COVID-19 Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANALYSIS OF COVID-19 DATASET \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section: Overview\n",
    "In this project, I am going to analyze the dataset of \"Our World in Data COVID-19\" which has been altered slightly for studying. I will first identify interesting trends and patterns by analyzing and thinking critically about the data. Then I will create a final dataset by Python3. My analysis will concentrate on business understanding, data understanding, and data preparation, which are the first three phases of the CRISP_DM process model.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section: Setup\n",
    "The following python libraries and functions are imported to help analyze the dataset.\n",
    "- pandas\n",
    "- numpy\n",
    "- matplotlib.pyplot\n",
    "- %matplotlib inline\n",
    "- seaborn\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "#from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# Section: 1 - Business Understanding\n",
    "    The COVID-19 dataset, which is maintained by *Our World in Data*, is a collection of COVID-19 data includes data on confirmed cases, deaths, testing, ages, geographical locations, and other variables about COVID-19 or potential influencing factors about COVID-19. The data of confirmed cases and deaths in this dataset comes from the European Centre for Disease Prevention and Control (ECDC), and the data of testing for COVID-19 comes from official reports and is collected by the *Our World in Data* team, and the data of other variables are collected from other sources like United Nations, World Bank, Global Burden of Disease, Blavatnik School of Government, etc. Those organizations collect the data through a variety of sources like existing influenza and viral respiratory disease surveillance, syndromic surveillance, case reporting, commercial lab reporting, the healthcare safety system, ongoing research platforms, etc. Each row of the dataset represents the COVID-19 data of one day for a country. A collection of data makes up the COVID-19 data for a period of time in a country. And those collections of data constitute the COVID-19 data of each country over a period of time. The epidemic-related data is the basis for analysis and control of the epidemic. Through analyzing this data, users can visually see the daily or weekly patient growth rates, the cure rate, and some other related data, and so that they can get a quick understanding of the overall trends of the problems they might care. The hospitals and doctors might use this data to understand the characteristics of the infection of epidemics. And governments might use this data to get convincing feedbacks on the effectiveness of their epidemic control strategies so they can make adjustments to their epidemic control strategies or keep implementing the effective one.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section: 2 - Data Understanding\n",
    "In this section, I will focus on individual attributes and/or combinations of attributes and then perform exploratory data analysis of the dataset. \n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('ALTERED owid-covid-data.xlsx', sheet_name = 'ALTERED owid-covid-data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Section: 2.1 - Describe the meaning and type of data for each attribute.\n",
    "The dataset contains 42 attributes. I will describe the meaning and type of data for each attribute in table form.\n",
    "\n",
    "| Attribute Name | Description | Type |\n",
    "| :--- | :--- | :---|\n",
    "| RECID | Identity of a row of data | Discrete and nominal |\n",
    "| iso_code | ISO 3166-1 alpha-3 – three-letter country codes | Nominal |\n",
    "| continent | Continent of the geographical location | Nominal |\n",
    "| location | Geographical location | Nominal |\n",
    "| date | Date of observation | Interval |\n",
    "| total_cases | Total confirmed cases of COVID-19 | Discrete and ratio |\n",
    "| new_cases | New confirmed cases of COVID-19 | Discrete and ratio |\n",
    "| new_cases_smoothed | New confirmed cases of COVID-19 (7-day smoothed) | Continuous and ratio |\n",
    "| total_deaths | Total deaths attributed to COVID-19 | Discrete and ratio |\n",
    "| new_deaths | New deaths attributed to COVID-19 | Discrete and ratio |\n",
    "| new_deaths_smoothed | New deaths attributed to COVID-19 (7-day smoothed) | Continuous and ratio |\n",
    "| total_cases_per_million | Total confirmed cases of COVID-19 per 1,000,000 people | Continuous and ratio |\n",
    "| new_cases_per_million | New confirmed cases of COVID-19 per 1,000,000 people | Continuous and ratio |\n",
    "| new_cases_smoothed_per_million | New confirmed cases of COVID-19 (7-day smoothed) per 1,000,000 people | Continuous and ratio |\n",
    "| total_deaths_per_million | Total deaths attributed to COVID-19 per 1,000,000 people | Continuous and ratio |\n",
    "| new_deaths_per_million | New deaths attributed to COVID-19 per 1,000,000 people | Continuous and ratio |\n",
    "| new_deaths_smoothed_per_million | New deaths attributed to COVID-19 (7-day smoothed) per 1,000,000 people | Continuous and ratio |\n",
    "| new_tests | New tests for COVID-19 | Discrete and ratio |\n",
    "| total_tests | Total tests for COVID-19 | Discrete and ratio |\n",
    "| total_tests_per_thousand | Total tests for COVID-19 per 1,000 people | Continuous and ratio |\n",
    "| new_tests_per_thousand | New tests for COVID-19 per 1,000 people | Continuous and ratio |\n",
    "| new_tests_smoothed | New tests for COVID-19 (7-day smoothed). For countries that don't report testing data on a daily basis, we assume that testing changed equally on a daily basis over any periods in which no data was reported. This produces a complete series of daily figures, which is then averaged over a rolling 7-day window | Discrete and ratio |\n",
    "| new_tests_smoothed_per_thousand | New tests for COVID-19 (7-day smoothed) per 1,000 people | Continuous and ratio |\n",
    "| tests_per_case | Tests conducted per new confirmed case of COVID-19, given as a rolling 7-day average (this is the inverse of positive_rate) | Continuous and ratio |\n",
    "| positive_rate | The share of COVID-19 tests that are positive, given as a rolling 7-day average (this is the inverse of tests_per_case) | Continuous and ratio |\n",
    "| tests_units | Units used by the location to report its testing data | Nominal |\n",
    "| stringency_index | Government Response Stringency Index: composite measure based on 9 response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest response) | Continuous and ratio |\n",
    "| population | Population in 2020 | Discrete and ratio |\n",
    "| population_density | Number of people divided by land area, measured in square kilometers, most recent year available | Continuous and ratio |\n",
    "| median_age | Median age of the population, UN projection for 2020 | Continuous and ratio |\n",
    "| aged_65_older | Share of the population that is 65 years and older, most recent year available | Continuous and ratio |\n",
    "| aged_70_older | Share of the population that is 70 years and older in 2015 | Continuous and ratio |\n",
    "| gdp_per_capita | Gross domestic product at purchasing power parity (constant 2011 international dollars), most recent year available | Continuous and ratio |\n",
    "| extreme_poverty | Share of the population living in extreme poverty, most recent year available since 2010 | Continuous and ratio |\n",
    "| cardiovasc_death_rate | Death rate from cardiovascular disease in 2017 (annual number of deaths per 100,000 people) | Continuous and ratio |\n",
    "| diabetes_prevalence | Diabetes prevalence (% of population aged 20 to 79) in 2017 | Continuous and ratio |\n",
    "| female_smokers | Share of women who smoke, most recent year available | Continuous and ratio |\n",
    "| male_smokers | Share of men who smoke, most recent year available | Continuous and ratio |\n",
    "| handwashing_facilities | Share of the population with basic handwashing facilities on premises, most recent year available | Continuous and ratio |\n",
    "| hospital_beds_per_thousand | Hospital beds per 1,000 people, most recent year available since 2010 | Continuous and ratio |\n",
    "| life_expectancy | Life expectancy at birth in 2019 | Continuous and  |\n",
    "| NEW_CASE_TREND | The trend of new case | Nominal |\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RECID                                       int64\n",
      "iso_code                                   object\n",
      "continent                                  object\n",
      "location                                   object\n",
      "date                               datetime64[ns]\n",
      "total_cases                               float64\n",
      "new_cases                                 float64\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",
      "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",
      "tests_per_case                            float64\n",
      "positive_rate                             float64\n",
      "tests_units                                object\n",
      "stringency_index                          float64\n",
      "population                                float64\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                           float64\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",
      "NEW_CASE_TREND                             object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#print(df.columns)\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Section: 2.2 - Provide basic statistics for the attributes.\n",
    "The following are basic statistics for the attributes.\n",
    "- For the attributes whose values are number, the counts, percentiles, mean, median, and standard deviation of each of them are shown below.\n",
    "- For the attributes whose type is object, the counts, unique, top, frequency, first value, and last value of each of them are shown below.\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>RECID</th>\n",
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       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
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       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>...</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</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",
       "      <th>life_expectancy</th>\n",
       "      <th>NEW_CASE_TREND</th>\n",
       "    </tr>\n",
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       "      <th>count</th>\n",
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       "      <td>42395</td>\n",
       "      <td>42141</td>\n",
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       "      <td>41101.000000</td>\n",
       "      <td>42074.000000</td>\n",
       "      <td>41884.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>25098.000000</td>\n",
       "      <td>38079.000000</td>\n",
       "      <td>39410.000000</td>\n",
       "      <td>29942.000000</td>\n",
       "      <td>29567.000000</td>\n",
       "      <td>17731.000000</td>\n",
       "      <td>34461.000000</td>\n",
       "      <td>41867.000000</td>\n",
       "      <td>41037</td>\n",
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       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>211</td>\n",
       "      <td>6</td>\n",
       "      <td>212</td>\n",
