",
       " ('MS',),\n",
       " ('MT',),\n",
       " ('NC',),\n",
       " ('ND',),\n",
       " ('NE',),\n",
       " ('NH',),\n",
       " ('NJ',),\n",
       " ('NM',),\n",
       " ('NV',),\n",
       " ('NY',),\n",
       " ('OH',),\n",
       " ('OK',),\n",
       " ('OR',),\n",
       " ('PA',),\n",
       " ('PR',),\n",
       " ('RI',),\n",
       " ('SC',),\n",
       " ('SD',),\n",
       " ('TN',),\n",
       " ('TX',),\n",
       " ('UT',),\n",
       " ('VA',),\n",
       " ('VI',),\n",
       " ('VT',),\n",
       " ('WA',),\n",
       " ('WI',),\n",
       " ('WV',),\n",
       " ('WY',)]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT DISTINCT state\n",
    "FROM testing\n",
    "ORDER BY state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2abd8d3-abfc-4967-8362-973981d336c7",
   "metadata": {},
   "source": [
    "%%sql\n",
    "DELETE FROM testing\n",
    "WHERE state = 'VI' or state = 'AS' or state = 'PR' or state = 'MP' or state = 'GU'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3b730663-ae36-4244-ad7b-dfb6ed44a6b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>56</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(56,)]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT Count(DISTINCT state)\n",
    "FROM testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0f4c31-f209-441a-be90-d1c108ac0f32",
   "metadata": {},
   "source": [
    "Now we have only 51 states (which includes the 50 states plus District of Columbia), great!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b89d14a-ce95-4285-9453-505d1d3e813f",
   "metadata": {},
   "source": [
    "We will also remove the columns related to number of cases on the testing table. This is because this data is already available to us on the 'cases' table and having 2 records of this information would be redundant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6411a0d1-c256-42d3-8061-02c99f599798",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "ALTER table testing\n",
    "drop column cases_conf_probable, drop column cases_confirmed, drop column cases_probable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad866ee0-9421-4bed-83b5-0d243ab5fc20",
   "metadata": {},
   "source": [
    "Lets now have a look to see if there are many null values in other areas and whether we should drop any rows or columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e3b4cb0c-3bc8-46e6-96f0-b142dca56723",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>date</th>\n",
       "            <th>state</th>\n",
       "            <th>tests_viral_positive</th>\n",
       "            <th>tests_viral_negative</th>\n",
       "            <th>tests_viral_total</th>\n",
       "            <th>tests_antigen_positive</th>\n",
       "            <th>tests_antigen_total</th>\n",
       "            <th>people_viral_positive</th>\n",
       "            <th>people_viral_total</th>\n",
       "            <th>people_antigen_positive</th>\n",
       "            <th>people_antigen_total</th>\n",
       "            <th>encounters_viral_total</th>\n",
       "            <th>tests_combined_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2020-09-18</td>\n",
       "            <td>AR</td>\n",
       "            <td>None</td>\n",
       "            <td>794097</td>\n",
       "            <td>866435</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>72338</td>\n",
       "            <td>None</td>\n",
       "            <td>2010</td>\n",
       "            <td>9587</td>\n",
       "            <td>None</td>\n",
       "            <td>866435</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(datetime.date(2020, 9, 18), 'AR', None, 794097, 866435, None, None, 72338, None, 2010, 9587, None, 866435)]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from testing\n",
    "where people_antigen_positive is not null\n",
    "limit 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d21ab2e4-61f2-46af-bcb5-c0dd14f9d295",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>19447</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(19447,)]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "from testing\n",
    "where tests_viral_positive is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d2ddcb96-a773-40f6-b43a-491a7a944fa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>26268</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(26268,)]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "from testing\n",
    "where tests_viral_negative is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c08c5ef7-6395-4ec8-929d-eca47c6444c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>8722</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(8722,)]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "from testing\n",
    "where tests_viral_total is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c2142f24-1c11-4995-adf0-5c831600a87f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>30110</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(30110,)]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "from testing\n",
    "where tests_antigen_positive is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "43b71c1f-aecf-4eff-85ad-fb9028a04ca0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>26211</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(26211,)]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "from testing\n",
    "where tests_antigen_total is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "aaf233e5-2531-4bd8-bb8a-7d6a6855a7ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>34881</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(34881,)]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "from testing\n",
    "where people_antigen_positive is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1f0bdfab-bdb1-436f-b936-9d180478f65d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>34203</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(34203,)]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "from testing\n",
    "where people_antigen_total is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "40d0ed8a-5d43-4802-aed8-8b342117d164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>27230</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(27230,)]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "from testing\n",
    "where encounters_viral_total is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fedf145b-2009-4438-bab8-5338e383c2a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>211</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(211,)]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "from testing\n",
    "where tests_combined_total is null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a545ef6-50a2-4ca4-89be-38d6123394a9",
   "metadata": {},
   "source": [
    "As we can see from above, we are dealing with alot of null values in the testing table.\n",
    "It is possible that alot of these have been recorded as null, although they should be '0' instead. It is difficult to confirm this however, so we will not alter these null values.\n",
    "However, for simplicity sake, we will remove all the columns related to antigen tests, simply due to the sheer number of null values and also because our analysis will concentrate on PCR testing rather than antigen tests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0762091d-471f-4099-ac01-9929026135c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "ALTER table testing\n",
    "drop column tests_antigen_positive, drop column tests_antigen_total, drop column people_antigen_positive, drop column people_antigen_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "08539610-65bc-4fd7-97ea-dde1256b56e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>date</th>\n",
       "            <th>state</th>\n",
       "            <th>tests_viral_positive</th>\n",
       "            <th>tests_viral_negative</th>\n",
       "            <th>tests_viral_total</th>\n",
       "            <th>people_viral_positive</th>\n",
       "            <th>people_viral_total</th>\n",
       "            <th>encounters_viral_total</th>\n",
       "            <th>tests_combined_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2020-03-06</td>\n",
       "            <td>AK</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>8</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>8</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(datetime.date(2020, 3, 6), 'AK', None, None, 8, None, None, None, 8)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from testing\n",
    "limit 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80c0382-0b2e-40c5-9039-bc5882854c66",
   "metadata": {},
   "source": [
    "### Vaccine Type Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d1b59be9-a08a-4b10-be84-6a33b9ff4223",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-12-11 16:30:42--  https://raw.githubusercontent.com/govex/COVID-19/master/data_tables/vaccine_data/us_data/time_series/vaccine_data_us_timeline.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 10815775 (10M) [text/plain]\n",
      "Saving to: ‘vaccine_data_us_timeline.csv’\n",
      "\n",
      "vaccine_data_us_tim 100%[===================>]  10.31M  --.-KB/s    in 0.1s    \n",
      "\n",
      "2021-12-11 16:30:42 (104 MB/s) - ‘vaccine_data_us_timeline.csv’ saved [10815775/10815775]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/govex/COVID-19/master/data_tables/vaccine_data/us_data/time_series/vaccine_data_us_timeline.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9e2a33cc-58be-45c8-be63-2d3e3bd6f508",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1: date\n",
      "  2: state\n",
      "  3: cases_conf_probable\n",
      "  4: cases_confirmed\n",
      "  5: cases_probable\n",
      "  6: tests_viral_positive\n",
      "  7: tests_viral_negative\n",
      "  8: tests_viral_total\n",
      "  9: tests_antigen_positive\n",
      " 10: tests_antigen_total\n",
      " 11: people_viral_positive\n",
      " 12: people_viral_total\n",
      " 13: people_antigen_positive\n",
      " 14: people_antigen_total\n",
      " 15: encounters_viral_total\n",
      " 16: tests_combined_total\n"
     ]
    }
   ],
   "source": [
    "!csvcut -n time_series_covid19_US.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "554d51be-888f-45d5-b23d-392a3b66919c",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1. \"date\"\n",
      "\n",
      "\tType of data:          Date\n",
      "\tContains null values:  False\n",
      "\tUnique values:         697\n",
      "\tSmallest value:        2020-01-13\n",
      "\tLargest value:         2021-12-09\n",
      "\tMost common values:    2020-03-16 (56x)\n",
      "\t                       2020-03-17 (56x)\n",
      "\t                       2020-03-18 (56x)\n",
      "\t                       2020-03-19 (56x)\n",
      "\t                       2020-03-20 (56x)\n",
      "\n",
      "  2. \"state\"\n",
      "\n",
      "\tType of data:          Text\n",
      "\tContains null values:  False\n",
      "\tUnique values:         56\n",
      "\tLongest value:         2 characters\n",
      "\tMost common values:    WA (697x)\n",
      "\t                       MA (688x)\n",
      "\t                       VA (683x)\n",
      "\t                       FL (681x)\n",
      "\t                       NJ (669x)\n",
      "\n",
      "  3. \"cases_conf_probable\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         28343\n",
      "\tSmallest value:        0\n",
      "\tLargest value:         4861352\n",
      "\tSum:                   13706387381\n",
      "\tMean:                  380828.189853\n",
      "\tMedian:                132677\n",
      "\tStDev:                 640473.902336\n",
      "\tMost common values:    0 (665x)\n",
      "\t                       None (301x)\n",
      "\t                       183 (106x)\n",
      "\t                       2 (67x)\n",
      "\t                       1 (66x)\n",
      "\n",
      "  4. \"cases_confirmed\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         10190\n",
      "\tSmallest value:        0\n",
      "\tLargest value:         4861352\n",
      "\tSum:                   8453726469\n",
      "\tMean:                  620912.704297\n",
      "\tMedian:                398134\n",
      "\tStDev:                 792593.228413\n",
      "\tMost common values:    None (22677x)\n",
      "\t                       0 (142x)\n",
      "\t                       183 (101x)\n",
      "\t                       395 (24x)\n",
      "\t                       111156 (14x)\n",
      "\n",
      "  5. \"cases_probable\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         6734\n",
      "\tSmallest value:        0\n",
      "\tLargest value:         738537\n",
      "\tSum:                   896838270\n",
      "\tMean:                  65876.17673\n",
      "\tMedian:                17098.5\n",
      "\tStDev:                 102558.931575\n",
      "\tMost common values:    None (22678x)\n",
      "\t                       0 (4982x)\n",
      "\t                       15590 (14x)\n",
      "\t                       732 (13x)\n",
      "\t                       46144 (13x)\n",
      "\n",
      "  6. \"tests_viral_positive\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         14068\n",
      "\tSmallest value:        0\n",
      "\tLargest value:         4378438\n",
      "\tSum:                   7204351296\n",
      "\tMean:                  426949.821975\n",
      "\tMedian:                189026.5\n",
      "\tStDev:                 667094.524792\n",
      "\tMost common values:    None (19418x)\n",
      "\t                       0 (85x)\n",
      "\t                       618835 (44x)\n",
      "\t                       734205 (43x)\n",
      "\t                       2 (25x)\n",
      "\n",
      "  7. \"tests_viral_negative\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         8154\n",
      "\tSmallest value:        1\n",
      "\tLargest value:         39801779\n",
      "\tSum:                   38090024778\n",
      "\tMean:                  3785155.995031\n",
      "\tMedian:                2447303\n",
      "\tStDev:                 5432538.633255\n",
      "\tMost common values:    None (26229x)\n",
      "\t                       9223141 (44x)\n",
      "\t                       10118939 (43x)\n",
      "\t                       252506 (17x)\n",
      "\t                       3620798 (16x)\n",
      "\n",
      "  8. \"tests_viral_total\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         22420\n",
      "\tSmallest value:        0\n",
      "\tLargest value:         113686439\n",
      "\tSum:                   147972254136\n",
      "\tMean:                  5364812.346313\n",
      "\tMedian:                2175292\n",
      "\tStDev:                 10150962.319608\n",
      "\tMost common values:    None (8710x)\n",
      "\t                       2140 (371x)\n",
      "\t                       26131 (360x)\n",
      "\t                       3 (39x)\n",
      "\t                       1616 (31x)\n",
      "\n",
      "  9. \"tests_antigen_positive\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         4325\n",
      "\tSmallest value:        0\n",
      "\tLargest value:         542374\n",
      "\tSum:                   594054045\n",
      "\tMean:                  95430.368675\n",
      "\tMedian:                62598\n",
      "\tStDev:                 101385.458579\n",
      "\tMost common values:    None (30067x)\n",
      "\t                       1 (81x)\n",
      "\t                       0 (77x)\n",
      "\t                       3 (54x)\n",
      "\t                       248 (51x)\n",
      "\n",
      " 10. \"tests_antigen_total\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         6796\n",
      "\tSmallest value:        0\n",
      "\tLargest value:         9405890\n",
      "\tSum:                   11064337283\n",
      "\tMean:                  1093962.555171\n",
      "\tMedian:                712107\n",
      "\tStDev:                 1300592.333155\n",
      "\tMost common values:    None (26178x)\n",
      "\t                       123809 (80x)\n",
      "\t                       78973 (42x)\n",
      "\t                       748339 (41x)\n",
      "\t                       396680 (37x)\n",
      "\n",
      " 11. \"people_viral_positive\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         26507\n",
      "\tSmallest value:        0\n",
      "\tLargest value:         4861352\n",
      "\tSum:                   12315278829\n",
      "\tMean:                  374871.509467\n",
      "\tMedian:                143864\n",
      "\tStDev:                 621772.212559\n",
      "\tMost common values:    None (3440x)\n",
      "\t                       0 (405x)\n",
      "\t                       183 (104x)\n",
      "\t                       1 (43x)\n",
      "\t                       2 (35x)\n",
      "\n",
      " 12. \"people_viral_total\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         12877\n",
      "\tSmallest value:        1\n",
      "\tLargest value:         13374909\n",
      "\tSum:                   27658651691\n",
      "\tMean:                  1723495.244953\n",
      "\tMedian:                810477\n",
      "\tStDev:                 2324492.421271\n",
      "\tMost common values:    None (20244x)\n",
      "\t                       17542 (360x)\n",
      "\t                       694144 (226x)\n",
      "\t                       13374909 (190x)\n",
      "\t                       498100 (105x)\n",
      "\n",
      " 13. \"people_antigen_positive\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         1150\n",
      "\tSmallest value:        3\n",
      "\tLargest value:         168762\n",
      "\tSum:                   92598540\n",
      "\tMean:                  63250.368852\n",
      "\tMedian:                65184.5\n",
      "\tStDev:                 43597.209044\n",
      "\tMost common values:    None (34828x)\n",
      "\t                       81803 (35x)\n",
      "\t                       57190 (35x)\n",
      "\t                       57683 (35x)\n",
      "\t                       76255 (9x)\n",
      "\n",
      " 14. \"people_antigen_total\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         1742\n",
      "\tSmallest value:        3\n",
      "\tLargest value:         1286192\n",
      "\tSum:                   870798859\n",
      "\tMean:                  406725.296123\n",
      "\tMedian:                350434\n",
      "\tStDev:                 309936.821812\n",
      "\tMost common values:    None (34151x)\n",
      "\t                       481311 (35x)\n",
      "\t                       247999 (35x)\n",
      "\t                       580372 (35x)\n",
      "\t                       366813 (35x)\n",
      "\n",
      " 15. \"encounters_viral_total\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         8105\n",
      "\tSmallest value:        0\n",
      "\tLargest value:         82538008\n",
      "\tSum:                   59658907321\n",
      "\tMean:                  6553043.422781\n",
      "\tMedian:                2469614\n",
      "\tStDev:                 11591459.401699\n",
      "\tMost common values:    None (27188x)\n",
      "\t                       23272104 (263x)\n",
      "\t                       9842443 (64x)\n",
      "\t                       0 (40x)\n",
      "\t                       9958651 (22x)\n",
      "\n",
      " 16. \"tests_combined_total\"\n",
      "\n",
      "\tType of data:          Number\n",
      "\tContains null values:  True (excluded from calculations)\n",
      "\tUnique values:         30314\n",
      "\tSmallest value:        0\n",
      "\tLargest value:         113686439\n",
      "\tSum:                   193215989535\n",
      "\tMean:                  5355061.931072\n",
      "\tMedian:                1820630\n",
      "\tStDev:                 10537849.45487\n",
      "\tMost common values:    2140 (371x)\n",
      "\t                       17542 (280x)\n",
      "\t                       None (211x)\n",
      "\t                       0 (102x)\n",
      "\t                       9842443 (64x)\n",
      "\n",
      "Row count: 36292\n"
     ]
    }
   ],
   "source": [
    "!csvstat time_series_covid19_US.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8b7daa0c-a161-4de7-b31c-3ff5badeea08",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_series = sqlc.read.csv(\"time_series_covid19_US.csv\",  header=True, inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a77a70fd-3b76-4f25-8823-3956e0f2aa6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- date: string (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- cases_conf_probable: integer (nullable = true)\n",
      " |-- cases_confirmed: integer (nullable = true)\n",
      " |-- cases_probable: integer (nullable = true)\n",
      " |-- tests_viral_positive: integer (nullable = true)\n",
      " |-- tests_viral_negative: integer (nullable = true)\n",
      " |-- tests_viral_total: integer (nullable = true)\n",
      " |-- tests_antigen_positive: integer (nullable = true)\n",
      " |-- tests_antigen_total: integer (nullable = true)\n",
      " |-- people_viral_positive: integer (nullable = true)\n",
      " |-- people_viral_total: integer (nullable = true)\n",
      " |-- people_antigen_positive: integer (nullable = true)\n",
      " |-- people_antigen_total: integer (nullable = true)\n",
      " |-- encounters_viral_total: integer (nullable = true)\n",
      " |-- tests_combined_total: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "time_series.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8debe3b9-2664-4d20-bdd4-cbe9e5f6c85e",
   "metadata": {},
   "source": [
    "Based on the above, we expect that we can work with the following table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "57b0a210-9b0d-4416-be18-36dc2de32398",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "36292 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "COPY testing FROM '/home/ubuntu/notebooks/Final Group Project/Final Submission/time_series_covid19_US.csv'\n",
    "CSV\n",
    "HEADER;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6c2a814e-38dd-414d-b8c9-eb370d49dd95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "5 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>date</th>\n",
       "            <th>state</th>\n",
       "            <th>cases_conf_probable</th>\n",
       "            <th>cases_confirmed</th>\n",
       "            <th>cases_probable</th>\n",
       "            <th>tests_viral_positive</th>\n",
       "            <th>tests_viral_negative</th>\n",
       "            <th>tests_viral_total</th>\n",
       "            <th>tests_antigen_positive</th>\n",
       "            <th>tests_antigen_total</th>\n",
       "            <th>people_viral_positive</th>\n",
       "            <th>people_viral_total</th>\n",
       "            <th>people_antigen_positive</th>\n",
       "            <th>people_antigen_total</th>\n",
       "            <th>encounters_viral_total</th>\n",
       "            <th>tests_combined_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2020-03-06</td>\n",
       "            <td>AK</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>8</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>8</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2020-03-07</td>\n",
       "            <td>AK</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>12</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>12</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2020-03-08</td>\n",
       "            <td>AK</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>14</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>14</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2020-03-09</td>\n",
       "            <td>AK</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>23</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>23</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2020-03-10</td>\n",
       "            <td>AK</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>23</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>23</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(datetime.date(2020, 3, 6), 'AK', None, None, None, None, None, 8, None, None, None, None, None, None, None, 8),\n",
       " (datetime.date(2020, 3, 7), 'AK', None, None, None, None, None, 12, None, None, None, None, None, None, None, 12),\n",
       " (datetime.date(2020, 3, 8), 'AK', None, None, None, None, None, 14, None, None, None, None, None, None, None, 14),\n",
       " (datetime.date(2020, 3, 9), 'AK', None, None, None, None, None, 23, None, None, None, None, None, None, None, 23),\n",
       " (datetime.date(2020, 3, 10), 'AK', None, None, None, None, None, 23, None, None, None, None, None, None, None, 23)]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT * from testing limit 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41e042a1-4869-4e12-9bb4-7ebc9f760400",
