data_Normalization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing a dataset\n",
    "\n",
    "* How does the dataset handle invalid values? \n",
    "* What do we want to do with null values?\n",
    "* Do we want to summarise, group or filter the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>Unnamed: 57</th>\n",
       "      <th>Unnamed: 58</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2/25/2020</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.009</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
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       "      <td>67.27</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2/26/2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2/27/2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2/28/2020</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.009</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2/29/2020</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</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",
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       "      <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>389</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/20/2021</td>\n",
       "      <td>626802</td>\n",
       "      <td>3667</td>\n",
       "      <td>3086.000</td>\n",
       "      <td>13843.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>47.857</td>\n",
       "      <td>2837.591</td>\n",
       "      <td>16.601</td>\n",
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       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/21/2021</td>\n",
       "      <td>630471</td>\n",
       "      <td>3669</td>\n",
       "      <td>3288.286</td>\n",
       "      <td>13863.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>46.571</td>\n",
       "      <td>2854.200</td>\n",
       "      <td>16.610</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
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       "      <td>0.6</td>\n",
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       "      <td>0.557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/22/2021</td>\n",
       "      <td>633741</td>\n",
       "      <td>3270</td>\n",
       "      <td>3396.714</td>\n",
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       "      <td>72.0</td>\n",
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       "      <td>14.804</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/23/2021</td>\n",
       "      <td>637042</td>\n",
       "      <td>3301</td>\n",
       "      <td>3532.429</td>\n",
       "      <td>13965.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>44.143</td>\n",
       "      <td>2883.948</td>\n",
       "      <td>14.944</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/24/2021</td>\n",
       "      <td>640988</td>\n",
       "      <td>3946</td>\n",
       "      <td>3596.857</td>\n",
       "      <td>14028.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>44.429</td>\n",
       "      <td>2901.812</td>\n",
       "      <td>17.864</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>394 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     location       date  total_cases  new_cases  new_cases_smoothed  \\\n",
       "0    Pakistan  2/25/2020            2          2                 NaN   \n",
       "1    Pakistan  2/26/2020            2          0                 NaN   \n",
       "2    Pakistan  2/27/2020            2          0                 NaN   \n",
       "3    Pakistan  2/28/2020            4          2                 NaN   \n",
       "4    Pakistan  2/29/2020            4          0                 NaN   \n",
       "..        ...        ...          ...        ...                 ...   \n",
       "389  Pakistan  3/20/2021       626802       3667            3086.000   \n",
       "390  Pakistan  3/21/2021       630471       3669            3288.286   \n",
       "391  Pakistan  3/22/2021       633741       3270            3396.714   \n",
       "392  Pakistan  3/23/2021       637042       3301            3532.429   \n",
       "393  Pakistan  3/24/2021       640988       3946            3596.857   \n",
       "\n",
       "     total_deaths  new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "0             NaN         NaN                  NaN                    0.009   \n",
       "1             NaN         NaN                  NaN                    0.009   \n",
       "2             NaN         NaN                  NaN                    0.009   \n",
       "3             NaN         NaN                  NaN                    0.018   \n",
       "4             NaN         NaN                  NaN                    0.018   \n",
       "..            ...         ...                  ...                      ...   \n",
       "389       13843.0        44.0               47.857                 2837.591   \n",
       "390       13863.0        20.0               46.571                 2854.200   \n",
       "391       13935.0        72.0               48.571                 2869.004   \n",
       "392       13965.0        30.0               44.143                 2883.948   \n",
       "393       14028.0        63.0               44.429                 2901.812   \n",
       "\n",
       "     new_cases_per_million  ...  cardiovasc_death_rate  diabetes_prevalence  \\\n",
       "0                    0.009  ...                423.031                 8.35   \n",
       "1                    0.000  ...                423.031                 8.35   \n",
       "2                    0.000  ...                423.031                 8.35   \n",
       "3                    0.009  ...                423.031                 8.35   \n",
       "4                    0.000  ...                423.031                 8.35   \n",
       "..                     ...  ...                    ...                  ...   \n",
       "389                 16.601  ...                423.031                 8.35   \n",
       "390                 16.610  ...                423.031                 8.35   \n",
       "391                 14.804  ...                423.031                 8.35   \n",
       "392                 14.944  ...                423.031                 8.35   \n",
       "393                 17.864  ...                423.031                 8.35   \n",
       "\n",
       "     female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0               2.8          36.7                  59.607   \n",
       "1               2.8          36.7                  59.607   \n",
       "2               2.8          36.7                  59.607   \n",
       "3               2.8          36.7                  59.607   \n",
       "4               2.8          36.7                  59.607   \n",
       "..              ...           ...                     ...   \n",
       "389             2.8          36.7                  59.607   \n",
       "390             2.8          36.7                  59.607   \n",
       "391             2.8          36.7                  59.607   \n",
       "392             2.8          36.7                  59.607   \n",
       "393             2.8          36.7                  59.607   \n",
       "\n",
       "     hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                           0.6            67.27                    0.557   \n",
       "1                           0.6            67.27                    0.557   \n",
       "2                           0.6            67.27                    0.557   \n",
       "3                           0.6            67.27                    0.557   \n",
       "4                           0.6            67.27                    0.557   \n",
       "..                          ...              ...                      ...   \n",
       "389                         0.6            67.27                    0.557   \n",
       "390                         0.6            67.27                    0.557   \n",
       "391                         0.6            67.27                    0.557   \n",
       "392                         0.6            67.27                    0.557   \n",
       "393                         0.6            67.27                    0.557   \n",
       "\n",
       "     Unnamed: 57  Unnamed: 58  \n",
       "0            NaN          NaN  \n",
       "1            NaN          NaN  \n",
       "2            NaN          NaN  \n",
       "3            NaN          NaN  \n",
       "4            NaN          NaN  \n",
       "..           ...          ...  \n",