       "      <td>254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
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       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>SGP</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>2020-07-31 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GROWING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>257</td>\n",
       "      <td>11305</td>\n",
       "      <td>257</td>\n",
       "      <td>212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22612</td>\n",
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       "      <th>first</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31 00:00:00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2020-09-09 00:00:00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21325.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.458119e+04</td>\n",
       "      <td>1318.411231</td>\n",
       "      <td>1307.672213</td>\n",
       "      <td>3638.575843</td>\n",
       "      <td>42.884467</td>\n",
       "      <td>...</td>\n",
       "      <td>21072.662978</td>\n",
       "      <td>11.929194</td>\n",
       "      <td>251.049336</td>\n",
       "      <td>8.046783</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.757564e+05</td>\n",
       "      <td>12049.462316</td>\n",
       "      <td>11875.367143</td>\n",
       "      <td>34029.492765</td>\n",
       "      <td>364.814844</td>\n",
       "      <td>...</td>\n",
       "      <td>20517.439597</td>\n",
       "      <td>19.076276</td>\n",
       "      <td>117.718404</td>\n",
       "      <td>4.138276</td>\n",
       "      <td>10.485089</td>\n",
       "      <td>13.400078</td>\n",
       "      <td>31.554879</td>\n",
       "      <td>2.534914</td>\n",
       "      <td>7.358198</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-8261.000000</td>\n",
       "      <td>-552.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1918.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>661.240000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>79.370000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>1.188000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>53.280000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10663.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.300000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.429000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5591.597000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>153.507000</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>21.222000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>69.910000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21325.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.470000e+02</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.143000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14103.452000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>237.372000</td>\n",
       "      <td>7.110000</td>\n",
       "      <td>6.434000</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>55.182000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>75.490000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31987.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.928000e+03</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>32415.132000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>318.949000</td>\n",
       "      <td>10.180000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>40.900000</td>\n",
       "      <td>83.741000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>79.930000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42649.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.760941e+07</td>\n",
       "      <td>298039.000000</td>\n",
       "      <td>270541.143000</td>\n",
       "      <td>898087.000000</td>\n",
       "      <td>10491.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>116935.600000</td>\n",
       "      <td>77.600000</td>\n",
       "      <td>724.417000</td>\n",
       "      <td>23.360000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>78.100000</td>\n",
       "      <td>98.999000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>86.750000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               RECID iso_code continent   location                 date  \\\n",
       "count   42649.000000    42395     42141      42649                42649   \n",
       "unique           NaN      211         6        212                  254   \n",
       "top              NaN      SGP    Europe  Singapore  2020-07-31 00:00:00   \n",
       "freq             NaN      257     11305        257                  212   \n",
       "first            NaN      NaN       NaN        NaN  2019-12-31 00:00:00   \n",
       "last             NaN      NaN       NaN        NaN  2020-09-09 00:00:00   \n",
       "mean    21325.000000      NaN       NaN        NaN                  NaN   \n",
       "std     12311.850152      NaN       NaN        NaN                  NaN   \n",
       "min         1.000000      NaN       NaN        NaN                  NaN   \n",
       "25%     10663.000000      NaN       NaN        NaN                  NaN   \n",
       "50%     21325.000000      NaN       NaN        NaN                  NaN   \n",
       "75%     31987.000000      NaN       NaN        NaN                  NaN   \n",
       "max     42649.000000      NaN       NaN        NaN                  NaN   \n",
       "\n",
       "         total_cases      new_cases  new_cases_smoothed   total_deaths  \\\n",
       "count   4.207400e+04   41884.000000        41101.000000   42074.000000   \n",
       "unique           NaN            NaN                 NaN            NaN   \n",
       "top              NaN            NaN                 NaN            NaN   \n",
       "freq             NaN            NaN                 NaN            NaN   \n",
       "first            NaN            NaN                 NaN            NaN   \n",
       "last             NaN            NaN                 NaN            NaN   \n",
       "mean    8.458119e+04    1318.411231         1307.672213    3638.575843   \n",
       "std     8.757564e+05   12049.462316        11875.367143   34029.492765   \n",
       "min     0.000000e+00   -8261.000000         -552.000000       0.000000   \n",
       "25%     4.300000e+01       0.000000            0.429000       0.000000   \n",
       "50%     8.470000e+02       9.000000           13.143000      15.000000   \n",
       "75%     8.928000e+03     145.000000          151.000000     191.000000   \n",
       "max     2.760941e+07  298039.000000       270541.143000  898087.000000   \n",
       "\n",
       "          new_deaths  ...  gdp_per_capita  extreme_poverty  \\\n",
       "count   41884.000000  ...    37586.000000     25098.000000   \n",
       "unique           NaN  ...             NaN              NaN   \n",
       "top              NaN  ...             NaN              NaN   \n",
       "freq             NaN  ...             NaN              NaN   \n",
       "first            NaN  ...             NaN              NaN   \n",
       "last             NaN  ...             NaN              NaN   \n",
       "mean       42.884467  ...    21072.662978        11.929194   \n",
       "std       364.814844  ...    20517.439597        19.076276   \n",
       "min     -1918.000000  ...      661.240000         0.100000   \n",
       "25%         0.000000  ...     5591.597000         0.500000   \n",
       "50%         0.000000  ...    14103.452000         1.800000   \n",
       "75%         3.000000  ...    32415.132000        16.000000   \n",
       "max     10491.000000  ...   116935.600000        77.600000   \n",
       "\n",
       "        cardiovasc_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "count            38079.000000         39410.000000    29942.000000   \n",
       "unique                    NaN                  NaN             NaN   \n",
       "top                       NaN                  NaN             NaN   \n",
       "freq                      NaN                  NaN             NaN   \n",
       "first                     NaN                  NaN             NaN   \n",
       "last                      NaN                  NaN             NaN   \n",
       "mean               251.049336             8.046783       10.855732   \n",
       "std                117.718404             4.138276       10.485089   \n",
       "min                 79.370000             0.990000        0.100000   \n",
       "25%                153.507000             5.310000        1.900000   \n",
       "50%                237.372000             7.110000        6.434000   \n",
       "75%                318.949000            10.180000       19.600000   \n",
       "max                724.417000            23.360000       44.000000   \n",
       "\n",
       "        male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "count   29567.000000            17731.000000                34461.000000   \n",
       "unique           NaN                     NaN                         NaN   \n",
       "top              NaN                     NaN                         NaN   \n",
       "freq             NaN                     NaN                         NaN   \n",
       "first            NaN                     NaN                         NaN   \n",
       "last             NaN                     NaN                         NaN   \n",
       "mean       32.639923               52.705054                    3.120487   \n",
       "std        13.400078               31.554879                    2.534914   \n",
       "min         7.700000                1.188000                    0.100000   \n",
       "25%        21.400000               21.222000                    1.300000   \n",
       "50%        31.400000               55.182000                    2.500000   \n",
       "75%        40.900000               83.741000                    4.200000   \n",
       "max        78.100000               98.999000                   13.800000   \n",
       "\n",
       "        life_expectancy  NEW_CASE_TREND  \n",
       "count      41867.000000           41037  \n",
       "unique              NaN               4  \n",
       "top                 NaN         GROWING  \n",
       "freq                NaN           22612  \n",
       "first               NaN             NaN  \n",
       "last                NaN             NaN  \n",
       "mean          74.084896             NaN  \n",
       "std            7.358198             NaN  \n",
       "min           53.280000             NaN  \n",
       "25%           69.910000             NaN  \n",
       "50%           75.490000             NaN  \n",
       "75%           79.930000             NaN  \n",
       "max           86.750000             NaN  \n",
       "\n",
       "[13 rows x 42 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.describe()\n",
    "df.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Section: 2.3 - Visualize the most important or interesting attributes using appropriate techniques.\n",
    "I think new_cases_per_million and new_deaths_per_million are the most important attributes because those two variables reflect the growth trend and severity of the epidemic. I will give a brief interpretation of the graphs below in the following paragraphs.\n",
    "\n",
