initialization_COVID_DB_SQLite.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "klbsGgR1WpSH"
   },
   "source": [
    "# COVID Database setup and initialization in SQLite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r2isTTzJerSD"
   },
   "source": [
    "The database will be named as `COVID_DB` and it will be created and initialized using Python and SQLite; with SQLite as the Database Management System and Python for scripting.\n",
    "\n",
    "We'll use Python to create and initialize the Database. We'll also add the contents of the 2 COVID <abbr title=\"Comma Seperated Values\">CSV</abbr> files to the Database as Tables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rr3umb-Yf7-6"
   },
   "source": [
    "## Connecting to Google Drive (If necessary)\n",
    "While working on this project using Google Drive there is a need to import the `drive` object from the `google.colab` python module. This is only required while working on this `.ipynb` file and using the SQLite Database with this file if it's worked on in Google Drive.\n",
    "\n",
    "A `mountpoint` instance must be created to navigate Google Drive, in this case it can be called: `/content/drive`. It will ask for authentication to your Google Drive account. A username and password may be requested. Then simply \"Allow\" this let this `.ipynb` file create, add, delete, and modify files in whatever Google Drive account is in use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17505,
     "status": "ok",
     "timestamp": 1644690819467,
     "user": {
      "displayName": "James Wolfe",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjniFB9tD5BdLBJA15GSIxZ-ItGzbUjXLrJb8IrwQ=s64",
      "userId": "02458012257526617222"
     },
     "user_tz": 300
    },
    "id": "kUmjecDtiqiR",
    "outputId": "a7f6db58-b3b4-4874-daff-beee4de46469"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount(\"/content/drive\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Gz5e5dDk82j"
   },
   "source": [
    "## The <abbr title=\"Comma Seperated Values\">CSV</abbr> files to use in this Project\n",
    "The source for the COVID-19 data comes from [*Coronavirus (COVID-19) Deaths*](https://ourworldindata.org/covid-deaths) by <u>**Our World in Data**</u> and has already been seperated into 2 CSV files: `CovidDeaths.csv` and `CovidVaccinations.csv`. These 2 CSV files will each be their own table in the `COVID_DB` database. the code below shows the path to each of these files and should be modified if they are moved to a different location in Google Drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "stmGxAJppDPM"
   },
   "outputs": [],
   "source": [
    "path_covid_death = \"/content/drive/MyDrive/Documents/Data Analysis Projects/SQL Data Exploration/CovidDeaths.csv\"\n",
    "path_covid_vacc = \"/content/drive/MyDrive/Documents/Data Analysis Projects/SQL Data Exploration/CovidVaccinations.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EIz1w94UbwZO"
   },
   "source": [
    "## The Modules (Library) needed for this project.\n",
    "\n",
    "The Pandas Module provides a means to convert a `csv` file to a DataFrame which in turn can be convert into a table in `COVID_DB`. We'll need help from another Module: SQLAlchemy, to be able to modify the COVID_DB in a object oriented programmatic way. Of course we must also have a module for `SQLite` to create/modify/delete a simple database. Which is the `COVID_DB`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hcmN7cCycAp3"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tWeSUF6dfcra"
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zAOBOslI8aI5"
   },
   "source": [
    "If I'm using my Google Drive Account:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SIsgOL6J8aI5"
   },
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:////content/drive/MyDrive/Documents/Data Analysis Projects/SQL Data Exploration/COVID_DB.db\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3YEs7mvb8aI6"
   },
   "source": [
    "If I'm using my own computer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kz-wzuXU8aI6"
   },
   "outputs": [],
   "source": [
    "my_conn = create_engine(r\"sqlite:///C:\\Users\\jcwol\\Google Drive\\Documents\\Data Analysis Projects\\SQL Data Exploration/COVID_DB.db\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FfM0KwTyo4Ff"
   },
   "source": [
    "## Reading the `CSV` files and convert into a DataFrame\n",
    "Now we can get our hands dirty and have Pandas read the `csv` files and store them as a DataFrame. \n",
    "\n",
    "Disclaimer: In this project there is no primary keys we could use for when we can create the `COVID_DB` database. Also data cleaning will not be done in this Python Notebook. This is to just start creating and implementing a Database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7E2J_kXhpfBI"
   },
   "outputs": [],
   "source": [
    "df_covid_death = pd.read_csv(path_covid_death)\n",
    "df_covid_vacc = pd.read_csv(path_covid_vacc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 661
    },
    "executionInfo": {
     "elapsed": 348,
     "status": "ok",
     "timestamp": 1644312382768,
     "user": {
      "displayName": "James Wolfe",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjniFB9tD5BdLBJA15GSIxZ-ItGzbUjXLrJb8IrwQ=s64",
      "userId": "02458012257526617222"
     },
     "user_tz": 300
    },
    "id": "fROLyWu5p6Uw",