       "389          NaN          NaN  \n",
       "390          NaN          NaN  \n",
       "391          NaN          NaN  \n",
       "392          NaN          NaN  \n",
       "393          NaN          NaN  \n",
       "\n",
       "[394 rows x 59 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv(\"owid-covid-data-pak.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
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       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
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       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2/25/2020</td>\n",
       "      <td>2</td>\n",
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       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2/26/2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
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       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2/27/2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2/28/2020</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.009</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2/29/2020</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/1/2020</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/2/2020</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.005</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/3/2020</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/4/2020</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/5/2020</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.005</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/6/2020</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/7/2020</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/8/2020</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/9/2020</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>1.571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.045</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/10/2020</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.014</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/11/2020</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2.143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.005</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/12/2020</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>3.143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.036</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/13/2020</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>3.571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.014</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/14/2020</td>\n",
       "      <td>53</td>\n",
       "      <td>22</td>\n",
       "      <td>6.714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.100</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/15/2020</td>\n",
       "      <td>136</td>\n",
       "      <td>83</td>\n",
       "      <td>18.571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.376</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    location       date  total_cases  new_cases  new_cases_smoothed  \\\n",
       "0   Pakistan  2/25/2020            2          2               0.000   \n",
       "1   Pakistan  2/26/2020            2          0               0.000   \n",
       "2   Pakistan  2/27/2020            2          0               0.000   \n",
       "3   Pakistan  2/28/2020            4          2               0.000   \n",
       "4   Pakistan  2/29/2020            4          0               0.000   \n",
       "5   Pakistan   3/1/2020            4          0               0.571   \n",
       "6   Pakistan   3/2/2020            5          1               0.714   \n",
       "7   Pakistan   3/3/2020            5          0               0.429   \n",
       "8   Pakistan   3/4/2020            5          0               0.429   \n",
       "9   Pakistan   3/5/2020            6          1               0.571   \n",
       "10  Pakistan   3/6/2020            6          0               0.286   \n",
       "11  Pakistan   3/7/2020            6          0               0.286   \n",
       "12  Pakistan   3/8/2020            6          0               0.286   \n",
       "13  Pakistan   3/9/2020           16         10               1.571   \n",
       "14  Pakistan  3/10/2020           19          3               2.000   \n",
       "15  Pakistan  3/11/2020           20          1               2.143   \n",
       "16  Pakistan  3/12/2020           28          8               3.143   \n",
       "17  Pakistan  3/13/2020           31          3               3.571   \n",
       "18  Pakistan  3/14/2020           53         22               6.714   \n",
       "19  Pakistan  3/15/2020          136         83              18.571   \n",
       "\n",
       "    total_deaths  new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "0            0.0         0.0                  0.0                    0.009   \n",
       "1            0.0         0.0                  0.0                    0.009   \n",
       "2            0.0         0.0                  0.0                    0.009   \n",
       "3            0.0         0.0                  0.0                    0.018   \n",
       "4            0.0         0.0                  0.0                    0.018   \n",
       "5            0.0         0.0                  0.0                    0.018   \n",
       "6            0.0         0.0                  0.0                    0.023   \n",
       "7            0.0         0.0                  0.0                    0.023   \n",
       "8            0.0         0.0                  0.0                    0.023   \n",
       "9            0.0         0.0                  0.0                    0.027   \n",
       "10           0.0         0.0                  0.0                    0.027   \n",
       "11           0.0         0.0                  0.0                    0.027   \n",
       "12           0.0         0.0                  0.0                    0.027   \n",
       "13           0.0         0.0                  0.0                    0.072   \n",
       "14           0.0         0.0                  0.0                    0.086   \n",
       "15           0.0         0.0                  0.0                    0.091   \n",
       "16           0.0         0.0                  0.0                    0.127   \n",
       "17           0.0         0.0                  0.0                    0.140   \n",
       "18           0.0         0.0                  0.0                    0.240   \n",
       "19           0.0         0.0                  0.0                    0.616   \n",
       "\n",
       "    new_cases_per_million  ...  cardiovasc_death_rate  diabetes_prevalence  \\\n",
       "0                   0.009  ...                423.031                 8.35   \n",
       "1                   0.000  ...                423.031                 8.35   \n",
       "2                   0.000  ...                423.031                 8.35   \n",
       "3                   0.009  ...                423.031                 8.35   \n",
       "4                   0.000  ...                423.031                 8.35   \n",
       "5                   0.000  ...                423.031                 8.35   \n",
       "6                   0.005  ...                423.031                 8.35   \n",
       "7                   0.000  ...                423.031                 8.35   \n",
       "8                   0.000  ...                423.031                 8.35   \n",
       "9                   0.005  ...                423.031                 8.35   \n",
       "10                  0.000  ...                423.031                 8.35   \n",
       "11                  0.000  ...                423.031                 8.35   \n",
       "12                  0.000  ...                423.031                 8.35   \n",
       "13                  0.045  ...                423.031                 8.35   \n",
       "14                  0.014  ...                423.031                 8.35   \n",
       "15                  0.005  ...                423.031                 8.35   \n",
       "16                  0.036  ...                423.031                 8.35   \n",
       "17                  0.014  ...                423.031                 8.35   \n",
       "18                  0.100  ...                423.031                 8.35   \n",
       "19                  0.376  ...                423.031                 8.35   \n",