    "In the graphs of New cases per million in the UK/US/World, the number of new cases ballooned between March to April, which shows us that COVID-19 outbroke began around March in the UK and US. The new cases of the UK peaked during April and May. Then it rapidly decreased until July and ballooned again in September. Through the trend, we can predict that the new cases of the UK will keep increasing in the future until reaching another peak. The new cases of the US also peaked in April and May and then became steady until July. And then the new cases of the US reached a higher peak during the mid-July and August.  Through the trend, we can predict that the new cases of the US will decrease in the future. The new cases of the World were overall upward, and we can predict that the number will keep increasing in the future.\n",
    "\n",
    "The graphs of New deaths per million in the UK/US/World show us the number of new deaths ballooned between March to April, which corresponding to the ballooning of the number of new cases. This shows that Covid-19 is especially deadly, especially in the early stage of the outbreak. From the graphs of New deaths per million in the UK/US, we can see that the number of new deaths peaked during April and May, and then overall decreases even when the number of new cases was increasing. This trend suggests that people in the UK/US realized the severity of the epidemic and began to control it and have a better way to treats the patients who have COVID-19. From the graphs of New deaths per million in the World, we can see that the new deaths ballooned between March to April and then became steady. But it doesn’t mean the worldwide treatment has not improved because we should notice that the worldwide new cases were increasing at the same time.\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attributeName = np.array(df.columns)\n",
    "#attribute = np.array(df)\n",
    "worldAttr = np.array(df[df['location'].isin(['World'])])\n",
    "ukAttr = np.array(df[df['location'].isin(['United Kingdom'])])\n",
    "usAttr = np.array(df[df['location'].isin(['United States'])])\n",
    "\n",
    "index_new_cases_per_million = np.argwhere(attributeName == 'new_cases_per_million')\n",
    "index_new_deaths_per_million = np.argwhere(attributeName == 'new_deaths_per_million')\n",
    "index_date = np.argwhere(attributeName == 'date')\n",
    "\n",
    "uk_new_cases_per_million = ukAttr[:,index_new_cases_per_million[0,0]]\n",
    "uk_new_deaths_per_million = ukAttr[:,index_new_deaths_per_million[0,0]]\n",
    "uk_date = ukAttr[:,index_date[0,0]]\n",
    "\n",
    "us_new_cases_per_million = usAttr[:,index_new_cases_per_million[0,0]]\n",
    "us_new_deaths_per_million = usAttr[:,index_new_deaths_per_million[0,0]]\n",
    "us_date = usAttr[:,index_date[0,0]]\n",
    "\n",
    "world_new_cases_per_million = worldAttr[:,index_new_cases_per_million[0,0]]\n",
    "world_new_deaths_per_million = worldAttr[:,index_new_deaths_per_million[0,0]]\n",
    "world_date = worldAttr[:,index_date[0,0]]\n",
    "\n",
    "\n",
    "l1=plt.plot(uk_date,uk_new_cases_per_million,'r',label='UK')\n",
    "plt.title('New cases per million in the UK')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('New cases per million')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "l4=plt.plot(uk_date,uk_new_deaths_per_million,'r',label='UK')\n",
    "plt.title('New deaths per million in the UK')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('New deaths per million')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "l2=plt.plot(us_date,us_new_cases_per_million,'g',label='US')\n",
    "plt.title('New cases per million in the US')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('New cases per million')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "l5=plt.plot(us_date,us_new_deaths_per_million,'g',label='US')\n",
    "plt.title('New deaths per million in the US')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('New deaths per million')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "l3=plt.plot(world_date,world_new_cases_per_million,'b',label='World')\n",
    "plt.title('New cases per million in the World')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('New cases per million')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "l6=plt.plot(world_date,world_new_deaths_per_million,'b',label='World')\n",
    "plt.title('New deaths per million the World')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('New deaths per million')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Section: 2.4 - Verify data quality: explain any missing values, duplicate data, or outliers.\n",
    "After running the following code cell, the numbers of missing values of each attribute are shown below. There are lots of missing values except RECID, location, and date. This is probably because some of the data was not collected. To deal with the missing values problem, we can delete the sample data which contains missing values or fill the null value with zero, mean, or previous value. But in this dataset, I prefer not to modify them at the same time and just leave them blank now because the usual ways I mentioned are not very suitable for this dataset. The method of deleting sample data that contains missing value will cause the other data to be incomplete, and the method of filling the missing data with other values may cause some misunderstanding of the situation of the corresponding location. But it doesn’t mean I will never modify them. I might modify part of them when I need to use the data of corresponding attributes. For example, if I’ want to plot a graph of gdp_per_capital vs. life_expectancy, I will copy the DataFrame to a new one and modify the missing value in the new DataFrame by deleting the rows contain missing values. I will show the process in code in the next two sections.\n",
    "\n",
    "For duplicated data, the number of duplicated rows also shown below. There are four duplicated rows and I will drop three of them. \n",
    "\n",
    "For the outliers, I randomly choose seven attributes as samples to detect outliers and the graphs show that there are indeed some outliers. Some of the outliers might come from the extreme manifestations of some true and normal values. Some of them might be caused by the occasionality of conditions or errors in recoding and calculation. And in some of the outliers. To deal with the outliers, depending on the situation, we can delete the outliers, replace them by mean or other suitable values, and or keep them as they are. In the case of this dataset, I will remove the rows contain outliers. But I won't remove them from the original dataset directly. Instead, I will remove the outliers from the temporary nparray generated from dataset which contains the data I need. I will show the process in code in the next two sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of missing values: \n",
      "RECID                                  0\n",
      "iso_code                             254\n",
      "continent                            508\n",
      "location                               0\n",
      "date                                   0\n",
      "total_cases                          575\n",
      "new_cases                            765\n",
      "new_cases_smoothed                  1548\n",
      "total_deaths                         575\n",
      "new_deaths                           765\n",
      "new_deaths_smoothed                 1548\n",
      "total_cases_per_million              829\n",
      "new_cases_per_million                829\n",
      "new_cases_smoothed_per_million      1612\n",
      "total_deaths_per_million             829\n",
      "new_deaths_per_million               829\n",
      "new_deaths_smoothed_per_million     1612\n",
      "new_tests                          28319\n",
      "total_tests                        27965\n",
      "total_tests_per_thousand           27965\n",
      "new_tests_per_thousand             28319\n",
      "new_tests_smoothed                 26541\n",
      "new_tests_smoothed_per_thousand    26541\n",
      "tests_per_case                     27717\n",
      "positive_rate                      27337\n",
      "tests_units                        25760\n",
      "stringency_index                    7304\n",
      "population                           254\n",
      "population_density                  2163\n",
      "median_age                          4583\n",
      "aged_65_older                       5145\n",
      "aged_70_older                       4780\n",
      "gdp_per_capita                      5063\n",
      "extreme_poverty                    17551\n",
      "cardiovasc_death_rate               4570\n",
      "diabetes_prevalence                 3239\n",
      "female_smokers                     12707\n",
      "male_smokers                       13082\n",
      "handwashing_facilities             24918\n",
      "hospital_beds_per_thousand          8188\n",
      "life_expectancy                      782\n",
      "NEW_CASE_TREND                      1612\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "The number of duplicated rows: \n",
      "4\n",
      "\n",
      "duplicated rows are dropped!\n",
      "\n",
      "\n",
      "Outliers: \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7cvByukewb5qzz610f9LpHrpH51+ii+tdwF39fx4nAy9rre1qrd1F9+qVU5KcNqyxa/Q8IbtGJsmZdK8AOLS1ds+e9l/kYx9P90qC6dbaTaM8trQ7rgFraJK8iu7vAu6gWwf9feD8UcY3yYPp/oDinwAfM77an7gEoWE6gu61rNcAf0y35PG7Ix7DaXQv5XoE8DuDNyR5WbrfEJzv8qURj1PLkEsQWrb6J7wetZubd/Xr2NLQGGBJKuIShCQVMcCSVMQAS1IRAyxJRf4fgH/Tyf1UJZEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"The number of missing values: \")\n",
    "print(df.isnull().sum())\n",
    "#print(\"\\nMissing values: \")\n",
    "#print(df.isnull())\n",
    "#df.fillna(method='pad') #fill null with previous value\n",
    "#df.fillna(df.mean()) #fill null with mean\n",
    "\n",
    "df = df.drop(columns=['RECID'])\n",
    "duplicate_row = df[df.duplicated()]\n",
    "print(\"\\n\\nThe number of duplicated rows: \")\n",
    "print(duplicate_row.shape[0])\n",
    "#print(\"\\nDuplicate data: \")\n",
    "#print(duplicate_row)\n",
    "df.drop_duplicates()\n",
    "print(\"\\nduplicated rows are dropped!\")\n",
    "\n",
    "print(\"\\n\\nOutliers: \")\n",
    "sns.boxplot(x=df['total_cases_per_million'], color='lime')\n",
    "plt.xlabel('total_cases_per_million', fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(x=df['new_cases_per_million'], color='lime')\n",
    "plt.xlabel('new_cases_per_million', fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(x=df['new_deaths_per_million'], color='lime')\n",
    "plt.xlabel('new_deaths_per_million', fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(x=df['stringency_index'], color='lime')\n",
    "plt.xlabel('stringency_index', fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(x=df['population_density'], color='lime')\n",
    "plt.xlabel('population_density', fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(x=df['gdp_per_capita'], color='lime')\n",
    "plt.xlabel('gdp_per_capita', fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(x=df['hospital_beds_per_thousand'], color='lime')\n",
    "plt.xlabel('hospital_beds_per_thousand', fontsize=14)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Section: 2.5 - Explore the relationships among the attributes, excluding the class attribute.\n",