   "metadata": {},
   "source": [
    "Let's limit only to 51 states (50 US states + DC) like we did in the cases table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eafdee0-7559-4d6f-a21b-fd96cdc3480d",
   "metadata": {},
   "source": [
    "Like we did with our other tables, lets narrow our analysis down to just the 50 states plus DC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "580aa14e-5b36-4347-861c-2f016b37d743",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "56 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>state</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>AK</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AL</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AR</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AS</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AZ</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CO</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CT</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>DC</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>DE</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>FL</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>GA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>GU</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>HI</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>IA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ID</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>IL</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>IN</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>KS</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>KY</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>LA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MD</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ME</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MI</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MN</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MO</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MP</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MS</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MT</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NC</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ND</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NE</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NH</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NJ</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NM</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NV</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NY</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>OH</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>OK</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>OR</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>PA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>PR</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>RI</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SC</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SD</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>TN</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>TX</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>UT</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>VA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>VI</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>VT</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>WA</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>WI</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>WV</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>WY</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('AK',),\n",
       " ('AL',),\n",
       " ('AR',),\n",
       " ('AS',),\n",
       " ('AZ',),\n",
       " ('CA',),\n",
       " ('CO',),\n",
       " ('CT',),\n",
       " ('DC',),\n",
       " ('DE',),\n",
       " ('FL',),\n",
       " ('GA',),\n",
       " ('GU',),\n",
       " ('HI',),\n",
       " ('IA',),\n",
       " ('ID',),\n",
       " ('IL',),\n",
       " ('IN',),\n",
       " ('KS',),\n",
       " ('KY',),\n",
       " ('LA',),\n",
       " ('MA',),\n",
       " ('MD',),\n",
       " ('ME',),\n",
       " ('MI',),\n",
       " ('MN',),\n",
       " ('MO',),\n",
       " ('MP',),\n",
       " ('MS',),\n",
       " ('MT',),\n",
       " ('NC',),\n",
       " ('ND',),\n",
       " ('NE',),\n",
       " ('NH',),\n",
       " ('NJ',),\n",
       " ('NM',),\n",
       " ('NV',),\n",
       " ('NY',),\n",
       " ('OH',),\n",
       " ('OK',),\n",
       " ('OR',),\n",
       " ('PA',),\n",
       " ('PR',),\n",
       " ('RI',),\n",
       " ('SC',),\n",
       " ('SD',),\n",
       " ('TN',),\n",
       " ('TX',),\n",
       " ('UT',),\n",
       " ('VA',),\n",
       " ('VI',),\n",
       " ('VT',),\n",
       " ('WA',),\n",
       " ('WI',),\n",
       " ('WV',),\n",
       " ('WY',)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT DISTINCT state\n",
    "FROM testing\n",
    "ORDER BY state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "096ea91f-905c-470b-bf2c-a60e3ec1fe14",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%sql\n",
    "DELETE FROM testing\n",
    "WHERE state = 'VI' or state = 'AS' or state = 'PR' or state = 'MP' or state = 'GU'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "23bde55a-7330-420c-9487-7f2d8fa7d789",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>51</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(51,)]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT Count(DISTINCT state)\n",
    "FROM testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "243ea95e-6724-4bf6-aa3e-aeef4718c835",
   "metadata": {},
   "source": [
    "Now we have only 51 states (which includes the 50 states plus District of Columbia), great!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829d54f5-64be-4126-9373-cac2adf2ad1b",
   "metadata": {},
   "source": [
    "### Vaccine Type Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a8f32206-27da-4737-a234-24c04d09cdf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-12-10 22:53:15--  https://raw.githubusercontent.com/govex/COVID-19/master/data_tables/vaccine_data/us_data/time_series/vaccine_data_us_timeline.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.111.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 10782427 (10M) [text/plain]\n",
      "Saving to: ‘vaccine_data_us_timeline.csv’\n",
      "\n",
      "vaccine_data_us_tim 100%[===================>]  10.28M  --.-KB/s    in 0.04s   \n",
      "\n",
      "2021-12-10 22:53:15 (251 MB/s) - ‘vaccine_data_us_timeline.csv’ saved [10782427/10782427]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/govex/COVID-19/master/data_tables/vaccine_data/us_data/time_series/vaccine_data_us_timeline.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "cea37b19-e0d9-4a3f-ba67-f1d4b60ab5f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1: Province_State\n",
      "  2: Date\n",
      "  3: Vaccine_Type\n",
      "  4: FIPS\n",
      "  5: Country_Region\n",
      "  6: Lat\n",
      "  7: Long_\n",
      "  8: Doses_alloc\n",
      "  9: Doses_shipped\n",
      " 10: Doses_admin\n",
      " 11: Stage_One_Doses\n",
      " 12: Stage_Two_Doses\n",
      " 13: Combined_Key\n"
     ]
    }
   ],
   "source": [
    "!csvcut -n vaccine_data_us_timeline.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "3280f3da-d1e5-40b4-94bc-8a289bdb5b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.types import DateType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "5c7a78d0-95be-4073-8c56-2da1fcc31f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.types import IntegerType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "25a6311a-8a53-41ae-8fc7-6b9fab1d1c12",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vaccines_ship = sqlc.read.csv(\"vaccine_data_us_timeline.csv\",  header=True, inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "db7724f7-ded2-4860-b839-0a194eee17a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Province_State: string (nullable = true)\n",
      " |-- Date: string (nullable = true)\n",
      " |-- Vaccine_Type: string (nullable = true)\n",
      " |-- FIPS: integer (nullable = true)\n",
      " |-- Country_Region: string (nullable = true)\n",
      " |-- Lat: double (nullable = true)\n",
      " |-- Long_: double (nullable = true)\n",
      " |-- Doses_alloc: integer (nullable = true)\n",
      " |-- Doses_shipped: integer (nullable = true)\n",
      " |-- Doses_admin: integer (nullable = true)\n",
      " |-- Stage_One_Doses: integer (nullable = true)\n",
      " |-- Stage_Two_Doses: integer (nullable = true)\n",
      " |-- Combined_Key: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vaccines_ship.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f81644d5-f95c-4e3d-b090-99af161151fc",
   "metadata": {},
   "source": [
    "Based on the data above, we expect that the following table structure will be suitable: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "8b7e393e-4651-46df-995c-1ca7e80802e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP TABLE IF EXISTS vaccine_type;\n",
    "\n",
    "CREATE TABLE vaccine_type (\n",
    "    Province_State Varchar(64) NOT NULL,\n",
    "    Date DATE NOT NULL,\n",
    "    Vaccine_Type Varchar(64),\n",
    "    FIPS NUMERIC,\n",
    "    Country_Region Varchar(2),\n",
    "    Lat NUMERIC,\n",
    "    Long_ NUMERIC,\n",
    "    Doses_alloc INTEGER, \n",
    "    Doses_shipped INTEGER,\n",
    "    Doses_admin INTEGER,\n",
    "    Stage_One_Doses INTEGER,\n",
    "    Stage_Two_Doses INTEGER,\n",
    "    Combined_Key varchar(64)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "2a45e999-082d-4577-b485-26c42b6d4347",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "121372 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "COPY vaccine_type FROM '/home/ubuntu/notebooks/Final Group Project/Final Submission/vaccine_data_us_timeline.csv'\n",
    "CSV\n",
    "HEADER;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "b1a5b756-b560-4615-a166-11aa31ea6851",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "5 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "            <th>date</th>\n",
       "            <th>vaccine_type</th>\n",
       "            <th>fips</th>\n",
       "            <th>country_region</th>\n",
       "            <th>lat</th>\n",
       "            <th>long_</th>\n",
       "            <th>doses_alloc</th>\n",
       "            <th>doses_shipped</th>\n",
       "            <th>doses_admin</th>\n",
       "            <th>stage_one_doses</th>\n",
       "            <th>stage_two_doses</th>\n",
       "            <th>combined_key</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>2020-12-10</td>\n",
       "            <td>All</td>\n",
       "            <td>1</td>\n",
       "            <td>US</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>Alabama, US</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>2020-12-10</td>\n",
       "            <td>Moderna</td>\n",
       "            <td>1</td>\n",
       "            <td>US</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>Alabama, US</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>2020-12-10</td>\n",
       "            <td>Pfizer</td>\n",
       "            <td>1</td>\n",
       "            <td>US</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>Alabama, US</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>2020-12-11</td>\n",
       "            <td>All</td>\n",
       "            <td>1</td>\n",
       "            <td>US</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>Alabama, US</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>2020-12-11</td>\n",
       "            <td>Moderna</td>\n",
       "            <td>1</td>\n",
       "            <td>US</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>Alabama, US</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Alabama', datetime.date(2020, 12, 10), 'All', Decimal('1'), 'US', Decimal('32.3182'), Decimal('-86.9023'), None, None, None, None, None, 'Alabama, US'),\n",
       " ('Alabama', datetime.date(2020, 12, 10), 'Moderna', Decimal('1'), 'US', Decimal('32.3182'), Decimal('-86.9023'), None, None, None, None, None, 'Alabama, US'),\n",
       " ('Alabama', datetime.date(2020, 12, 10), 'Pfizer', Decimal('1'), 'US', Decimal('32.3182'), Decimal('-86.9023'), None, None, None, None, None, 'Alabama, US'),\n",
       " ('Alabama', datetime.date(2020, 12, 11), 'All', Decimal('1'), 'US', Decimal('32.3182'), Decimal('-86.9023'), None, None, None, None, None, 'Alabama, US'),\n",
       " ('Alabama', datetime.date(2020, 12, 11), 'Moderna', Decimal('1'), 'US', Decimal('32.3182'), Decimal('-86.9023'), None, None, None, None, None, 'Alabama, US')]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT * from vaccine_type limit 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "bd2e3fad-78f5-4a87-aa7b-58edf097c282",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>65</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(65,)]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(distinct province_state)\n",
    "FROM vaccine_type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f3561f-082b-4d27-aa59-0f0b4ab731ed",
   "metadata": {},
   "source": [
    "We note that there are 65 states over here! We need to get this down to the 51 states we are looking to analyze. Let's see which ones have null latitude or longitude values first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "eb087e82-59b6-46a6-905b-385bc688ddc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "9 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Bureau of Prisons</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Department of Defense</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Federal Bureau of Prisons</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Federated States of Micronesia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indian Health Services</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Long Term Care (LTC) Program</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Marshall Islands</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Republic of Palau</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Veterans Health Administration</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Bureau of Prisons',),\n",
       " ('Department of Defense',),\n",
       " ('Federal Bureau of Prisons',),\n",
       " ('Federated States of Micronesia',),\n",
       " ('Indian Health Services',),\n",
       " ('Long Term Care (LTC) Program',),\n",
       " ('Marshall Islands',),\n",
       " ('Republic of Palau',),\n",
       " ('Veterans Health Administration',)]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT distinct province_state FROM vaccine_type\n",
    "WHERE lat is null or long_ is null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f8d59f-8b3f-43ba-b5b5-20152fa74cb5",
   "metadata": {},
   "source": [
    "First, let's remove all of those states from the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "4573dc3b-e4a5-4afc-8ac1-37d3135c0b70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "10572 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DELETE FROM vaccine_type\n",
    "WHERE lat is null or long_ is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "5b6965f3-481d-4d2d-b534-14dc9f22d8cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "56 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alaska</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>American Samoa</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arizona</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arkansas</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>California</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Colorado</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Connecticut</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Delaware</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>District of Columbia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Florida</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Georgia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Guam</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Hawaii</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Idaho</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Illinois</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indiana</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Iowa</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Kansas</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Kentucky</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Louisiana</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Maine</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Maryland</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Massachusetts</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Michigan</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Minnesota</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Mississippi</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Missouri</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Montana</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Nebraska</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Nevada</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Hampshire</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Jersey</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Mexico</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New York</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>North Carolina</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>North Dakota</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Northern Mariana Islands</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Ohio</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Oklahoma</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Oregon</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Pennsylvania</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Puerto Rico</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Rhode Island</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Carolina</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Dakota</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Tennessee</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Texas</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Utah</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Vermont</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Virgin Islands</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Virginia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Washington</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>West Virginia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Wisconsin</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Wyoming</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Alabama',),\n",
       " ('Alaska',),\n",
       " ('American Samoa',),\n",
       " ('Arizona',),\n",
       " ('Arkansas',),\n",
       " ('California',),\n",
       " ('Colorado',),\n",
       " ('Connecticut',),\n",
       " ('Delaware',),\n",
       " ('District of Columbia',),\n",
       " ('Florida',),\n",
       " ('Georgia',),\n",
       " ('Guam',),\n",
       " ('Hawaii',),\n",
       " ('Idaho',),\n",
       " ('Illinois',),\n",
       " ('Indiana',),\n",
       " ('Iowa',),\n",
       " ('Kansas',),\n",
       " ('Kentucky',),\n",
       " ('Louisiana',),\n",
       " ('Maine',),\n",
       " ('Maryland',),\n",
       " ('Massachusetts',),\n",
       " ('Michigan',),\n",
       " ('Minnesota',),\n",
       " ('Mississippi',),\n",
       " ('Missouri',),\n",
       " ('Montana',),\n",
       " ('Nebraska',),\n",
       " ('Nevada',),\n",
       " ('New Hampshire',),\n",
       " ('New Jersey',),\n",
       " ('New Mexico',),\n",
       " ('New York',),\n",
       " ('North Carolina',),\n",
       " ('North Dakota',),\n",
       " ('Northern Mariana Islands',),\n",
       " ('Ohio',),\n",
       " ('Oklahoma',),\n",
       " ('Oregon',),\n",
       " ('Pennsylvania',),\n",
       " ('Puerto Rico',),\n",
       " ('Rhode Island',),\n",
       " ('South Carolina',),\n",
       " ('South Dakota',),\n",
       " ('Tennessee',),\n",
       " ('Texas',),\n",
       " ('Utah',),\n",
       " ('Vermont',),\n",
       " ('Virgin Islands',),\n",
       " ('Virginia',),\n",
       " ('Washington',),\n",
       " ('West Virginia',),\n",
       " ('Wisconsin',),\n",
       " ('Wyoming',)]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT distinct province_state\n",
    "FROM vaccine_type\n",
    "ORDER BY province_state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e20e0a-be12-44e5-8c38-e01f0befeb64",
   "metadata": {},
   "source": [
    "Now let's remove the US territories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "afeca431-2d3b-443c-977b-4235bf411d9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "9897 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DELETE FROM vaccine_type\n",
    "WHERE province_state = 'Virgin Islands' or province_state = 'American Samoa' or province_state = 'Puerto Rico' or province_state = 'Northern Mariana Islands' or province_state = 'Guam'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "b369bd4c-fcbd-4931-8ecc-405ba6506b5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>51</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(51,)]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(distinct province_state)\n",
    "FROM vaccine_type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "041734ab-75d1-44c6-9893-ecfb17bb0d72",
   "metadata": {},
   "source": [
    "We now just have 51 states, great"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "dc1d1f21-86c1-4db2-9e3c-18df9114fb83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "            <th>date</th>\n",
       "            <th>vaccine_type</th>\n",
       "            <th>fips</th>\n",
       "            <th>country_region</th>\n",
       "            <th>lat</th>\n",
       "            <th>long_</th>\n",
       "            <th>doses_alloc</th>\n",
       "            <th>doses_shipped</th>\n",
       "            <th>doses_admin</th>\n",
       "            <th>stage_one_doses</th>\n",
       "            <th>stage_two_doses</th>\n",
       "            <th>combined_key</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>2020-12-10</td>\n",
       "            <td>All</td>\n",
       "            <td>1</td>\n",
       "            <td>US</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>Alabama, US</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Alabama', datetime.date(2020, 12, 10), 'All', Decimal('1'), 'US', Decimal('32.3182'), Decimal('-86.9023'), None, None, None, None, None, 'Alabama, US')]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select * from vaccine_type limit 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "83c5f6cd-6091-494f-9c8c-a31c765c079d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>85105</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(85105,)]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "FROM vaccine_type\n",
    "where doses_alloc is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "4832d9b0-a6a6-486e-af5b-75beb8832a17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>13556</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(13556,)]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "FROM vaccine_type\n",
    "where doses_shipped is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1d7bc10f-eb61-4ab2-9875-108f6c72c42d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>10320</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(10320,)]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "FROM vaccine_type\n",
    "where doses_admin is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "5033bc14-97ef-419e-a101-e8d03e9296d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>50336</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(50336,)]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "FROM vaccine_type\n",
    "where stage_one_doses is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "bfc05bcb-3807-4ca2-acce-a6a58353e128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>41677</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(41677,)]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select count(*)\n",
    "FROM vaccine_type\n",
    "where stage_two_doses is null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f6e828-b6ab-4b36-ac06-e65589772de0",
   "metadata": {},
   "source": [
    "We see alot of null values in these columns for this table. It is likely that these null values correspond to 'zeros'. However, we can not be sure of this. Since these columns still provide useful information that other people can use for their analysis, we will keep them in our table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adec306a-a6c7-4f8d-9502-4ee62e2b63ab",
   "metadata": {},
   "source": [