       "\n",
       "    female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0              2.8          36.7                  59.607   \n",
       "1              2.8          36.7                  59.607   \n",
       "2              2.8          36.7                  59.607   \n",
       "3              2.8          36.7                  59.607   \n",
       "4              2.8          36.7                  59.607   \n",
       "5              2.8          36.7                  59.607   \n",
       "6              2.8          36.7                  59.607   \n",
       "7              2.8          36.7                  59.607   \n",
       "8              2.8          36.7                  59.607   \n",
       "9              2.8          36.7                  59.607   \n",
       "10             2.8          36.7                  59.607   \n",
       "11             2.8          36.7                  59.607   \n",
       "12             2.8          36.7                  59.607   \n",
       "13             2.8          36.7                  59.607   \n",
       "14             2.8          36.7                  59.607   \n",
       "15             2.8          36.7                  59.607   \n",
       "16             2.8          36.7                  59.607   \n",
       "17             2.8          36.7                  59.607   \n",
       "18             2.8          36.7                  59.607   \n",
       "19             2.8          36.7                  59.607   \n",
       "\n",
       "    hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                          0.6            67.27                    0.557   \n",
       "1                          0.6            67.27                    0.557   \n",
       "2                          0.6            67.27                    0.557   \n",
       "3                          0.6            67.27                    0.557   \n",
       "4                          0.6            67.27                    0.557   \n",
       "5                          0.6            67.27                    0.557   \n",
       "6                          0.6            67.27                    0.557   \n",
       "7                          0.6            67.27                    0.557   \n",
       "8                          0.6            67.27                    0.557   \n",
       "9                          0.6            67.27                    0.557   \n",
       "10                         0.6            67.27                    0.557   \n",
       "11                         0.6            67.27                    0.557   \n",
       "12                         0.6            67.27                    0.557   \n",
       "13                         0.6            67.27                    0.557   \n",
       "14                         0.6            67.27                    0.557   \n",
       "15                         0.6            67.27                    0.557   \n",
       "16                         0.6            67.27                    0.557   \n",
       "17                         0.6            67.27                    0.557   \n",
       "18                         0.6            67.27                    0.557   \n",
       "19                         0.6            67.27                    0.557   \n",
       "\n",
       "    Unnamed: 57  Unnamed: 58  \n",
       "0           0.0          0.0  \n",
       "1           0.0          0.0  \n",
       "2           0.0          0.0  \n",
       "3           0.0          0.0  \n",
       "4           0.0          0.0  \n",
       "5           0.0          0.0  \n",
       "6           0.0          0.0  \n",
       "7           0.0          0.0  \n",
       "8           0.0          0.0  \n",
       "9           0.0          0.0  \n",
       "10          0.0          0.0  \n",
       "11          0.0          0.0  \n",
       "12          0.0          0.0  \n",
       "13          0.0          0.0  \n",
       "14          0.0          0.0  \n",
       "15          0.0          0.0  \n",
       "16          0.0          0.0  \n",
       "17          0.0          0.0  \n",
       "18          0.0          0.0  \n",
       "19          0.0          0.0  \n",
       "\n",
       "[20 rows x 59 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location                                  object\n",
       "date                                      object\n",
       "total_cases                                int64\n",
       "new_cases                                  int64\n",
       "new_cases_smoothed                       float64\n",
       "total_deaths                             float64\n",
       "new_deaths                               float64\n",
       "new_deaths_smoothed                      float64\n",
       "total_cases_per_million                  float64\n",
       "new_cases_per_million                    float64\n",
       "new_cases_smoothed_per_million           float64\n",
       "total_deaths_per_million                 float64\n",
       "new_deaths_per_million                   float64\n",
       "new_deaths_smoothed_per_million          float64\n",
       "reproduction_rate                        float64\n",
       "icu_patients                             float64\n",
       "icu_patients_per_million                 float64\n",
       "hosp_patients                            float64\n",
       "hosp_patients_per_million                float64\n",
       "weekly_icu_admissions                    float64\n",
       "weekly_icu_admissions_per_million        float64\n",
       "weekly_hosp_admissions                   float64\n",
       "weekly_hosp_admissions_per_million       float64\n",
       "new_tests                                float64\n",
       "total_tests                              float64\n",
       "total_tests_per_thousand                 float64\n",
       "new_tests_per_thousand                   float64\n",
       "new_tests_smoothed                       float64\n",
       "new_tests_smoothed_per_thousand          float64\n",
       "positive_rate                            float64\n",
       "tests_per_case                           float64\n",
       "tests_units                               object\n",
       "total_vaccinations                       float64\n",
       "people_vaccinated                        float64\n",
       "people_fully_vaccinated                  float64\n",
       "new_vaccinations                         float64\n",
       "new_vaccinations_smoothed                float64\n",
       "total_vaccinations_per_hundred           float64\n",
       "people_vaccinated_per_hundred            float64\n",
       "people_fully_vaccinated_per_hundred      float64\n",
       "new_vaccinations_smoothed_per_million    float64\n",
       "stringency_index                         float64\n",
       "population                                 int64\n",
       "population_density                       float64\n",
       "median_age                               float64\n",
       "aged_65_older                            float64\n",
       "aged_70_older                            float64\n",
       "gdp_per_capita                           float64\n",
       "extreme_poverty                            int64\n",
       "cardiovasc_death_rate                    float64\n",
       "diabetes_prevalence                      float64\n",
       "female_smokers                           float64\n",
       "male_smokers                             float64\n",
       "handwashing_facilities                   float64\n",
       "hospital_beds_per_thousand               float64\n",
       "life_expectancy                          float64\n",
       "human_development_index                  float64\n",
       "Unnamed: 57                              float64\n",
       "Unnamed: 58                              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>new_cases_smoothed_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>Unnamed: 57</th>\n",
       "      <th>Unnamed: 58</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>389.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>389.00000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>389.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.940000e+02</td>\n",
       "      <td>3.940000e+02</td>\n",
       "      <td>3.940000e+02</td>\n",
       "      <td>3.940000e+02</td>\n",
       "      <td>3.940000e+02</td>\n",
       "      <td>3.940000e+02</td>\n",
       "      <td>3.940000e+02</td>\n",