    "In this section, I will first show the averages of population, population_density, total_cases_per_million, _deaths_per_million, new_cases_per_million, new_deaths_per_million, gdp_per_capita, and hospital_beds_per_thousand grouped by location. And then show the scatter plots graphs and linear regression graphs of gdp_per_capita vs. total_deaths_per_million, hospital_beds_per_thousand vs. total_deaths_per_milliond, gdp_per_capita vs. hospital_beds_per_thousand, total_cases_per_million vs. total_deaths_per_million, new_cases_per_million vs. new_deaths_per_million, total_deaths_per_million vs. population.\n",
    "    \n",
    "- gdp_per_capita vs. total_deaths_per_million\n",
    "\n",
    "In the scatter plots graph, we can see that most of the points are at the bottom left of the graph, but they don’t have any clear pattern. In the linear regression graph, we can see that there is a positive correlation between these two variables. This seems to be a counterintuitive result. I guess there are some interfering factors caused this result. \n",
    "\n",
    "- hospital_beds_per_thousand vs. total_deaths_per_milliond\n",
    "\n",
    "In the scatter plots graph, we can see that most of the points are at the bottom left of the graph. In the linear regression graph, we can see that the two variables are slightly positively correlated.\n",
    "\n",
    "- gdp_per_capita vs. hospital_beds_per_thousand\n",
    "\n",
    "In the scatter plots graph, we can see that most of the points are at the bottom left of the graph. In the linear regression graph, we can see that the two variables are positively correlated.\n",
    "\n",
    "- total_cases_per_million vs. total_deaths_per_million\n",
    "\n",
    "In the scatter plots graph, we can see that most of the points are at the bottom left of the graph. In the linear regression graph, we can see that the two variables are positively correlated, and their correlation is relatively close.\n",
    "\n",
    "- new_cases_per_million vs. new_deaths_per_million\n",
    "\n",
    "In the scatter plots graph, we can see that most of the points are at the bottom left of the graph. In the linear regression graph, we can see that the two variables are positively correlated. \n",
    "\n",
    "- total_deaths_per_million vs. population\n",
    "\n",
    "In the scatter plots graph, we can see that most of the points are at the bottom left of the graph, but they don’t have any clear pattern. In the linear regression graph, we can see that the regression line is nearly horizontal, and it suggests that there is no relationship between the two variables. But this conclusion does not seem accurate in reality. Maybe there are some interfering factors -- like too many outliers -- that caused this result.\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-a6bc3cdba97e>:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  print(df.groupby(\"location\")[\"population\", \"population_density\",\n",
      "<ipython-input-7-a6bc3cdba97e>:28: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  arr_temp = np.array(df.groupby(\"location\")[\"gdp_per_capita\", \"total_deaths_per_million\"].mean())\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group-wise averages:\n",
      "                  population  population_density  total_cases_per_million  \\\n",
      "location                                                                    \n",
      "Afghanistan     3.892834e+07              54.422               375.682626   \n",
      "Albania         2.877800e+06             104.871               987.444118   \n",
      "Algeria         4.385104e+07              17.348               261.020474   \n",
      "Andorra         7.726500e+04             163.755             10012.439094   \n",
      "Angola          3.286627e+07              23.890                18.948337   \n",
      "...                      ...                 ...                      ...   \n",
      "Western Sahara  5.973300e+05                 NaN               640.636723   \n",
      "World           7.794799e+09              58.045               898.353878   \n",
      "Yemen           2.982597e+07              53.508                32.316974   \n",
      "Zambia          1.838396e+07              22.995               169.139091   \n",
      "Zimbabwe        1.486293e+07              42.729               109.461977   \n",
      "\n",
      "                total_deaths_per_million  new_cases_per_million  \\\n",
      "location                                                          \n",
      "Afghanistan                    11.382210               4.072074   \n",
      "Albania                        29.429935              19.741387   \n",
      "Algeria                        12.591635               4.298791   \n",
      "Andorra                       552.571467              90.669133   \n",
      "Angola                          0.838192               0.527343   \n",
      "...                                  ...                    ...   \n",
      "Western Sahara                  1.258555               9.360387   \n",
      "World                          38.668783              13.945004   \n",
      "Yemen                           8.927627               0.436980   \n",
      "Zambia                          4.118491               4.025880   \n",
      "Zimbabwe                        2.681896               2.873260   \n",
      "\n",
      "                new_deaths_per_million  gdp_per_capita  \\\n",
      "location                                                 \n",
      "Afghanistan                   0.149901        1803.987   \n",
      "Albania                       0.599565       11803.431   \n",
      "Algeria                       0.143859       13913.839   \n",
      "Andorra                       3.810778             NaN   \n",
      "Angola                        0.021163        5819.495   \n",
      "...                                ...             ...   \n",
      "Western Sahara                0.012219             NaN   \n",
      "World                         0.453579       15469.207   \n",
      "Yemen                         0.126359        1479.147   \n",
      "Zambia                        0.091989        3689.251   \n",
      "Zimbabwe                      0.084780        1899.775   \n",
      "\n",
      "                hospital_beds_per_thousand  \n",
      "location                                    \n",
      "Afghanistan                          0.500  \n",
      "Albania                              2.890  \n",
      "Algeria                              1.900  \n",
      "Andorra                                NaN  \n",
      "Angola                                 NaN  \n",
      "...                                    ...  \n",
      "Western Sahara                         NaN  \n",
      "World                                2.705  \n",
      "Yemen                                0.700  \n",
      "Zambia                               2.000  \n",
      "Zimbabwe                             1.700  \n",
      "\n",
      "[212 rows x 8 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-a6bc3cdba97e>:65: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  arr_temp = np.array(df.groupby(\"location\")[\"hospital_beds_per_thousand\", \"total_deaths_per_million\"].mean())\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-a6bc3cdba97e>:103: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  arr_temp = np.array(df.groupby(\"location\")[\"gdp_per_capita\",\"hospital_beds_per_thousand\"].mean())\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-a6bc3cdba97e>:141: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  arr_temp = np.array(df.groupby(\"location\")[\"total_cases_per_million\",\"total_deaths_per_million\"].max())\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-a6bc3cdba97e>:177: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  arr_temp = np.array(df.groupby(\"location\")[\"new_cases_per_million\",\"new_deaths_per_million\"].max())\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-a6bc3cdba97e>:212: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  arr_temp = np.array(df.groupby(\"location\")[\"total_deaths_per_million\",\"population\"].max())\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oV5FKeEgnYu46ziKLee7ciUjM2XUeFxlPBtRaZK6jyLkWWwaFrIk0NI7MbKNZi0Ehsz29qHsj4Wkf/0pnv/d7iv4qtwOZsGvRNRSF+jGyekI2mStitlBGqeJPPvzQ6Ayq1RWYpmTDUK0qvPnf7ghN+DuZK6JQrjrxmVmIuKwDFtlsXruvbcfKt8jK2NqX0q8DrsXBrHYt2pnvs6k4knE9nVuhVEHaTFFlr2NdiEmTkg9oUQsKWSLSeawXI4sKGF2LrYdC1kyMubVkhEzVj5E1YpH5+ysfEB08LwBsClhkc4VwQwEEhWzxcWWTOf16ymyfODmPX/vID3DzgbEV1Y9sDKYXSvjW/cfwg8ACrfa+mVoohvbrWWSz+XI4g9bFyCoYdvenjoklYx760nEUylWXoJSOe0ibuRWtReaEbKGMZFxbZ8Ep3hJxCcfIIhMB13t27LMbHWPGSYNbB4WsBTRyIy8WIwN8Ky0ReZiiqcGNEs2QFPFnAc+XdMxhIJtAIiaYNzEHO6WPC87XschsEN9uj07lzXbBHXNwbA4Hx2ZXVF/SGdzxxATeePXPUDb3ip3kN5gQNBXp7MxEOkQ1QmY6UvFAhu18sYzhXi1kuaLOWswkY8gmtTDZDlUmGUMmGcNsvoxyVSEVjwVci6VQsodlcdfi8jGyWET07GPJmT2aD4WszVinWzRGVm9SUufmiIcFbNXjyMzn0/EYPE+QjHtuQHRPMo6eVBzzhXKkcal1LVqr0jVS5r1J05BNBBqyv/zafXj39f70Q6RzyRXLbjkUAPjBIyfw3YfG3KwctkMzGchStJaYE7J8ZLsQdi1aK2xbfzoQI9OzdiRjnhtHlg0Kmbnf0gktXPZeDSZ7zObLSFnXYiwqZOHhLYAvdlH3YbBs0fT7pX5EsiZQyJrISuZa9B8GvR+dBV9vbWwsvL/S9Puo1ZdO6M9bN8x8oYzeVBw9yTjmCmUnXoBvkdnGoWzmulNKucbJNiRWwIIN2dhMAWMz/nyNhydyeP1nf8qZ9juQy/71J/jAtw64/ZNzxdB2Yt7eD3qbL/nxq+kGXIsz+ZJLvd/Wn0K+VEWpUsV8sYxsKoZsKqZjZKWKsb60V+Gkue8yiRgyiZi7VjoRQ8pYZHPFMlIJm7UYc9dMxjzXUQTqJHnUjZGFRa52hWhKWbOhkLWApbKW/Jk9wjd9dOE+oE5sLB4uXylxJ2Qxt7XJHtlUHD2pGHKFihOvTZlEjUVmXy8E3I/OIpuvtchOzBVcQwcAtz96Et9/eBwHRv2ZHW579IRbRJGsDybni3jndfeEOhyPjs3h4Nic27f/Zysk7v+fC1thgO/+qxWyEtIJD56EXYvbN6UB6DjZvPUYJOM6RraIRZZKeEgtYpEphVCyhyURi8TIIrPpxOqNI5NIR9O5FhkjaxUUsiayknFkNVmL9SyyyDiyROTBWenzYh/KGiErVtCbimnXYtG3yHYOZGqSPezryVxtrGwi4loqlquYyZcxW/BdUtYNFZzq6i1fuBP//P2Dbj9XLOOR44yrtZIHjk7jZ49PuP3bHj2J6+4YwV1PTgKwqySUcSLQKTlpF8A028lIzNS6FXVZOFsxV6ygVKliNl9GfzqB3lQcs/mycy1u78+4a1QVtEWWjCFXLCNXLCObiCNjhGwiZJF57lrB9HsANTGy6HAWoDYOHe1MAr7rv8Yis5MG07nYdChkLWDpcWQ2axFmu3j6fXSZiOjM3CvFPq+2l5pOeG5hzZ5kHL0p61rUjcmuAR2nUEpheqHkvtf0Qik0W0OtizEcM9Nl+rUVsHHTIOZLFUzmSjgeWC7m6tuewG/80w9D8ZiHRmdC++TUuPH+0dCs8h/41gH8xfX3uf1x8/8Ynw13PE4GOiDOIpuLdmRKqFZVyCILZi36mYQlzBZK6M8k0JdOhFyLO4xFNjajr9ebMjHcorbIMskYsomwkKWNa3HKWWR++j1QK2QJM2VV0MMRdetH42DhY8LPJ9PvWweFbJ3gRXpx9dLvExGLzApYcoXJHpZ6FlnOZC1mU3Hd4y1UQhZZxcTEphfK2NGvGxctZLVuo5NOyGyjF+i9z1khM9tIA2kbLAB4cmIe+VLVNaJTuSJ+42M/xLX7R9wxj47PYX/AgiCL8+j4HL56p//bPXFyHn/0b3fia3cddWXHpvM4Nu13Jqzl7AuZ70a04wQnorFRs61UFWbz5dA9Mp0roVjW8yru2ZzVZQslzCyU0ZeOoz+TCFlk24yQ2Q5ONqld3/Nm9vtMIhayyOLGskonYm6m+3Qg/R7wn5to0kc9iyyarRiaa7Em49jM7BEZUkOaB4WsiaiGxpHprX/TL2GRRafJqTNl1UrwY2Q22SPmGh/rWgxaZDsHtHtnNl/GzEIp1ABZayvmiQuuT7r0a53ZaF1PgC9yNQI2ay00/1grava9kckFlKsKT56cd8f8/bcfxtuvvdvtTy+U8KfX3BWKzwUXVNyoKKVqFj+94qv34XsHjrv9q297HO+47h43cN0Okxid9odJjM0WMJMvu+merIA5ITPbSlVhakFbXFGLbDLUuSk6q703FcdUruQyFfcM6vtqeqGE2XwJfekE+tJxzCyUXIxsmxk3dmxaX7cnGUM2GXcDooMxsolcERnTOcsEXImpuJ/sAdSzyMIZikAgHu1FOpF1Y2Th59Mf3kIlazYUsiYSTeSof4xdWBNmu5TrIhwbS0bHtKzwefGzFvXDnUp4rjHqSWnX4nyx7JI9rHtnNl/C9EIJpxkhmwlYZLsHMy5LzW6LFT3tVVBUrJV2IhIjsw3lybkCKqanb3vhNtvRNtTHAlbb0akFjE7lnXWw//EJfO3uo/jxoZPumD/43M9CKw+PTi/gQ995ODTzyLHpvGu81yMHx+ZcBwkAvnrnCL79wDG3/+0HjuMFH/ie+63ypQq+9NMn8a37/GNGp/OoKv+3dr/ntP8ZGwONCpjtYIQ6JXMFLWamWsFkD3vrT+SKzsV3xpYsJnNFdw3bIZoxy7f0pePoT5sYWaGMVNzDZjOT/XHzvez9mQu4Fm3W4uR80QlWKiRkeoC0pZ5rMbgFFl+HzO6L1GYBx6OuRZBmQyFrM/4q0mE3RHCy4JgX7jFGF/sL9iBXgrPI4r5r0QqKi0GY9Pu+VBybzNx1k7kS5gpl7BrMQCQcIztjqCc0fsx9Zr4YTgyYC8fI7L5tMKvKd1FZi+y42dpe+fFAGv/odB7lqsIJ08Aena4djP3g6AweDGRHfuOeo/jo9w7i0AmdeVetKlzykVvxr7cecsdcf9cI/vDz+91+qVLFX9/wQMh6ueOJidA0TMVyFV+760hIcH5y6CSeCFiQo9MLuG7/YbevlML7//MhPHzMT2r5zA8fw9uuucuv/9EZvOxD/4XbH/XF+WPfOxiq78PHZlGqKBw6MW9+K2tt+b+V/d2OzYQF7HhEtABf5BZzLdr3JgLCZl9P5IrYZaz4yfkipnJ6Wqjt/RlML5SckJ0WdC3my+hPx9GXTmC2oGNk9l4MXr/HJHuELDIjWuWqQiapn4mGLLJYVMhqx5G5TMRFRCv4OprswQHRzYdC1kQayVp048girsXwMhF6G488cPVmGVgJ9oGzsYVMIubmnMsm4+hJxlCqaJdRn2lcAODIlE6NH8gk0J9OuBhZNhnD1r6UTsc3y2k8ZWsvAC1KE/MFkxUmODlfRKlSde6nqEUG6Ea0UlUBt2O4AbYNcqlSdQ2ta7iNgNkGfL6gYzRBYbMutSNT1tIrYCpXws8DKeXfPzCOmx487qy0h0Zn8LnbHsd3HvBdde//zwN47zcedPs3PXgcf/rvd+POJ6dc2R9/4U58+Kafu/0v/eRJvPPL97pkiWMzeXzy1kP4xj1+nOr7D4/hxvuPOUE8OK7r9Yipn1IKR6YWcCTwnazA+ttat6EtcwJmOwrTYYs3+F6Na3EuaJEVXUdkqCcZipGdNaz//5O5EqYXitiUTWAwm8BUrlRjkVnXYr9xLdoYWW86jt503NQnGCOLY9bGyJJ+1iIQ7Jz5TVwqEiNzsbFFLLPg62hCRzT5Q79m+n27oJCtExYbTKnLogOhw0keVuBWmuZbMyA68ND3mBgZoBu+vnQC/aYxGZnQjeKmbAKbMgmXfj+YTWIgm8BkruissrOGewDo3vnJuSI29yQx1JPCyblCILvMcz38sYCQjc8WcHKu4FxWtlEdC1gSOiZUcB2CaCMdbdhPzhedKI1M6rIjdmsE+sikP4ZtxLy2x/pb/5jDkzkcnsg5wXliQltDT5rtXKGMk/NFPDER/Ez4fIfNb3o4cN4jkwsolKvut/Hrqbcn5ooolqs4PpN38S77XjTuNWp+q1Kl6kRodDrcIfBduMH4pHbXOot5zrfIrLV9IvC/fMrWXpycL6JS1ZmtZ5r/v7XIBjIJDGQTmFooulk89gxqITsxW0ChXDWdJt+1aAfnB+vYk9TJSEXX8dIZiVZXgp0zSzoeQ9wTd8xiMbLwFFX149FRrwhQGx6ga7F1UMiaSGOz35uEkEj6fWicinm52MO02qxFK4ipgGvREnTnHJvOhywy2/huyvhCNr1QxEA2gYFsEvlS1TWgrkc+X8TJ+SKGepLY3JPEybmi693/wrY+zBV0YsH4bAG9ATfS8WCjOutbLwBQKFcxs1AOZdfZ10enww263QJ+A26tMytgIxGhCJZZ4To8Ybe6PF+q4PhMAfPFirMua8Qp8plQmRPKcB2stRWsjy+09rvpbVUFvvdU+D37XXPFCmYWyiHRP2Z+IytkU7kS8qVKqDMxNqvjX+WqwuaeJKZyJRTKFZyYLeCs4R7EPMGJuQJOGCE7e1svZvNlnDAdkD2DWcQ9wYTp3OjOTjKUhTrcl0Iy7rnvqTtNCVSqOnGlNxVHzBP0JGPufuhJxdx9AmghExFkjeCl69zTqYROr7dli1liMc9fmT3qbqyZGDigUtEQAJM9WgeFrAU0sh6ZtaaiKbxAbcA5UdNLPDXXYjD93pJN+r3gYzN510sGgBHToAaFbDJXMkKmxe7QuLZGrJBNzBdxcq6Aod4khnqTOGmEDQDO3d4PQAvX+FwBT93RB0A3otbNtbUvFbLEbANybCacJm5FLmqZhV2KYYGwImdFZGy2gEK5EmrUaywy8xscDYleWOyi4nRirlBjDS5m6Y3PFZyb15YdcdeuFdyjUwtQStWIdOh7Ty848dK/ld8xsA3z2EwBx83+1r4UxmYLTnDO26H/Tyfnijg5X8BwX8p1SiaMxfYU8/9+1LhBh3qTGOxJYiqnLbJNgXvkiZPh+8h+z2Cn6dh03glWTyruLLCeVNyJFlB7D6eT4S3gj5dMRWJj9dPul3YlRicIDh4bTb+njjUfClmbiVptbhmXwM1vNS26HlmyTirwSoi7cWQ2/d6/HXrNFFWATl7oSyeQTcYQ86SuRTaZK2Igm3Sr8j5mkg3O2JJFzBNM5oqYmC9ic08KQz1JnJwvuBTuc41wnZjTZadt7kFfKh6yyH5x1yYnKsdn8jh7q/7MsZm8s/760nHnbhydzkNEx3vKlWqoQT8ytYC5QtnFaKIuO6WA0al8zWeAxV2NgG9xLWaR2c8XyhXnIouK3vEZLaLB80avZV2f0fpNL5Tc2lxWnEOiP513QjeYTeDYtBa/sZkCfmG7/3uOzRawpTeFHZvSOD6T94Vsp9/hODFXxJbeFLb0poxrsYD+dNxNJfWoiePpeyKBiXmdpTiQSbi1756YyCGTiCEZ94yQ+RaZ7TSdmCu6+JjdeqLFyN6fAFzqvd3aezl4T1vPQ8ot32L3w88TUJvBWJO1WDdGFrHW7MweVLKmQyFrIv44suVvZHECtrxFFn2oRCQ0pVWj1Kbf+w1D1HXTl45DRNCbirsGtD+TQH8mgZmFEqYD8Q/At8g296QwmE1iYr6Ek3PatTjUm8LEXNHFamwjOm56/8N9KQz3pTA+py0yEeD8nf2YmC9irlDGZK6EC3ZvAqATFI5N55FOePiFbX0YnV7AyXkdO/qFbX2oKp2Nd2Qqj6GeJETCIpVO+C6tI5ML7v9wZGphSTGZyunEhMOReFq1qgKWU1j0AC1uR6fyzhKPip6tR/jaOedq9EQ37vmSFjvbCTk6teDEa2tfynetTjcLKrEAACAASURBVOfxVGNJaYtMH/OMPQMYnc5jMldCsVLFBbsH9O9phGxbfwrDfWljJevPWItMf84KWRIn5ozbuDeFzT16vJedg3Gz6dxM5nRCUNBqf/LkPPoz+h7rT8edyPYHrH8A7j60Sw31JOMhNyJQK2SZyBYIWGSJRZI84v4zlIwIWNQLEvMEItGkrPDzyRhZ66CQtYAlNca5Fu2xVsj8Q3x/fWQb6G2ucAJ8AMH0+/AMH4CfFWaxrp6+dByliq500CKbWjDJHqa3fejEHET0MZt7Ejg+k8dsoexiZPNF3RCn4h5OH9IJAY+Oz6NYqWK4L4UtfSlnkQ31JN1g7PuPTAMALtjjN7yjM3ns2JTBjoGMtjpMg/6s0wcB6AzGo1MLOH0oiy29KRydWnBi88w9gzg2o622I1MLrrE+MulnA565pceJycjkArabGU1GjOAkYoL+dByHJ3MYmy2gWKlic08SR6f0eQ9P5tw4qBGTGAIAW3qTfoxsKufG6Y1M+vXbu6UHRyYXMJXT1tb5O7WAHzHf6bTNWWzpTbp9AHjOGZv1oPV8CaPTC7hg9wBinmB0Sot+JhHDOdv7MDZTcNbs023HYCaPsZk8hvvS2NofcS0ai+znx2ehlK6/b5HpRB77PW2G5WBPApt7kjg2ncdCqYKBbNIliYxMLrjXmzIJN25QW2QJd+9FLbIe52r079dMQpdlnEUW3iZjnnPbp+NLx8iCr6MrTERn3Km/SoV5hjmzR8ugkLUZP9kjOj6ldgqc6EOVqNMbXAm1MTJ/7I0OrvtC5vecE+7YVDyGTZkEShWFSlVhIJvAYI9+//ETOQxkEoh5gsFs0vXQh0wvHgAePj6LLb3a1Qjo1HYAvkU2W8D4rN+oAsB9I1rITtuc1e4xEyPb3p/Gjk1pjE7nnTXy7NO0kB2d1u7HnQMZ7BzI4Oi0L1LP2bsZlarC8dkCRiZz2Hf6IDzRcaiRyRzinuCZpw1iZFJbegulCi48awiAbogPT+SwcyCD04ayRti0MD3vTH3e0ek8Dk/k8Iw9A0jFPRyeXHDi9bwzh3BkcgGlShWjU3k870z/vCNG/M7Z1quFzdT3uXs3A/CF1n6nI1OB720E/ND4PKZyJewezGBrXwqj01r0t29KY0d/GsVKFQ+N6nFrZ2/rRTrhOYtsa38K2/rSmJgv4uiUtnhPH8qG/k++RVYIZKQaITP/b5vcYX8XfY/oY8pVFRIyix0QbelN+pYYoCcMDu4Dy1tkobR7t3yL3epjQkIWr995jFpg9dYls8+nF/CYkOZCIWsiKxlHZnExsiVcFlE3B6B7fyt9XmqEzDzQ9Xq8QYsMQN0GKBgjK1aqrsEaClgeQ71J5356+NgstvSlkE7E0JeO+0LWm8Jwr2+RbetPYWuftlbuNRbZtv4UtvXrGM6x6Tx2bEpjW38ahXLVncdaZEenFnB0Oo9dAxnsGki7sVdxT/BMY9ndf2Qa+VIVZ2zpwbb+tHPv7RhI4/ShLMZnCy7u87wztZiMTOYwMrmA3YMZ7B7I4vBEzn3PC40oHZ7M4cjkAvYMZrBrMIPDEzlnxT379EEUylXcf2Qa5arCs08fRNwTd15d37BAOiEzFpg+JoMjkzkcncojGfOc2/XOJ/RM9Ts2WZFfwHEj+ts3aQv3nsNT5vfUv9/I5AIm5ovY2pdynYcHj85guC+FVDyGgWwCB8yg7S19KQz16nXCRiZzGOrR1lbMExyfKSAZ95BNxrC5J+GGUAxkkhgI3DO2Y9QfKVvSIkvW3p9OwIxllooMKUkFx5NFLLFo0gdQb17TehaZV3dO1FgsbIlRxpoPhawFNOBZ9NPvI1lRgO+iiLo7gj3IU7PIwq7F3lRY0AC4HrJtYOoJ2WA2oRcvNA3EZiNqg9mkE2wdI9Pl0wslDJvXw70pNxvF1n7diM4Vynji5HyoUb1vRDe8203De3Qqj+PWyjCuuTufnEIy5uH0zVn0peK478g0iuWqtl42ZTA6lccRI1J7NusG/aeP6QmHrTA4kRrIYvdg+Jin7xlAJhFzArNnMIs9mzPGQtNW0S8ZIXvw6AxmC2Xs2ZzFnsFsyIqzFs6PD+nznjHUg50DGRw25909mMHuwQwWShXcZwT8Wadpsfv58VlM5koBi0yL8/ZNaewy9b3jSStk2u06apI9dgR+q3vM77m1T/+e9x+1HYU0tpr5DR8cncFwb8r9nx43M5QM9WjXIgDMFyvY3JOE5wkGTQxsczYJEXGdG0BbZNlkzHXG6t1Hvem48wAAdWJkde7PTNQii2QxBhfPdEkfkQHRwRk9oolU0Ym7Ad2xDFto4c+4icBpkTUdClkTUQ2MJItOLFxv0uBYNGMqYplFj28UN44s4lq0QfRU3O9xWkusf0mLTL+2DZe1yGzcBNCuxaHAvm0It/SmXIxkuC/lGs6ZfBlb+9IY6kkh5gkeP5lDymS5be9P4+DYHMpVhR2b0i5j7u4nJ7F9UxqeJ9i+KR22TAa0MDxwdBo7N2Vc7M0JmbGcjkwFxUQLzu1m3sbdg1rcHhmbw4m5ojumUK7i7sNTGO5LYe+WHnii1/HSn9GidDhgxe2pOa85ZiKHI1MLbh8AfnJoAtlkDFt6k9i+Ke3WCts9qIU3X6rq7zSQxta+NGKe4I7H9ffeOZDGzk1pHJ1awNhsHtsCv9VDozMY6kkiGfewvT/thHhrn7Z4AT2ge9iI2nBfynVKtEUW/t8CwJCxuKP3g71fRASbTCy1P3If2TFj1r1ty4CAp8C6GEOuRVsWScMPzCNqSS+W7LFE1mJ0iipAPz+xOgOiXTyN6fctg0LWAhq5kWuyFqXWIquZoirwEK2m1+eWcYkMHrUNh4gehAo07lrUW79HDoQbss0ma9HihKxPH5OKe+hLxV3DCWg3YswTJ27b+tMQEWzblHarUm/r962MmXzZvbaWCKBn7981YNLDx+exazCDbDKOwWwCDxhLxIrU6HQex2cKbh8A7nhiEoNZvejj7sGMWzZmz+ZsyLLbM5hBIuZhx6aME8jdg1ns2ZzFVK6ER47PYs9g1llO+x+fgAiwYyCN3YMZPDQ6g3ypaq6txe6ekSnsGshARLBrIIMHj86472TF+ND4PHYOZBDzBNv7025M3TbjSiyUqyhVtOhv6dW/aaminGBt6/d/8619vkUGICRkgG78+1Jx9z8B4DoomxfZBu8Nu7VC1h+5v0TEvY66FrOpsGgBtRZYdPb7ehZZbdZieByZXWgTqD8dnB44jcB+ONOR48haB4WszURtNidodRb3iwacg+nCq7LIYlHXot4GYw/OrRNxLUZ70gBc7MMJWW+4IbPZfT3JmGs8bOKHFbThvhREJCRkwyY+Zt2LNmvQbgHtPhvuTbm4hBWynZv8Y3YFGn0A2G1e7xrMoKrgJkbeNZB11uGuwQy29acR9wSFctUJy57NWTdmK2i1zRk3oi2fM8uQWNcioN1wu42IDvUkkStWsK0vjVQ85iw7W18rdqWKcq9tfe0xVmj199WvdxrBHupJIp2IhX6H7f3aYrNLo1gB2xb4Pbf26/iX/T2He9NmazoePdptuCUgZE64esMW+WBAyJy1ng3fP/Z+CqbdOyGrSb/3kzOScQ8iQW9C2LvguxZXHiNL1B0CEy4LD5OJZC0y2aNlUMiayMqSPaJZi4tnQznLrE5m40qoGUcWr4099KTCAraURWZf28Zqcx0Xo4joBtCUbenzXYtAbc8f8Btam/BhF1kMWhDbN6URj3nuczuMSO0wDXsmoRMVgkJmX9vGPygUlt2D2sLZYYTBikZQPPYErLbwMVn3m23KJMKfsWIXEL3oeXdvzmBTxh8cbGeStwIcM7Nv1P1O9vsP+Japxf4m1r243f2eeuuJFsCYJ856jv5f7P8taG3Z184yi9wHiZg4obGuxeh9FEzy6EuF77lo+j2gRS6TiDmxiLoWM/WELBEWrlS9GFncC2Uk1starI2RRbIWmezRMihkTaSx9cjsMXobXWATWHwcWb3U35VgXZbReEIorXkRi2xToActomNnVmBdbCTSoNnYCeD32oMxMgDOnTXU41sDW/vDFplvSejyRExc42mz8awF4rsYtTvSxoOAWuGyQrFroI4oDUQFR+8n4x629KacdQX4k+Bad+OegBVXc16zte8Fj3HCNZitu93er8V7MJtwDba1xHZGhDxokW3bZCxbU2Y7CHZ/qDfl/pe2s1AjZL2+i9HeC0MRC9xaXQM99p5JumdhIGKRbVrCIovGxnoCLsXggpoA3JpkUUssNOdig+PIwqJVfyaPWETYglum37cOClkLaOQ29pM99LbWFy/ugaibtSgrnft+8dnvwz3eGET8sTw2m8w2PJ4n6EvFXXwM8GNlm3usoIUbOsAXNetatO/ZhjJkDdjYWKTBDVoSttHY0W8FLGyRWFEQEdeo74wIV1RcbKwpWLanjgVlr707IkZRQRvMJlwj7N4brG+RDWT9FPTdUcGNCK+I1AhYVMiHelOIe9qC2NJjXbRhy8z+vsHYmBW5qIANhRJ4wgI2FHEp9qXiiHvixAvw3dA2eSg6TlGX+QkgQG2MDNDilokIG+BbYp4nSMW9+hbZkkIm4f06npK4F55RJ7oEk2eeSepY86GQNZFGXIv2IBG/9xYMMgNm4GVgP2qZAeGFOBul3sKagJ9+D+iGojcVd+ePWmQA3BpTlsFItlq0gQu+jlpkNhajX+tJaW1DszUSy9mcTSIRE9dYA36j7FxqziKpdb9FLTArEOmEzg7cYdyVQNAaCltZtjz4Xu1WHyMi2D2YdVZcvWO39qWRiEldV+Vi9dWvs+b71rfIYp64sWJO9CMu2ujvq+uztGsR0P+7vlTcWTq2A2L/7yKCwZ7w+DFnkWUbsMiSNkaWMPsB4UrFkE3UDozOhKy08IKafvq9P+sHEF6gNhELuxaj48kA3aGM14llB59dT4SNbAuIL39I99KXWtnPIwgnb5xhxgnZNZnq8Yu7N+GekemQEPSZZdzdfjoeGjDqEi4CvddMIoZ8A1oW7Jn2peNIxj330Pck40jGvJp0+VDihXMBBlxVfelQQN/Pgku7uvWn46E4zc6BDHoDq07vHEhDpDY+FRTrqIvO88SM4fLF5PQhPUmxdQXuHEjrMWVb/GPO2NKDx07MO+E+Y4v+/+zd0uuO2bulJ+SO2mv+h2eY6bQGjHifucX/35413ItU3HPisXdLD0TC//+ztuoyKyZ2dQB77ZgnOGOoJ1SXM00qv11JecdAGqm45+pij3nw6LS7N+y0X/Ye1N8zi2rV7eK0obBVmE7EsK0/VRPL08Kr/787NulEkWjSjF0hXP/m+r1toWSctItt2vdEfMtcrzkWC91rethF0v1Wm531HsyUTCEVL7n9qFtTvw6L6GA2gWTcc5ZZ1tz3wdlENplFY4P7tp6WvnT0OU1o70WgLJ3wQiJKmoOohsyGzkREbgSwZRUf3QLgxLJHtQ7WZ2lYn6VZT/VZT3UBOqs+J5RSl7SyMp3Chhay1SIi+5VS+9pdDwvrszSsz9Ksp/qsp7oArM9Gge5bQgghHQ2FjBBCmoiIfEZExkTk/gYOT4rIzSJyr4jcIiK7m17BDQCFrD5XtbsCEVifpWF9lmY91Wc91QVoTX0+B6DR2FYRwOeVUhcAeC+A9zerUhsJxsgIIaTJiMgZAL6plHqa2T8LwD8DGAaQA/CHSqkDIvIAgIuVUiOix+RMK6X621TtjoEWGSGEtJ6rAPyJUurZAN4B4OOm/B4ArzavfxNAn4gMtaF+HQXHkRFCSAsRkV4AzwdwXWD6Kjs47h0A/klEXgfgVgBHAJRbXcdOg0JGCCGtxQMwpZR6RvQNpdRRAL8FOMF7tVJqusX16zjoWiSEkBailJoB8JiIvAYARPN083qLiNh2+QoAn2lTNTuKDS1kl1xyiYKeNYp//OMf/9ryd9lll6nt27c/Fo/Hz9+9e7f69Kc/rQ4dOnThxRdffO3Tn/509dSnPrX6nve8524A6rrrrht/ylOeUjnnnHPUG97whnfn8/m/CpyrYTZo27coGzprcd++fWr//v3trgYhhKwFDc8MvkHbvkW//4a2yAghhGx8KGSEEEI6GgoZIYSQjoZCRgghpKOhkBFCCOloKGSEEEI6GgoZIYSQjoZCRgghpKOhkBFCCOloKGSEEEI6GgoZIYSQjoZCRgghG4xKdePOoVsPrkcW4Ix3/UdN2eMf+PU21IQQQlbPsZl8u6vQUmiRGeqJ2FLlhBCyXpleKKFcqba7Gi2DQkYIIRuMSlXhJ49NtLsaLYNCRgghGxBPGl6+rOOhkBFCyAYj7gmec8Zgu6vRMihkhBCywcgm44jHuqd5755vugyLZScya5EQ0ml0W/o9hczArEVCyEahoihka4aIfEZExkTk/kDZ34nIARG5V0SuF5GBwHtXiMhBEXlYRC4OlD9bRO4z731UpIuimIQQskLiXnc1kc22yD4H4JJI2U0AnqaUugDAzwFcAQAich6ASwGcbz7zcRGJmc98AsCbAJxt/qLnJIQQYohRyNYOpdStACYiZd9RSpXN7o8B7DavXwngGqVUQSn1GICDAJ4rIjsA9CulbldKKQCfB/CqZtabEEJI59DuGNkfAPiWeb0LwOHAeyOmbJd5HS2vi4i8SUT2i8j+8fHxNa4uIYSsT4JtXz5faHd1WkrbhExE/gJAGcAXbFGdw9QS5XVRSl2llNqnlNo3PDzccH2YtUgI6WSCbV8ylWx3dVpKW4RMRF4L4OUA/odxFwLa0toTOGw3gKOmfHed8jXn3G09S+4TQkgn0E2zegBtEDIRuQTAnwN4hVIqF3jrBgCXikhKRPZCJ3X8VCk1CmBWRJ5nshV/H8DX17pel3z4Fhw4Ph8qO3B8Hpd8+Ja1vhQhhDSV7pKxJi/jIiJfAnARgC0iMgLgSugsxRSAm0wW/Y+VUn+klHpARK4F8CC0y/EtSqmKOdWboTMgM9AxtW9hjYmK2HLlhBCyXumy8dDNFTKl1GV1ij+9xPHvA/C+OuX7ATxtDatGCCEblioHRBNCCOlkus21SCEzLJbYwYQPQkjH0WVKRiEz3Pj2i+pmLd749ovaUyFCCFkl0mVKRiELMJhNLrlPCCGdQJdl31PILJd98jbc/thkqOz2xyZx2Sdva1ONCCGENAKFzBAVseXKCSFkvdJlBhmFjBBCNhxdpmQUMkIIIR0Nhcxw4d7BFZUTQsi6pbvGQ1PILF+6/Pk1onXh3kF86fLnt6lGhBBCGqGpU1R1Gpe/+CmAHMLhyRz2DGZx+YvObHeVCCGELAMtMsMtB8bwv294AGOzeQxkEhibzeN/3/AAbjkw1u6qEUIIWQIKmeGTtx5CIibIJuMQ0dtETPDJWw+1u2qEEEKWgEJmODyZQyYRC5VlEjGMTOYW+QQhhKxPuizXg0Jm2TOYxUKpEipbKFWwezDbphoRQghpBAqZ4fIXnYlSRSFXLEMpvS1VFBM+CCFknUMhM1x07la89xXnY2tfGtMLJWztS+O9rzgfF527td1VI4QQsgRMvw9w0blbKVyEENJh0CIjhBDS0VDICCGEdDQUMkIIIR0NhYwQQkhHQyEjhBDS0TBrMcDbr7kTN9x7DJWqQswTvOKC7fjwpc9qd7UIIWRFdNm6mrTILG+/5k5cf/coKlU9uUulqnD93aN4+zV3trlmhBBCloJCZrjh3mMAABH/L1hOCCFkfUIhM1hLrNFyQggh6wMKmSHm1fcqL1ZOCCFkfUAhM7zigu0AAKX8v2A5IYSQ9QmFzPDhS5+F33zGDmeBxTzBbz5jB7MWCSFknUMhC7B3Sy96kjHEPEFPMoa9W3rbXSVCCCHLwHFkho9+9+f4yPcOwhMg7ulFNT/yvYMAgLe+7Jw2144QQlZAl4X2aZEZPvXDx4yIefDEM1tdTgghZP1CITPMFyuIJih6ossJIYSsXyhkhp5kDNEhY1WlywkhhKxfKGSGN75wL6oKKFerqKqq2epyQggh6xcKmeGtLzsHO/qSKFUUCmWFUkVhR1+SiR6EELLOaaqQichnRGRMRO4PlG0WkZtE5BGzHQy8d4WIHBSRh0Xk4kD5s0XkPvPeR0VkzXNyLvvkbRiZLoTKRqYLuOyTt631pQghhKwhzbbIPgfgkkjZuwDcrJQ6G8DNZh8ich6ASwGcbz7zcRGxAapPAHgTgLPNX/Scp8ztj02uqJwQQsj6oKlCppS6FcBEpPiVAK42r68G8KpA+TVKqYJS6jEABwE8V0R2AOhXSt2ulFIAPh/4DCGEkC6nHTGybUqpUQAw262mfBeAw4HjRkzZLvM6Wl4XEXmTiOwXkf3j4+NrWnFCCFmvBNu+Qr6w/Ac2EOsp2aNe3EstUV4XpdRVSql9Sql9w8PDDV/8wr2DKyonhJD1RLDtS6VT7a5OS2mHkB037kKY7ZgpHwGwJ3DcbgBHTfnuOuVrypcuf36NaF24dxBfuvz5a30pQggha0g75lq8AcBrAXzAbL8eKP+iiHwIwE7opI6fKqUqIjIrIs8D8BMAvw/gY82oGEWLEEI6j6YKmYh8CcBFALaIyAiAK6EF7FoReQOAJwG8BgCUUg+IyLUAHgRQBvAWpZSdH+rN0BmQGQDfMn+EEEJIc4VMKXXZIm+9dJHj3wfgfXXK9wN42hpWjRBCyAZhPSV7EEIIISuGQkYIIaSjoZARQshGY9EBShsTChkhhJCOph3p9+uWyz55W2huRY4jI4SQ9Q8tMkNUxAA9YTBnvyeEkPUNhczA2e8JIaQzoZARQgjpaChkhBBCOhoKmYGz3xNCSGdCITPUm/2+N+nh8hc/pU01IoQQ0ggUsgCXv/gpGO5NIh33EPeAchV455fvwS0Hxpb/MCGEkLZAIQvwtzcewMR8EYVyFeUqUChXMTFfxN/eeKDdVSOEELIIHBAd4ODYHCqBqV0UgIrS5YQQQtYntMgClKv1JyhbrJwQQkj7oZAFWEyuKGOEELJ+oZARQgjpaChkhBBCOhoKmYEp9oQQ0plQyAyfvPXQou950sKKEELIKdJtcX0KmeHwZG7R9+JUMkIIWbdQyAx7BrOLvleudFv/hhBCOgcKmeHyF