    "We will be merging the two vaccination tables later on, and will remove some columns then"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c08e583-df4e-4e89-a6b8-d13cb776c517",
   "metadata": {
    "tags": []
   },
   "source": [
    "### People Vaccinated Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "b355fbe9-5323-4115-abb1-5138aa11f039",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-12-11 16:36:56--  https://raw.githubusercontent.com/govex/COVID-19/master/data_tables/vaccine_data/us_data/time_series/people_vaccinated_us_timeline.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1649934 (1.6M) [text/plain]\n",
      "Saving to: ‘people_vaccinated_us_timeline.csv.1’\n",
      "\n",
      "people_vaccinated_u 100%[===================>]   1.57M  --.-KB/s    in 0.008s  \n",
      "\n",
      "2021-12-11 16:36:56 (194 MB/s) - ‘people_vaccinated_us_timeline.csv.1’ saved [1649934/1649934]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/govex/COVID-19/master/data_tables/vaccine_data/us_data/time_series/people_vaccinated_us_timeline.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "a6a2d309-6709-4476-9b96-b0e05ab97620",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1: FIPS\n",
      "  2: Province_State\n",
      "  3: Country_Region\n",
      "  4: Date\n",
      "  5: Lat\n",
      "  6: Long_\n",
      "  7: Combined_Key\n",
      "  8: People_Fully_Vaccinated\n",
      "  9: People_Partially_Vaccinated\n"
     ]
    }
   ],
   "source": [
    "!csvcut -n people_vaccinated_us_timeline.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "3747a6c0-1e7b-4145-a21c-19552e83166c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1. FIPS: Number\n",
      "  2. Province_State: Text\n",
      "  3. Country_Region: Text\n",
      "  4. Date: Date\n",
      "  5. Lat: Number\n",
      "  6. Long_: Number\n",
      "  7. Combined_Key: Text\n",
      "  8. People_Fully_Vaccinated: Number\n",
      "  9. People_Partially_Vaccinated: Number\n"
     ]
    }
   ],
   "source": [
    "!csvstat --type people_vaccinated_us_timeline.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "640e61b2-13ff-444a-8f49-b53110502f3c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "people_vaccinated = sqlc.read.csv(\"people_vaccinated_us_timeline.csv\",  header=True, inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "b88704aa-43c8-4d14-b942-695f7bc6dbd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- FIPS: integer (nullable = true)\n",
      " |-- Province_State: string (nullable = true)\n",
      " |-- Country_Region: string (nullable = true)\n",
      " |-- Date: string (nullable = true)\n",
      " |-- Lat: double (nullable = true)\n",
      " |-- Long_: double (nullable = true)\n",
      " |-- Combined_Key: string (nullable = true)\n",
      " |-- People_Fully_Vaccinated: integer (nullable = true)\n",
      " |-- People_Partially_Vaccinated: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "people_vaccinated.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "f5b519f6-9f51-4104-bd63-d96869829750",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP TABLE IF EXISTS people_vaccinated;\n",
    "\n",
    "CREATE TABLE people_vaccinated (\n",
    "    FIPS INTEGER,\n",
    "    PROVINCE_STATE VARCHAR(64) NOT NULL,\n",
    "    Country_Region VARCHAR(2) NOT NULL,\n",
    "    Date DATE NOT NULL,\n",
    "    Lat NUMERIC,\n",
    "    Long_ NUMERIC,\n",
    "    Combined_Key VARCHAR(50),\n",
    "    People_Fully_Vaccinated INTEGER,\n",
    "    People_Partially_Vaccinated INTEGER\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "fc7c817b-68e0-40f5-b58f-ba4e2181ce72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "22272 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "COPY people_vaccinated FROM '/home/ubuntu/notebooks/Final Group Project/Final Submission/people_vaccinated_us_timeline.csv'\n",
    "CSV\n",
    "HEADER;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "dfd5dc9d-c45f-41d5-a9d0-7f61a763459c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "10 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>fips</th>\n",
       "            <th>province_state</th>\n",
       "            <th>country_region</th>\n",
       "            <th>date</th>\n",
       "            <th>lat</th>\n",
       "            <th>long_</th>\n",
       "            <th>combined_key</th>\n",
       "            <th>people_fully_vaccinated</th>\n",
       "            <th>people_partially_vaccinated</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-10</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-11</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-12</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-13</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-14</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-15</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-16</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-17</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-18</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-19</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1, 'Alabama', 'US', datetime.date(2020, 12, 10), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None),\n",
       " (1, 'Alabama', 'US', datetime.date(2020, 12, 11), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None),\n",
       " (1, 'Alabama', 'US', datetime.date(2020, 12, 12), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None),\n",
       " (1, 'Alabama', 'US', datetime.date(2020, 12, 13), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None),\n",
       " (1, 'Alabama', 'US', datetime.date(2020, 12, 14), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None),\n",
       " (1, 'Alabama', 'US', datetime.date(2020, 12, 15), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None),\n",
       " (1, 'Alabama', 'US', datetime.date(2020, 12, 16), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None),\n",
       " (1, 'Alabama', 'US', datetime.date(2020, 12, 17), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None),\n",
       " (1, 'Alabama', 'US', datetime.date(2020, 12, 18), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None),\n",
       " (1, 'Alabama', 'US', datetime.date(2020, 12, 19), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None)]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT * FROM people_vaccinated limit 10;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "d5b9fe9a-08c0-403f-b027-f392506fbd35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>61</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(61,)]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(distinct province_state) \n",
    "FROM people_vaccinated"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3202d289-8d3e-4d6e-b446-d69db805adbf",
   "metadata": {},
   "source": [
    "Like we did with the previous 3 tables, let's get this down to just 51 province_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "c10dc2a5-70ca-4291-913c-b139a52cd934",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "5 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Department of Defense</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Federal Bureau of Prisons</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indian Health Services</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Long Term Care (LTC) Program</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Veterans Health Administration</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Department of Defense',),\n",
       " ('Federal Bureau of Prisons',),\n",
       " ('Indian Health Services',),\n",
       " ('Long Term Care (LTC) Program',),\n",
       " ('Veterans Health Administration',)]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT distinct province_state FROM people_vaccinated\n",
    "WHERE lat is null or long_ is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "f0192a72-1c3f-4f3b-aecc-4472c3971c2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1776 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DELETE FROM people_vaccinated\n",
    "WHERE lat is null or long_ is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "34d620dc-34d7-46bc-aed9-9385caa8fca2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "56 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alaska</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>American Samoa</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arizona</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arkansas</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>California</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Colorado</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Connecticut</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Delaware</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>District of Columbia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Florida</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Georgia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Guam</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Hawaii</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Idaho</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Illinois</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indiana</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Iowa</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Kansas</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Kentucky</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Louisiana</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Maine</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Maryland</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Massachusetts</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Michigan</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Minnesota</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Mississippi</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Missouri</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Montana</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Nebraska</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Nevada</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Hampshire</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Jersey</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Mexico</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New York</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>North Carolina</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>North Dakota</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Northern Mariana Islands</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Ohio</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Oklahoma</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Oregon</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Pennsylvania</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Puerto Rico</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Rhode Island</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Carolina</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Dakota</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Tennessee</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Texas</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Utah</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Vermont</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Virgin Islands</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Virginia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Washington</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>West Virginia</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Wisconsin</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Wyoming</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Alabama',),\n",
       " ('Alaska',),\n",
       " ('American Samoa',),\n",
       " ('Arizona',),\n",
       " ('Arkansas',),\n",
       " ('California',),\n",
       " ('Colorado',),\n",
       " ('Connecticut',),\n",
       " ('Delaware',),\n",
       " ('District of Columbia',),\n",
       " ('Florida',),\n",
       " ('Georgia',),\n",
       " ('Guam',),\n",
       " ('Hawaii',),\n",
       " ('Idaho',),\n",
       " ('Illinois',),\n",
       " ('Indiana',),\n",
       " ('Iowa',),\n",
       " ('Kansas',),\n",
       " ('Kentucky',),\n",
       " ('Louisiana',),\n",
       " ('Maine',),\n",
       " ('Maryland',),\n",
       " ('Massachusetts',),\n",
       " ('Michigan',),\n",
       " ('Minnesota',),\n",
       " ('Mississippi',),\n",
       " ('Missouri',),\n",
       " ('Montana',),\n",
       " ('Nebraska',),\n",
       " ('Nevada',),\n",
       " ('New Hampshire',),\n",
       " ('New Jersey',),\n",
       " ('New Mexico',),\n",
       " ('New York',),\n",
       " ('North Carolina',),\n",
       " ('North Dakota',),\n",
       " ('Northern Mariana Islands',),\n",
       " ('Ohio',),\n",
       " ('Oklahoma',),\n",
       " ('Oregon',),\n",
       " ('Pennsylvania',),\n",
       " ('Puerto Rico',),\n",
       " ('Rhode Island',),\n",
       " ('South Carolina',),\n",
       " ('South Dakota',),\n",
       " ('Tennessee',),\n",
       " ('Texas',),\n",
       " ('Utah',),\n",
       " ('Vermont',),\n",
       " ('Virgin Islands',),\n",
       " ('Virginia',),\n",
       " ('Washington',),\n",
       " ('West Virginia',),\n",
       " ('Wisconsin',),\n",
       " ('Wyoming',)]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT distinct province_state\n",
    "FROM people_vaccinated\n",
    "ORDER BY province_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "18eb8d76-6ce3-43fe-bdc3-de17049c871e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1830 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DELETE FROM people_vaccinated\n",
    "WHERE province_state = 'Virgin Islands' or province_state = 'American Samoa' or province_state = 'Puerto Rico' or province_state = 'Northern Mariana Islands' or province_state = 'Guam'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "8ee8947f-45fe-4086-90b8-ae3ec78a0504",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>51</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(51,)]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT count(distinct province_state) \n",
    "FROM people_vaccinated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "6cadae6f-b2d5-4fc5-acd0-6408c56d0764",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>fips</th>\n",
       "            <th>province_state</th>\n",
       "            <th>country_region</th>\n",
       "            <th>date</th>\n",
       "            <th>lat</th>\n",
       "            <th>long_</th>\n",
       "            <th>combined_key</th>\n",
       "            <th>people_fully_vaccinated</th>\n",
       "            <th>people_partially_vaccinated</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-10</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1, 'Alabama', 'US', datetime.date(2020, 12, 10), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None)]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT *\n",
    "FROM people_vaccinated\n",
    "limit 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2adecd3f-b129-4d06-8508-8dd58e59feb6",
   "metadata": {},
   "source": [
    "We don't need to remove any fact based columns here. We will remove the location based columns later on when we combine this table with the 'vaccine_type' table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76645a47-5b10-4f4f-8ed8-f01203cbb6a8",
   "metadata": {},
   "source": [
    "## Ask 4: Producing Analytical Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e93e022-4113-48b7-862b-59a6ccda35c4",
   "metadata": {},
   "source": [
    "After multiple group discussions, we decided to proceed with the following analytical questions:\n",
    "\n",
    "Question 1: How did key COVID cases and the number of vaccinated people differ in states that voted for the Democrat party vs the Republican party in the 2020 election?\n",
    "\n",
    "Question 2: How did vaccination rates affect the total number of cases and and the total number of deaths over time?\n",
    "\n",
    "Question 3: How does the quality of education affect the number of fully vaccinated people across different states?\n",
    "\n",
    "Question 4: What was the vaccination rate trend over time? Which vaccine types were most popular at different points during the pandemic?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49614e27-e2d2-4bbb-8672-78936e6123db",
   "metadata": {},
   "source": [
    "To answer these questions, we would have to design our dimensional database accordingly, which brings us to Ask 5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0933822-bcb1-44d7-be4a-d7819db84e28",
   "metadata": {},
   "source": [
    "## Ask 5: Designing the dimensional database"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fbdca35-e940-45f1-8e64-ce35b3817180",
   "metadata": {},
   "source": [
    "Our group had two approaches on how to design our star schema db. \n",
    "\n",
    "Our initial thinking was to create 1 giant combined fact tables. This would hold the metrics for the cases, testing data and people vaccinated. There would be surrogate keys to represent the location and time dimensions. \n",
    "\n",
    "Below is the schema for this first approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c6e88e01-928b-43af-a784-725ce4624f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://i.ibb.co/WgTW2Jd/final-DB-schema-1fact.png\" width=\"700\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url='https://i.ibb.co/WgTW2Jd/final-DB-schema-1fact.png', width = 700)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "938631fc-2b89-4949-8da6-96d0fa023e3b",
   "metadata": {},
   "source": [
    "Our second option and what we ultimately decided to go with, was having 3 fact tables to keep the metrics related to vaccine, cases and testing separate. In our opinion it kept the structure cleaner and provided a simpler interpretation. It also allowed during our analysis to only query and deal with the information that we needed. And if it was necessary to combine information we could use the state and time keys to join on.\n",
    "\n",
    "In both scenarios it was critical for us that we kept the vaccine type information into a separated table since each record in this table represented a type and would drastically increase the number of rows if combined with another table. We used a surrogate key in scenario 1 to isolate and the state and time key in scenario 2.  \n",
    "\n",
    "Approach 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f7e877c8-ac5d-4148-b4f4-e2c0a8e39133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://i.ibb.co/3sYhnft/final-DB-schema-3facts.png\" width=\"900\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='https://i.ibb.co/3sYhnft/final-DB-schema-3facts.png', width = 900)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8b0f879-5302-4afb-8ba4-e8f73f047b63",
   "metadata": {},
   "source": [
    "We will now design a dimensional database with the schema above (approach 2) in mind"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be19079-a541-4626-a387-2c0868eb31e0",
   "metadata": {},
   "source": [
    "### Creating location dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "d98c7974-b809-47f4-b27b-49cdaee35a78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP TABLE IF EXISTS location cascade;\n",
    "\n",
    "CREATE TABLE location (\n",
    "    key SERIAL PRIMARY KEY,\n",
    "    state VARCHAR(64),\n",
    "    state_abbrev VARCHAR(2),\n",
    "    country VARCHAR(64),\n",
    "    FIPS NUMERIC,\n",
    "    Lat NUMERIC(9,4),\n",
    "    Long NUMERIC(9,4),\n",
    "    dem_or_rep varchar(3),\n",
    "    education_score numeric,\n",
    "    pop integer\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "4e6e8df4-23c5-455b-aa09-2f1f72a2f21f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "51 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "INSERT INTO location (state, country, FIPS, Lat, Long)\n",
    "select distinct province_state as state, country_region as country, FIPS, Lat, Long_ as Long\n",
    "from cases;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "a5d67367-d511-4449-9ac1-46605a287557",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "5 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>key</th>\n",
       "            <th>state</th>\n",
       "            <th>state_abbrev</th>\n",
       "            <th>country</th>\n",
       "            <th>fips</th>\n",
       "            <th>lat</th>\n",
       "            <th>long</th>\n",
       "            <th>dem_or_rep</th>\n",
       "            <th>education_score</th>\n",
       "            <th>pop</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>45</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>None</td>\n",
       "            <td>US</td>\n",
       "            <td>1</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>35</td>\n",
       "            <td>Alaska</td>\n",
       "            <td>None</td>\n",
       "            <td>US</td>\n",
       "            <td>2</td>\n",
       "            <td>61.3707</td>\n",
       "            <td>-152.4044</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>12</td>\n",
       "            <td>Arizona</td>\n",
       "            <td>None</td>\n",
       "            <td>US</td>\n",
       "            <td>4</td>\n",
       "            <td>33.7298</td>\n",
       "            <td>-111.4312</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>28</td>\n",
       "            <td>Arkansas</td>\n",
       "            <td>None</td>\n",
       "            <td>US</td>\n",
       "            <td>5</td>\n",
       "            <td>34.9697</td>\n",
       "            <td>-92.3731</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>19</td>\n",
       "            <td>California</td>\n",
       "            <td>None</td>\n",
       "            <td>US</td>\n",
       "            <td>6</td>\n",
       "            <td>36.1162</td>\n",
       "            <td>-119.6816</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(45, 'Alabama', None, 'US', Decimal('1'), Decimal('32.3182'), Decimal('-86.9023'), None, None, None),\n",
       " (35, 'Alaska', None, 'US', Decimal('2'), Decimal('61.3707'), Decimal('-152.4044'), None, None, None),\n",
       " (12, 'Arizona', None, 'US', Decimal('4'), Decimal('33.7298'), Decimal('-111.4312'), None, None, None),\n",
       " (28, 'Arkansas', None, 'US', Decimal('5'), Decimal('34.9697'), Decimal('-92.3731'), None, None, None),\n",
       " (19, 'California', None, 'US', Decimal('6'), Decimal('36.1162'), Decimal('-119.6816'), None, None, None)]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select * from location\n",
    "order by state\n",
    "limit 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c80a459-077d-491a-b775-c07981c8d566",
   "metadata": {},
   "source": [
    "### Additional Data Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8c17701-129d-4eb8-809c-e1c46d3e5e38",
   "metadata": {},
   "source": [
    "Some of our analytical questions require the use of external data. Namely education data for each state and whether the state voted Republican or Democrat in the 2020 elections. We will now import this data and combine it with our existing 'location' table.\n",
    "We will also add the state abbreviations from the election table into our location table so that we will be able to link it with the testing table.\n",
    "\n",
    "All new data added:\n",
    "* usa_state_code (state abbreviation)\n",
    "* dem_or_rep (which party the state voted for in the 2020 presidential election)\n",
    "* educationScore (overall education metric assigned to each state which compared 20 different metrics including share of adults with bachelor's degree, quality of public school systems, public college graduation rate etc.)\n",
    "* pop (population of the state)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af612387-5106-4764-b6d9-0be0b4bb54b2",
   "metadata": {},
   "source": [
    "The data was compiled from the following sources:\n",
    "1. https://www.kaggle.com/paultimothymooney/2020-usa-election-vote-percentages-by-state/notebook\n",
    "2. https://worldpopulationreview.com/state-rankings/most-educated-states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "f9411ae5-d343-4920-a250-bb7646ac23d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP TABLE IF EXISTS additional_data;\n",
    "\n",
    "CREATE TABLE additional_data (\n",
    "    State VARCHAR(30) NOT NULL,\n",
    "    usa_state_code varchar(2),\n",
    "    dem_or_rep varchar(3),\n",
    "    educationScore Numeric,\n",
    "    pop numeric\n",
    "    \n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "a89d6889-a3c4-4649-be19-fe4b9aa85d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "additional_state_data = sqlc.read.csv(\"additional_state_data.csv\",  header=True, inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "122074f6-d243-4e89-8433-48c280c26347",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- state: string (nullable = true)\n",