       "      <td>3.940000e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>287125.535533</td>\n",
       "      <td>1626.873096</td>\n",
       "      <td>1620.060208</td>\n",
       "      <td>6424.620968</td>\n",
       "      <td>37.709677</td>\n",
       "      <td>35.69226</td>\n",
       "      <td>1299.843850</td>\n",
       "      <td>7.365008</td>\n",
       "      <td>7.334183</td>\n",
       "      <td>29.084871</td>\n",
       "      <td>...</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>8.350000e+00</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>3.670000e+01</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>6.727000e+01</td>\n",
       "      <td>5.570000e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>196256.135980</td>\n",
       "      <td>1446.514199</td>\n",
       "      <td>1300.114718</td>\n",
       "      <td>4097.792988</td>\n",
       "      <td>34.603439</td>\n",
       "      <td>28.69063</td>\n",
       "      <td>888.469669</td>\n",
       "      <td>6.548514</td>\n",
       "      <td>5.885737</td>\n",
       "      <td>18.551092</td>\n",
       "      <td>...</td>\n",
       "      <td>1.024482e-12</td>\n",
       "      <td>5.691569e-14</td>\n",
       "      <td>2.045408e-14</td>\n",
       "      <td>2.418917e-13</td>\n",
       "      <td>2.134338e-14</td>\n",
       "      <td>3.890721e-15</td>\n",
       "      <td>3.699520e-13</td>\n",
       "      <td>4.224212e-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.286000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>8.350000e+00</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>3.670000e+01</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>6.727000e+01</td>\n",
       "      <td>5.570000e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>81663.250000</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>611.714000</td>\n",
       "      <td>3343.750000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.57100</td>\n",
       "      <td>369.696750</td>\n",
       "      <td>2.635000</td>\n",
       "      <td>2.769000</td>\n",
       "      <td>15.137750</td>\n",
       "      <td>...</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>8.350000e+00</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>3.670000e+01</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>6.727000e+01</td>\n",
       "      <td>5.570000e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>299844.500000</td>\n",
       "      <td>1307.500000</td>\n",
       "      <td>1317.000000</td>\n",
       "      <td>6418.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.85700</td>\n",
       "      <td>1357.424000</td>\n",
       "      <td>5.919000</td>\n",
       "      <td>5.962000</td>\n",
       "      <td>29.055000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>8.350000e+00</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>3.670000e+01</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>6.727000e+01</td>\n",
       "      <td>5.570000e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>447885.750000</td>\n",
       "      <td>2414.750000</td>\n",
       "      <td>2317.857000</td>\n",
       "      <td>9494.750000</td>\n",
       "      <td>56.250000</td>\n",
       "      <td>51.71400</td>\n",
       "      <td>2027.620500</td>\n",
       "      <td>10.931750</td>\n",
       "      <td>10.493000</td>\n",
       "      <td>42.983750</td>\n",
       "      <td>...</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>8.350000e+00</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>3.670000e+01</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>6.727000e+01</td>\n",
       "      <td>5.570000e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>640988.000000</td>\n",
       "      <td>12073.000000</td>\n",
       "      <td>6533.286000</td>\n",
       "      <td>14028.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>135.71400</td>\n",
       "      <td>2901.812000</td>\n",
       "      <td>54.656000</td>\n",
       "      <td>29.577000</td>\n",
       "      <td>63.506000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>8.350000e+00</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>3.670000e+01</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>6.727000e+01</td>\n",
       "      <td>5.570000e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         total_cases     new_cases  new_cases_smoothed  total_deaths  \\\n",
       "count     394.000000    394.000000          389.000000    372.000000   \n",
       "mean   287125.535533   1626.873096         1620.060208   6424.620968   \n",
       "std    196256.135980   1446.514199         1300.114718   4097.792988   \n",
       "min         2.000000      0.000000            0.286000      2.000000   \n",
       "25%     81663.250000    582.000000          611.714000   3343.750000   \n",
       "50%    299844.500000   1307.500000         1317.000000   6418.000000   \n",
       "75%    447885.750000   2414.750000         2317.857000   9494.750000   \n",
       "max    640988.000000  12073.000000         6533.286000  14028.000000   \n",
       "\n",
       "       new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "count  372.000000            389.00000               394.000000   \n",
       "mean    37.709677             35.69226              1299.843850   \n",
       "std     34.603439             28.69063               888.469669   \n",
       "min      0.000000              0.00000                 0.009000   \n",
       "25%     10.000000              9.57100               369.696750   \n",
       "50%     32.000000             32.85700              1357.424000   \n",
       "75%     56.250000             51.71400              2027.620500   \n",
       "max    313.000000            135.71400              2901.812000   \n",
       "\n",
       "       new_cases_per_million  new_cases_smoothed_per_million  \\\n",
       "count             394.000000                      389.000000   \n",
       "mean                7.365008                        7.334183   \n",
       "std                 6.548514                        5.885737   \n",
       "min                 0.000000                        0.001000   \n",
       "25%                 2.635000                        2.769000   \n",
       "50%                 5.919000                        5.962000   \n",
       "75%                10.931750                       10.493000   \n",
       "max                54.656000                       29.577000   \n",
       "\n",
       "       total_deaths_per_million  ...  cardiovasc_death_rate  \\\n",
       "count                372.000000  ...           3.940000e+02   \n",
       "mean                  29.084871  ...           4.230310e+02   \n",
       "std                   18.551092  ...           1.024482e-12   \n",
       "min                    0.009000  ...           4.230310e+02   \n",
       "25%                   15.137750  ...           4.230310e+02   \n",
       "50%                   29.055000  ...           4.230310e+02   \n",
       "75%                   42.983750  ...           4.230310e+02   \n",
       "max                   63.506000  ...           4.230310e+02   \n",
       "\n",
       "       diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "count         3.940000e+02    3.940000e+02  3.940000e+02   \n",
       "mean          8.350000e+00    2.800000e+00  3.670000e+01   \n",
       "std           5.691569e-14    2.045408e-14  2.418917e-13   \n",
       "min           8.350000e+00    2.800000e+00  3.670000e+01   \n",
       "25%           8.350000e+00    2.800000e+00  3.670000e+01   \n",
       "50%           8.350000e+00    2.800000e+00  3.670000e+01   \n",
       "75%           8.350000e+00    2.800000e+00  3.670000e+01   \n",
       "max           8.350000e+00    2.800000e+00  3.670000e+01   \n",
       "\n",
       "       handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "count            3.940000e+02                3.940000e+02     3.940000e+02   \n",
       "mean             5.960700e+01                6.000000e-01     6.727000e+01   \n",
       "std              2.134338e-14                3.890721e-15     3.699520e-13   \n",
       "min              5.960700e+01                6.000000e-01     6.727000e+01   \n",
       "25%              5.960700e+01                6.000000e-01     6.727000e+01   \n",
       "50%              5.960700e+01                6.000000e-01     6.727000e+01   \n",
       "75%              5.960700e+01                6.000000e-01     6.727000e+01   \n",
       "max              5.960700e+01                6.000000e-01     6.727000e+01   \n",