5256HuUMUIIWb80JGQi8lsi8oiITIvIjIjMishMsyvXSi46d+ui71HICCFk/dLoFFUfBPAbSqmHmlkZQgghZKU06lo8vtFF7Ix3/Ue7q0AIIWQVNGqR7ReRfwfwNQAFW6iU+mpTakUIIWTVqC6LhzQqZP0AcgB+NVCmAFDICCFknaG6LLLfkJAppV7f7IoQQghZG7rNIms0a3G3iFwvImMiclxEviIiu5tdOUIIISuHQlafzwK4AcBOALsAfMOUdQWc2IMQ0ll0l5I1KmTDSqnPKqXK5u9zAIabWK91BaeoIoR0Et22FnCjQnZCRH5XRGLm73cBnGxmxdYT1W6z0wkhpINoVMj+AMBvAzgGYBTA/2vKugIRWmSEkM4h1mVepEazFp8E8Iom12Xd0l23BCGk0+m2vveSQiYi/0sp9UER+RjqRA+VUm9tWs3WEZz9nhDSSUiXdb+Xs8jstFT71/rCIvJ2AG+EFsj7ALweQBbAvwM4A8DjAH5bKTVpjr8CwBsAVAC8VSn17bWu02Ko7ronCCGko1hSyJRS3zDbq9fyoiKyC8BbAZynlFoQkWsBXArgPAA3K6U+ICLvAvAuAH8uIueZ98+HHgLwXRE5RylVWct6LVrfVlyEEELIqljOtfgNLDEgQSl1KnGzOICMiJSgLbGjAK4AcJF5/2oAtwD4cwCvBHCNUqoA4DEROQjguQBuP4XrN17RLgucEkJIJ7Gca/Hvm3FRpdQREfl7AE8CWADwHaXUd0Rkm1Jq1BwzKiJ2kbBdAH4cOMWIKatBRN4E4E0AcNppp61JfRkjI4Ssd4JtX//OxRcK3ogs51r8r2ZcVEQGoa2svQCmAFxnxqYt+pF61at3oFLqKgBXAcC+ffvWRIEYIyOErHeCbd/w3vO6qve9nGvxPiztWrxgldd9GYDHlFLj5jpfBfB8AMdFZIexxnYAGDPHjwDYE/j8bmhXZEugjhFCOgmm34d5eZOu+ySA54lIFtq1+FLozMh5AK8F8AGz/bo5/gYAXxSRD0Ene5wN4KdNqlsNjJERQsj6ZTnX4hPNuKhS6ici8mUAdwIoA7gL2iTuBXCtiLwBWuxeY45/wGQ2PmiOf0urMhYBTlFFCCHrmeVciz9USr1QRGYRdjEKAKWU6l/thZVSVwK4MlJcgLbO6h3/PgDvW+31ToWY1+hMXoQQQlrNchbZC822rzXVWZ/sHcq2uwqEEEIWoaG5FgGXabgn+Bml1J3NqNR6412/9tR2V4EQQsgiNCRkIvI3AF4H4BCAqilWAH65OdVaX1x07tblDyKEENIWGrXIfhvAWUqpYjMrQwghhKyURrMY7gcw0MyKEEIIWRu6bcBQoxbZ+wHcJSL3Q2cWAjjluRYJIYSQU6ZRIbsawN9CL7dSXebYjsQToFpnuBjHQhNCyPqmUSE7oZT6aFNr0mYW0yvqGCGErG8aFbI7ROT90FNFBV2LGyb9vp41tlQ5IYSQ9UGjQvZMs31eoGxDpd8vplfUMUIIWd80JGRKqZcs9b6IvHatV5FuNYL6okXXIiGk0+i2DvhaTSL4tjU6T9ugRUYIIZ3JWglZxxsui63f023r+hBCSKexVkLW8YbLYiu1cAUXQghZ39AiMyw2XozjyAghZH2zrJCJiCciv73MYT9ao/q0DVpkhBDSmSwrZEqpKoD/ucwxS77fCXiLmF6LlRNCyHql2zrgjboWbxKRd4jIHhHZbP+aWrMWk1jkl1isnBBCyPqg0QHRf2C2bwmUKQBnrm112ke+XL8Ls1g5IYSQ9UGjA6L3NrsihBBCyGpoyHEmIlkR+UsRucrsny0iL29u1QghhJDlaTQC9FkABusKmgAAHYZJREFURQDPN/sjAP5PU2pECCHklKh02WznjQrZWUqpDwIoAYBSagEbYOwYIYRsREqVDbls5KI0KmRFEcnAzOAhImchsJwLIYSQ9UO1y/LvG81avBLAjQD2iMgXALwAwOuaVSlCCCGrJx7rrnFDjWYt3iQid0KvRyYA3qaUOtHUmq0jbjkwhovO3druahBCSEMkumwih5XI9osBvBTASwD8P82pzvrkb2880O4qEEJIw3Tbqh2Npt9/HMAfAbgPwP0ALheRf25mxdYTh07Mt7sKhBBCFqHRGNmLATxNKWWTPa6GFrWuoNsCp4QQ0kk06lp8GMBpgf09AO5d++q0j6V8ytUuG5NBCCGdRKNCNgTgIRG5RURuAfAggGERuUFEbmha7VrIUlZXRemED0IIIeuPRl2L/7uptVgHVJYxut56zV24968vbk1lCCGENEyj6ff/tdT7InK7UurCtanS+mQmX253FQghhNRhrUbNpdfoPIQQQsiKWCshYzYEIYSsE7ot0bq75jEhhJAuoNRlmdZrJWRdNo6cEELWL2XOfl+LiPSIiGdenyMirxCRROCQ31vphUVkQES+LCIHROQhEblQRDaLyE0i8ojZDgaOv0JEDorIwyLSlvTBWw6M4bKrfowX/u33cNlVP2ZKPiFkXcL1yOpzK4C0iOwCcDOA1wP4nH1TKXX/Kq79EQA3KqXOBfB0AA8BeBeAm5VSZ5vrvAsAROQ8AJcCOB/AJQA+LiKxVVzzlPjjL96J2w+dxMjkAm4/dBJ//MU7KWaEkHWHAlAsd49V1qiQiVIqB+C3AHxMKfWbAM5b7UVFpB/AiwB8GgCUUkWl1BSAVwK42hx2NYBXmdevBHCNUqqglHoMwEEAz13t9VdLrlip2X/Hl+9pdTUIIWRJBECsi2bAb1jIRORCAP8DwH+YskYHU9fjTADjAD4rIneJyKdEpAfANqXUKACYrV07ZReAw4HPj5iyehV9k4jsF5H94+Pjp1DFxjgxV2z6NQghZDmCbZ8HRSGrw9sAXAHgeqXUAyJyJoDvn8J14wCeBeATSqlnApiHcSMuQr3/SF0nsFLqKqXUPqXUvuHh4VOoIiGEdA7Bti+ROBU7o/NodGaPW6HjZHb/EIC3nsJ1RwCMKKV+Yva/DC1kx0Vkh1JqVER2ABgLHL8n8PndAI6ewvUJIWTDUmLWYi0mU/EqEfmOiHzP/q32okqpYwAOi8gvmKKXQk9EfAOA15qy1wL4unl9A4BLRSQlInsBnA3gp6u9/lpz1rv/Exf89bfx0e/+vN1VIYQQVKoK+VJl+QM3CI3an9cB+BcAnwKwVr/OnwD4gogkARyCzoT0AFwrIm8A8CSA1wCAcWdeCy12ZQBvUUqtm/9Spaowky/jw999BADw1ped0+YaEUK6nWKlinSi5cndbaFRISsrpT6xlhdWSt0NYF+dt166yPHvA/C+tazDWqMAfOz7BylkhJC2U15uSY8NxJJCJiKbzctviMgfA7geQMG+r5SaaGLdOpJSF908hJD1S3+6exI+lvumd0AbGjZr8J2B9xR0Gj0hhJB1RCLmIR7rnql0lxQypdReABCRtFIqH3xPRLh0CyGErENi0j1jyIDGx5Hd1mAZIYSQdtNdOrZsjGw79AwaGRF5Jvyfpx9Atsl1I4QQQpZluRjZxQBeBz0A+UOB8lkA725SnQghhJwCXWaQLRsjuxrA1SLyaqXUV1pUJ0IIIaRhGp2i6isi8uvQy6ikA+XvbVbFCCGEkEZodIqqfwHw36Fn4xDoGTdOb2K9CCGEkIZoNGvx+Uqp3wcwqZR6D4ALEZ7ElxBCCGkLjQrZgtnmRGQngBKAvc2pEiGEENI4jc5h8k0RGQDwdwDuhJ7V41NNqxUhhJBV02XjoRtO9vgb8/IrIvJNAGml1HTzqkUIIYQ0RqPJHlkR+SsR+VelVAHAVhF5eZPrRgghhCxLozGyz0LPen+h2R8B8H+aUiNCCCFkBTQqZGcppT4IneQBpdQCum/wOCGEdATd1jg3KmRFEclAJ3lARM5CYF0yQggh64nukrJGsxavBHAjgD0i8gUAL4Ceg5EQQghpK41mLd4kIncCeB601L9NKXWiqTUjhBBCGmC5ZVyeFSkaNdvTROQ0pdSdzakWIYQQ0hjLWWT/YLZpAPsA3ANtkV0A4CcAXti8qhFCCCHLs2Syh1LqJUqplwB4AsCzlFL7lFLPBvBMAAdbUUFCCCEro9tm9mg0a/FcpdR9dkcpdT+AZzSnSoQQQk6FqlLtrkJLaTRr8SER+RSAf4NOwf9dAA81rVaEEEJWTalCIavH6wG8GcDbzP6tAD7RlBoRQgg5JcqVarur0FIaTb/PA/iw+atBRL6ilHr1WlaMEELI6pAuC5I1GiNbjjPX6DyEEEJOEa+7dGzNhKy7HLKEELKOoUVGCCGko6FFtjq67GcjhJD1i3RZk7xWQvbna3QeQgghp0p36diycy3eh/rxLwGglFIXQL/4ThPqRgghZBV0mY4tm37/8pbUghBCCFklSwqZUuqJVlWEEELI2tBlSYuNxchE5Hki8jMRmRORoohURGSm2ZUjhBCycrpMxxpO9vgnAJcBeARABsAbAXysWZUihBByKnSXlDWctaiUOgggppSqKKU+C+Alp3pxEYmJyF0i8k2zv1lEbhKRR8x2MHDsFSJyUEQeFpGLT/XahBCyUaFrsT45EUkCuFtEPigibwfQswbXfxvCs+i/C8DNSqmzAdxs9iEi5wG4FMD5AC4B8HERia3B9QkhhHQ4jQrZ75lj/yeAeQB7APzWqVxYRHYD+HUAnwoUvxLA1eb11QBeFSi/RilVUEo9Br2o53NP5fqEELJRUV22HlmjQvYqpVReKTWjlHqPUurPcOqp+f8I4H8BCK43sE0pNQoAZrvVlO8CcDhw3Igpq0FE3iQi+0Vk//j4+ClWkRBCOoNg21coldtdnZbSqJC9tk7Z61Z7URF5OYAxpdQdjX6kTlndLodS6iql1D6l1L7h4eHVVpEQQjqKYNsnXndFXpab2eMyAL8DYK+I3BB4qx/AyVO47gsAvEJE/huANIB+Efk3AMdFZIdSalREdgAYM8ePQLszLbsBHD2F6xNCyIYl3mWzBi83s8dtAEYBbAHwD4HyWQD3rvaiSqkrAFwBACJyEYB3KKV+V0T+Dtr6+4DZft185AYAXxSRDwHYCeBsAD9d7fUJIWQjQyELYGb2eALAhSKyDcBzzFsPKaWa4YT9AIBrReQNAJ4E8BpTjwdE5FoADwIoA3iLUqrShOsTQkjHU+2uXI9lLTIAgIi8BsDfA7gFOl71MRF5p1Lqy6daAaXULea8UEqdBPDSRY57H4D3ner1CCFko1PpMiVrSMgA/CWA5yilxgBARIYBfBfAKQsZIYSQtUXVz4XbsDSatehZETOcXMFnCSGEtJBuW1izUYvsWyLybQBfMvv/HcB/NqdKhBBCToVCuYKFYgWZZHek4TdqVSkAnwRwAYCnA7iqaTUihBBySigAPzx4ot3VaBmNCtmvKKW+qpT6M6XU25VS1wP4tWZWjBBCyOp5dHyu3VVoGcsNiH4zgD8GcKaIBMeN9QH4UTMrRgghZPXsGcy2uwotY7kY2RcBfAvA+2FmojfMKqUmmlYrQgghp8TeLWuxQElnsNyA6GkA09CLahJCCOkQztjSPRYZU+gJIWSDEfcE2WSjSemdD4WMEEI2GF6XLRFNISOEENLRUMgIIYR0NBQyQgghHQ2FjBBCNhhVxUmDCSGEdDDlquqqpVwoZIQQsgGZWSi1uwotg0JGCCEbkJ4Ux5ERQgjpUDwRJOPd07x3zzclhJAuoaoUqoyREUII6WSmGSMjhBDSycwVyu2uQsugkBFCyAakWKm2uwotg0JGCCEbkFgXTRxMISOEkA3IzoFMu6vQMihkhBCywYh7TL8nhBBCOgYKGSGEbDC6bM5gCtlak4x1T4CVELI+6aI8DwAUsjWnXOmyrhAhZN0hXaZkFLI1pntGbhBC1iuVqoLqIv8ihYwQQjYYVaUwPltodzVaBoWMEEI2IJxrkRBCSEezbVO63VVoGRQyQgjZYAiA/nSi3dVoGRSyNSaT4E9KCGkvCmCyB1k9ca+70l4JIeuTQrl7cqgpZGsMh5ERQtYDKc612FxEZI+IfF9EHhKRB0TkbaZ8s4jcJCKPmO1g4DNXiMhBEXlYRC5uR70bIV+stLsKhJAuJybSVYOi2yXZZQD/n1LqqQCeB+AtInIegHcBuFkpdTaAm80+zHuXAjgfwCUAPi4isbbUfBlokBFC2k1FKXz97iPtrkbLaIuQKaVGlVJ3mtezAB4CsAvAKwFcbQ67GsCrzOtXArhGKVVQSj0G4CCA57a21o1BISOEtJtkzMNfXn8/jkwttLsqLaHtTlQROQPAMwH8BMA2pdQooMUOwFZz2C4AhwMfGzFl9c73JhHZLyL7x8fHm1VtQghZVwTbvlglj9lCGQ8enWl3tVpCW4VMRHoBfAXAnyqllvrF6zl76xo/SqmrlFL7lFL7hoeH16KahBCy7gm2fZV4Gtv703jROVvaXa2W0DYhE5EEtIh9QSn1VVN8XER2mPd3ABgz5SMA9gQ+vhvA0VbVdSUw+54Q0m6K5Spe94IzkIqvy1SCNaddWYsC4NMAHlJKfSjw1g0AXmtevxbA1wPll4pISkT2AjgbwE9bVd+VwHFkhJD1wHBvqt1VaBnxNl33BQB+D8B9InK3KXs3gA8AuFZE3gDgSQCvAQCl1AMici3+//buPTiu8rzj+PfZXUmWbSy7yNjGxrXBxMHkwsUDDbjBE0NDQgpNaQecthMoLWmGACHT6ZDMZJJ0SggzmaRD06RxuZkSYAgkLYOBJFxcoIONjQkYfAsYbAtsZDv4IgOSdvfpH+estV4keSWv9J6z+/vM7OzZs2fPPrKl/Z33Pe++B9YRjXi8yt0TOc692EDfpheR5OotNM4XooMEmbs/Q//nvQAWDfCaG4AbRqyoGmmk726ISHK1NjdGtyIkYNRivVGMiUgSTGjVpMEyTHnNUSUiCaApqmTYFGMiIqNLQVZjCjIRSYLu3sYZ7KEgExGpQzv3d4cuYdQoyGpMXyMTkSTYdUBBJsPUSCdYRSS5Mg30VSB96taYZvYQkSRopM8iBVmNafS9iCRBm75HJsPVk0/kzFki0mAmH6W5FmWYNEWViPTn5sc2ccszr3Ogp8C45ix/t2A215z7oRF7v+YGOl+vIKsxxZiIVLr5sU384LHfHXy87/38wccjFWb6HpkMm6aoEpFKPywLsWrW18JLHXtHbN9JoyCrMVeTTEQqDHR4O1KHvS25DHc/t6VhLuWiIKuxrIJMRAIb15Lj7X3dvLB1T+hSRoWCrMamThgTugQRaXCl4+lGGfDRGD+liEgDKcZ9lhMb5LtkGrVYAwaYRfc7u3pClyMiDS5fjM6NTW1rjB4iBVkNjGmKLileKDbGiVURSbZC0Zk6YczBz6Z6p67FGnivt8D7vQUKRWf20WNDlyMiDc4w9r/fq1GLMjRONM/iZz86LXQpItLgWpoyHOgp8OI2jVqUITLgkZd3hC5DRBpcabRiowy/1zmyGnLg1c6u0GWISIPLmtE2tonXdx8IXcqoUIusxvJFTVElIuG1tTbxyNrtHOjOhy5lxCnIaiwJMbZ8QyeLl6xgwU1PsHjJCpZv6AxdkoiMsnPnTWHPu738y7L1oUsZcQqyGgt9UdblGzq5Yukqnt28m4533uPZzbu5YukqhZlIgzm+fTwLTmznnue2snLz7tDljCidIytzzP7d/MPKB+jN5ihkMvRmchQyWfKZ7MHl3mw2us9kyZfWZeJ12RyWy8ITrZDLRbempuhWWj7cusyRHVt86b9Wf+Aq1QWP1m+84bNHtG8RSZdzT5rC2jf3cueKLZx5/NGhyxkxCrIyk9/dw98+/+CR7+jeI3it2QdDrtoQzOX4yWvv9AVtNkc+k6VgUQDzzkP9v7a//Q623RBrOuQ+m41+RhEZcU3ZDJ8/dTp3rdjCnnd7mDi2OXRJI0JBVqaruZXfzDmTXDFPtlikqZgnWyzQVCiQK+bJFQvxLV4uFGgq5sl4kVyxeHB9c7EAPsyzZe7Q0xPdhuFTgz354rB2WXvVBGO1IVv++uGG63BqGGydgloSZNvv36O34Ny3uoPxLTm+cObM0CXVnIKszJZJx/L3F3/ziPaRMdh84wVQKEA+D729ffeDLZdvX/58f9sPsr8lj28kVyyQ9SiAs3HwNhULXHjyMdXvr3Jd5fojmY6rtL96lc32BWM2O3DgVhOahwvX5uZDX1NNWA836Ev32caY9qhebNyxj/bxzYxpqt8hEQqyGsuVRntks9GtpWVU3/+71y8b8LkLv3dB7d6oWDx80B4uDIe6zVD3N5QDgsoDjyMJ2kIhunV31+7fO0nMDg3FysDt7/FQWuCHC9Lm5oEDt3L7UtCXB/7hWvl11v2dLzq7unr47sPrmTetjWkTx3D2Ce11dYkXBVmN5StHWtSrTCb6cGiuzz533KMwqiYMhxK+Q21t9/T0v11Pz6H1lcJ4oPoqb729R9b9XXqfelXemq5sXVfel4fvAC3wf1u3Mxo0ls3yblMrNy68nO6m0TnI/fLCE3its4u1b+5l3fa9XH77Ktpam/j0yVO46JTpnD2nfVTqGEkKshprkBirf+WtjtbW0NWMjMru7Gpa2IM939PT91x5AJcCuTxsS89X2/0+0EFF+c9QWi7dCoXh/9uU9lGjVvWfVjy+6ZzLarLfauQyGeZOncDcqRPIF4pMn9TKspe288jaHdy3uoOHrl7AR6a3jVo9I0FBVmMKMkmNQN3fo6aa7u+BgniwW2VLudQ6Lg/riv0/uHrLwcFipa/qhJDLZlh00hQWnTSF5Rs7uez2VXTn0z9DvoJMROpTgrq/rxnk3PVou3vlVnoLRZat3Q7AE+s72bhjf+CqBlbNKEsFmYhIA9my+wAPrHmTXV3dnD5zEtMmpv8q0qkatmJm55vZRjN71cyuD11P2sxK0FGhiIyennyRdW/t5d5VW1ny1GbyxSKXnTWLi0+fQaYORmimpkVmZlng34HzgA5glZk96O7rwlaWLrOuX8YbtRyGLyKJ9F5PgQ079rFu+z42vb2f3oIzpinDWScczbnzptCSq5/vA6YmyIAzgFfdfTOAmd0LXAQoyEREyuzq6uaGh9dRdDhqTI7TZk7i5GPbmN0+jmzomc1HQJqCbDqwrexxB3BmoFpERBKrJ19kwZx25h3bxoxJrXXRfTiYNAVZf/8THxjtbmZXAlcCzJxZf3OKiYj0p/yzr2XqHG6//Iy6bH31J02DPTqA48oezwDeqtzI3Ze4+3x3nz958uRRK05EJKTyzz4H1m/fF7qkUZOmIFsFnGhms82sGbgUqME1V0RE6svs9nHMOWZ86DJGTWqCzN3zwFeAXwHrgfvc/ZVa7b9WI/lCjwg83PuHrk+kEQ30dzdSf4/jW3KMaaqfUYmHYz7ciUNTYP78+b569erQZYiI1ELVJ7zq9LNvwJ8/NS0yERGR/ijIREQk1RRkIiKSagoyERFJNQWZiIikmoJMRERSTUEmIiKppiATEZFUU5CJiEiqKchERCTVFGQiIpJqdT3XopntBLYM46XtwK4al3MkVM/gVM/gklRPkmqBdNWzy93Pr2YnZvZotdvWg7oOsuEys9XuPj90HSWqZ3CqZ3BJqidJtYDqqRfqWhQRkVRTkImISKopyPq3JHQBFVTP4FTP4JJUT5JqAdVTF3SOTEREUk0tMhERSTUFmYiIpJqCrIyZnW9mG83sVTO7PgH13GZmnWb2cgJqOc7MnjSz9Wb2ipldG7ieMWb2nJm9GNfznZD1lJhZ1sxeMLOHElDLG2a21sx+a2arE1DPRDO738w2xL9HnwhYy9z436V022dmXw1VT1zTdfHv8stmdo+ZjQlZT5roHFnMzLLAJuA8oANYBSx293UBa/ok0AXc6e4fCVVHXMs0YJq7rzGzo4DngT8L9e9jZgaMc/cuM2sCngGudfcVIeopq+trwHxggrt/LnAtbwDz3T0RX/g1s6XA0+5+i5k1A2PdfU8C6soCbwJnuvtwJlCoRQ3TiX6H57n7e2Z2H/Cwu98Rop60UYuszxnAq+6+2d17gHuBi0IW5O5PAb8PWUOJu2939zXx8n5gPTA9YD3u7l3xw6b4FvSozMxmABcAt4SsI4nMbALwSeBWAHfvSUKIxRYBr4UKsTI5oNXMcsBY4K3A9aSGgqzPdGBb2eMOAn5QJ5mZzQJOBVYGriNrZr8FOoHfuHvQeoB/Bf4JKAauo8SBX5vZ82Z2ZeBajgd2ArfHXa+3mNm4wDWVXArcE7IAd38T+D6wFdgO7HX3X4esKU0UZH2sn3Xqd61gZuOBB4Cvuvu+kLW4e8HdTwFmAGeYWbDuVzP7HNDp7s+HqqEfZ7v7acBngKvirupQcsBpwE/c/VTgAJCE89DNwIXAzwPXMYmoB2g2cCwwzsz+OmRNaaIg69MBHFf2eAZq2h8iPhf1APAzd/9F6HpK4i6q5UDISVLPBi6Mz0vdC3zKzO4KWA/u/lZ83wn8kqj7PJQOoKOs1Xw/UbCF9hlgjbu/HbiOc4HX3X2nu/cCvwDOClxTaijI+qwCTjSz2fFR2qXAg4FrSox4cMWtwHp3/0EC6plsZhPj5VaiD4INoepx96+7+wx3n0X0u/OEuwc7ojazcfGgHOIuvD8Bgo1+dfcdwDYzmxuvWgQEG0hVZjGBuxVjW4E/MrOx8d/aIqLz0FKFXOgCksLd82b2FeBXQBa4zd1fCVmTmd0DLATazawD+Ja73xqonLOBvwHWxuelAL7h7g8HqmcasDQecZYB7nP34EPeE2QK8MvoM5EccLe7Pxq2JK4GfhYfKG4GLg9ZjJmNJRql/KWQdQC4+0ozux9YA+SBF9B0VVXT8HsREUk1dS2KiEiqKchERCTVFGQiIpJqCjIREUk1BZmIyAgayuTfZvaHZva4mb1kZsvjac/kMBRkIsNkZrMO9+EUb/OFssfzzezmka9OEuQOqv+y/veJJgn/GPDPwI0jVVQ9UZCJjKxZwMEgc/fV7n5NuHJktPU3+beZnWBmj8bzYD5tZh+On5oHPB4vP0ngicvTQkEmdStuDW0ws6VxV8398cwJi+KJa9fG3T4t8fZvmNlN8XXOnjOzOfH6O8zsL8r22zXAez1tZmviW2l6oe8Bfxxf8+o6M1tYulaZmf2Bmf13XNsKM/tYvP7bcV3LzWyzmSn46s8S4Gp3Px34R+DH8foXgYvj5c8DR5nZ0QHqSxUFmdS7ucCSuKtmH/A1oq6eS9z9o0SzXny5bPt97n4G8COi2eyr1QmcF0/SewlQ6j68nugaXKe4+w8rXvMd4IW4tm8Ad5Y992Hg00TzI34rnudS6kA88fZZwM/jWXJ+SjRTDUShdo6ZvQCcQ3SdtHyQQlNEU1RJvdvm7v8XL98FfJNoctZN8bqlwFX0hdY9ZfeVwTOYJuBHZnYKUAA+VMVrFhAffbv7E2Z2tJm1xc8tc/duoNvMOommnOoYQj2SXBlgT3zlhkPEEz3/ORwMvIvdfe8o15c6apFJvRvqHGzez3Ke+G8lntC1uZ/XXQe8DXyc6ArR/W1TabBLB3WXrSugg866EV/+6HUz+0uIfqfM7OPxcruZlT6Xvw7cFqjMVFGQSb2baWafiJcXA48Bs0rnv4gmQv7fsu0vKbt/Nl5+Azg9Xr6IqPVVqQ3Y7u7FeJ/ZeP1+4KgBansK+CsAM1sI7Ap9jTepvXjy72eBuWbWYWZXEP2/X2FmLwKv0DeoYyGw0cw2EbXCbwhQcuroKE/q3Xrgi2b2U+B3wLXACqLzEzmiy/f8R9n2LWa2kuggb3G87j+B/zGz54hGlB3o531+DDwQH2U/WbbNS0A+/sC6g2hW85JvE10x+SXgXeCLR/ajShK5++IBnvrAkHx3v5/oWm0yBJr9XuqWmc0CHnL3qq4cHV8Uc7677xrBskSkxtS1KCIiqaYWmYiIpJpaZCIikmoKMhERSTUFmYiIpJqCTEREUk1BJiIiqfb/6wWe5blLprQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Group-wise averages:\")\n",