      " |-- usa_state_code: string (nullable = true)\n",
      " |-- DEM or REP: string (nullable = true)\n",
      " |-- educationScore: double (nullable = true)\n",
      " |-- Pop: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "additional_state_data.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "503ff842-b154-428d-b34e-b7ba4be724f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "51 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "COPY additional_data FROM '/home/ubuntu/notebooks/Final Group Project/Final Submission/additional_state_data.csv'\n",
    "CSV\n",
    "HEADER;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "a00e8c0d-461a-4751-bb7f-8fc797afee8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "5 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>state</th>\n",
       "            <th>usa_state_code</th>\n",
       "            <th>dem_or_rep</th>\n",
       "            <th>educationscore</th>\n",
       "            <th>pop</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>AL</td>\n",
       "            <td>REP</td>\n",
       "            <td>30.23</td>\n",
       "            <td>4934193</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alaska</td>\n",
       "            <td>AK</td>\n",
       "            <td>REP</td>\n",
       "            <td>49.19</td>\n",
       "            <td>724357</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arizona</td>\n",
       "            <td>AZ</td>\n",
       "            <td>DEM</td>\n",
       "            <td>42.61</td>\n",
       "            <td>7520103</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arkansas</td>\n",
       "            <td>AR</td>\n",
       "            <td>REP</td>\n",
       "            <td>27.9</td>\n",
       "            <td>3033946</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>California</td>\n",
       "            <td>CA</td>\n",
       "            <td>DEM</td>\n",
       "            <td>50.03</td>\n",
       "            <td>39613493</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Alabama', 'AL', 'REP', Decimal('30.23'), Decimal('4934193')),\n",
       " ('Alaska', 'AK', 'REP', Decimal('49.19'), Decimal('724357')),\n",
       " ('Arizona', 'AZ', 'DEM', Decimal('42.61'), Decimal('7520103')),\n",
       " ('Arkansas', 'AR', 'REP', Decimal('27.9'), Decimal('3033946')),\n",
       " ('California', 'CA', 'DEM', Decimal('50.03'), Decimal('39613493'))]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT * FROM\n",
    "additional_data\n",
    "LIMIT 5;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d0fab0-7273-4232-bd60-82676476b576",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Update Location Dimension Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da562b9e-42fe-4650-ba68-39b601a64c14",
   "metadata": {},
   "source": [
    "We will now update the location dimension table to incorporate the following columns which we added in the additional_data table:\n",
    "1. usa_state_code (state abbreviation)\n",
    "2. dem_or_rep (which party the state voted for in 2020)\n",
    "3. education_score\n",
    "4. pop (population)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "217e0839-af7e-414a-9fbb-425b8bba4d74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "51 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "update location A\n",
    "set \n",
    "state_abbrev = usa_state_code,\n",
    "dem_or_rep = b.dem_or_rep,\n",
    "education_score = educationscore,\n",
    "pop = b.pop\n",
    "FROM additional_data B\n",
    "where a.state = b.state "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "a35f35a0-0643-46f1-933b-e68616d8a2de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "5 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>key</th>\n",
       "            <th>state</th>\n",
       "            <th>state_abbrev</th>\n",
       "            <th>country</th>\n",
       "            <th>fips</th>\n",
       "            <th>lat</th>\n",
       "            <th>long</th>\n",
       "            <th>dem_or_rep</th>\n",
       "            <th>education_score</th>\n",
       "            <th>pop</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>45</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>AL</td>\n",
       "            <td>US</td>\n",
       "            <td>1</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>REP</td>\n",
       "            <td>30.23</td>\n",
       "            <td>4934193</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>35</td>\n",
       "            <td>Alaska</td>\n",
       "            <td>AK</td>\n",
       "            <td>US</td>\n",
       "            <td>2</td>\n",
       "            <td>61.3707</td>\n",
       "            <td>-152.4044</td>\n",
       "            <td>REP</td>\n",
       "            <td>49.19</td>\n",
       "            <td>724357</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>12</td>\n",
       "            <td>Arizona</td>\n",
       "            <td>AZ</td>\n",
       "            <td>US</td>\n",
       "            <td>4</td>\n",
       "            <td>33.7298</td>\n",
       "            <td>-111.4312</td>\n",
       "            <td>DEM</td>\n",
       "            <td>42.61</td>\n",
       "            <td>7520103</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>28</td>\n",
       "            <td>Arkansas</td>\n",
       "            <td>AR</td>\n",
       "            <td>US</td>\n",
       "            <td>5</td>\n",
       "            <td>34.9697</td>\n",
       "            <td>-92.3731</td>\n",
       "            <td>REP</td>\n",
       "            <td>27.9</td>\n",
       "            <td>3033946</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>19</td>\n",
       "            <td>California</td>\n",
       "            <td>CA</td>\n",
       "            <td>US</td>\n",
       "            <td>6</td>\n",
       "            <td>36.1162</td>\n",
       "            <td>-119.6816</td>\n",
       "            <td>DEM</td>\n",
       "            <td>50.03</td>\n",
       "            <td>39613493</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(45, 'Alabama', 'AL', 'US', Decimal('1'), Decimal('32.3182'), Decimal('-86.9023'), 'REP', Decimal('30.23'), 4934193),\n",
       " (35, 'Alaska', 'AK', 'US', Decimal('2'), Decimal('61.3707'), Decimal('-152.4044'), 'REP', Decimal('49.19'), 724357),\n",
       " (12, 'Arizona', 'AZ', 'US', Decimal('4'), Decimal('33.7298'), Decimal('-111.4312'), 'DEM', Decimal('42.61'), 7520103),\n",
       " (28, 'Arkansas', 'AR', 'US', Decimal('5'), Decimal('34.9697'), Decimal('-92.3731'), 'REP', Decimal('27.9'), 3033946),\n",
       " (19, 'California', 'CA', 'US', Decimal('6'), Decimal('36.1162'), Decimal('-119.6816'), 'DEM', Decimal('50.03'), 39613493)]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select * \n",
    "from location\n",
    "limit 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8dfc14-fd75-4c9e-9894-a50761e41cc5",
   "metadata": {},
   "source": [
    "We now have the final version of our location table, great! This contains all the data we need. \n",
    "We will now add the state key into all of our tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e369627-6d3d-4e39-bd34-0b41bedf52e5",
   "metadata": {},
   "source": [
    "Let's now add the state_key into our cases table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "09504bd2-0d4d-4f07-90ba-e23556d0d1f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "ALTER TABLE cases\n",
    "ADD COLUMN state_key INTEGER,\n",
    "ADD CONSTRAINT fk_state\n",
    "    FOREIGN KEY (state_key)\n",
    "    REFERENCES location (key);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "10a32a77-f9d8-4675-b1fa-d5776e5adb05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "30498 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "UPDATE cases\n",
    "SET state_key = location.key\n",
    "FROM location\n",
    "WHERE location.FIPS=cases.FIPS;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "fb4af956-6fc8-450f-88b0-115a8e5e0ab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "            <th>country_region</th>\n",
       "            <th>last_update</th>\n",
       "            <th>lat</th>\n",
       "            <th>long_</th>\n",
       "            <th>confirmed</th>\n",
       "            <th>deaths</th>\n",
       "            <th>recovered</th>\n",
       "            <th>active</th>\n",
       "            <th>fips</th>\n",
       "            <th>incident_rate</th>\n",
       "            <th>mortality_rate</th>\n",
       "            <th>uid</th>\n",
       "            <th>iso3</th>\n",
       "            <th>state_key</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-04-12</td>\n",
       "            <td>32.318200</td>\n",
       "            <td>-86.902300</td>\n",
       "            <td>3667</td>\n",
       "            <td>93</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>1</td>\n",
       "            <td>74.78812241430825</td>\n",
       "            <td>2.5361330788110172</td>\n",
       "            <td>84000001</td>\n",
       "            <td>USA</td>\n",
       "            <td>45</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Alabama', 'US', datetime.date(2020, 4, 12), Decimal('32.318200'), Decimal('-86.902300'), 3667, 93, None, None, Decimal('1'), Decimal('74.78812241430825'), Decimal('2.5361330788110172'), Decimal('84000001'), 'USA', 45)]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from cases\n",
    "limit 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d557dc5-71fe-42d6-a08a-0169f24b3511",
   "metadata": {},
   "source": [
    "Let's add the state_key into our testing table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "9ca9768a-38b5-43f7-bcad-86426be8f796",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "ALTER TABLE testing\n",
    "ADD COLUMN state_key INTEGER,\n",
    "ADD CONSTRAINT fk_state\n",
    "    FOREIGN KEY (state_key)\n",
    "    REFERENCES location (key);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "31b6e2f3-f300-4985-a11f-c2137e5cccbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "33173 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "UPDATE testing\n",
    "SET state_key = location.key\n",
    "FROM location\n",
    "WHERE location.state_abbrev=testing.state;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "fb76a6c2-174f-4cc8-82fb-725c7726e178",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>date</th>\n",
       "            <th>state</th>\n",
       "            <th>tests_viral_positive</th>\n",
       "            <th>tests_viral_negative</th>\n",
       "            <th>tests_viral_total</th>\n",
       "            <th>people_viral_positive</th>\n",
       "            <th>people_viral_total</th>\n",
       "            <th>encounters_viral_total</th>\n",
       "            <th>tests_combined_total</th>\n",
       "            <th>state_key</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2020-04-21</td>\n",
       "            <td>AR</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>27441</td>\n",
       "            <td>2227</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>27441</td>\n",
       "            <td>28</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(datetime.date(2020, 4, 21), 'AR', None, None, 27441, 2227, None, None, 27441, 28)]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from testing\n",
    "limit 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e44b45e0-d82c-4f04-9509-6307a670e804",
   "metadata": {},
   "source": [
    "We will add the state_key to our vaccine table in a later section, once we have combined the 2 vaccine tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4d37bc3-851a-4e71-a3ed-0a322349af0c",
   "metadata": {},
   "source": [
    "### Creating time dimension"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d465d430-c39b-4519-b0b2-854dd07af34e",
   "metadata": {},
   "source": [
    "We will create a new time dimension table that will extract all the time elements from all of the dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "47572697-b34d-4a55-a0af-05cd4172d543",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP TABLE IF EXISTS time;\n",
    "\n",
    "CREATE TABLE time (\n",
    "    key SERIAL PRIMARY KEY,\n",
    "    day DATE,\n",
    "    year INTEGER,\n",
    "    month_of_year_str VARCHAR(9),\n",
    "    month_of_year INTEGER,\n",
    "    day_of_month INTEGER,\n",
    "    day_of_week_str CHAR(9),\n",
    "    day_of_week INTEGER,\n",
    "    quarter_of_year INTEGER\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b73b06c-c559-4ff5-90fa-bafe33d39d47",
   "metadata": {},
   "source": [
    "Over here we insert a union of all the dates from cases, testing, and the 2 vaccine tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "dba9614e-467a-429c-b349-bbe9383367fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "698 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "INSERT INTO time (day)\n",
    "SELECT DISTINCT\n",
    "    last_update AS day\n",
    "FROM cases\n",
    "UNION\n",
    "SELECT DISTINCT\n",
    "    date AS day\n",
    "FROM testing\n",
    "UNION\n",
    "SELECT DISTINCT\n",
    "    date AS day\n",
    "FROM vaccine_type\n",
    "UNION\n",
    "SELECT DISTINCT\n",
    "    date AS day\n",
    "FROM people_vaccinated"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc4ddb0-2fa4-4d1f-b07b-75cabdddd81d",
   "metadata": {},
   "source": [
    "Now we update the table to extract all the other columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "fd2ea36b-63b4-46db-9394-a2c8024184a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "698 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "UPDATE time\n",
    "Set year = CAST(TO_CHAR(day, 'YYYY') AS INTEGER),\n",
    "    month_of_year_str = TO_CHAR(day, 'Month'),\n",
    "    month_of_year = CAST(TO_CHAR(day, 'MM') AS INTEGER),\n",
    "    day_of_month = CAST(TO_CHAR(day, 'DD') AS INTEGER),\n",
    "    day_of_week_str = TO_CHAR(day, 'Day'),\n",
    "    day_of_week = CAST(TO_CHAR(day, 'D') AS INTEGER),\n",
    "    quarter_of_year = CAST(TO_CHAR(day, 'Q') AS INTEGER)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b720fe-a6ee-4c5e-9eee-d91b0b419296",
   "metadata": {},
   "source": [
    "Let's now add the time_key into our cases table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "929ce9b7-9eac-4432-8252-c10038899af2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "ALTER TABLE cases\n",
    "ADD COLUMN time_key INTEGER,\n",
    "ADD CONSTRAINT fk_time\n",
    "    FOREIGN KEY (time_key)\n",
    "    REFERENCES time (key);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "dee38d8f-c42e-4ba5-92cd-3a51a97d6482",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "30498 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "UPDATE cases\n",
    "SET time_key = time.key\n",
    "FROM time\n",
    "WHERE cases.last_update = time.day;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "191de710-51ee-44a8-8bfc-969267858e4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "            <th>country_region</th>\n",
       "            <th>last_update</th>\n",
       "            <th>lat</th>\n",
       "            <th>long_</th>\n",
       "            <th>confirmed</th>\n",
       "            <th>deaths</th>\n",
       "            <th>recovered</th>\n",
       "            <th>active</th>\n",
       "            <th>fips</th>\n",
       "            <th>incident_rate</th>\n",
       "            <th>mortality_rate</th>\n",
       "            <th>uid</th>\n",
       "            <th>iso3</th>\n",
       "            <th>state_key</th>\n",
       "            <th>time_key</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Arizona</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-04-12</td>\n",
       "            <td>33.729800</td>\n",
       "            <td>-111.431200</td>\n",
       "            <td>3542</td>\n",
       "            <td>115</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>4</td>\n",
       "            <td>48.662422237325615</td>\n",
       "            <td>3.2467532467532467</td>\n",
       "            <td>84000004</td>\n",
       "            <td>USA</td>\n",
       "            <td>12</td>\n",
       "            <td>616</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Arizona', 'US', datetime.date(2020, 4, 12), Decimal('33.729800'), Decimal('-111.431200'), 3542, 115, None, None, Decimal('4'), Decimal('48.662422237325615'), Decimal('3.2467532467532467'), Decimal('84000004'), 'USA', 12, 616)]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select * \n",
    "from cases\n",
    "limit 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd1cb8f4-fccc-4c13-8e9f-0ee3ebab049a",
   "metadata": {},
   "source": [
    "Lets now add the time key to the testing table as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "45ca5e35-25a2-4135-9f51-2af6d9a15e39",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "ALTER TABLE testing\n",
    "ADD COLUMN time_key INTEGER,\n",
    "ADD CONSTRAINT fk_time\n",
    "    FOREIGN KEY (time_key)\n",
    "    REFERENCES time (key);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "0572c38b-569b-484e-95ba-f64331d985b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "33173 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "UPDATE testing\n",
    "SET time_key = time.key\n",
    "FROM time\n",
    "WHERE Testing.date = time.day;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "0512f905-ba81-4582-a47e-b1fd17e740a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>date</th>\n",
       "            <th>state</th>\n",
       "            <th>tests_viral_positive</th>\n",
       "            <th>tests_viral_negative</th>\n",
       "            <th>tests_viral_total</th>\n",
       "            <th>people_viral_positive</th>\n",
       "            <th>people_viral_total</th>\n",
       "            <th>encounters_viral_total</th>\n",
       "            <th>tests_combined_total</th>\n",
       "            <th>state_key</th>\n",
       "            <th>time_key</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2020-04-21</td>\n",
       "            <td>AR</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>27441</td>\n",
       "            <td>2227</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>27441</td>\n",
       "            <td>28</td>\n",
       "            <td>190</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(datetime.date(2020, 4, 21), 'AR', None, None, 27441, 2227, None, None, 27441, 28, 190)]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from testing\n",
    "limit 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f263be-5acd-461f-9f89-ebb21fd49765",
   "metadata": {},
   "source": [
    "We will add the time_key to our vaccine table in a later section, once we have combined the 2 vaccine tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ae2054-f4ba-4230-a0e1-1d43fa098f04",
   "metadata": {},
   "source": [
    "### Removing unnecessary columns in Cases and Testing Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a122f0-f1d7-4b16-8c8f-f7e9ccb8bd5d",
   "metadata": {},
   "source": [
    "Let's remove all of the location and time related columns as we can already identify these through state_key and time_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "4bd7c081-ade2-45a4-8a86-4941fd616066",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "ALTER table cases\n",
    "drop column province_state, drop column country_region, drop column last_update, drop column lat, drop column long_, drop column FIPS, drop column uid, drop column iso3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "8f0ff0ec-9619-4891-b40c-39b20a1ae65c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "ALTER table testing\n",
    "drop column date, drop column state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a2b4e4-bb67-475b-b8ca-1b960b5aa54c",
   "metadata": {},
   "source": [
    "Here's what the final version of our cases and testing table look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "24829c95-9a20-430a-9bd6-ec575716fdda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "3 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>confirmed</th>\n",
       "            <th>deaths</th>\n",
       "            <th>recovered</th>\n",
       "            <th>active</th>\n",
       "            <th>incident_rate</th>\n",
       "            <th>mortality_rate</th>\n",
       "            <th>state_key</th>\n",
       "            <th>time_key</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>3542</td>\n",
       "            <td>115</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>48.662422237325615</td>\n",
       "            <td>3.2467532467532467</td>\n",
       "            <td>12</td>\n",
       "            <td>616</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>272</td>\n",
       "            <td>8</td>\n",
       "            <td>66</td>\n",
       "            <td>198</td>\n",
       "            <td>37.18158144748443</td>\n",
       "            <td>2.9411764705882355</td>\n",
       "            <td>35</td>\n",
       "            <td>616</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3667</td>\n",
       "            <td>93</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>74.78812241430825</td>\n",
       "            <td>2.5361330788110172</td>\n",
       "            <td>45</td>\n",
       "            <td>616</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(3542, 115, None, None, Decimal('48.662422237325615'), Decimal('3.2467532467532467'), 12, 616),\n",
       " (272, 8, Decimal('66'), Decimal('198'), Decimal('37.18158144748443'), Decimal('2.9411764705882355'), 35, 616),\n",
       " (3667, 93, None, None, Decimal('74.78812241430825'), Decimal('2.5361330788110172'), 45, 616)]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from cases\n",
    "limit 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "f23e3a0c-3356-4f56-baf6-43b144af2b0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "3 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>tests_viral_positive</th>\n",
       "            <th>tests_viral_negative</th>\n",
       "            <th>tests_viral_total</th>\n",
       "            <th>people_viral_positive</th>\n",
       "            <th>people_viral_total</th>\n",
       "            <th>encounters_viral_total</th>\n",
       "            <th>tests_combined_total</th>\n",
       "            <th>state_key</th>\n",
       "            <th>time_key</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>146387</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>134930</td>\n",
       "            <td>882838</td>\n",
       "            <td>2452384</td>\n",
       "            <td>2452384</td>\n",
       "            <td>40</td>\n",
       "            <td>698</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>65371</td>\n",
       "            <td>654565</td>\n",
       "            <td>2304153</td>\n",
       "            <td>2304153</td>\n",
       "            <td>18</td>\n",
       "            <td>698</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>447050</td>\n",
       "            <td>10356976</td>\n",
       "            <td>10815373</td>\n",
       "            <td>369819</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>10815373</td>\n",
       "            <td>14</td>\n",
       "            <td>698</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(146387, None, None, 134930, 882838, 2452384, 2452384, 40, 698),\n",
       " (None, None, None, 65371, 654565, 2304153, 2304153, 18, 698),\n",
       " (447050, 10356976, 10815373, 369819, None, None, 10815373, 14, 698)]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from testing\n",
    "order by time_key desc\n",
    "limit 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6040f1-b1cf-4d5b-bde0-c718bf7bb6ca",
   "metadata": {},
   "source": [
    "### Combining Vaccine tables and adding dimensional keys"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bada3104-66c6-49f2-b4b1-b4f13eb0e773",
   "metadata": {},
   "source": [
    "The goal here is to combine the vaccine_type table with the people_vaccinated table. We will be extracting the relevant columns and then adding the state_key and time_key into the final vaccine table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "4dd0283b-7c20-4079-9262-84a33ecc9614",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>province_state</th>\n",
       "            <th>date</th>\n",
       "            <th>vaccine_type</th>\n",
       "            <th>fips</th>\n",
       "            <th>country_region</th>\n",
       "            <th>lat</th>\n",
       "            <th>long_</th>\n",
       "            <th>doses_alloc</th>\n",
       "            <th>doses_shipped</th>\n",
       "            <th>doses_admin</th>\n",
       "            <th>stage_one_doses</th>\n",
       "            <th>stage_two_doses</th>\n",
       "            <th>combined_key</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>2020-12-10</td>\n",
       "            <td>All</td>\n",