       "\n",
       "       human_development_index  Unnamed: 57  Unnamed: 58  \n",
       "count             3.940000e+02          0.0          0.0  \n",
       "mean              5.570000e-01          NaN          NaN  \n",
       "std               4.224212e-15          NaN          NaN  \n",
       "min               5.570000e-01          NaN          NaN  \n",
       "25%               5.570000e-01          NaN          NaN  \n",
       "50%               5.570000e-01          NaN          NaN  \n",
       "75%               5.570000e-01          NaN          NaN  \n",
       "max               5.570000e-01          NaN          NaN  \n",
       "\n",
       "[8 rows x 56 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>new_cases_smoothed_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>Unnamed: 57</th>\n",
       "      <th>Unnamed: 58</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>287125.535533</td>\n",
       "      <td>1626.873096</td>\n",
       "      <td>1620.060208</td>\n",
       "      <td>6424.620968</td>\n",
       "      <td>37.709677</td>\n",
       "      <td>35.69226</td>\n",
       "      <td>1299.84385</td>\n",
       "      <td>7.365008</td>\n",
       "      <td>7.334183</td>\n",
       "      <td>29.084871</td>\n",
       "      <td>...</td>\n",
       "      <td>423.031</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.27</td>\n",
       "      <td>0.557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_cases    new_cases  new_cases_smoothed  total_deaths  \\\n",
       "location                                                                 \n",
       "Pakistan  287125.535533  1626.873096         1620.060208   6424.620968   \n",
       "\n",
       "          new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "location                                                             \n",
       "Pakistan   37.709677             35.69226               1299.84385   \n",
       "\n",
       "          new_cases_per_million  new_cases_smoothed_per_million  \\\n",
       "location                                                          \n",
       "Pakistan               7.365008                        7.334183   \n",
       "\n",
       "          total_deaths_per_million  ...  cardiovasc_death_rate  \\\n",
       "location                            ...                          \n",
       "Pakistan                 29.084871  ...                423.031   \n",
       "\n",
       "          diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "location                                                      \n",
       "Pakistan                 8.35             2.8          36.7   \n",
       "\n",
       "          handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "location                                                                        \n",
       "Pakistan                  59.607                         0.6            67.27   \n",
       "\n",
       "          human_development_index  Unnamed: 57  Unnamed: 58  \n",
       "location                                                     \n",
       "Pakistan                    0.557          NaN          NaN  \n",
       "\n",
       "[1 rows x 56 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"location\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of      location       date  total_cases  new_cases  new_cases_smoothed  \\\n",
       "0    Pakistan  2/25/2020            2          2                 NaN   \n",
       "1    Pakistan  2/26/2020            2          0                 NaN   \n",
       "2    Pakistan  2/27/2020            2          0                 NaN   \n",
       "3    Pakistan  2/28/2020            4          2                 NaN   \n",
       "4    Pakistan  2/29/2020            4          0                 NaN   \n",
       "..        ...        ...          ...        ...                 ...   \n",
       "389  Pakistan  3/20/2021       626802       3667            3086.000   \n",
       "390  Pakistan  3/21/2021       630471       3669            3288.286   \n",
       "391  Pakistan  3/22/2021       633741       3270            3396.714   \n",
       "392  Pakistan  3/23/2021       637042       3301            3532.429   \n",
       "393  Pakistan  3/24/2021       640988       3946            3596.857   \n",
       "\n",
       "     total_deaths  new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "0             NaN         NaN                  NaN                    0.009   \n",
       "1             NaN         NaN                  NaN                    0.009   \n",
       "2             NaN         NaN                  NaN                    0.009   \n",
       "3             NaN         NaN                  NaN                    0.018   \n",
       "4             NaN         NaN                  NaN                    0.018   \n",
       "..            ...         ...                  ...                      ...   \n",
       "389       13843.0        44.0               47.857                 2837.591   \n",
       "390       13863.0        20.0               46.571                 2854.200   \n",
       "391       13935.0        72.0               48.571                 2869.004   \n",
       "392       13965.0        30.0               44.143                 2883.948   \n",
       "393       14028.0        63.0               44.429                 2901.812   \n",
       "\n",
       "     new_cases_per_million  ...  cardiovasc_death_rate  diabetes_prevalence  \\\n",
       "0                    0.009  ...                423.031                 8.35   \n",
       "1                    0.000  ...                423.031                 8.35   \n",
       "2                    0.000  ...                423.031                 8.35   \n",
       "3                    0.009  ...                423.031                 8.35   \n",
       "4                    0.000  ...                423.031                 8.35   \n",
       "..                     ...  ...                    ...                  ...   \n",
       "389                 16.601  ...                423.031                 8.35   \n",
       "390                 16.610  ...                423.031                 8.35   \n",
       "391                 14.804  ...                423.031                 8.35   \n",
       "392                 14.944  ...                423.031                 8.35   \n",
       "393                 17.864  ...                423.031                 8.35   \n",
       "\n",
       "     female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0               2.8          36.7                  59.607   \n",
       "1               2.8          36.7                  59.607   \n",
       "2               2.8          36.7                  59.607   \n",
       "3               2.8          36.7                  59.607   \n",
       "4               2.8          36.7                  59.607   \n",
       "..              ...           ...                     ...   \n",
       "389             2.8          36.7                  59.607   \n",
       "390             2.8          36.7                  59.607   \n",
       "391             2.8          36.7                  59.607   \n",
       "392             2.8          36.7                  59.607   \n",
       "393             2.8          36.7                  59.607   \n",
       "\n",
       "     hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                           0.6            67.27                    0.557   \n",
       "1                           0.6            67.27                    0.557   \n",
       "2                           0.6            67.27                    0.557   \n",
       "3                           0.6            67.27                    0.557   \n",
       "4                           0.6            67.27                    0.557   \n",
       "..                          ...              ...                      ...   \n",
       "389                         0.6            67.27                    0.557   \n",
       "390                         0.6            67.27                    0.557   \n",
       "391                         0.6            67.27                    0.557   \n",
       "392                         0.6            67.27                    0.557   \n",
       "393                         0.6            67.27                    0.557   \n",
       "\n",
       "     Unnamed: 57  Unnamed: 58  \n",
       "0            NaN          NaN  \n",
       "1            NaN          NaN  \n",