    "print(df.groupby(\"location\")[\"population\", \"population_density\",\n",
    "                            \"total_cases_per_million\",\n",
    "                            \"total_deaths_per_million\",\n",
    "                            \"new_cases_per_million\",\n",
    "                            \"new_deaths_per_million\",\n",
    "                            \"gdp_per_capita\",\n",
    "                            \"hospital_beds_per_thousand\"].mean())\n",
    "\n",
    "# df_temp = df.groupby(\"location\")[\"population\", \"population_density\",\n",
    "#                             \"total_cases_per_million\",\n",
    "#                             \"total_deaths_per_million\",\n",
    "#                             \"new_cases_per_million\",\n",
    "#                             \"new_deaths_per_million\",\n",
    "#                             \"gdp_per_capita\",\n",
    "#                             \"hospital_beds_per_thousand\"].mean()\n",
    "# #drop the rows contain missing values\n",
    "# df_temp.dropna(axis=0, how='any', inplace=True)\n",
    "\n",
    "# pd.crosstab(df_temp.population_density, \n",
    "#             df_temp.total_cases_per_million,margins = True)\n",
    "\n",
    "##############\n",
    "df_temp = df.loc[:,[\"location\", \"gdp_per_capita\", \"total_deaths_per_million\"]]\n",
    "#drop the rows contain missing values\n",
    "df_temp.dropna(axis=0, how='any', inplace=True)\n",
    "\n",
    "arr_temp = np.array(df.groupby(\"location\")[\"gdp_per_capita\", \"total_deaths_per_million\"].mean())\n",
    "\n",
    "#remove outliers and sort\n",
    "arr_temp = arr_temp[arr_temp[:,1].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "arr_temp = arr_temp[arr_temp[:,0].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "\n",
    "f1 = plt.figure()\n",
    "ax1 = f1.add_subplot(111)\n",
    "ax1.set_title('gdp_per_capita vs. total_deaths_per_million')\n",
    "plt.xlabel('gdp_per_capita')\n",
    "plt.ylabel('total_deaths_per_milliond')\n",
    "ax1.scatter(arr_temp[:,0],arr_temp[:,1],marker = '.')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# l1=plt.plot(arr_temp[:,0],arr_temp[:,1],'b')\n",
    "# plt.title('gdp_per_capita vs. total_deaths_per_million')\n",
    "# plt.xlabel('gdp_per_capita')\n",
    "# plt.ylabel('total_deaths_per_milliond')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "np.random.seed(0)\n",
    "g = sns.jointplot(x=\"gdp_per_capita\", y=\"total_deaths_per_million\", data=df_temp, kind='reg',\n",
    "                  joint_kws={'line_kws':{'color':'red'}})\n",
    "\n",
    "##############\n",
    "df_temp = df.loc[:,[\"location\", \"hospital_beds_per_thousand\", \"total_deaths_per_million\"]]\n",
    "#drop the rows contain missing values\n",
    "df_temp.dropna(axis=0, how='any', inplace=True)\n",
    "\n",
    "arr_temp = np.array(df.groupby(\"location\")[\"hospital_beds_per_thousand\", \"total_deaths_per_million\"].mean())\n",
    "\n",
    "#remove outliers and sort\n",
    "arr_temp = arr_temp[arr_temp[:,1].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "arr_temp = arr_temp[arr_temp[:,0].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "\n",
    "f1 = plt.figure()\n",
    "ax1 = f1.add_subplot(111)\n",
    "ax1.set_title('hospital_beds_per_thousand vs. total_deaths_per_million')\n",
    "plt.xlabel('hospital_beds_per_thousand')\n",
    "plt.ylabel('total_deaths_per_milliond')\n",
    "ax1.scatter(arr_temp[:,0],arr_temp[:,1],marker = '.')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# l1=plt.plot(arr_temp[:,0],arr_temp[:,1],'b')\n",
    "# plt.title('hospital_beds_per_thousand vs. total_deaths_per_million')\n",
    "# plt.xlabel('hospital_beds_per_thousand')\n",
    "# plt.ylabel('total_deaths_per_milliond')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "g = sns.jointplot(x=\"hospital_beds_per_thousand\", y=\"total_deaths_per_million\", data=df_temp, kind='reg',\n",
    "                  joint_kws={'line_kws':{'color':'red'}})\n",
    "\n",
    "##############\n",
    "df_temp = df.loc[:,[\"location\", \"gdp_per_capita\", \"hospital_beds_per_thousand\"]]\n",
    "#drop the rows contain missing values\n",
    "df_temp.dropna(axis=0, how='any', inplace=True)\n",
    "\n",
    "#df_temp[(np.abs(stats.zscore(df)) < 3).total_cases_per_million(axis=1)]\n",
    "\n",
    "arr_temp = np.array(df.groupby(\"location\")[\"gdp_per_capita\",\"hospital_beds_per_thousand\"].mean())\n",
    "\n",
    "#remove outliers and sort\n",
    "arr_temp = arr_temp[arr_temp[:,1].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "arr_temp = arr_temp[arr_temp[:,0].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "\n",
    "f1 = plt.figure()\n",
    "ax1 = f1.add_subplot(111)\n",
    "ax1.set_title('gdp_per_capita vs. hospital_beds_per_thousand')\n",
    "plt.xlabel('gdp_per_capitan')\n",
    "plt.ylabel('hospital_beds_per_thousand')\n",
    "ax1.scatter(arr_temp[:,0],arr_temp[:,1],marker = '.')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# l1=plt.plot(arr_temp[:,0],arr_temp[:,1],'b')\n",
    "# plt.title('gdp_per_capita vs. hospital_beds_per_thousand')\n",
    "# plt.xlabel('gdp_per_capita')\n",
    "# plt.ylabel('hospital_beds_per_thousand')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "g = sns.jointplot(x=\"gdp_per_capita\", y=\"hospital_beds_per_thousand\", data=df_temp, kind='reg',\n",
    "                  joint_kws={'line_kws':{'color':'red'}})\n",
    "\n",
    "##############\n",
    "df_temp = df.loc[:,[\"location\", \"total_cases_per_million\", \"total_deaths_per_million\"]]\n",
    "#drop the rows contain missing values\n",
    "df_temp.dropna(axis=0, how='any', inplace=True)\n",
    "\n",
    "#df_temp[(np.abs(stats.zscore(df)) < 3).total_cases_per_million(axis=1)]\n",
    "\n",
    "arr_temp = np.array(df.groupby(\"location\")[\"total_cases_per_million\",\"total_deaths_per_million\"].max())\n",
    "\n",
    "#remove outliers and sort\n",
    "arr_temp = arr_temp[arr_temp[:,1].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "arr_temp = arr_temp[arr_temp[:,0].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "\n",
    "f1 = plt.figure()\n",
    "ax1 = f1.add_subplot(111)\n",
    "ax1.set_title('total_cases_per_million vs. total_deaths_per_million')\n",
    "plt.xlabel('total_cases_per_million')\n",
    "plt.ylabel('total_deaths_per_million')\n",
    "ax1.scatter(arr_temp[:,0],arr_temp[:,1],marker = '.')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# l1=plt.plot(arr_temp[:,0],arr_temp[:,1],'b')\n",
    "# plt.title('total_cases_per_million vs. total_deaths_per_million')\n",
    "# plt.xlabel('total_cases_per_million')\n",
    "# plt.ylabel('total_deaths_per_milliond')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "g = sns.jointplot(x=\"total_cases_per_million\", y=\"total_deaths_per_million\", data=df_temp, kind='reg',\n",
    "                  joint_kws={'line_kws':{'color':'red'}})\n",
    "\n",
    "##############\n",
    "df_temp = df.loc[:,[\"location\", \"new_cases_per_million\",\"new_deaths_per_million\"]]\n",
    "#drop the rows contain missing values\n",
    "df_temp.dropna(axis=0, how='any', inplace=True)\n",
    "\n",
    "arr_temp = np.array(df.groupby(\"location\")[\"new_cases_per_million\",\"new_deaths_per_million\"].max())\n",
    "#remove outliers and sort\n",
    "arr_temp = arr_temp[arr_temp[:,1].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "arr_temp = arr_temp[arr_temp[:,0].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "\n",
    "f2 = plt.figure()\n",
    "ax2 = f2.add_subplot(111)\n",
    "ax2.set_title('new_cases_per_million vs. new_deaths_per_million')\n",
    "plt.xlabel('new_cases_per_million')\n",
    "plt.ylabel('new_deaths_per_million')\n",
    "ax2.scatter(arr_temp[:,0],arr_temp[:,1],marker = '.')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# l1=plt.plot(arr_temp[:,0],arr_temp[:,1],'b')\n",
    "# plt.title('new_cases_per_million vs. new_deaths_per_million')\n",
    "# plt.xlabel('new_cases_per_million')\n",
    "# plt.ylabel('new_deaths_per_million')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "g = sns.jointplot(x=\"new_cases_per_million\", y=\"new_deaths_per_million\", data=df_temp, kind='reg',\n",
    "                  joint_kws={'line_kws':{'color':'red'}})\n",
    "\n",
    "##############\n",
    "df_temp = df.loc[:,[\"location\", \"total_deaths_per_million\",\"population\"]]\n",
    "#drop the rows contain missing values\n",
    "df_temp.dropna(axis=0, how='any', inplace=True)\n",
    "\n",
    "arr_temp = np.array(df.groupby(\"location\")[\"total_deaths_per_million\",\"population\"].max())\n",
    "#remove outliers and sort\n",
    "arr_temp = arr_temp[arr_temp[:,1].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "arr_temp = arr_temp[arr_temp[:,0].argsort()]\n",
    "for i in range(5):\n",
    "    arr_temp = np.delete(arr_temp, arr_temp.shape[0]-1, axis = 0)\n",
    "    arr_temp = np.delete(arr_temp, 0, axis = 0)\n",
    "\n",
    "f3 = plt.figure()\n",
    "ax3 = f3.add_subplot(111)\n",
    "ax3.set_title('population vs. total_deaths_per_million')\n",
    "plt.xlabel('population')\n",
    "plt.ylabel('total_deaths_per_million')\n",
    "ax3.scatter(arr_temp[:,0],arr_temp[:,1],marker = '.')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# l1=plt.plot(arr_temp[:,0],arr_temp[:,1],'b')\n",
    "# plt.title('population vs. total_deaths_per_million')\n",
    "# plt.xlabel('population')\n",
    "# plt.ylabel('total_deaths_per_million')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "g = sns.jointplot(x=\"population\", y=\"total_deaths_per_million\", data=df_temp, kind='reg',\n",
    "                  joint_kws={'line_kws':{'color':'red'}})\n",
    "\n",
    "del df_temp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Section: 2.6 - Identify and explain any interesting relationships between the class attribute and the other attributes.\n",
    "In this section, I will identify and explain the relationships between the class attribute NEW_CASE_TREND and new_cases_per_million, and the relationships between NEW_CASE_TREND and total_cases_per_million. And I will still take the data of the UK/US/World for example and I refer to the graphs of New cases per million in the UK/US/World drawn in section 2.3. \n",
    "\n",
    "Let’s recall the situation of the New cases per million graphs. From the graphs of New cases per million in the US/UK/World, we can see the number of new cases ballooned between March to April. And then the US data peaked in April and May and then became steady until July. And then the new cases of the US ballooned again and reached a higher peak during the mid-July and August. From the graph of the UK, we see that the number of new cases peaked during April and May, and then it rapidly decreased until July and ballooned again in September. In the graph of the World, the number of new cases is overall upward since March.\n",
    "\n",