       "            <td>1</td>\n",
       "            <td>US</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>Alabama, US</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Alabama', datetime.date(2020, 12, 10), 'All', Decimal('1'), 'US', Decimal('32.3182'), Decimal('-86.9023'), None, None, None, None, None, 'Alabama, US')]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from vaccine_type\n",
    "limit 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "56d093a5-559f-451b-9d88-77b9ab663e79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>fips</th>\n",
       "            <th>province_state</th>\n",
       "            <th>country_region</th>\n",
       "            <th>date</th>\n",
       "            <th>lat</th>\n",
       "            <th>long_</th>\n",
       "            <th>combined_key</th>\n",
       "            <th>people_fully_vaccinated</th>\n",
       "            <th>people_partially_vaccinated</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>US</td>\n",
       "            <td>2020-12-10</td>\n",
       "            <td>32.3182</td>\n",
       "            <td>-86.9023</td>\n",
       "            <td>Alabama, US</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1, 'Alabama', 'US', datetime.date(2020, 12, 10), Decimal('32.3182'), Decimal('-86.9023'), 'Alabama, US', None, None)]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from people_vaccinated\n",
    "limit 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e25d16-9841-469a-ae2c-6c351a720699",
   "metadata": {},
   "source": [
    "Only creating new table to copy into so the column order can be correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "6d622216-4de4-4f6b-a228-5e19f6686092",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP TABLE IF EXISTS vaccine;\n",
    "\n",
    "CREATE TABLE vaccine (\n",
    "    state_key numeric(9),\n",
    "    province_state varchar(35),\n",
    "    date_key numeric(9),\n",
    "    date date,  \n",
    "    vaccine_type varchar(50),\n",
    "    doses_alloc integer,\n",
    "    doses_shipped integer,\n",
    "    doses_admin integer,\n",
    "    stage_one_doses integer,\n",
    "    stage_two_doses integer,\n",
    "    full_vac integer,\n",
    "    part_vac integer\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88094a2e-831b-4899-9179-eebccd7f3266",
   "metadata": {},
   "source": [
    "Insert vaccine type into the specific columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "45e81345-6d73-46f2-b211-e201af82c3a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "100903 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "insert into vaccine (province_state, date, vaccine_type, doses_alloc, doses_shipped, doses_admin, stage_one_doses, stage_two_doses)\n",
    "select \n",
    "province_state,\n",
    "date,\n",
    "vaccine_type,\n",
    "doses_alloc,\n",
    "doses_shipped,\n",
    "doses_admin,\n",
    "stage_one_doses,\n",
    "stage_two_doses\n",
    "from vaccine_type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c75ab6ca-8f00-402d-bcbc-943fe69d7e22",
   "metadata": {},
   "source": [
    "Update state key from location into vaccine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "5843d5ba-5e9d-4ff2-9bff-bc76feaa323d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "100903 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "update vaccine\n",
    "set state_key = key\n",
    "from location \n",
    "where province_state = state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c75d414-5fd6-408c-866c-b407c529429f",
   "metadata": {},
   "source": [
    "Update time key into vaccine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "2d124503-c6a2-4424-88f6-27b2f22855e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "100903 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "update vaccine\n",
    "set date_key = key\n",
    "from time\n",
    "where date=day"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a56b96-58f4-4939-b73d-860374de310c",
   "metadata": {},
   "source": [
    "Update people vaccinated into vaccine table. This is where we combine the two tables. We decided to only add people vaccinated to where vaccine type 'All', instead of adding for all records. So for totals of vaccines, need to filter to type 'All'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "9682e231-cc47-4612-ad87-2d0f282426a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "16833 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "update vaccine\n",
    "set \n",
    "full_vac = people_fully_vaccinated,\n",
    "part_vac = people_partially_vaccinated\n",
    "from people_vaccinated\n",
    "where vaccine.province_state = people_vaccinated.province_state and vaccine.date=people_vaccinated.date and (people_fully_vaccinated > 0 or people_partially_vaccinated > 0) and vaccine_type='All'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "8bcfcdc7-970b-4a80-a2ea-7bee15f9b92f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "16833 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "update vaccine\n",
    "set \n",
    "full_vac = people_fully_vaccinated,\n",
    "part_vac = people_partially_vaccinated\n",
    "from people_vaccinated\n",
    "where vaccine.province_state = people_vaccinated.province_state and vaccine.date=people_vaccinated.date and (people_fully_vaccinated > 0 or people_partially_vaccinated > 0) and vaccine_type='All'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815ff6fc-3b30-4964-8f04-98ac922c87e6",
   "metadata": {},
   "source": [
    "Drop columns we no longer need since we have keys now to join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "6d970fd5-464f-4421-9f5c-a1a42dc6e73f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "alter table vaccine\n",
    "drop column province_state,\n",
    "drop column date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "c0882307-6c50-4e71-aad4-2edb4ac833b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "3 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>state_key</th>\n",
       "            <th>date_key</th>\n",
       "            <th>vaccine_type</th>\n",
       "            <th>doses_alloc</th>\n",
       "            <th>doses_shipped</th>\n",
       "            <th>doses_admin</th>\n",
       "            <th>stage_one_doses</th>\n",
       "            <th>stage_two_doses</th>\n",
       "            <th>full_vac</th>\n",
       "            <th>part_vac</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>40</td>\n",
       "            <td>516</td>\n",
       "            <td>All</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>24</td>\n",
       "            <td>204</td>\n",
       "            <td>Unknown</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>48</td>\n",
       "            <td>516</td>\n",
       "            <td>All</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(Decimal('40'), Decimal('516'), 'All', None, None, None, None, None, None, None),\n",
       " (Decimal('24'), Decimal('204'), 'Unknown', None, None, None, None, None, None, None),\n",
       " (Decimal('48'), Decimal('516'), 'All', None, None, None, None, None, None, None)]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from vaccine\n",
    "limit 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd35b19-f540-4a6c-9e29-3aef3ade8950",
   "metadata": {},
   "source": [
    "Above is our final vaccine table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0185f4c0-8a64-48bb-bf12-aa8e2385aff7",
   "metadata": {},
   "source": [
    "## Ask 6: Answering the Analytical Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77adce87-c4a3-4973-bfec-a9a7a638759d",
   "metadata": {},
   "source": [
    "### Question 1: How did cases and the number of vaccinated people differ across Democratic vs Republican states?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "509d784d-973c-429b-ac49-34416715f30b",
   "metadata": {},
   "source": [
    "For the purposes of this question, we are considering states that voted for the democratic party in 2020 to be a 'democratic state', and those that voted for the republican party in 2020 to be a 'republican state'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e0f4a42-a056-4034-ae76-9014ae411da5",
   "metadata": {},
   "source": [
    "Firstly, let's create a query that will put the state, which party the state voted for in 2020, the total cases per 100k and total fully vaccinated people per 100k all in one table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "22fd8673-92af-453a-8169-480ef92f684b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "51 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>state</th>\n",
       "            <th>dem_or_rep</th>\n",
       "            <th>total_cases_per_100k</th>\n",
       "            <th>full_vaccines_per_100k</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>North Dakota</td>\n",
       "            <td>REP</td>\n",
       "            <td>21297</td>\n",
       "            <td>49132</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alaska</td>\n",
       "            <td>REP</td>\n",
       "            <td>20641</td>\n",
       "            <td>55886</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Tennessee</td>\n",
       "            <td>REP</td>\n",
       "            <td>19276</td>\n",
       "            <td>50205</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Wyoming</td>\n",
       "            <td>REP</td>\n",
       "            <td>19194</td>\n",
       "            <td>46212</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Dakota</td>\n",
       "            <td>REP</td>\n",
       "            <td>18788</td>\n",
       "            <td>55011</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Utah</td>\n",
       "            <td>REP</td>\n",
       "            <td>18584</td>\n",
       "            <td>56014</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Rhode Island</td>\n",
       "            <td>DEM</td>\n",
       "            <td>18102</td>\n",
       "            <td>74351</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Montana</td>\n",
       "            <td>REP</td>\n",
       "            <td>17872</td>\n",
       "            <td>52013</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Carolina</td>\n",
       "            <td>REP</td>\n",
       "            <td>17836</td>\n",
       "            <td>50897</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Kentucky</td>\n",
       "            <td>REP</td>\n",
       "            <td>17591</td>\n",
       "            <td>54495</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arkansas</td>\n",
       "            <td>REP</td>\n",
       "            <td>17524</td>\n",
       "            <td>49807</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arizona</td>\n",
       "            <td>DEM</td>\n",
       "            <td>17445</td>\n",
       "            <td>53873</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Florida</td>\n",
       "            <td>REP</td>\n",
       "            <td>17369</td>\n",
       "            <td>60937</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Mississippi</td>\n",
       "            <td>REP</td>\n",
       "            <td>17276</td>\n",
       "            <td>48118</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>REP</td>\n",
       "            <td>17249</td>\n",
       "            <td>46473</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Idaho</td>\n",
       "            <td>REP</td>\n",
       "            <td>17170</td>\n",
       "            <td>46921</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Oklahoma</td>\n",
       "            <td>REP</td>\n",
       "            <td>16854</td>\n",
       "            <td>51932</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Wisconsin</td>\n",
       "            <td>DEM</td>\n",
       "            <td>16845</td>\n",
       "            <td>60326</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Iowa</td>\n",
       "            <td>REP</td>\n",
       "            <td>16654</td>\n",
       "            <td>57557</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Louisiana</td>\n",
       "            <td>REP</td>\n",
       "            <td>16583</td>\n",
       "            <td>49646</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>West Virginia</td>\n",
       "            <td>REP</td>\n",
       "            <td>16449</td>\n",
       "            <td>71931</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indiana</td>\n",
       "            <td>REP</td>\n",
       "            <td>16357</td>\n",
       "            <td>51266</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Minnesota</td>\n",
       "            <td>DEM</td>\n",
       "            <td>16178</td>\n",
       "            <td>63162</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Nebraska</td>\n",
       "            <td>REP</td>\n",
       "            <td>16017</td>\n",
       "            <td>58046</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Kansas</td>\n",
       "            <td>REP</td>\n",
       "            <td>16000</td>\n",
       "            <td>55567</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Delaware</td>\n",
       "            <td>DEM</td>\n",
       "            <td>15732</td>\n",
       "            <td>61618</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Georgia</td>\n",
       "            <td>DEM</td>\n",
       "            <td>15677</td>\n",
       "            <td>48874</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Mexico</td>\n",
       "            <td>DEM</td>\n",
       "            <td>14988</td>\n",
       "            <td>65410</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Texas</td>\n",
       "            <td>REP</td>\n",
       "            <td>14954</td>\n",
       "            <td>54566</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Nevada</td>\n",
       "            <td>DEM</td>\n",
       "            <td>14858</td>\n",
       "            <td>53463</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Missouri</td>\n",
       "            <td>REP</td>\n",
       "            <td>14850</td>\n",
       "            <td>52027</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Michigan</td>\n",
       "            <td>DEM</td>\n",
       "            <td>14770</td>\n",
       "            <td>55485</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>North Carolina</td>\n",
       "            <td>REP</td>\n",
       "            <td>14626</td>\n",
       "            <td>56683</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Ohio</td>\n",
       "            <td>REP</td>\n",
       "            <td>14460</td>\n",
       "            <td>53970</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Colorado</td>\n",
       "            <td>DEM</td>\n",
       "            <td>14403</td>\n",
       "            <td>63161</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Illinois</td>\n",
       "            <td>DEM</td>\n",
       "            <td>14283</td>\n",
       "            <td>63359</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Jersey</td>\n",
       "            <td>DEM</td>\n",
       "            <td>14104</td>\n",
       "            <td>71021</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New York</td>\n",
       "            <td>DEM</td>\n",
       "            <td>14031</td>\n",
       "            <td>70708</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Pennsylvania</td>\n",
       "            <td>DEM</td>\n",
       "            <td>13568</td>\n",
       "            <td>62054</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Massachusetts</td>\n",
       "            <td>DEM</td>\n",
       "            <td>13335</td>\n",
       "            <td>72555</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>California</td>\n",
       "            <td>DEM</td>\n",
       "            <td>12879</td>\n",
       "            <td>65911</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Hampshire</td>\n",
       "            <td>DEM</td>\n",
       "            <td>11859</td>\n",
       "            <td>64811</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Connecticut</td>\n",
       "            <td>DEM</td>\n",
       "            <td>11828</td>\n",
       "            <td>73505</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Virginia</td>\n",
       "            <td>DEM</td>\n",
       "            <td>11354</td>\n",
       "            <td>65898</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Washington</td>\n",
       "            <td>DEM</td>\n",
       "            <td>10170</td>\n",
       "            <td>64881</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Maryland</td>\n",
       "            <td>DEM</td>\n",
       "            <td>9695</td>\n",
       "            <td>68809</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>District of Columbia</td>\n",
       "            <td>DEM</td>\n",
       "            <td>9481</td>\n",
       "            <td>80841</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Oregon</td>\n",
       "            <td>DEM</td>\n",
       "            <td>9273</td>\n",
       "            <td>64049</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Maine</td>\n",
       "            <td>DEM</td>\n",
       "            <td>8902</td>\n",
       "            <td>73345</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Vermont</td>\n",
       "            <td>DEM</td>\n",
       "            <td>8015</td>\n",
       "            <td>75194</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Hawaii</td>\n",
       "            <td>DEM</td>\n",
       "            <td>6192</td>\n",
       "            <td>73373</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('North Dakota', 'REP', Decimal('21297'), Decimal('49132')),\n",
       " ('Alaska', 'REP', Decimal('20641'), Decimal('55886')),\n",
       " ('Tennessee', 'REP', Decimal('19276'), Decimal('50205')),\n",
       " ('Wyoming', 'REP', Decimal('19194'), Decimal('46212')),\n",
       " ('South Dakota', 'REP', Decimal('18788'), Decimal('55011')),\n",
       " ('Utah', 'REP', Decimal('18584'), Decimal('56014')),\n",
       " ('Rhode Island', 'DEM', Decimal('18102'), Decimal('74351')),\n",
       " ('Montana', 'REP', Decimal('17872'), Decimal('52013')),\n",
       " ('South Carolina', 'REP', Decimal('17836'), Decimal('50897')),\n",
       " ('Kentucky', 'REP', Decimal('17591'), Decimal('54495')),\n",
       " ('Arkansas', 'REP', Decimal('17524'), Decimal('49807')),\n",
       " ('Arizona', 'DEM', Decimal('17445'), Decimal('53873')),\n",
       " ('Florida', 'REP', Decimal('17369'), Decimal('60937')),\n",
       " ('Mississippi', 'REP', Decimal('17276'), Decimal('48118')),\n",
       " ('Alabama', 'REP', Decimal('17249'), Decimal('46473')),\n",
       " ('Idaho', 'REP', Decimal('17170'), Decimal('46921')),\n",
       " ('Oklahoma', 'REP', Decimal('16854'), Decimal('51932')),\n",
       " ('Wisconsin', 'DEM', Decimal('16845'), Decimal('60326')),\n",
       " ('Iowa', 'REP', Decimal('16654'), Decimal('57557')),\n",
       " ('Louisiana', 'REP', Decimal('16583'), Decimal('49646')),\n",
       " ('West Virginia', 'REP', Decimal('16449'), Decimal('71931')),\n",
       " ('Indiana', 'REP', Decimal('16357'), Decimal('51266')),\n",
       " ('Minnesota', 'DEM', Decimal('16178'), Decimal('63162')),\n",
       " ('Nebraska', 'REP', Decimal('16017'), Decimal('58046')),\n",
       " ('Kansas', 'REP', Decimal('16000'), Decimal('55567')),\n",
       " ('Delaware', 'DEM', Decimal('15732'), Decimal('61618')),\n",
       " ('Georgia', 'DEM', Decimal('15677'), Decimal('48874')),\n",
       " ('New Mexico', 'DEM', Decimal('14988'), Decimal('65410')),\n",
       " ('Texas', 'REP', Decimal('14954'), Decimal('54566')),\n",
       " ('Nevada', 'DEM', Decimal('14858'), Decimal('53463')),\n",
       " ('Missouri', 'REP', Decimal('14850'), Decimal('52027')),\n",
       " ('Michigan', 'DEM', Decimal('14770'), Decimal('55485')),\n",
       " ('North Carolina', 'REP', Decimal('14626'), Decimal('56683')),\n",
       " ('Ohio', 'REP', Decimal('14460'), Decimal('53970')),\n",
       " ('Colorado', 'DEM', Decimal('14403'), Decimal('63161')),\n",
       " ('Illinois', 'DEM', Decimal('14283'), Decimal('63359')),\n",
       " ('New Jersey', 'DEM', Decimal('14104'), Decimal('71021')),\n",
       " ('New York', 'DEM', Decimal('14031'), Decimal('70708')),\n",
       " ('Pennsylvania', 'DEM', Decimal('13568'), Decimal('62054')),\n",
       " ('Massachusetts', 'DEM', Decimal('13335'), Decimal('72555')),\n",
       " ('California', 'DEM', Decimal('12879'), Decimal('65911')),\n",
       " ('New Hampshire', 'DEM', Decimal('11859'), Decimal('64811')),\n",
       " ('Connecticut', 'DEM', Decimal('11828'), Decimal('73505')),\n",
       " ('Virginia', 'DEM', Decimal('11354'), Decimal('65898')),\n",
       " ('Washington', 'DEM', Decimal('10170'), Decimal('64881')),\n",
       " ('Maryland', 'DEM', Decimal('9695'), Decimal('68809')),\n",
       " ('District of Columbia', 'DEM', Decimal('9481'), Decimal('80841')),\n",
       " ('Oregon', 'DEM', Decimal('9273'), Decimal('64049')),\n",
       " ('Maine', 'DEM', Decimal('8902'), Decimal('73345')),\n",
       " ('Vermont', 'DEM', Decimal('8015'), Decimal('75194')),\n",
       " ('Hawaii', 'DEM', Decimal('6192'), Decimal('73373'))]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select \n",
    "  l.state\n",
    ", l.dem_or_rep\n",
    ", round(max(c.Incident_Rate),0) as total_cases_per_100k\n",
    ", round((max(v.full_vac)/cast(l.pop as numeric))*100000,0) as full_vaccines_per_100k\n",
    "from location l\n",
    "left join vaccine v on v.state_key = l.key\n",
    "left join cases c on c.state_key = l.key\n",
    "where full_vac is not null\n",
    "group by l.state, pop, l.dem_or_rep\n",
    "order by total_cases_per_100k desc\n",
    "; "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e0b9c0-8121-4a3f-a20a-5fe20bd81ea7",
   "metadata": {},
   "source": [
    "Lets now turn this into a view so that we can export it into a csv. The reason we want this as a CSV is so that we can import it into Tableau for some data visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "64cc12a3-82fb-40a9-bff7-cecd8dfca3c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP VIEW IF EXISTS party;\n",
    "create view party as\n",
    "select \n",
    "  l.state\n",
    ", l.dem_or_rep\n",
    ", round(max(c.Incident_Rate),0) as total_cases_per_100k\n",
    ", round((max(v.full_vac)/cast(l.pop as numeric))*100000,0) as full_vaccines_per_100k\n",
    "from location l\n",
    "left join vaccine v on v.state_key = l.key\n",
    "left join cases c on c.state_key = l.key\n",
    "where full_vac is not null\n",
    "group by l.state, pop, l.dem_or_rep\n",
    "order by total_cases_per_100k desc\n",
    "; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3f0952f4-1733-48a0-b3cd-13209f567094",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "51 rows affected.\n"
     ]
    }
   ],
   "source": [
    "party_df = %sql SELECT * FROM party;\n",
    "party_df = pd.DataFrame(party_df, columns = ('state', 'party','total_cases_per_100k','full_vaccines_per_100k'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b6ac857f-6112-4757-8af0-b9aff737a098",
   "metadata": {},
   "outputs": [],
   "source": [
    "party_df.to_csv('party_df.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd428d5-f054-4b8c-a12d-a35429c4e0f2",
   "metadata": {},
   "source": [
    "#### How do the number of cases differ in states that voted for the democratic party vs the republican party in 2020?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e2e0a193-6d71-4b8e-864b-27e34c7cedcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://i.ibb.co/Y8vYHC1/Dem-vs-Rep-Cases.png\" width=\"300\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='https://i.ibb.co/Y8vYHC1/Dem-vs-Rep-Cases.png', width = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef7d896-a3ac-434e-92c7-7f98f7b50c0e",
   "metadata": {},
   "source": [
    "* As we can see, cases generally seemed to be lower in 'blue' states compared to 'red' states.\n",
    "* The median number of cases for blue states was roughly 13,800 (per 100k) where as it was 17,249 (per 100k) in red states\n",
    "* We also notice 2 anomalies from the republican states - Alaska and North Dakota, which were both well above the other states.\n",
    "    * Upon further analysis, we see that these 2 states are as 'red' as they get. Both states have voted for the republcian party in every single election since 1968.\n",
    "\n",
    "Why is it that republican voting states have higher cases than democrat voting states?\n",
    "* It is likely that states that voted republican also have republican governers. We know that, broadly speaking, republican politicians have had a more relaxed approach to COVID in terms of restrictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d16be6b-73d1-46a9-b1d8-0a6f5deea53f",
   "metadata": {},
   "source": [