    "outputId": "a7bba2db-00a8-4c5f-800b-bd4fd3ff1d88"
   },
   "outputs": [
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       "  "
      ],
      "text/plain": [
       "       iso_code  ... weekly_hosp_admissions_per_million\n",
       "0           AFG  ...                                NaN\n",
       "1           AFG  ...                                NaN\n",
       "2           AFG  ...                                NaN\n",
       "3           AFG  ...                                NaN\n",
       "4           AFG  ...                                NaN\n",
       "...         ...  ...                                ...\n",
       "157471      ZWE  ...                                NaN\n",
       "157472      ZWE  ...                                NaN\n",
       "157473      ZWE  ...                                NaN\n",
       "157474      ZWE  ...                                NaN\n",
       "157475      ZWE  ...                                NaN\n",
       "\n",
       "[157476 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covid_death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "executionInfo": {
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     "status": "ok",
     "timestamp": 1644312401843,
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    },
    "id": "EfB4YOLip_DT",
    "outputId": "243c325b-1269-4f0c-bf0f-43710105d486"
   },
   "outputs": [
    {
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       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157472</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-23</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>1824420.0</td>\n",
       "      <td>120.885</td>\n",
       "      <td>0.102</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.0795</td>\n",
       "      <td>12.6</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>7512903.0</td>\n",
       "      <td>4242647.0</td>\n",
       "      <td>3270256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6117.0</td>\n",
       "      <td>10631.0</td>\n",
       "      <td>49.78</td>\n",
       "      <td>28.11</td>\n",
       "      <td>21.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>704.0</td>\n",
       "      <td>5182.0</td>\n",
       "      <td>0.034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157473</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-24</td>\n",
       "      <td>4913.0</td>\n",
       "      <td>1829333.0</td>\n",
       "      <td>121.211</td>\n",
       "      <td>0.326</td>\n",
       "      <td>4043.0</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.0735</td>\n",
       "      <td>13.6</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>7517985.0</td>\n",
       "      <td>4245063.0</td>\n",
       "      <td>3272922.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5082.0</td>\n",
       "      <td>10273.0</td>\n",
       "      <td>49.81</td>\n",
       "      <td>28.13</td>\n",
       "      <td>21.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681.0</td>\n",
       "      <td>5009.0</td>\n",
       "      <td>0.033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
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       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>157474</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7525574.0</td>\n",
       "      <td>4248576.0</td>\n",
       "      <td>3276998.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7589.0</td>\n",
       "      <td>9579.0</td>\n",
       "      <td>49.86</td>\n",
       "      <td>28.15</td>\n",
       "      <td>21.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>635.0</td>\n",
       "      <td>4638.0</td>\n",
       "      <td>0.031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>157475</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157476 rows × 46 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88e43e26-26ee-4ae5-bd10-30c6468a8d54')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "       iso_code  ... excess_mortality_cumulative_per_million\n",
       "0           AFG  ...                                     NaN\n",
       "1           AFG  ...                                     NaN\n",
       "2           AFG  ...                                     NaN\n",
       "3           AFG  ...                                     NaN\n",
       "4           AFG  ...                                     NaN\n",
       "...         ...  ...                                     ...\n",
       "157471      ZWE  ...                                     NaN\n",
       "157472      ZWE  ...                                     NaN\n",
       "157473      ZWE  ...                                     NaN\n",
       "157474      ZWE  ...                                     NaN\n",
       "157475      ZWE  ...                                     NaN\n",
       "\n",
       "[157476 rows x 46 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covid_vacc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MfKqQ3AKLhdv"
   },
   "source": [
    "## Creating the Tables in `COVID_DB`\n",
    "\n",
    "At this point using Pandas we can create the tables `Covid_Deaths` and `Covid Vaccinations` off of the `df_covid_death` and `df_covid_vacc` dataframes and also indirectly create the `COVID_DB` the moment we use Pandas' `to_sql` method.\n",
    "\n",