       "2            NaN          NaN  \n",
       "3            NaN          NaN  \n",
       "4            NaN          NaN  \n",
       "..           ...          ...  \n",
       "389          NaN          NaN  \n",
       "390          NaN          NaN  \n",
       "391          NaN          NaN  \n",
       "392          NaN          NaN  \n",
       "393          NaN          NaN  \n",
       "\n",
       "[394 rows x 59 columns]>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it looks like they are using `0` values where they dont have data. I don't think theyre using `NaN` at all, but if they were we could either drop those rows (`dropna`) or fill them to some value (`fillna`). Because they're using `0` already, it might be prudent to do this just in case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1,2,np.nan,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, nan, 4]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_a = pd.DataFrame(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
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      "text/plain": [
       "       0\n",
       "0    1.0\n",
       "1    2.0\n",
       "2 -999.0\n",
       "3    4.0"
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     "execution_count": 25,
     "metadata": {},
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   ],
   "source": [
    "df_a.fillna(-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we know there are `NaN` values. We could also have just, you know, checked for `NaN`, but now I'm trying to show functions you can use.\n",
    "\n",
    "So, what to do with these zero values. In some cases we could fill them with something sensible, but that normally just biases the data. So mostly we'd ignore them. So what we want to do is ask *how* we want to use the data. Will we be using `SkinThickness`? Do we care if there are non-physical outliers?\n",
    "\n",
    "If we cared about \ttotal_cases, total_deaths and \tcardiovasc_death_rate, we could get rid of a ton of these issues but only looking at those columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[[\"location\",\"date\" ,\"total_cases\" , \"cardiovasc_death_rate\",\"total_deaths\" , \"handwashing_facilities\" ,\"total_cases_per_million\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>Pakistan</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.018</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2/29/2020</td>\n",
       "      <td>4</td>\n",
       "      <td>423.031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/1/2020</td>\n",
       "      <td>4</td>\n",
       "      <td>423.031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/2/2020</td>\n",
       "      <td>5</td>\n",
       "      <td>423.031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.023</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/3/2020</td>\n",
       "      <td>5</td>\n",
       "      <td>423.031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/4/2020</td>\n",
       "      <td>5</td>\n",
       "      <td>423.031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/5/2020</td>\n",
       "      <td>6</td>\n",
       "      <td>423.031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   location       date  total_cases  cardiovasc_death_rate  total_deaths  \\\n",
       "0  Pakistan  2/25/2020            2                423.031           0.0   \n",
       "1  Pakistan  2/26/2020            2                423.031           0.0   \n",
       "2  Pakistan  2/27/2020            2                423.031           0.0   \n",
       "3  Pakistan  2/28/2020            4                423.031           0.0   \n",
       "4  Pakistan  2/29/2020            4                423.031           0.0   \n",
       "5  Pakistan   3/1/2020            4                423.031           0.0   \n",
       "6  Pakistan   3/2/2020            5                423.031           0.0   \n",
       "7  Pakistan   3/3/2020            5                423.031           0.0   \n",
       "8  Pakistan   3/4/2020            5                423.031           0.0   \n",
       "9  Pakistan   3/5/2020            6                423.031           0.0   \n",
       "\n",
       "   handwashing_facilities  total_cases_per_million  \n",
       "0                  59.607                    0.009  \n",
       "1                  59.607                    0.009  \n",
       "2                  59.607                    0.009  \n",
       "3                  59.607                    0.018  \n",
       "4                  59.607                    0.018  \n",
       "5                  59.607                    0.018  \n",
       "6                  59.607                    0.023  \n",
       "7                  59.607                    0.023  \n",
       "8                  59.607                    0.023  \n",
       "9                  59.607                    0.027  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>count</th>\n",
       "      <td>394.000000</td>\n",
       "      <td>3.940000e+02</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>3.940000e+02</td>\n",
       "      <td>394.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>287125.535533</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>6065.885787</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>1299.843850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>196256.135980</td>\n",
       "      <td>1.024482e-12</td>\n",
       "      <td>4246.585220</td>\n",
       "      <td>2.134338e-14</td>\n",
       "      <td>888.469669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>0.009000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>81663.250000</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>1708.500000</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>369.696750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>299844.500000</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>6362.000000</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>1357.424000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>447885.750000</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>9062.500000</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>2027.620500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>640988.000000</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>14028.000000</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>2901.812000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total_cases  cardiovasc_death_rate  total_deaths  \\\n",
       "count     394.000000           3.940000e+02    394.000000   \n",
       "mean   287125.535533           4.230310e+02   6065.885787   \n",
       "std    196256.135980           1.024482e-12   4246.585220   \n",
       "min         2.000000           4.230310e+02      0.000000   \n",
       "25%     81663.250000           4.230310e+02   1708.500000   \n",
       "50%    299844.500000           4.230310e+02   6362.000000   \n",
       "75%    447885.750000           4.230310e+02   9062.500000   \n",
       "max    640988.000000           4.230310e+02  14028.000000   \n",
       "\n",
       "       handwashing_facilities  total_cases_per_million  \n",
       "count            3.940000e+02               394.000000  \n",
       "mean             5.960700e+01              1299.843850  \n",
       "std              2.134338e-14               888.469669  \n",
       "min              5.960700e+01                 0.009000  \n",
       "25%              5.960700e+01               369.696750  \n",
       "50%              5.960700e+01              1357.424000  \n",
       "75%              5.960700e+01              2027.620500  \n",
       "max              5.960700e+01              2901.812000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But now lets get rid of any stray zeros. \n",
    "\n",
    "What we want to do is find a row with *any* number of zeros and remove that row. Or in terms of applying a mask, find the rows which have any zero (True), invert that (to False) so when we apply the mask using `loc`, the False entries get dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>total_cases</th>\n",
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       "      <th>389</th>\n",
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       "      <td>423.031</td>\n",
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       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>630471</td>\n",
       "      <td>423.031</td>\n",
       "      <td>2854.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>633741</td>\n",