    "#### How do the number of vaccinations differ in states that voted for the democratic party vs the republican party in 2020?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "85ffbe9d-490c-45ce-8f9d-9e9265f49dec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://i.ibb.co/XzXv5pT/Dem-vs-Rep-Vaccines.png\" width=\"300\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "Image(url='https://i.ibb.co/XzXv5pT/Dem-vs-Rep-Vaccines.png', width = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ba34937-afad-411e-932d-c495705d7d90",
   "metadata": {},
   "source": [
    "* As we can see, the number of fully vaccinated people (per 100k) generally seemed to be higher in 'blue' states compared to 'red' states.\n",
    "* The median number of fully vaccinated people for blue states was roughly 65,000 (per 100k) where as it was just 52,000 (per 100k) in red states\n",
    "* We also notice 1 significant anomaly from the republican states - West Virginia. In fact, this was the only state that had more fully vaccinated people (69,000 per 100k) than the median number of fully vaccinated people in blue states.\n",
    "    * After some research, we see that the reason for this is likely West Virginia's decision to opt out of early federal vaccination rollout plan (that partnered with only CVS's and Walgreen's), and instead partnered with over 200 local pharmacies across the state. This gave West Virginia far more operational mobility and allowed them to vaccinate citizens with much higher efficiency. (read more here -https://www.nytimes.com/2021/01/24/us/west-virginia-vaccine.html) \n",
    "    * Another reason may be the ageing population. West Virginia is 3rd in the US in terms of median age. With COVID-19 most deeply affecting the elderly, West Virginia residents would likely have more incentive to receive the vaccination than many other states (https://worldpopulationreview.com/state-rankings/median-age-by-state)\n",
    "\n",
    "Why is it that democrat voting states have more people vaccinated than republican voting states?\n",
    "* It is likely that states that voted democrats also have democratic governers. We know that, broadly speaking, democratic politicians have been pushing vaccines harder than republicans. In many blue states, there are vaccination mandates for many activities (one example being New York). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67b1c7f-d6af-4fc3-946d-062d3c2a05aa",
   "metadata": {},
   "source": [
    "### Question 2: How did vaccination rates affect the total number of cases and and the total number of deaths over time?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "141432a3-e8f0-45c1-8cc9-455f4ee120ab",
   "metadata": {},
   "source": [
    "The Query below combined data from our Vaccine and Cases table. The results of this query produce the total amount of Vaccinations administered, Deaths, and Confirmed cases by the State. Keeping our data from April 2020 until December 08, 2021."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "3106913c-fc2d-4dcb-9992-b93506ed66f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "51 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>state</th>\n",
       "            <th>state_key</th>\n",
       "            <th>vac</th>\n",
       "            <th>date</th>\n",
       "            <th>confirmed</th>\n",
       "            <th>deaths</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Alabama</td>\n",
       "            <td>16</td>\n",
       "            <td>2286632</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>845761</td>\n",
       "            <td>16119</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Alaska</td>\n",
       "            <td>30</td>\n",
       "            <td>403344</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>150996</td>\n",
       "            <td>880</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arizona</td>\n",
       "            <td>20</td>\n",
       "            <td>4036491</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>1269780</td>\n",
       "            <td>22307</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Arkansas</td>\n",
       "            <td>4</td>\n",
       "            <td>1508237</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>528838</td>\n",
       "            <td>8667</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>California</td>\n",
       "            <td>35</td>\n",
       "            <td>25995926</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>5088615</td>\n",
       "            <td>74473</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Colorado</td>\n",
       "            <td>12</td>\n",
       "            <td>3708753</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>829437</td>\n",
       "            <td>9193</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Connecticut</td>\n",
       "            <td>25</td>\n",
       "            <td>2602944</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>421692</td>\n",
       "            <td>8905</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Delaware</td>\n",
       "            <td>50</td>\n",
       "            <td>607618</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>153190</td>\n",
       "            <td>2178</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>District of Columbia</td>\n",
       "            <td>33</td>\n",
       "            <td>569133</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>66912</td>\n",
       "            <td>1196</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Florida</td>\n",
       "            <td>8</td>\n",
       "            <td>13322353</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>3730395</td>\n",
       "            <td>61548</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Georgia</td>\n",
       "            <td>41</td>\n",
       "            <td>5289598</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>1664456</td>\n",
       "            <td>30510</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Hawaii</td>\n",
       "            <td>23</td>\n",
       "            <td>1030560</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>87664</td>\n",
       "            <td>1018</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Idaho</td>\n",
       "            <td>13</td>\n",
       "            <td>869951</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>306845</td>\n",
       "            <td>3929</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Illinois</td>\n",
       "            <td>38</td>\n",
       "            <td>7937710</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>1809875</td>\n",
       "            <td>28847</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indiana</td>\n",
       "            <td>3</td>\n",
       "            <td>3477651</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>1101185</td>\n",
       "            <td>17557</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Iowa</td>\n",
       "            <td>39</td>\n",
       "            <td>1816132</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>525458</td>\n",
       "            <td>7354</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Kansas</td>\n",
       "            <td>49</td>\n",
       "            <td>1614441</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>466125</td>\n",
       "            <td>6689</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Kentucky</td>\n",
       "            <td>34</td>\n",
       "            <td>2441766</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>785926</td>\n",
       "            <td>10943</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Louisiana</td>\n",
       "            <td>15</td>\n",
       "            <td>2291295</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>770894</td>\n",
       "            <td>14801</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Maine</td>\n",
       "            <td>27</td>\n",
       "            <td>987696</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>119662</td>\n",
       "            <td>1324</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Maryland</td>\n",
       "            <td>45</td>\n",
       "            <td>4158287</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>586143</td>\n",
       "            <td>11206</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Massachusetts</td>\n",
       "            <td>21</td>\n",
       "            <td>4991521</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>919097</td>\n",
       "            <td>19404</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Michigan</td>\n",
       "            <td>36</td>\n",
       "            <td>5529817</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>1475053</td>\n",
       "            <td>25399</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Minnesota</td>\n",
       "            <td>9</td>\n",
       "            <td>3586890</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>912370</td>\n",
       "            <td>9506</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Mississippi</td>\n",
       "            <td>46</td>\n",
       "            <td>1423193</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>514171</td>\n",
       "            <td>10285</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Missouri</td>\n",
       "            <td>48</td>\n",
       "            <td>3195477</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>911403</td>\n",
       "            <td>13086</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Montana</td>\n",
       "            <td>26</td>\n",
       "            <td>561672</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>191012</td>\n",
       "            <td>2671</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Nebraska</td>\n",
       "            <td>7</td>\n",
       "            <td>1128676</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>309828</td>\n",
       "            <td>3155</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Nevada</td>\n",
       "            <td>44</td>\n",
       "            <td>1698239</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>457643</td>\n",
       "            <td>8016</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Hampshire</td>\n",
       "            <td>1</td>\n",
       "            <td>887741</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>161244</td>\n",
       "            <td>1716</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Jersey</td>\n",
       "            <td>37</td>\n",
       "            <td>6280370</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>1252705</td>\n",
       "            <td>28390</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New Mexico</td>\n",
       "            <td>43</td>\n",
       "            <td>1376884</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>314264</td>\n",
       "            <td>5367</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>New York</td>\n",
       "            <td>11</td>\n",
       "            <td>13488689</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>2729595</td>\n",
       "            <td>57534</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>North Carolina</td>\n",
       "            <td>42</td>\n",
       "            <td>6048542</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>1534005</td>\n",
       "            <td>18740</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>North Dakota</td>\n",
       "            <td>5</td>\n",
       "            <td>377032</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>162294</td>\n",
       "            <td>1930</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Ohio</td>\n",
       "            <td>51</td>\n",
       "            <td>6298327</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>1690217</td>\n",
       "            <td>27008</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Oklahoma</td>\n",
       "            <td>40</td>\n",
       "            <td>2065916</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>666927</td>\n",
       "            <td>11913</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Oregon</td>\n",
       "            <td>19</td>\n",
       "            <td>2737980</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>391099</td>\n",
       "            <td>5161</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Pennsylvania</td>\n",
       "            <td>14</td>\n",
       "            <td>7945527</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>1736920</td>\n",
       "            <td>33421</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Rhode Island</td>\n",
       "            <td>22</td>\n",
       "            <td>785126</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>191763</td>\n",
       "            <td>2932</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Carolina</td>\n",
       "            <td>29</td>\n",
       "            <td>2678806</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>918321</td>\n",
       "            <td>14210</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Dakota</td>\n",
       "            <td>31</td>\n",
       "            <td>490780</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>166208</td>\n",
       "            <td>2334</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Tennessee</td>\n",
       "            <td>10</td>\n",
       "            <td>3476474</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>1316384</td>\n",
       "            <td>17033</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Texas</td>\n",
       "            <td>6</td>\n",
       "            <td>16162657</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>4335900</td>\n",
       "            <td>73828</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Utah</td>\n",
       "            <td>2</td>\n",
       "            <td>1845288</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>595801</td>\n",
       "            <td>3528</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Vermont</td>\n",
       "            <td>28</td>\n",
       "            <td>468649</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>50014</td>\n",
       "            <td>410</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Virginia</td>\n",
       "            <td>17</td>\n",
       "            <td>5659228</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>969116</td>\n",
       "            <td>14710</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Washington</td>\n",
       "            <td>24</td>\n",
       "            <td>5038688</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>774465</td>\n",
       "            <td>9303</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>West Virginia</td>\n",
       "            <td>47</td>\n",
       "            <td>1236368</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>294794</td>\n",
       "            <td>4868</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Wisconsin</td>\n",
       "            <td>18</td>\n",
       "            <td>3515995</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>980783</td>\n",
       "            <td>10005</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Wyoming</td>\n",
       "            <td>32</td>\n",
       "            <td>267943</td>\n",
       "            <td>2021-12-08</td>\n",
       "            <td>111089</td>\n",
       "            <td>1428</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Alabama', Decimal('16'), 2286632, datetime.date(2021, 12, 8), 845761, 16119),\n",
       " ('Alaska', Decimal('30'), 403344, datetime.date(2021, 12, 8), 150996, 880),\n",
       " ('Arizona', Decimal('20'), 4036491, datetime.date(2021, 12, 8), 1269780, 22307),\n",
       " ('Arkansas', Decimal('4'), 1508237, datetime.date(2021, 12, 8), 528838, 8667),\n",
       " ('California', Decimal('35'), 25995926, datetime.date(2021, 12, 8), 5088615, 74473),\n",
       " ('Colorado', Decimal('12'), 3708753, datetime.date(2021, 12, 8), 829437, 9193),\n",
       " ('Connecticut', Decimal('25'), 2602944, datetime.date(2021, 12, 8), 421692, 8905),\n",
       " ('Delaware', Decimal('50'), 607618, datetime.date(2021, 12, 8), 153190, 2178),\n",
       " ('District of Columbia', Decimal('33'), 569133, datetime.date(2021, 12, 8), 66912, 1196),\n",
       " ('Florida', Decimal('8'), 13322353, datetime.date(2021, 12, 8), 3730395, 61548),\n",
       " ('Georgia', Decimal('41'), 5289598, datetime.date(2021, 12, 8), 1664456, 30510),\n",
       " ('Hawaii', Decimal('23'), 1030560, datetime.date(2021, 12, 8), 87664, 1018),\n",
       " ('Idaho', Decimal('13'), 869951, datetime.date(2021, 12, 8), 306845, 3929),\n",
       " ('Illinois', Decimal('38'), 7937710, datetime.date(2021, 12, 8), 1809875, 28847),\n",
       " ('Indiana', Decimal('3'), 3477651, datetime.date(2021, 12, 8), 1101185, 17557),\n",
       " ('Iowa', Decimal('39'), 1816132, datetime.date(2021, 12, 8), 525458, 7354),\n",
       " ('Kansas', Decimal('49'), 1614441, datetime.date(2021, 12, 8), 466125, 6689),\n",
       " ('Kentucky', Decimal('34'), 2441766, datetime.date(2021, 12, 8), 785926, 10943),\n",
       " ('Louisiana', Decimal('15'), 2291295, datetime.date(2021, 12, 8), 770894, 14801),\n",
       " ('Maine', Decimal('27'), 987696, datetime.date(2021, 12, 8), 119662, 1324),\n",
       " ('Maryland', Decimal('45'), 4158287, datetime.date(2021, 12, 8), 586143, 11206),\n",
       " ('Massachusetts', Decimal('21'), 4991521, datetime.date(2021, 12, 8), 919097, 19404),\n",
       " ('Michigan', Decimal('36'), 5529817, datetime.date(2021, 12, 8), 1475053, 25399),\n",
       " ('Minnesota', Decimal('9'), 3586890, datetime.date(2021, 12, 8), 912370, 9506),\n",
       " ('Mississippi', Decimal('46'), 1423193, datetime.date(2021, 12, 8), 514171, 10285),\n",
       " ('Missouri', Decimal('48'), 3195477, datetime.date(2021, 12, 8), 911403, 13086),\n",
       " ('Montana', Decimal('26'), 561672, datetime.date(2021, 12, 8), 191012, 2671),\n",
       " ('Nebraska', Decimal('7'), 1128676, datetime.date(2021, 12, 8), 309828, 3155),\n",
       " ('Nevada', Decimal('44'), 1698239, datetime.date(2021, 12, 8), 457643, 8016),\n",
       " ('New Hampshire', Decimal('1'), 887741, datetime.date(2021, 12, 8), 161244, 1716),\n",
       " ('New Jersey', Decimal('37'), 6280370, datetime.date(2021, 12, 8), 1252705, 28390),\n",
       " ('New Mexico', Decimal('43'), 1376884, datetime.date(2021, 12, 8), 314264, 5367),\n",
       " ('New York', Decimal('11'), 13488689, datetime.date(2021, 12, 8), 2729595, 57534),\n",
       " ('North Carolina', Decimal('42'), 6048542, datetime.date(2021, 12, 8), 1534005, 18740),\n",
       " ('North Dakota', Decimal('5'), 377032, datetime.date(2021, 12, 8), 162294, 1930),\n",
       " ('Ohio', Decimal('51'), 6298327, datetime.date(2021, 12, 8), 1690217, 27008),\n",
       " ('Oklahoma', Decimal('40'), 2065916, datetime.date(2021, 12, 8), 666927, 11913),\n",
       " ('Oregon', Decimal('19'), 2737980, datetime.date(2021, 12, 8), 391099, 5161),\n",
       " ('Pennsylvania', Decimal('14'), 7945527, datetime.date(2021, 12, 8), 1736920, 33421),\n",
       " ('Rhode Island', Decimal('22'), 785126, datetime.date(2021, 12, 8), 191763, 2932),\n",
       " ('South Carolina', Decimal('29'), 2678806, datetime.date(2021, 12, 8), 918321, 14210),\n",
       " ('South Dakota', Decimal('31'), 490780, datetime.date(2021, 12, 8), 166208, 2334),\n",
       " ('Tennessee', Decimal('10'), 3476474, datetime.date(2021, 12, 8), 1316384, 17033),\n",
       " ('Texas', Decimal('6'), 16162657, datetime.date(2021, 12, 8), 4335900, 73828),\n",
       " ('Utah', Decimal('2'), 1845288, datetime.date(2021, 12, 8), 595801, 3528),\n",
       " ('Vermont', Decimal('28'), 468649, datetime.date(2021, 12, 8), 50014, 410),\n",
       " ('Virginia', Decimal('17'), 5659228, datetime.date(2021, 12, 8), 969116, 14710),\n",
       " ('Washington', Decimal('24'), 5038688, datetime.date(2021, 12, 8), 774465, 9303),\n",
       " ('West Virginia', Decimal('47'), 1236368, datetime.date(2021, 12, 8), 294794, 4868),\n",
       " ('Wisconsin', Decimal('18'), 3515995, datetime.date(2021, 12, 8), 980783, 10005),\n",
       " ('Wyoming', Decimal('32'), 267943, datetime.date(2021, 12, 8), 111089, 1428)]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select\n",
    "state\n",
    ", vaccine.state_key\n",
    ", max(full_vac) as vac\n",
    ", max(day) as date\n",
    ", max(confirmed) as confirmed\n",
    ", max(deaths) as deaths\n",
    "from vaccine \n",
    "left outer join time t on date_key = t.key\n",
    "left outer join location l on vaccine.state_key = l.key\n",
    "left outer join cases c on c.state_key = vaccine.state_key\n",
    "where vaccine_type = 'All' and full_vac > 0\n",
    "group by state, vaccine.state_key\n",
    "order by state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c53d86b9-6ab5-4fbc-bcf2-916ef16f9b01",
   "metadata": {},
   "source": [
    "The Query below produces the total amount of Vaccinations, Confirmed cases & Deaths within the United States. The Data is categorized by months of the year within 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "76997316-67ea-4a1f-be54-6234a34b3c94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "11 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>month_of_year</th>\n",
       "            <th>year</th>\n",
       "            <th>vac_total</th>\n",
       "            <th>confirmed_total</th>\n",
       "            <th>deaths_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>2021</td>\n",
       "            <td>21140</td>\n",
       "            <td>21153740</td>\n",
       "            <td>361854</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2</td>\n",
       "            <td>2021</td>\n",
       "            <td>4529058</td>\n",
       "            <td>26341080</td>\n",
       "            <td>448190</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3</td>\n",
       "            <td>2021</td>\n",
       "            <td>28974693</td>\n",
       "            <td>28921030</td>\n",
       "            <td>521482</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4</td>\n",
       "            <td>2021</td>\n",
       "            <td>96674593</td>\n",
       "            <td>32059963</td>\n",
       "            <td>569530</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5</td>\n",
       "            <td>2021</td>\n",
       "            <td>126356608</td>\n",
       "            <td>32956934</td>\n",
       "            <td>584475</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>6</td>\n",
       "            <td>2021</td>\n",
       "            <td>136924946</td>\n",
       "            <td>33252153</td>\n",
       "            <td>592873</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7</td>\n",
       "            <td>2021</td>\n",
       "            <td>159516198</td>\n",
       "            <td>33996745</td>\n",
       "            <td>605688</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8</td>\n",
       "            <td>2021</td>\n",
       "            <td>168668201</td>\n",
       "            <td>37546830</td>\n",
       "            <td>624908</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9</td>\n",
       "            <td>2021</td>\n",
       "            <td>174113751</td>\n",
       "            <td>39776362</td>\n",
       "            <td>644713</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>10</td>\n",
       "            <td>2021</td>\n",
       "            <td>185654285</td>\n",
       "            <td>44751154</td>\n",
       "            <td>722285</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>11</td>\n",
       "            <td>2021</td>\n",
       "            <td>192324822</td>\n",
       "            <td>46801193</td>\n",
       "            <td>759801</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1, 2021, 21140, 21153740, 361854),\n",
       " (2, 2021, 4529058, 26341080, 448190),\n",
       " (3, 2021, 28974693, 28921030, 521482),\n",
       " (4, 2021, 96674593, 32059963, 569530),\n",
       " (5, 2021, 126356608, 32956934, 584475),\n",
       " (6, 2021, 136924946, 33252153, 592873),\n",
       " (7, 2021, 159516198, 33996745, 605688),\n",
       " (8, 2021, 168668201, 37546830, 624908),\n",
       " (9, 2021, 174113751, 39776362, 644713),\n",
       " (10, 2021, 185654285, 44751154, 722285),\n",
       " (11, 2021, 192324822, 46801193, 759801)]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "\n",
    "with months as (\n",
    "select \n",
    "max(day) as date\n",
    ", max(key) as max_key\n",
    ", month_of_year\n",
    ", year\n",
    "from time\n",
    "where year = 2021\n",
    "group by month_of_year, year\n",
    "order by month_of_year, year),\n",
    "\n",
    "state_tot as (select\n",
    "location.key\n",
    ", state\n",
    ", case when full_vac is null then 0 else full_vac end as full_vac\n",
    ", date\n",
    ", month_of_year\n",
    ", year\n",
    ", confirmed\n",
    ", deaths\n",
    "from months\n",
    "inner join vaccine on date_key = max_key and vaccine_type = 'All'\n",