    "Just incase if the entire Python Notebook needs to be started from top to bottom we can just recreate the tables in `COVID_DB` so that no errors are produced when rerunning these particular code blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Oymk6JiuMoNF"
   },
   "outputs": [],
   "source": [
    "df_covid_death.to_sql(\"CovidDeaths\", engine, if_exists='replace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "e1jRCaMEONS0"
   },
   "outputs": [],
   "source": [
    "df_covid_vacc.to_sql(\"CovidVaccinations\", engine, if_exists='replace')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pTxhsGk1ObsO"
   },
   "source": [
    "## Checking if the Datababe and Tables exist!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AEAdLMi_OoJd"
   },
   "outputs": [],
   "source": [
    "sql_df_death = pd.read_sql(\"CovidDeaths\", engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 661
    },
    "executionInfo": {
     "elapsed": 274,
     "status": "ok",
     "timestamp": 1644691237939,
     "user": {
      "displayName": "James Wolfe",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjniFB9tD5BdLBJA15GSIxZ-ItGzbUjXLrJb8IrwQ=s64",
      "userId": "02458012257526617222"
     },
     "user_tz": 300
    },
    "id": "pHETIoFKPJJN",
    "outputId": "143acb2e-31e1-4579-f776-4a430ba0caae"
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>39835428.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>157471</th>\n",
       "      <td>157471</td>\n",
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       "      <td>Africa</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157472</th>\n",
       "      <td>157472</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-23</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>228254.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>310.857</td>\n",
       "      <td>5294.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>15124.000</td>\n",
       "      <td>4.969</td>\n",
       "      <td>20.597</td>\n",
       "      <td>350.778</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>157473</th>\n",
       "      <td>157473</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-24</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>228541.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>297.286</td>\n",
       "      <td>5305.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.714</td>\n",
       "      <td>15143.017</td>\n",
       "      <td>19.016</td>\n",
       "      <td>19.698</td>\n",
       "      <td>351.507</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.445</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157474</th>\n",
       "      <td>157474</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-25</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>228776.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>330.857</td>\n",
       "      <td>5316.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.286</td>\n",
       "      <td>15158.588</td>\n",
       "      <td>15.571</td>\n",
       "      <td>21.922</td>\n",
       "      <td>352.236</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.549</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157475</th>\n",
       "      <td>157475</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-26</td>\n",
       "      <td>15092171.0</td>\n",
       "      <td>228943.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>293.714</td>\n",
       "      <td>5321.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.857</td>\n",
       "      <td>15169.653</td>\n",
       "      <td>11.065</td>\n",
       "      <td>19.461</td>\n",
       "      <td>352.567</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157476 rows × 27 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eca9befb-f42a-4506-83a4-d71623ef6406')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "\n",
       "      <script>\n",
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       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-eca9befb-f42a-4506-83a4-d71623ef6406');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         index  ... weekly_hosp_admissions_per_million\n",
       "0            0  ...                                NaN\n",
       "1            1  ...                                NaN\n",
       "2            2  ...                                NaN\n",
       "3            3  ...                                NaN\n",
       "4            4  ...                                NaN\n",
       "...        ...  ...                                ...\n",
       "157471  157471  ...                                NaN\n",
       "157472  157472  ...                                NaN\n",
       "157473  157473  ...                                NaN\n",
       "157474  157474  ...                                NaN\n",
       "157475  157475  ...                                NaN\n",
       "\n",
       "[157476 rows x 27 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_df_death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VRy1EoA0PNX8"
   },
   "outputs": [],
   "source": [
    "sql_df_vacc = pd.read_sql(\"CovidVaccinations\", engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 679
    },
    "executionInfo": {
     "elapsed": 264,
     "status": "ok",
     "timestamp": 1644691306125,
     "user": {