       "      <td>423.031</td>\n",
       "      <td>2869.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>637042</td>\n",
       "      <td>423.031</td>\n",
       "      <td>2883.948</td>\n",
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       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>640988</td>\n",
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       "      <td>2901.812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>394 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_cases  cardiovasc_death_rate  total_cases_per_million\n",
       "0              2                423.031                    0.009\n",
       "1              2                423.031                    0.009\n",
       "2              2                423.031                    0.009\n",
       "3              4                423.031                    0.018\n",
       "4              4                423.031                    0.018\n",
       "..           ...                    ...                      ...\n",
       "389       626802                423.031                 2837.591\n",
       "390       630471                423.031                 2854.200\n",
       "391       633741                423.031                 2869.004\n",
       "392       637042                423.031                 2883.948\n",
       "393       640988                423.031                 2901.812\n",
       "\n",
       "[394 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[['total_cases','cardiovasc_death_rate','total_cases_per_million']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>393</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>394 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     location   date  total_cases  cardiovasc_death_rate  total_deaths  \\\n",
       "0       False  False        False                  False          True   \n",
       "1       False  False        False                  False          True   \n",
       "2       False  False        False                  False          True   \n",
       "3       False  False        False                  False          True   \n",
       "4       False  False        False                  False          True   \n",
       "..        ...    ...          ...                    ...           ...   \n",
       "389     False  False        False                  False         False   \n",
       "390     False  False        False                  False         False   \n",
       "391     False  False        False                  False         False   \n",
       "392     False  False        False                  False         False   \n",
       "393     False  False        False                  False         False   \n",
       "\n",
       "     handwashing_facilities  \n",
       "0                     False  \n",
       "1                     False  \n",
       "2                     False  \n",
       "3                     False  \n",
       "4                     False  \n",
       "..                      ...  \n",
       "389                   False  \n",
       "390                   False  \n",
       "391                   False  \n",
       "392                   False  \n",
       "393                   False  \n",
       "\n",
       "[394 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.columns[:-1]] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "  <tbody>\n",
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       "      <th>22</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/18/2020</td>\n",
       "      <td>454</td>\n",
       "      <td>423.031</td>\n",
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       "      <td>Pakistan</td>\n",
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       "      <td>730</td>\n",
       "      <td>423.031</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>3.305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/21/2020</td>\n",
       "      <td>776</td>\n",
       "      <td>423.031</td>\n",
       "      <td>5.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>3.513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/22/2020</td>\n",
       "      <td>875</td>\n",
       "      <td>423.031</td>\n",
       "      <td>6.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>3.961</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/20/2021</td>\n",
       "      <td>626802</td>\n",
       "      <td>423.031</td>\n",
       "      <td>13843.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>2837.591</td>\n",
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       "    <tr>\n",
       "      <th>390</th>\n",
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       "      <td>630471</td>\n",
       "      <td>423.031</td>\n",
       "      <td>13863.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>2854.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/22/2021</td>\n",
       "      <td>633741</td>\n",
       "      <td>423.031</td>\n",
       "      <td>13935.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>2869.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/23/2021</td>\n",
       "      <td>637042</td>\n",
       "      <td>423.031</td>\n",
       "      <td>13965.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>2883.948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/24/2021</td>\n",
       "      <td>640988</td>\n",
       "      <td>423.031</td>\n",
       "      <td>14028.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>2901.812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>372 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     location       date  total_cases  cardiovasc_death_rate  total_deaths  \\\n",
       "22   Pakistan  3/18/2020          454                423.031           2.0   \n",
       "23   Pakistan  3/19/2020          501                423.031           3.0   \n",
       "24   Pakistan  3/20/2020          730                423.031           3.0   \n",
       "25   Pakistan  3/21/2020          776                423.031           5.0   \n",
       "26   Pakistan  3/22/2020          875                423.031           6.0   \n",
       "..        ...        ...          ...                    ...           ...   \n",
       "389  Pakistan  3/20/2021       626802                423.031       13843.0   \n",
       "390  Pakistan  3/21/2021       630471                423.031       13863.0   \n",
       "391  Pakistan  3/22/2021       633741                423.031       13935.0   \n",
       "392  Pakistan  3/23/2021       637042                423.031       13965.0   \n",
       "393  Pakistan  3/24/2021       640988                423.031       14028.0   \n",
       "\n",
       "     handwashing_facilities  total_cases_per_million  \n",
       "22                   59.607                    2.055  \n",
       "23                   59.607                    2.268  \n",
       "24                   59.607                    3.305  \n",
       "25                   59.607                    3.513  \n",
       "26                   59.607                    3.961  \n",
       "..                      ...                      ...  \n",
       "389                  59.607                 2837.591  \n",
       "390                  59.607                 2854.200  \n",
       "391                  59.607                 2869.004  \n",
       "392                  59.607                 2883.948  \n",
       "393                  59.607                 2901.812  \n",
       "\n",
       "[372 rows x 7 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[~(df2[df2.columns[:-1]] == 0).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_cases</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>372.000000</td>\n",
       "      <td>3.720000e+02</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>3.720000e+02</td>\n",
       "      <td>372.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>304103.672043</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>6424.620968</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>1376.705444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>188743.551647</td>\n",
       "      <td>3.984398e-13</td>\n",
       "      <td>4097.792988</td>\n",
       "      <td>2.134499e-14</td>\n",