    "inner join location on location.key = state_key\n",
    "inner join cases on cases.state_key = key and time_key = max_key)\n",
    "\n",
    "select \n",
    "  month_of_year\n",
    ", year\n",
    ", sum(full_vac) as vac_total\n",
    ", sum(confirmed) as confirmed_total\n",
    ", sum(deaths) as deaths_total\n",
    "from state_tot\n",
    "group by month_of_year, year\n",
    "order by month_of_year, year\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d71da2c2-7d87-4c57-947d-dfddb78247f4",
   "metadata": {},
   "source": [
    "The Query Below is similar to the query above, in that it provides the total amount of Vaccinations, Confirmed cases & Deaths within the United States grouped by months. But it also includes the total percentage change monthly of Vaccaintions, confirmed acses and Deaths within th eUnited States.The data in this query starts from April 2020 until November 2021."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "d93b2da2-8a08-4ff8-9e5c-2a7e2deb65e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "20 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>month_of_year</th>\n",
       "            <th>year</th>\n",
       "            <th>vac_total</th>\n",
       "            <th>confirmed_total</th>\n",
       "            <th>deaths_total</th>\n",
       "            <th>vac_change</th>\n",
       "            <th>conf_change</th>\n",
       "            <th>death_change</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>4</td>\n",
       "            <td>2020</td>\n",
       "            <td>0</td>\n",
       "            <td>757582</td>\n",
       "            <td>41021</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5</td>\n",
       "            <td>2020</td>\n",
       "            <td>0</td>\n",
       "            <td>1485372</td>\n",
       "            <td>89816</td>\n",
       "            <td>0.0</td>\n",
       "            <td>96.06748840389555</td>\n",
       "            <td>118.95126886229004</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>6</td>\n",
       "            <td>2020</td>\n",
       "            <td>0</td>\n",
       "            <td>1849008</td>\n",
       "            <td>107033</td>\n",
       "            <td>0.0</td>\n",
       "            <td>24.481140078041054</td>\n",
       "            <td>19.169190344704727</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7</td>\n",
       "            <td>2020</td>\n",
       "            <td>0</td>\n",
       "            <td>3001295</td>\n",
       "            <td>130960</td>\n",
       "            <td>0.0</td>\n",
       "            <td>62.31920034959286</td>\n",
       "            <td>22.354787775732717</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8</td>\n",
       "            <td>2020</td>\n",
       "            <td>0</td>\n",
       "            <td>5353273</td>\n",
       "            <td>168898</td>\n",
       "            <td>0.0</td>\n",
       "            <td>78.36543891886669</td>\n",
       "            <td>28.96915088576665</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9</td>\n",
       "            <td>2020</td>\n",
       "            <td>0</td>\n",
       "            <td>7089211</td>\n",
       "            <td>203906</td>\n",
       "            <td>0.0</td>\n",
       "            <td>32.427600834106535</td>\n",
       "            <td>20.72730286918732</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>10</td>\n",
       "            <td>2020</td>\n",
       "            <td>0</td>\n",
       "            <td>9034797</td>\n",
       "            <td>229332</td>\n",
       "            <td>0.0</td>\n",
       "            <td>27.44432349382745</td>\n",
       "            <td>12.469471226937904</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>11</td>\n",
       "            <td>2020</td>\n",
       "            <td>0</td>\n",
       "            <td>12970903</td>\n",
       "            <td>264346</td>\n",
       "            <td>0.0</td>\n",
       "            <td>43.56607016184204</td>\n",
       "            <td>15.267821324542584</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>12</td>\n",
       "            <td>2020</td>\n",
       "            <td>0</td>\n",
       "            <td>16600582</td>\n",
       "            <td>303685</td>\n",
       "            <td>0.0</td>\n",
       "            <td>27.983240642536604</td>\n",
       "            <td>14.881632406013331</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1</td>\n",
       "            <td>2021</td>\n",
       "            <td>21140</td>\n",
       "            <td>21153740</td>\n",
       "            <td>361854</td>\n",
       "            <td>0.0</td>\n",
       "            <td>27.427701028795255</td>\n",
       "            <td>19.154386947001004</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2</td>\n",
       "            <td>2021</td>\n",
       "            <td>4529058</td>\n",
       "            <td>26341080</td>\n",
       "            <td>448190</td>\n",
       "            <td>21324.11542100284</td>\n",
       "            <td>24.522093965416992</td>\n",
       "            <td>23.85934658729764</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3</td>\n",
       "            <td>2021</td>\n",
       "            <td>28974693</td>\n",
       "            <td>28921030</td>\n",
       "            <td>521482</td>\n",
       "            <td>539.7509813298925</td>\n",
       "            <td>9.7943971925221</td>\n",
       "            <td>16.35288605279011</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4</td>\n",
       "            <td>2021</td>\n",
       "            <td>96674593</td>\n",
       "            <td>32059963</td>\n",
       "            <td>569530</td>\n",
       "            <td>233.65182851117697</td>\n",
       "            <td>10.853461996339687</td>\n",
       "            <td>9.213740838609962</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5</td>\n",
       "            <td>2021</td>\n",
       "            <td>126356608</td>\n",
       "            <td>32956934</td>\n",
       "            <td>584475</td>\n",
       "            <td>30.703015217245344</td>\n",
       "            <td>2.797791750414684</td>\n",
       "            <td>2.624093550822608</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>6</td>\n",
       "            <td>2021</td>\n",
       "            <td>136924946</td>\n",
       "            <td>33252153</td>\n",
       "            <td>592873</td>\n",
       "            <td>8.363898150858876</td>\n",
       "            <td>0.895772039959785</td>\n",
       "            <td>1.4368450318662047</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7</td>\n",
       "            <td>2021</td>\n",
       "            <td>159516198</td>\n",
       "            <td>33996745</td>\n",
       "            <td>605688</td>\n",
       "            <td>16.499003768093505</td>\n",
       "            <td>2.2392294417747927</td>\n",
       "            <td>2.1615084512197384</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8</td>\n",
       "            <td>2021</td>\n",
       "            <td>168668201</td>\n",
       "            <td>37546830</td>\n",
       "            <td>624908</td>\n",
       "            <td>5.737350259564235</td>\n",
       "            <td>10.442426179329816</td>\n",
       "            <td>3.1732509146623347</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9</td>\n",
       "            <td>2021</td>\n",
       "            <td>174113751</td>\n",
       "            <td>39776362</td>\n",
       "            <td>644713</td>\n",
       "            <td>3.2285575868565766</td>\n",
       "            <td>5.938003288160412</td>\n",
       "            <td>3.16926651603116</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>10</td>\n",
       "            <td>2021</td>\n",
       "            <td>185654285</td>\n",
       "            <td>44751154</td>\n",
       "            <td>722285</td>\n",
       "            <td>6.628157703638237</td>\n",
       "            <td>12.506905483211362</td>\n",
       "            <td>12.032020449409272</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>11</td>\n",
       "            <td>2021</td>\n",
       "            <td>192324822</td>\n",
       "            <td>46801193</td>\n",
       "            <td>759801</td>\n",
       "            <td>3.592988440853924</td>\n",
       "            <td>4.58097460458785</td>\n",
       "            <td>5.1940715922385206</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(4, 2020, 0, 757582, 41021, None, None, None),\n",
       " (5, 2020, 0, 1485372, 89816, 0.0, 96.06748840389555, 118.95126886229004),\n",
       " (6, 2020, 0, 1849008, 107033, 0.0, 24.481140078041054, 19.169190344704727),\n",
       " (7, 2020, 0, 3001295, 130960, 0.0, 62.31920034959286, 22.354787775732717),\n",
       " (8, 2020, 0, 5353273, 168898, 0.0, 78.36543891886669, 28.96915088576665),\n",
       " (9, 2020, 0, 7089211, 203906, 0.0, 32.427600834106535, 20.72730286918732),\n",
       " (10, 2020, 0, 9034797, 229332, 0.0, 27.44432349382745, 12.469471226937904),\n",
       " (11, 2020, 0, 12970903, 264346, 0.0, 43.56607016184204, 15.267821324542584),\n",
       " (12, 2020, 0, 16600582, 303685, 0.0, 27.983240642536604, 14.881632406013331),\n",
       " (1, 2021, 21140, 21153740, 361854, 0.0, 27.427701028795255, 19.154386947001004),\n",
       " (2, 2021, 4529058, 26341080, 448190, 21324.11542100284, 24.522093965416992, 23.85934658729764),\n",
       " (3, 2021, 28974693, 28921030, 521482, 539.7509813298925, 9.7943971925221, 16.35288605279011),\n",
       " (4, 2021, 96674593, 32059963, 569530, 233.65182851117697, 10.853461996339687, 9.213740838609962),\n",
       " (5, 2021, 126356608, 32956934, 584475, 30.703015217245344, 2.797791750414684, 2.624093550822608),\n",
       " (6, 2021, 136924946, 33252153, 592873, 8.363898150858876, 0.895772039959785, 1.4368450318662047),\n",
       " (7, 2021, 159516198, 33996745, 605688, 16.499003768093505, 2.2392294417747927, 2.1615084512197384),\n",
       " (8, 2021, 168668201, 37546830, 624908, 5.737350259564235, 10.442426179329816, 3.1732509146623347),\n",
       " (9, 2021, 174113751, 39776362, 644713, 3.2285575868565766, 5.938003288160412, 3.16926651603116),\n",
       " (10, 2021, 185654285, 44751154, 722285, 6.628157703638237, 12.506905483211362, 12.032020449409272),\n",
       " (11, 2021, 192324822, 46801193, 759801, 3.592988440853924, 4.58097460458785, 5.1940715922385206)]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "with months as (\n",
    "select \n",
    "  max(day) as date\n",
    ", max(key) as max_key\n",
    ", month_of_year\n",
    ", year\n",
    "from time\n",
    "\n",
    "group by year, month_of_year\n",
    "order by year, month_of_year),\n",
    "\n",
    "state_tot as (\n",
    "select\n",
    "location.key\n",
    ", state\n",
    ", case when full_vac is null then 0 else full_vac end as full_vac\n",
    ", date\n",
    ", month_of_year\n",
    ", year\n",
    ", confirmed\n",
    ", deaths\n",
    "from cases\n",
    "inner join location on location.key = cases.state_key\n",
    "inner join months on time_key = max_key\n",
    "left outer join vaccine on date_key = max_key and vaccine.state_key = cases.state_key and vaccine_type = 'All'\n",
    "\n",
    "),\n",
    "\n",
    "totals as (\n",
    "select \n",
    "  month_of_year\n",
    ", year\n",
    ", sum(full_vac) as vac_total\n",
    ", sum(confirmed) as confirmed_total\n",
    ", sum(deaths) as deaths_total\n",
    "from state_tot\n",
    "group by month_of_year, year\n",
    "order by year, month_of_year)\n",
    "\n",
    "select \n",
    "month_of_year\n",
    ", year\n",
    ", vac_total\n",
    ", confirmed_total\n",
    ", deaths_total\n",
    ", case when lag(vac_total, 1) over (order by year, month_of_year) = 0 then 0 else (vac_total - lag(vac_total, 1) over (order by year, month_of_year))/cast(lag(vac_total, 1) over (order by year, month_of_year) as float) *100 end as vac_change\n",
    ", (confirmed_total - lag(confirmed_total, 1) over (order by year, month_of_year))/cast(lag(confirmed_total, 1) over (order by year, month_of_year) as float) *100 as conf_change\n",
    ", (deaths_total - lag(deaths_total, 1) over (order by year, month_of_year))/cast(lag(deaths_total, 1) over (order by year, month_of_year) as float) *100 as death_change\n",
    "from totals\n",
    "order by year, month_of_year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f44a4e7-0952-41c7-8783-3b1e086cf616",
   "metadata": {},
   "source": [
    "We then exported to csv and ran some visualizations using Excel. Below are a few examples of the visualizations we did."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "24dc620f-baf7-4238-b3c0-96a25511cb7c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://i.ibb.co/bHFDstG/jamir-1.jpg\" width=\"600\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url='https://i.ibb.co/bHFDstG/jamir-1.jpg', width = 600)\n",
    "#This Graph depicts the progression over time of cases confirmed, deaths and vaccinations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "52c389a6-cd67-4e9a-86f8-63a8351704c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://i.ibb.co/drJ25c7/jamir-2.jpg\" width=\"1000\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url='https://i.ibb.co/drJ25c7/jamir-2.jpg', width = 1000)\n",
    "#This image includes 3 graphs: \n",
    "# 1. Graph which depicts the percent change of vaccinations \n",
    "# 1. Graph which depicts the percent change of Confirmed Cases\n",
    "# 1. Graph which depicts the percent change of Confirmed Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fae5ce83-914a-422a-bda0-52750fa7106a",
   "metadata": {},
   "source": [
    "Conclusion: Based on our data and its correlating graphs, it can be inferred that Vaccines had an impact on total cases confirmed and total deaths. This conclusion was reached because when we take a look at our percent changes graphs, for both Confirmed cases and Deaths, we witness a stark drop in their percent changes starting around February 2021. Which is the same period when Vaccinations saw their greatest increase in percent change. In February 2021 Confirmed cases had a percent change of 24.5% meanwhile Deaths had a change of 23.9%, after February 2021, these percent changes never rose above 20% mainly staying below the 10% threshold. Signifying that Vaccines had an impact on total confirmed cases and deaths"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f35b8a-c248-479a-b4e7-4b5335fbb3ce",
   "metadata": {},
   "source": [
    "### Question 3: How does the quality of education affect the number of fully vaccinated people across different states?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "64517374-31a6-4e22-938e-c01e3916ab12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "51 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>education_score</th>\n",
       "            <th>state</th>\n",
       "            <th>ratio</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>80.06</td>\n",
       "            <td>Massachusetts</td>\n",
       "            <td>0.73</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>73.21</td>\n",
       "            <td>Maryland</td>\n",
       "            <td>0.69</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>70.07</td>\n",
       "            <td>Vermont</td>\n",
       "            <td>0.75</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>68.22</td>\n",
       "            <td>Connecticut</td>\n",
       "            <td>0.74</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>67.97</td>\n",
       "            <td>Colorado</td>\n",
       "            <td>0.63</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>66.98</td>\n",
       "            <td>Virginia</td>\n",
       "            <td>0.66</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>66.32</td>\n",
       "            <td>New Hampshire</td>\n",
       "            <td>0.65</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>65.57</td>\n",
       "            <td>Minnesota</td>\n",
       "            <td>0.63</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>64.53</td>\n",
       "            <td>Utah</td>\n",
       "            <td>0.56</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>63.77</td>\n",
       "            <td>Washington</td>\n",
       "            <td>0.65</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>62.93</td>\n",
       "            <td>New Jersey</td>\n",
       "            <td>0.71</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>56.73</td>\n",
       "            <td>New York</td>\n",
       "            <td>0.71</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>55.71</td>\n",
       "            <td>Oregon</td>\n",
       "            <td>0.64</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>55.65</td>\n",
       "            <td>Hawaii</td>\n",
       "            <td>0.73</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>55.62</td>\n",
       "            <td>Illinois</td>\n",
       "            <td>0.63</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>55.4</td>\n",
       "            <td>Delaware</td>\n",
       "            <td>0.62</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>54.91</td>\n",
       "            <td>Maine</td>\n",
       "            <td>0.73</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>53.37</td>\n",
       "            <td>Montana</td>\n",
       "            <td>0.52</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>53.31</td>\n",
       "            <td>Kansas</td>\n",
       "            <td>0.56</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>53.23</td>\n",
       "            <td>Wisconsin</td>\n",
       "            <td>0.60</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>52.95</td>\n",
       "            <td>Rhode Island</td>\n",
       "            <td>0.74</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>52.08</td>\n",
       "            <td>Nebraska</td>\n",
       "            <td>0.58</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>52.02</td>\n",
       "            <td>North Dakota</td>\n",
       "            <td>0.49</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>51.25</td>\n",
       "            <td>Wyoming</td>\n",
       "            <td>0.46</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>50.03</td>\n",
       "            <td>California</td>\n",
       "            <td>0.66</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>49.69</td>\n",
       "            <td>Iowa</td>\n",
       "            <td>0.58</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>49.19</td>\n",
       "            <td>Alaska</td>\n",
       "            <td>0.56</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>48.86</td>\n",
       "            <td>Florida</td>\n",
       "            <td>0.61</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>48.74</td>\n",
       "            <td>Pennsylvania</td>\n",
       "            <td>0.62</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>48.66</td>\n",
       "            <td>Michigan</td>\n",
       "            <td>0.55</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>46.03</td>\n",
       "            <td>North Carolina</td>\n",
       "            <td>0.57</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>45.08</td>\n",
       "            <td>Ohio</td>\n",
       "            <td>0.54</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>44.96</td>\n",
       "            <td>Georgia</td>\n",
       "            <td>0.49</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>44.5</td>\n",
       "            <td>Missouri</td>\n",
       "            <td>0.52</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>44.29</td>\n",
       "            <td>District of Columbia</td>\n",
       "            <td>0.81</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>44.12</td>\n",
       "            <td>South Dakota</td>\n",
       "            <td>0.55</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>42.61</td>\n",
       "            <td>Arizona</td>\n",
       "            <td>0.54</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>41.82</td>\n",
       "            <td>Indiana</td>\n",
       "            <td>0.51</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>41.66</td>\n",
       "            <td>Idaho</td>\n",
       "            <td>0.47</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>37.12</td>\n",
       "            <td>Texas</td>\n",
       "            <td>0.55</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>36.32</td>\n",
       "            <td>New Mexico</td>\n",
       "            <td>0.65</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>35.85</td>\n",
       "            <td>Tennessee</td>\n",
       "            <td>0.50</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>34.77</td>\n",
       "            <td>South Carolina</td>\n",
       "            <td>0.51</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>34.43</td>\n",
       "            <td>Oklahoma</td>\n",
       "            <td>0.52</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>33.31</td>\n",
       "            <td>Nevada</td>\n",
       "            <td>0.53</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>32.15</td>\n",
       "            <td>Kentucky</td>\n",
       "            <td>0.54</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>30.23</td>\n",
       "            <td>Alabama</td>\n",
       "            <td>0.46</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>27.9</td>\n",
       "            <td>Arkansas</td>\n",
       "            <td>0.50</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>22.57</td>\n",
       "            <td>Louisiana</td>\n",
       "            <td>0.50</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>21.02</td>\n",
       "            <td>West Virginia</td>\n",
       "            <td>0.72</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>19.32</td>\n",
       "            <td>Mississippi</td>\n",
       "            <td>0.48</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(Decimal('80.06'), 'Massachusetts', Decimal('0.73')),\n",
       " (Decimal('73.21'), 'Maryland', Decimal('0.69')),\n",
       " (Decimal('70.07'), 'Vermont', Decimal('0.75')),\n",
       " (Decimal('68.22'), 'Connecticut', Decimal('0.74')),\n",
       " (Decimal('67.97'), 'Colorado', Decimal('0.63')),\n",
       " (Decimal('66.98'), 'Virginia', Decimal('0.66')),\n",
       " (Decimal('66.32'), 'New Hampshire', Decimal('0.65')),\n",
       " (Decimal('65.57'), 'Minnesota', Decimal('0.63')),\n",
       " (Decimal('64.53'), 'Utah', Decimal('0.56')),\n",
       " (Decimal('63.77'), 'Washington', Decimal('0.65')),\n",
       " (Decimal('62.93'), 'New Jersey', Decimal('0.71')),\n",
       " (Decimal('56.73'), 'New York', Decimal('0.71')),\n",
       " (Decimal('55.71'), 'Oregon', Decimal('0.64')),\n",
       " (Decimal('55.65'), 'Hawaii', Decimal('0.73')),\n",
       " (Decimal('55.62'), 'Illinois', Decimal('0.63')),\n",
       " (Decimal('55.4'), 'Delaware', Decimal('0.62')),\n",
       " (Decimal('54.91'), 'Maine', Decimal('0.73')),\n",
       " (Decimal('53.37'), 'Montana', Decimal('0.52')),\n",
       " (Decimal('53.31'), 'Kansas', Decimal('0.56')),\n",
       " (Decimal('53.23'), 'Wisconsin', Decimal('0.60')),\n",
       " (Decimal('52.95'), 'Rhode Island', Decimal('0.74')),\n",
       " (Decimal('52.08'), 'Nebraska', Decimal('0.58')),\n",
       " (Decimal('52.02'), 'North Dakota', Decimal('0.49')),\n",
       " (Decimal('51.25'), 'Wyoming', Decimal('0.46')),\n",
       " (Decimal('50.03'), 'California', Decimal('0.66')),\n",
       " (Decimal('49.69'), 'Iowa', Decimal('0.58')),\n",
       " (Decimal('49.19'), 'Alaska', Decimal('0.56')),\n",
       " (Decimal('48.86'), 'Florida', Decimal('0.61')),\n",
       " (Decimal('48.74'), 'Pennsylvania', Decimal('0.62')),\n",
       " (Decimal('48.66'), 'Michigan', Decimal('0.55')),\n",
       " (Decimal('46.03'), 'North Carolina', Decimal('0.57')),\n",
       " (Decimal('45.08'), 'Ohio', Decimal('0.54')),\n",
       " (Decimal('44.96'), 'Georgia', Decimal('0.49')),\n",
       " (Decimal('44.5'), 'Missouri', Decimal('0.52')),\n",
       " (Decimal('44.29'), 'District of Columbia', Decimal('0.81')),\n",
       " (Decimal('44.12'), 'South Dakota', Decimal('0.55')),\n",
       " (Decimal('42.61'), 'Arizona', Decimal('0.54')),\n",
       " (Decimal('41.82'), 'Indiana', Decimal('0.51')),\n",
       " (Decimal('41.66'), 'Idaho', Decimal('0.47')),\n",
       " (Decimal('37.12'), 'Texas', Decimal('0.55')),\n",
       " (Decimal('36.32'), 'New Mexico', Decimal('0.65')),\n",
       " (Decimal('35.85'), 'Tennessee', Decimal('0.50')),\n",
       " (Decimal('34.77'), 'South Carolina', Decimal('0.51')),\n",
       " (Decimal('34.43'), 'Oklahoma', Decimal('0.52')),\n",
       " (Decimal('33.31'), 'Nevada', Decimal('0.53')),\n",
       " (Decimal('32.15'), 'Kentucky', Decimal('0.54')),\n",
       " (Decimal('30.23'), 'Alabama', Decimal('0.46')),\n",