      "displayName": "James Wolfe",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjniFB9tD5BdLBJA15GSIxZ-ItGzbUjXLrJb8IrwQ=s64",
      "userId": "02458012257526617222"
     },
     "user_tz": 300
    },
    "id": "kojMSkK0PN15",
    "outputId": "8b6fe8cc-5ed8-4d41-9d51-3fe061a749b5"
   },
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>iso_code</th>\n",
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       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>8.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>157471</th>\n",
       "      <td>157471</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>2626.0</td>\n",
       "      <td>1822879.0</td>\n",
       "      <td>120.783</td>\n",
       "      <td>0.174</td>\n",
       "      <td>4145.0</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.0876</td>\n",
       "      <td>11.4</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>7506786.0</td>\n",
       "      <td>4239537.0</td>\n",
       "      <td>3267249.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9904.0</td>\n",
       "      <td>10567.0</td>\n",
       "      <td>49.74</td>\n",
       "      <td>28.09</td>\n",
       "      <td>21.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "      <td>5058.0</td>\n",
       "      <td>0.034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157472</th>\n",
       "      <td>157472</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-23</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>1824420.0</td>\n",
       "      <td>120.885</td>\n",
       "      <td>0.102</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.0795</td>\n",
       "      <td>12.6</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>7512903.0</td>\n",
       "      <td>4242647.0</td>\n",
       "      <td>3270256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6117.0</td>\n",
       "      <td>10631.0</td>\n",
       "      <td>49.78</td>\n",
       "      <td>28.11</td>\n",
       "      <td>21.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>704.0</td>\n",
       "      <td>5182.0</td>\n",
       "      <td>0.034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157473</th>\n",
       "      <td>157473</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-24</td>\n",
       "      <td>4913.0</td>\n",
       "      <td>1829333.0</td>\n",
       "      <td>121.211</td>\n",
       "      <td>0.326</td>\n",
       "      <td>4043.0</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.0735</td>\n",
       "      <td>13.6</td>\n",
       "      <td>tests performed</td>\n",
       "      <td>7517985.0</td>\n",
       "      <td>4245063.0</td>\n",
       "      <td>3272922.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5082.0</td>\n",
       "      <td>10273.0</td>\n",
       "      <td>49.81</td>\n",
       "      <td>28.13</td>\n",
       "      <td>21.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681.0</td>\n",
       "      <td>5009.0</td>\n",
       "      <td>0.033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157474</th>\n",
       "      <td>157474</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>7525574.0</td>\n",
       "      <td>4248576.0</td>\n",
       "      <td>3276998.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7589.0</td>\n",
       "      <td>9579.0</td>\n",
       "      <td>49.86</td>\n",
       "      <td>28.15</td>\n",
       "      <td>21.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>635.0</td>\n",
       "      <td>4638.0</td>\n",
       "      <td>0.031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157475</th>\n",
       "      <td>157475</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-01-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.822</td>\n",
       "      <td>1.882</td>\n",
       "      <td>1899.775</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157476 rows × 47 columns</p>\n",
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       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-80579f52-3946-45b6-b6df-532135343681 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-80579f52-3946-45b6-b6df-532135343681');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         index  ... excess_mortality_cumulative_per_million\n",
       "0            0  ...                                     NaN\n",
       "1            1  ...                                     NaN\n",
       "2            2  ...                                     NaN\n",
       "3            3  ...                                     NaN\n",
       "4            4  ...                                     NaN\n",
       "...        ...  ...                                     ...\n",
       "157471  157471  ...                                     NaN\n",
       "157472  157472  ...                                     NaN\n",
       "157473  157473  ...                                     NaN\n",
       "157474  157474  ...                                     NaN\n",
       "157475  157475  ...                                     NaN\n",
       "\n",
       "[157476 rows x 47 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_df_vacc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WXFPBI8Z96tH"
   },
   "source": [
    "## Database creation, initialization, and Setup has been completed."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "initialization_COVID_DB_SQLite.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}