       "      <td>854.459505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>454.000000</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>2.055000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>170015.000000</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>3343.750000</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>769.673750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>305987.500000</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>6418.000000</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>1385.234000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>461207.500000</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>9494.750000</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>2087.929250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>640988.000000</td>\n",
       "      <td>4.230310e+02</td>\n",
       "      <td>14028.000000</td>\n",
       "      <td>5.960700e+01</td>\n",
       "      <td>2901.812000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total_cases  cardiovasc_death_rate  total_deaths  \\\n",
       "count     372.000000           3.720000e+02    372.000000   \n",
       "mean   304103.672043           4.230310e+02   6424.620968   \n",
       "std    188743.551647           3.984398e-13   4097.792988   \n",
       "min       454.000000           4.230310e+02      2.000000   \n",
       "25%    170015.000000           4.230310e+02   3343.750000   \n",
       "50%    305987.500000           4.230310e+02   6418.000000   \n",
       "75%    461207.500000           4.230310e+02   9494.750000   \n",
       "max    640988.000000           4.230310e+02  14028.000000   \n",
       "\n",
       "       handwashing_facilities  total_cases_per_million  \n",
       "count            3.720000e+02               372.000000  \n",
       "mean             5.960700e+01              1376.705444  \n",
       "std              2.134499e-14               854.459505  \n",
       "min              5.960700e+01                 2.055000  \n",
       "25%              5.960700e+01               769.673750  \n",
       "50%              5.960700e+01              1385.234000  \n",
       "75%              5.960700e+01              2087.929250  \n",
       "max              5.960700e+01              2901.812000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.loc[~(df2[df2.columns[:-1]] == 0).any(axis=1)]\n",
    "df3.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, so we've selected the data we cared about, made sure it has no `null`-like values. We'll go on to checking things look sane with some plots in the next section. One final thing we could do is to group the data by outcome. It might make it easier to look for patterns in diagnoses.\n",
    "\n",
    "We can do this either by splitting out the DataFrame into two (one for yes and one for no), or if we wanted summary statistics we could use the `groupBy` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_cases</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>304103.672043</td>\n",
       "      <td>423.031</td>\n",
       "      <td>6424.620968</td>\n",
       "      <td>59.607</td>\n",
       "      <td>1376.705444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_cases  cardiovasc_death_rate  total_deaths  \\\n",
       "location                                                       \n",
       "Pakistan  304103.672043                423.031   6424.620968   \n",
       "\n",
       "          handwashing_facilities  total_cases_per_million  \n",
       "location                                                   \n",
       "Pakistan                  59.607              1376.705444  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby(\"location\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And what this can tell us is that, in general, the higher your glucose level, the more overweight you are, and the older you are, the greater your chance of being diagnosed with diabetes. Which, perhaps, is not that surprising.\n",
    "\n",
    "We can do other things using the `groupby` statement, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>423.031</td>\n",
       "      <td>6418.0</td>\n",
       "      <td>512134.425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cardiovasc_death_rate  total_deaths  total_cases_per_million\n",
       "location                                                              \n",
       "Pakistan                423.031        6418.0               512134.425"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby(\"location\").agg({\"cardiovasc_death_rate\": \"mean\", \"total_deaths\": \"median\", \"total_cases_per_million\": \"sum\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">total_cases</th>\n",
       "      <th colspan=\"2\" halign=\"left\">cardiovasc_death_rate</th>\n",
       "      <th colspan=\"2\" halign=\"left\">total_deaths</th>\n",
       "      <th colspan=\"2\" halign=\"left\">handwashing_facilities</th>\n",
       "      <th colspan=\"2\" halign=\"left\">hospital_beds_per_thousand</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>304103.672043</td>\n",
       "      <td>305987.5</td>\n",
       "      <td>423.031</td>\n",
       "      <td>423.031</td>\n",
       "      <td>6424.620968</td>\n",
       "      <td>6418.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_cases           cardiovasc_death_rate          total_deaths  \\\n",
       "                   mean    median                  mean   median         mean   \n",
       "location                                                                        \n",
       "Pakistan  304103.672043  305987.5               423.031  423.031  6424.620968   \n",
       "\n",
       "                 handwashing_facilities         hospital_beds_per_thousand  \\\n",
       "          median                   mean  median                       mean   \n",
       "location                                                                     \n",
       "Pakistan  6418.0                 59.607  59.607                        0.6   \n",
       "\n",
       "                 \n",
       "         median  \n",
       "location         \n",
       "Pakistan    0.6  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby(\"location\").agg([\"mean\", \"median\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also split the dataset into positive and negative outcomes if we wanted. *If*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 7) (0, 7)\n"
     ]
    }
   ],
   "source": [
    "positive = df3.loc[df3[\"location\"] == 1]\n",
    "negative = df3.loc[df3[\"location\"] == 0]\n",
    "print(positive.shape, negative.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We won't use this splitting just yet, so lets save out the cleaned and prepared dataset, `df3` to file, so our analysis code can load it in the future without having to copy-paste the data prep code into future notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.to_csv(\"covid_pak.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/18/2020</td>\n",
       "      <td>454</td>\n",
       "      <td>423.031</td>\n",
       "      <td>2.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/19/2020</td>\n",
       "      <td>501</td>\n",
       "      <td>423.031</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/20/2020</td>\n",
       "      <td>730</td>\n",
       "      <td>423.031</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/21/2020</td>\n",
       "      <td>776</td>\n",
       "      <td>423.031</td>\n",
       "      <td>5.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>3/22/2020</td>\n",
       "      <td>875</td>\n",
       "      <td>423.031</td>\n",
       "      <td>6.0</td>\n",
       "      <td>59.607</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    location       date  total_cases  cardiovasc_death_rate  total_deaths  \\\n",
       "22  Pakistan  3/18/2020          454                423.031           2.0   \n",
       "23  Pakistan  3/19/2020          501                423.031           3.0   \n",
       "24  Pakistan  3/20/2020          730                423.031           3.0   \n",
       "25  Pakistan  3/21/2020          776                423.031           5.0   \n",
       "26  Pakistan  3/22/2020          875                423.031           6.0   \n",
       "\n",
       "    handwashing_facilities  hospital_beds_per_thousand  \n",
       "22                  59.607                         0.6  \n",
       "23                  59.607                         0.6  \n",
       "24                  59.607                         0.6  \n",
       "25                  59.607                         0.6  \n",
       "26                  59.607                         0.6  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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