       " (Decimal('27.9'), 'Arkansas', Decimal('0.50')),\n",
       " (Decimal('22.57'), 'Louisiana', Decimal('0.50')),\n",
       " (Decimal('21.02'), 'West Virginia', Decimal('0.72')),\n",
       " (Decimal('19.32'), 'Mississippi', Decimal('0.48'))]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select \n",
    "  l.education_score as education_score,\n",
    "l.state\n",
    ", round(max(v.full_vac) / cast(l.pop as numeric), 2) as ratio\n",
    "from vaccine v\n",
    "left join location l on l.key = v.state_key \n",
    "where full_vac is not null\n",
    "group by l.state, education_score, l.pop\n",
    "order by education_score desc\n",
    "; "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad37d32-8a47-4e6b-8619-626ad735afef",
   "metadata": {},
   "source": [
    "Lets now turn this into a view so that we can export it into a csv. The reason we want this as a CSV is so that we can import it into Tableau for some data visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ea06ff17-d7d9-47b3-8e07-7c2705dd23cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP view if exists ed_vac;\n",
    "create view ed_vac as\n",
    "select \n",
    "  l.education_score as education_score,\n",
    "l.state\n",
    ", round(max(v.full_vac) / cast(l.pop as numeric), 2) as ratio\n",
    "from vaccine v\n",
    "left join location l on l.key = v.state_key \n",
    "where full_vac is not null\n",
    "group by l.state, education_score, l.pop\n",
    "order by education_score desc\n",
    "; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8a6ee88c-fc97-4604-863b-fd14b78392bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "51 rows affected.\n"
     ]
    }
   ],
   "source": [
    "ed_vac = %sql SELECT * FROM ed_vac;\n",
    "ed_vac_pd = pd.DataFrame(ed_vac, columns = (\"education_score\", \"state\", \"percentage_vaccinated\"))\n",
    "ed_vac_pd.to_csv('ed_vac.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7974a54-c21e-48e4-bd30-890ab809e267",
   "metadata": {},
   "source": [
    "We then used the dataframe (ed_vac_pd) to run some analysis and also to run a regression to see the true relationship between education and vaccine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c4d86ba-6630-4887-94d6-483b7dbf11b3",
   "metadata": {},
   "source": [
    "We need to download some packages to run this regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7cd0f5b9-6734-4ff7-bec2-307d4dc55b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: statsmodels in /home/ubuntu/.local/lib/python3.8/site-packages (0.13.1)\n",
      "Requirement already satisfied: patsy>=0.5.2 in /home/ubuntu/.local/lib/python3.8/site-packages (from statsmodels) (0.5.2)\n",
      "Requirement already satisfied: pandas>=0.25 in /home/ubuntu/.local/lib/python3.8/site-packages (from statsmodels) (1.3.2)\n",
      "Requirement already satisfied: numpy>=1.17 in /home/ubuntu/.local/lib/python3.8/site-packages (from statsmodels) (1.21.2)\n",
      "Requirement already satisfied: scipy>=1.3 in /home/ubuntu/.local/lib/python3.8/site-packages (from statsmodels) (1.7.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/ubuntu/.local/lib/python3.8/site-packages (from pandas>=0.25->statsmodels) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /home/ubuntu/.local/lib/python3.8/site-packages (from pandas>=0.25->statsmodels) (2021.1)\n",
      "Requirement already satisfied: six in /home/ubuntu/.local/lib/python3.8/site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)\n",
      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b498a7ba-4218-46b8-9b2e-2c21f1a59cef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting scikits.bootstrap\n",
      "  Downloading scikits.bootstrap-1.0.1-py3-none-any.whl (10 kB)\n",
      "Requirement already satisfied: numpy in /home/ubuntu/.local/lib/python3.8/site-packages (from scikits.bootstrap) (1.21.2)\n",
      "Collecting pyerf\n",
      "  Downloading pyerf-1.0.1-py3-none-any.whl (11 kB)\n",
      "Installing collected packages: pyerf, scikits.bootstrap\n",
      "Successfully installed pyerf-1.0.1 scikits.bootstrap-1.0.1\n",
      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install scikits.bootstrap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d2371af-d686-4d11-9c29-7ebf46512100",
   "metadata": {},
   "source": [
    "Lets convert the columns into the relevant data types, where required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ed70f529-be2c-445f-a798-1f7b9c156bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ed_vac_pd[[\"education_score\", \"percentage_vaccinated\"]] = ed_vac_pd[[\"education_score\", \"percentage_vaccinated\"]].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c7820dd0-3314-4805-a393-3ff3ad67777f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ed_vac_pd[[\"state\"]] = ed_vac_pd[[\"state\"]].astype('string')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8b9613dc-a3ed-42a9-af19-a450532730a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education_score          float64\n",
       "state                     string\n",
       "percentage_vaccinated    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ed_vac_pd.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95c3e3b1-ef64-4bb1-8459-dcbec7f318d4",
   "metadata": {},
   "source": [
    "#### Regression\n",
    "\n",
    "Let's now run the actual regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc083d7a-75fa-4e99-b650-8378dba55ea5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>percentage_vaccinated</td> <th>  R-squared (uncentered):</th>      <td>   0.951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                     <td>OLS</td>          <th>  Adj. R-squared (uncentered):</th> <td>   0.950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>Least Squares</td>     <th>  F-statistic:       </th>          <td>   969.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>               <td>Sat, 11 Dec 2021</td>    <th>  Prob (F-statistic):</th>          <td>2.10e-34</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>21:19:15</td>        <th>  Log-Likelihood:    </th>          <td>  30.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>        <td>    51</td>         <th>  AIC:               </th>          <td>  -58.51</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>            <td>    50</td>         <th>  BIC:               </th>          <td>  -56.58</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                <td>     1</td>         <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>nonrobust</td>       <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>education_score</th> <td>    0.0115</td> <td>    0.000</td> <td>   31.142</td> <td> 0.000</td> <td>    0.011</td> <td>    0.012</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.629</td> <th>  Durbin-Watson:     </th> <td>   0.618</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.008</td> <th>  Jarque-Bera (JB):  </th> <td>   9.381</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.830</td> <th>  Prob(JB):          </th> <td> 0.00918</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.289</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                  OLS Regression Results                                  \n",
       "==========================================================================================\n",
       "Dep. Variable:     percentage_vaccinated   R-squared (uncentered):                   0.951\n",
       "Model:                               OLS   Adj. R-squared (uncentered):              0.950\n",
       "Method:                    Least Squares   F-statistic:                              969.8\n",
       "Date:                   Sat, 11 Dec 2021   Prob (F-statistic):                    2.10e-34\n",
       "Time:                           21:19:15   Log-Likelihood:                          30.257\n",
       "No. Observations:                     51   AIC:                                     -58.51\n",
       "Df Residuals:                         50   BIC:                                     -56.58\n",
       "Df Model:                              1                                                  \n",
       "Covariance Type:               nonrobust                                                  \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "education_score     0.0115      0.000     31.142      0.000       0.011       0.012\n",
       "==============================================================================\n",
       "Omnibus:                        9.629   Durbin-Watson:                   0.618\n",
       "Prob(Omnibus):                  0.008   Jarque-Bera (JB):                9.381\n",
       "Skew:                           0.830   Prob(JB):                      0.00918\n",
       "Kurtosis:                       4.289   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "X = ed_vac_pd[\"education_score\"]\n",
    "y = ed_vac_pd[\"percentage_vaccinated\"]\n",
    "\n",
    "# Note the difference in argument order\n",
    "model = sm.OLS(y, X).fit()\n",
    "predictions = model.predict(X) # make the predictions by the model\n",
    "\n",
    "# Print out the statistics\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "271d7f57-5bd8-44fc-aa5e-a9d1e6195223",
   "metadata": {
    "tags": []
   },
   "source": [
    "We did a single liner reggresion to predict the rate of fully vaccinated people per state using education scorces. We find there is quite strong relationship between the rate of fully vaccinated people and education scorces per state seen in our R-sqaured of 0.951 and our p-value being far below 0.05 which means the relationship statically signifiant. The regreesion also tells us that it's a positive relationship based on the slope which is the rate of change that education score have the expected vaccintion rate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a3b8176-e096-4c1c-b441-79d0acb94935",
   "metadata": {},
   "source": [
    "Let's visualize this relationship with the use of a scatter plot. The plot below was done using Tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cf592f61-c7f0-45e5-a367-f34608377f2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://i.ibb.co/rbZ7ZmR/ed-vac.png\" width=\"600\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url='https://i.ibb.co/rbZ7ZmR/ed-vac.png', width = 600) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46b29e3c-467b-4b42-bb76-4d8cbf1f1057",
   "metadata": {},
   "source": [
    "This graph also confirms what is in the regresion, a positive relationship between education score and the percentage of people vaccinated. We can also see the two biggest outlier DC and West Virgina. In DC, it is possible that because many government workers live in DC and with the government vaccine mandates in place, that's why the rate of fully vaccinated people is so high. For West Virgina, as discussed in question three, West Virgina had a very effection rollout so that is another possible explaintion for why it's an outiier."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15105782-d3e0-4cf7-b0ad-5e17e3ffa08b",
   "metadata": {},
   "source": [
    "### Question 4: What was the vaccination rate trend over time? Which vaccine types were most popular at different points during the pandemic?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "e2431334-b299-4796-ad56-d525599787ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a560988e-8239-4871-85a0-72748a9f04ae",
   "metadata": {},
   "source": [
    "##### Inspecting Table - Time :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "116f3efa-c3af-4e8f-9d1d-80619374084e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>key</th>\n",
       "            <th>day</th>\n",
       "            <th>year</th>\n",
       "            <th>month_of_year_str</th>\n",
       "            <th>month_of_year</th>\n",
       "            <th>day_of_month</th>\n",
       "            <th>day_of_week_str</th>\n",
       "            <th>day_of_week</th>\n",
       "            <th>quarter_of_year</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>4147</td>\n",
       "            <td>2020-06-14</td>\n",
       "            <td>2020</td>\n",
       "            <td>June     </td>\n",
       "            <td>6</td>\n",
       "            <td>14</td>\n",
       "            <td>Sunday   </td>\n",
       "            <td>1</td>\n",
       "            <td>2</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(4147, datetime.date(2020, 6, 14), 2020, 'June     ', 6, 14, 'Sunday   ', 1, 2)]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select * \n",
    "from time limit 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ea61860-cfa7-4db4-959b-5508abeb321e",
   "metadata": {},
   "source": [
    "##### Inspecting Table - Vaccine :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "6696e079-290f-4fdb-b62f-112da5bdaff8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>state_key</th>\n",
       "            <th>date_key</th>\n",
       "            <th>vaccine_type</th>\n",
       "            <th>doses_alloc</th>\n",
       "            <th>doses_shipped</th>\n",
       "            <th>doses_admin</th>\n",
       "            <th>stage_one_doses</th>\n",
       "            <th>stage_two_doses</th>\n",
       "            <th>full_vac</th>\n",
       "            <th>part_vac</th>\n",
       "            <th>population</th>\n",
       "            <th>ratio</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>27</td>\n",
       "            <td>178</td>\n",
       "            <td>Unknown</td>\n",
       "            <td>None</td>\n",
       "            <td>0</td>\n",
       "            <td>22783</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>1354522</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(Decimal('27'), Decimal('178'), 'Unknown', None, 0, 22783, None, None, None, None, 1354522, None)]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from vaccine limit 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b31cf3-dca1-4a4c-8615-0ff2149ea2e9",
   "metadata": {},
   "source": [
    "##### Creating a View - vaccine_1 that has the data of only the columns that we'll need to find the popularity of vaccine types over time\n",
    "* Vaccine Table - vaccine_type, date_key, doses_admin\n",
    "* Time Table - year, month_of_year\n",
    "\n",
    "We group the data using year, month_of_year and date_key and order by year and month_of_year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "19e75166-0c75-4522-b959-1b9051602fa0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "DROP view IF EXISTS vaccine_1;\n",
    "\n",
    "create view vaccine_1 as\n",
    "select v.vaccine_type,v.date_key,sum(v.doses_admin) as max_doses_administered, t.year, t.month_of_year\n",
    "from vaccine v\n",
    "left join time t on t.key = v.date_key\n",
    "group by vaccine_type, t.year, t.month_of_year,v.date_key\n",
    "order by t.year,t.month_of_year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "854fc220-7479-471c-acd0-739e32a259ed",
   "metadata": {},
   "source": [
    "##### Reviewing View - vaccine_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a64aef8b-9fe2-4078-8377-52db798ad408",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>vaccine_type</th>\n",
       "            <th>date_key</th>\n",
       "            <th>max_doses_administered</th>\n",
       "            <th>year</th>\n",
       "            <th>month_of_year</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>All</td>\n",
       "            <td>5</td>\n",
       "            <td>884744</td>\n",
       "            <td>2020</td>\n",
       "            <td>12</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('All', Decimal('5'), 884744, 2020, 12)]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select *\n",
    "from vaccine_1\n",
    "limit 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "8a89311b-6fa8-4271-a1ac-dbffb0074687",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://student@/project_final\n",
      "1987 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['All', 'Moderna', 'Pfizer', 'Unassigned', 'Unknown', 'Janssen',\n",
       "       'unknown'], dtype=object)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine_1 = %sql SELECT * FROM vaccine_1;\n",
    "vaccine_1_pd = pd.DataFrame(vaccine_1, columns = ('vaccine_type','date_key','max_doses_administered','year','month_of_year'))\n",
    "vaccine_1_pd.vaccine_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "c227d700-2039-4635-b798-f28aa0fec299",
   "metadata": {},
   "outputs": [],
   "source": [
    "vaccine_1_pd[\"vaccine_type\"].replace({\"unknown\": \"Unknown\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "d7713db2-9999-41fb-8cca-36b269cfbdce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['All', 'Moderna', 'Pfizer', 'Unassigned', 'Unknown', 'Janssen'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine_1_pd.vaccine_type.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ca1a0e-fb88-489d-9186-1eb33013a095",
   "metadata": {},
   "source": [
    "##### Creating a column 'timeperiod' in View - vaccine_1 that is a concatination of year and month_of_year from the Table - time. This column will help us in visualizing the data displayed below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "929f89c2-13f2-4da3-b858-471018562d5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "vaccine_1_pd[\"timeperiod\"] = vaccine_1_pd[\"year\"].astype(str) + '/' + vaccine_1_pd[\"month_of_year\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "92727168-b01c-42f6-9a86-51f43592a5a7",
   "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>vaccine_type</th>\n",
       "      <th>date_key</th>\n",
       "      <th>max_doses_administered</th>\n",
       "      <th>year</th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>timeperiod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All</td>\n",
       "      <td>5</td>\n",
       "      <td>884744.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>2020/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Pfizer</td>\n",
       "      <td>594</td>\n",
       "      <td>491857.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>2020/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Pfizer</td>\n",
       "      <td>566</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>2020/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Pfizer</td>\n",
       "      <td>497</td>\n",
       "      <td>617991.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>2020/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Pfizer</td>\n",
       "      <td>458</td>\n",
       "      <td>97737.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>2020/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>Moderna</td>\n",
       "      <td>232</td>\n",
       "      <td>146142933.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>2021/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>Moderna</td>\n",
       "      <td>243</td>\n",
       "      <td>146971314.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>2021/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>Moderna</td>\n",
       "      <td>270</td>\n",
       "      <td>145341328.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>2021/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>Moderna</td>\n",
       "      <td>327</td>\n",
       "      <td>145138793.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>2021/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>All</td>\n",
       "      <td>327</td>\n",
       "      <td>373138508.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>2021/9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1987 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     vaccine_type date_key  max_doses_administered  year  month_of_year  \\\n",
       "0             All        5                884744.0  2020             12   \n",
       "63         Pfizer      594                491857.0  2020             12   \n",
       "62         Pfizer      566                    50.0  2020             12   \n",
       "61         Pfizer      497                617991.0  2020             12   \n",
       "60         Pfizer      458                 97737.0  2020             12   \n",
       "...           ...      ...                     ...   ...            ...   \n",
       "1493      Moderna      232             146142933.0  2021              9   \n",
       "1494      Moderna      243             146971314.0  2021              9   \n",
       "1495      Moderna      270             145341328.0  2021              9   \n",
       "1497      Moderna      327             145138793.0  2021              9   \n",
       "1437          All      327             373138508.0  2021              9   \n",
       "\n",
       "     timeperiod  \n",
       "0       2020/12  \n",
       "63      2020/12  \n",
       "62      2020/12  \n",
       "61      2020/12  \n",
       "60      2020/12  \n",
       "...         ...  \n",
       "1493     2021/9  \n",
       "1494     2021/9  \n",
       "1495     2021/9  \n",
       "1497     2021/9  \n",
       "1437     2021/9  \n",
       "\n",
       "[1987 rows x 6 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine_1_pd.sort_values(by=['timeperiod'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "175bf755-7e24-49a4-bb53-a82dd26edfd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vaccine_type               object\n",
       "date_key                   object\n",
       "max_doses_administered    float64\n",
       "year                        int64\n",
       "month_of_year               int64\n",
       "timeperiod                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine_1_pd.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f5c0aa-7d2a-48f1-926e-ab1bd1092046",
   "metadata": {},
   "source": [
    "##### Ploting a line graph that displays the popularity of vaccines over time in the US."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "74ac0ee3-afbc-49c6-98bc-d7c838f872b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x2520 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (30,35))\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plot = sns.lineplot(x=\"timeperiod\", y=\"max_doses_administered\", hue=\"vaccine_type\", marker='*', ms=20, data=vaccine_1_pd)\n",
    "plot.axvline('2020/12', color = 'red', linestyle = '--') # FDA approves both Morderna and Pfizer and the first deliveries of the COVID-19 vaccines began \n",
    "plot.axvline('2021/3', color = 'magenta', linestyle = '--') # FDA approves JnJ and and on March-02-2021 the Acting HHS Secretary directed that vaccination was avaible for teachers, school staff, and child care workers\n",
    "plot.axvline('2021/4', color = '#800080', linestyle = '--') # on April 19 2021 White House COVID Response Team announced that all people age 16 and older are eligible for the COVID-19 vaccine\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel(\"Maximum Doses Admin (in one hundred million)\")\n",
    "plt.title('\"Vaccine Types Popular over Time\"')\n",
    "plot.legend\n",
    "plt.savefig('vaccine.png')"
   ]
  },
  {
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    "##### About the graph:\n",
    " * The <span style=\"color:red\">*red*</span> dashed vertical line represents the yyyy/mm of FDA approvals of both Morderna(December-20-2020) and Pfizer(December-13-2020 ) and on December-14-2020 the first deliveries of the COVID-19 vaccines began\n",
    " * The <span style=\"color:#FF00FF\">*megenta*</span> dashed vertical line represents the yyyy/mm of FDA approval of JnJ(Feburary-27-2021) ~ 2021/3 and on March-02-2021 the Acting HHS Secretary directed that vaccination was avaible for teachers, school staff, and child care workers\n",
    " * The <span style=\"color:#800080\">*purple*</span> dashed vertical line represents the yyyy/mm on April-19-2021 White House COVID Response Team announced that all people age 16 and older are eligible for the COVID-19 vaccine"
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   "id": "88a2b4e4-e795-42a5-a714-f14a598c0a8a",
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    "##### Analysis:\n",
    "   * A decline in the vaccine_type 'Unassigned' is observed after the FDA approves JnJ - here we can suggest that JnJ's clicnical trials ended and as such the number of drugs administered through the type 'Unassigned' reduced\n",
    "   * The lines that represent Morderna and Pfizer have an exponential increase in the month of March 2021 - here we can suggest that this might be because the Acting HHS Secretary directed that vaccination was avaible for teachers, school staff, and child care workers \n",
    "   * We observe that Pfizer was more popular in the US when compared to Morderna and JnJ with about 2.6 hundred million doses being administered across the US\n",
    "   * Morderna comes next with about 1.8 hundred million doses being adminstered and JnJ comes later with about 30 million doses being administered across the US"
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