    "The values of NEW_CASE_TREND of the US are FLATs before 3/15/2020, and the values between 3/15/2020 and 6/26/20 are “GROWING”, and then became “GROWING_FAST” until 9/9/20, the last recorded day of the dataset. The values of NEW_CASE_TREND of the UK are FLATs before 3/11/2020, and then the values became “GROWING” until 9/9/20, the last recorded day of the dataset. The values of NEW_CASE_TREND of the World are FLATs before 3/16/2020, and then the values became “GROWING” until 9/9/20, the last recorded day of the dataset. The values of NEW_CASE_TREND of the US/UK/World share a similar feature – all of them became “GROWING” in March, which corresponding to the trend of new cases per million in the US/UK/World in March. But after March, the patterns of NEW_CASE_TREND do not correspond exactly with the values of new_cases_per_million except the data of the World. \n",
    "\n",
    "Notice that the values of new_cases_per_million are not accumulated values. They are the number of daily new cases. With this in mind, no matter what’s the trend of new_cases_per_million, the corresponding accumulated value would continue to increase as long as the values of new_cases_per_million larger than zero. And the trends of the corresponding accumulated value might be more fitting with the patterns of NEW_CASE_TREND. We can easily associate new_cases_per_million with total_cases_per_million whose values are accumulated value. So I generate the graphs of total_cases_per_million in UK/US/World, and we can see that the trends of total_cases_per_million in UK/US/World indeed fit with the patterns of NEW_CASE_TREND. So we can conclude that NEW_CASE_TREND reflect the trend of total_cases_per_million or the trend of total COVID-19 cases.\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Y8HvgFUm1gfyOWO6lVYXN+2Wh2VAzyzCzjAYNGhQjbOecKz1PfvIkv3v7d5y272m8ePaLVNolPbtGKvVEIqkycA4wMrfMzLaY2eowPQtYAOxLdAaSeClSE2BpmM4CmiZssw4FVKU551xZM+CjAfxuXJREXjv/NapWqhp3SAWK44zkJGCumf1UZSWpgaRKYbolUaP6/8xsGbBeUsfQ/tEDGB1WGwP0DNPnAe9bWb0RxDnnEgz4aAC3TbyNiw66iFEXjGLXyul9j1jKEomk4cA0YD9JWZJ6hVnd+XUj+3HAZ5L+Q9Rwfq2Z5Z5dXAc8A8wnOlMZH8r/AdSTNJ+oOuy2VL0W55wrLWPmjfkpibx49oul2h38zlJF+xGfkZFhmZmZcYfhnHO/MmfFHDr+oyP719+fKZdPYbcqu8Ud0k8kzTKzjPzmeRcpzjmXBlZtWsUZI86gZtWavHnhm2mVRIriXaQ451zMtmRv4awRZ/HtD98y+fLJpT4wVXF5InHOuRiZGb3G9OLjJR8z4twRdGzSMe6QdphXbTnnXIzunXIvL3/+Mv1/258LD7ow7nB2iicS55yLyXOfPsc9k++h56E9uePYO+IOZ6d5InHOuRg8Ov1RrhxzJSe1PImhpw9Ny65PkuWJxDnnStlznz7Hje/cyDkHnMNbF72V1netJ8Mb251zrhTNWjqL3m/1pnPLzgw/d3iZTyLgZyTOOVdqNm3bxKVvXErDGg0Zed7IcpFEwM9InHOu1Nz63q3MXTWXf132L3bfbfe4wykxfkbinHOlYPzX43nikye4qeNNnNjyxLjDKVGeSJxzLsVWbVrFlWOu5KA9D+L+E++PO5wS51VbzjmXQmZG77G9WfPjGiZcMiHtu4TfGZ5InHMuhYbNHsYbc9/gr53/yqF7HRp3OCnhVVvOOZciny77lD7j+vDb5r/l90f9Pu5wUsYTiXPOpcDKjSs5a+RZ1Ktej+HnDmcXld+vW6/acs65ErYtZxsXvH4BKzau4MMrPqRhzYZxh5RSqRxq91lJKyR9kVDWT9K3kmaHR7eEebdLmi9pnqSTE8rbS/o8zBsSxm5HUjVJI0P5DEnNU/VanHNuR/zh3T8wedFkhp42lIy98x1UsFxJ5bnWMKBrPuUPm1m78BgHIKkt0VjuB4Z1/iapUlj+SaA30CY8crfZC1hrZq2Bh4EBqXohzjmXrOGfD2fIzCHc1PEmLjv0srjDKRUpSyRmNgVYk+TiZwIjzGyLmS0E5gMdJDUCapvZNIsGl38BOCthnefD9OvAiSrL3Wc658q8peuX8rtxv+OoJkcxsPPAuMMpNXG0/lwv6bNQ9ZXbR0BjYEnCMlmhrHGYzlv+i3XMLBv4HqiX3w4l9ZaUKSlz5cqVJfdKnHMuMDOuHns1W7K38PxZz1N5l4rTBF3aieRJoBXQDlgGDA7l+Z1JWCHlha3z60KzoWaWYWYZDRo02LGInXMuCc/Nfo5xX4/jwZMepE29NnGHU6pKNZGY2XIzyzGz7cDfgQ5hVhbQNGHRJsDSUN4kn/JfrCOpMlCH5KvSnHOuxHyz7htunHAjnZp34voO18cdTqkr8txLUjXgXKB54vJm9pcd3ZmkRma2LDw9G8i9omsM8Iqkh4C9iRrVZ5pZjqT1kjoCM4AewGMJ6/QEpgHnAe+HdhTnnCs12207vcb0wjCeO/O5cn2/SEGSqcQbTdT+MAvYkuyGJQ0HOgH1JWUB9wCdJLUjqoJaBFwDYGZzJL0KfAlkA33MLCds6jqiK8B2A8aHB8A/gBclzSc6E+mebGzOOVdSnsp8iokLJ/L0aU/TvG7zuMOJhYr6ES/pCzM7qJTiSbmMjAzLzMyMOwznXDkwf818Dn3qUI5tdizjLxlfpsddL4qkWWaW700xyZyDTZV0cAnH5JxzZVrO9hyuGH0FVXapwjNnPFOuk0hRkqnaOga4XNJCoqotAWZmh6Q0MuecS2P3fXgfHy3+iOfPep4mtZsUvUI5lkwiOSXlUTjnXBky6stR3DP5Hi475DIuO6Ri3L1emCKrtszsG6AucHp41A1lzjlX4cz+bjY93uxBxyYdGXr60ApdpZWryEQiqS/wMrBneLwk6YZUB+acc+lm49aNnPvqueyx2x68ceEb5XK0w52RTNVWL+BIM9sIIGkA0b0bjxW6lnPOlTN3TLyD/639Hx9c/gF71dwr7nDSRjJXbQnISXieQ/7dkzjnXLn16bJPeWzmY/Q5og/H7XNc3OGklWTOSJ4DZkh6Izw/i+hmQOecqxDMjL4T+lKvej36n9A/7nDSTpGJxMwekjSZ6DJgAVeY2aepDsw559LFG3Pf4MPFH/LUqU9Rd9e6cYeTdgpMJJJqm9kPkvYg6s5kUcK8PczMO0h0zpV72duzuX3i7RxQ/wB6Hd4r7nDSUmFnJK8ApxH1sZXYj4rC85YpjMs559LCs58+y39X/5c3L3yzQo0xsiMKPCpmdlr426L0wnHOufSxcetG+k3ux9FNj+aM/c6IO5y0VVjV1uGFrWhm/y75cJxzLn08NO0hlm1Yxqvnv+o3HhaisPO0wYXMM+CEEo7FOefSxpcrv6T/h/05r+15HNPsmLjDSWuFVW39tjQDcc65dJG9PZsrRl9Braq1ePyUx+MOJ+0VVrV1TmErmtk/Sz4c55yL38PTHmbmtzMZfu5wGtZsGHc4aa+wqq3TC5lngCcS51y5M3fVXO6adBdn7382Fx54YdzhlAmFVW1dUZwNS3qW6PLhFbkjLEr6K1GC2gosILq5cZ2k5sBXwLyw+nQzuzas056fh9odB/Q1Mwtjyb8AtAdWAxea2aLixOycq9hytudw5egrqVG1Bn879W/ewJ6kAvvaknRp+Pv7/B5JbHsY0DVP2XvAQWFQrP8CtyfMW2Bm7cLj2oTyJ4HeQJvwyN1mL2CtmbUGHgYGJBGTc84VaOisoUzLmsaQrkO8U8YdUFinjTXC31oFPAplZlOANXnK3jWz7PB0OlDosGKSGgG1zWyaRYPLv0DU1xfAmcDzYfp14ET5zwfn3E5avWk1f5r0J37b/LdcfPDFcYdTphRWtfV0+PvnFO37SmBkwvMWkj4FfgD+ZGYfAo2BrIRlskIZ4e+SEGO2pO+BesCqvDuS1JvorIZmzZqV8MtwzpUHD3z0AOs2r+PRro96ldYOKvJ+f0ktgBuA5onLm9lO3+Yp6U4gm2jALIBlQDMzWx3aRN6UdCD5d1ef211LYfN+WWg2FBgKkJGRke8yzrmKa+XGlTyZ+SQXH3wxBzc8OO5wypxkOo55k6jb+LHA9uLuUFJPokb4E0N1FWa2BdgSpmdJWgDsS3QGklj91QRYGqazgKZAlqTKQB3yVKU551wyBn48kB+3/cidx94ZdyhlUjKJZLOZDSmJnUnqCvwRON7MNiWUNwDWmFmOpJZEjer/M7M1ktZL6gjMAHrw88iMY4CeRKM1nge8n5uYnHMuWV+t/IpHZzxKz3Y92b/+/nGHUyYlk0gelXQP8C7hrAGK7mtL0nCgE1BfUhZwD9FVWtWA90IdZO5lvscBf5GUTTQC47UJ3dRfx8+X/44PD4jOkl6UNJ/oTKR7Eq/FOed+YmbcMP4GalStwYCT/MLPnZVMIjkYuIyob63cqq0i+9oys4vyKc53ZEUzGwWMKmBeJnBQPuWbgfMLi8E55wozfv54Ji6cyJCuQ9izxp5xh1NmJZNIzgZamtnWVAfjnHOlJWd7Dre+dyut92jNNRnXxB1OmZZMIvkPUBdYkeJYnHOu1Dz/n+eZs3IOr53/GlUrVY07nDItmUTSEJgr6RN+2Ubio7w458qkjVs3ctekuziy8ZGce8C5cYdT5iWTSO5JeRTOOVeK7nz/TpauX8rI80b6zYcloMhEYmYflEYgzjlXGqZ8M4VHZzzK9Udc7wNWlZDC+tpyzrlyZWvOVq556xpa1G3Bgyc9GHc45UYyVVvOOVcuDJo6iLmr5vL2xW9To2qNoldwSSn0jERSJUkvlVYwzjmXKgvXLuTeKfdyzgHn0K1Nt7jDKVcKTSRmlgM0kOTXxjnnyrS+E/pSSZV45ORH4g6l3EmmamsR8LGkMcDG3EIzeyhVQTnnXEn6ePHHjP3vWO4/4X6a1mkadzjlTjKJZGl47EISA1o551w6MTPufP9OGtZoyP8d+X9xh1MuJXP5758BJNUws41FLe+cc+lk4sKJfPDNBwzpOsQb2FOkyMt/JR0l6Uvgq/D8UEl/S3lkzjlXTLlnI83qNKN3+95xh1NuJXMfySPAycBqADP7D1G37845l9benPsmM7+dyd3H3U21ytXiDqfcSuqGRDNbkqcoJwWxOOdcidmSvYVb3ruFAxscSM92PeMOp1xLprF9iaSjAQuXAf8foZrLOefS1ROfPMGCtQuYcMkEKu/i916nUjJnJNcCfYDGwLdAu/C8UJKelbRC0hcJZXtIek/S1+Hv7gnzbpc0X9I8SScnlLeX9HmYN0ShhzVJ1SSNDOUzJDVP9kU758q3VZtW8ZcP/sIprU/h5NYnF72CK5YiE4mZrTKzS8ysoZk1MLNLzWx1EtseBnTNU3YbMNHM2gATw3MktSUaKvfAsM7fJFUK6zwJ9CYax71NwjZ7AWvNrDXwMODjZDrnALjr/bvYsHUDg7oMijuUCiGZq7ZaShoraWU4wxgtqWVR65nZFKKx1BOdCTwfpp8HzkooH2FmW8xsITAf6CCpEVDbzKaZmQEv5Fknd1uvAyfmnq045yquyYsm89Ssp/i/I/+Ptg3axh1OhZBM1dYrwKtAI2Bv4DVg+E7ur6GZLQMIf3MHSW4MJDboZ4WyxmE6b/kv1jGzbOB7oN5OxuWcKwd+3PYjV425ila7t6L/Cf3jDqfCSCaRyMxeNLPs8HgJsBKOI78zCSukvLB1fr1xqbekTEmZK1eu3MkQnXPp7v4P72fB2gUMPX0o1atUjzucCiOZRDJJ0m2SmkvaR9KtwNuh4XyPHdzf8lBdRfibOw58FpDYAU4Tom5ZssJ03vJfrCOpMlCHX1elAWBmQ80sw8wyGjRosIMhO+fKgrmr5jLg4wFcesilnNDihLjDqVCSSSQXAtcAk4DJwHXAlcAsIHMH9zcGyL2guycwOqG8e7gSqwVRo/rMUP21XlLH0P7RI886uds6D3g/tKM45yoYM+N3b/+OGlVrMKizN7CXtmT62mqxMxuWNBzoBNSXlEU09vuDwKuSegGLgfPDPuZIehX4EsgG+oQu7CFKXMOA3YDx4QHwD+BFSfOJzkS670yczrmy7+XPX2bSokk8eeqTNKzZMO5wKhxVtB/xGRkZlpm5oydSzrl0tfbHtez3+H603L0lU3tNZRf5COKpIGmWmWXkN89v93TOlWm3T7yd1T+u5t3L3vUkEhM/6s65Mmt61nSGzhpK3yP70m6vdnGHU2Elc0PibyTVCNOXSnpI0j6pD8055wq2NWcrV4+9mr1r7c2fO/057nAqtGTOSJ4ENkk6FLgV+IboDnPnnItN/yn9+WLFFzx12lPUquaDt8YpmUSSHS6rPRN41MwexYfcdc7FaPZ3s3ngowe47JDLOG3f0+IOp8JLprF9vaTbgcuAY0NnilVSG5ZzzuVvS/YWrhh9BfWr1+eRro/EHY4j+RsStwBXmtl3RH1c/TWlUTnnXAFueucmZn83m6dPe5o9dtvRzjVcKiTTjfx3wCggd5zKVcAbqQzKOefy88J/XuDJzCe55ehbOGO/M+IOxwXJXLV1NVE37U+HosbAm6kMyjnn8vps+Wdc89Y1dGreiftPvD/ucFyCZKq2+gC/AX4AMLOv+bn7d+ecS7n1W9Zz3qvnsfuuuzPi3BE+dG6aSebd2GJmW3PHjAo97VasflWcc7ExM3q/1ZsFaxfwfo/3vS+tNJTMGckHku4AdpPUmWhgq7GpDcs55yJDZw1lxBcjuPe393J88+PjDsflI5lEchuwEvicqDv5ccCfUhmUc85BdL9I3wl96dKqC7cdc1vc4bgCJNON/Hbg78Dfw0BWTXzcD+dcqv2w5QcueO0C6lWvx4tnv+gdMqaxIhOJpMnAGWHZ2cBKSR+Y2e9THJtzroIyM3qPjdpFJvWcxJ41/PqedJZMiq9jZj8A524d88sAABhqSURBVADPmVl74KTUhuWcq8gGfDyAkXNG0v+3/Tlun+PiDscVIZlEUjmMr34B8FaK43HOVXBDZw3l9om3c/HBF/PHY/4YdzguCckkkr8A7wDzzewTSS2Br3d2h5L2kzQ74fGDpBsl9ZP0bUJ5t4R1bpc0X9I8SScnlLeX9HmYN0S51yg758qk1798nWvfupZubbox7Mxh3i5SRsQ61G7oAPJb4EjgCmCDmQ3Ks0xbYDjQAdgb+Bewr5nlSJoJ9AWmE11NNsTMxlMIH2rXufT03oL3OPWVU+nQuAPvXvYu1atUjzskl6BYQ+1K2hXoBRwI7JpbbmZXlkBsJwILzOybQk4mzgRGmNkWYKGk+UAHSYuA2mY2LcT5AnAWUGgicc6lnxlZMzh75Nkc0OAA3rr4LU8iZUwy540vAnsBJwMfAE2A9SW0/+5EZxu5rpf0maRnJe0eyhoDSxKWyQpljcN03vJfkdRbUqakzJUrV5ZQ6M65kvDVyq/o9ko39qq5F+9c+g51d60bd0huByWTSFqb2V3ARjN7HjgVOLi4O5ZUleiy4tdC0ZNAK6AdsAwYnLtoPqtbIeW/LjQbamYZZpbRoEGDYsXtnCs5S9cvpevLXamySxXevexd9qq5V9whuZ2QTCLZFv6uk3QQUAdoXgL7PgX4t5ktBzCz5WaWk3ADZIewXBbQNGG9JsDSUN4kn3LnXBnww5Yf6PZyN9b8uIZxl4yj5e4t4w7J7aRkEsnQUM30J2AM8CUwsAT2fREJ1VrhEuNcZwNfhOkxQHdJ1SS1ANoAM81sGdHojR3D1Vo9gNElEJdzLsVWbFzBiS+cyJyVcxh1wSgOb3R43CG5Ykimi5RnwuQUoER+MkiqDnQm6rsr10BJ7YiqpxblzjOzOZJeJUpg2UAfM8sJ61wHDAN2I2pk94Z259LcvFXz6PZKN5atX8aoC0bRpVWXuENyxVTk5b+S7gcGmtm68Hx34GYzK5MdN/rlv87FZ/KiyZwz8hyqVKrC6O6j6dikY9whuSQVdvlvMlVbp+QmEQAzWwt0K2R555z7hezt2fzlg7/Q+cXO7FVzL6b3mu5JpBxJZmCrSpKqhfs4kLQbP4/f7pxzhdqwdQMXvHYB4+eP55KDL+Hxbo/7Jb7lTDKJ5CVgoqTniNovrgSeT2lUzrly4cuVX3L+a+czd9Vcnj7taXq37x13SC4FkmlsHyjpM6IefwXca2bvpDwy51yZZWYMmz2M68dfT82qNXnn0nc4qaV3Gl5eJXNGgplNACakOBbnXDmwaN0irn3rWt5Z8A6dmnfilXNeoVGtRkWv6MqspBKJc84VZd3mdTw07SEGTxvMLtqFx055jOsyrqPSLpXiDs2lmCcS51yxZG/P5pHpj3Dfh/exbvM6zm97PoO7DKZpnaZFr+zKBU8kzrmdNvu72Vw99moyl2ZyaptT6X9Cf9rt1S7usFwpKzCRSPqc/DtBFGBmdkjKonLOpbX1W9bTf0p/Bk8bTL3q9Rhx7gguOPACfGy5iqmwM5LTSi0K51yZsGnbJp6Y+QQDpw5k1aZVXHXYVQzoPIA9dtsj7tBcjApMJGb2TWkG4pxLX9tytvHMv5/hL1P+wncbvqNr6678udOf6dC4Q9Eru3IvmRESOwKPAQcAVYFKRGOT1E5xbM65NDDu63H0ndCX+Wvmc2yzY3n9/Nf5TbPfxB2WSyPJNLY/TjSS4WtABlF37a1TGZRzLn6Lv1/M79/5PaO+GsX+9ffn7Yvf5pTWp3g7iPuVZG9InC+pUui+/TlJU1Mcl3MuJqs2reKBDx/giU+eQBL3n3A/Nx99M1UrVY07NJemkkkkm8KwuLMlDSQaBrdGasNyzpW2DVs38PC0h/nr1L+ycdtGeh7ak36d+tGsTrO4Q3NpLplEchlRd/PXAzcRDXt7TiqDcs6Vni3ZWxg6ayj3TrmXlZtWcvb+Z9P/hP60bdA27tBcGZFMIjnLzB4FNgN/BpDUF3g0lYE551JvyjdT6D22N/NWz6NT8048eOKDHNnkyLjDcmVMMgNb9cyn7PLi7FTSIkmfS5otKTOU7SHpPUlfh7+7Jyx/u6T5kuZJOjmhvH3YznxJQ+StgM4l5dsfvuWqMVdx/LDj2ZKzhbcueov3e7zvScTtlMLubL8IuBhoIWlMwqzawOoS2PdvzWxVwvPbgIlm9qCk28LzP0pqS3TV2IHA3sC/JO0bGv6fBHoD04FxQFd83HbnCrR8w3Ie/OhBnsx8ku22nVuOvoV7jr+HGlW92dPtvMKqtqYSNazXBwYnlK8HPktBLGcCncL088Bk4I+hfEQYoXGhpPlAB0mLgNpmNg1A0gvAWXgice5X1v64loEfD2TIzCFsyd5Cz0N7ctfxd9G8bvO4Q3PlQFF3tn8DHCWpIXBEmPWVmWUXc78GvCvJgKfNbCjQ0MyWhX0vk7RnWLYx0RlHrqxQti1M5y3/FUm9ic5caNbMr0BxFcfGrRt5dMajDPx4ID9s+YGLDr6Ifsf3o029NnGH5sqRZO5sPx8YRHSGIOAxSbeY2evF2O9vzGxpSBbvSZpbWAj5lFkh5b8ujBLVUICMjIx8l3GuPDEzRs4ZyU3v3MR3G77j9H1Pp/8J/Tmkofe16kpeMldt/Qk4wsxWAEhqAPwL2OlEYmZLw98Vkt4AOgDLJTUKZyONgBVh8SyiS45zNQGWhvIm+ZQ7V6Et/n4xfcb14a3/vsURex/BqAtGcXTTo+MOy5VjyVy1tUtuEglWJ7leviTVkFQrdxroAnwBjOHnK8R6AqPD9Bigu6RqkloAbYCZoRpsvaSO4WqtHgnrOFfhrN+ynrvev4v9Ht+P9xe+z0NdHmJar2meRFzKJXNGMkHSO8Dw8PxCiteg3RB4I1ypWxl4xcwmSPoEeFVSL2AxcD6Amc2R9CrwJZAN9AlXbAFcBwwDdgsxeUO7q3C25mxl6Kyh9J/Sn+Ubl3PxwRdz/wn3s0/dfeIOzVUQMiu6yUDSOcAxRO0SU8zsjVQHlioZGRmWmZkZdxjOlYix88bSd0JfFq5byHH7HMfAkwb6vSAuJSTNMrOM/OYl09g+wMz+CPwznzLnXAzW/LiGvhP68tJnL3HQngcx/pLxnNzqZO+Z18UimbaOzvmUnVLSgTjnimZmjJ47mgP/diAjvhjB3cfdzazes+jauqsnERebwu5svw74HdBSUuINiLWAj1MdmHPul6YtmcbtE2/ng28+4JCGhzDu4nEc1uiwuMNyrtCqrVeIGq8fIOquJNd6M1uT0qiccz/5fPnn3Pn+nYz971ga1mjIY6c8Ru/2vX18EJc2Cruz/Xvge+Ci0gvHOZdr3eZ1/P6d3zNs9jBqV6vNfSfcR98j+3q/WC7tJDVConOudE1eNJkeb/Rg6fql/OHoP3DbMbexx257xB2Wc/nyROJcGtmSvYW7J93NX6f+lVZ7tOLjKz/2y3ld2vNE4lyamPi/ifQZ14d5q+dxTftrGNxlsFdjuTLBE4lzMVu2fhk3v3szw78YTqvdWzHhkgmc3Prkold0Lk14InEuRm/OfZPL37yczdmbuef4e7jtmNvYtfKucYfl3A7xROJcDLblbOOOiXcwaNogjtj7CF4+52UfI8SVWZ5InCtlS9cv5cLXL+SjxR/R54g+DO4ymGqVq8UdlnM7zROJc6Xo/YXvc9Goi9i4dSMvn/MyFx98cdwhOVdsOz2uiHMueRu3bqTv+L6c+MKJ1NutHjOvnulJxJUbfkbiXIpNXjSZq8ZcxYK1C7ihww08cOIDflmvK1c8kTiXIus2r+OWd2/hmU+fodXurZjcczLHNz8+7rCcK3GeSJxLgU3bNtFpWCe+WPEFtx59K/d0uofqVarHHZZzKVHqbSSSmkqaJOkrSXMk9Q3l/SR9K2l2eHRLWOd2SfMlzZN0ckJ5e0mfh3lD5AMyuDSwatMqLvnnJXy2/DNGdx/NgM4DPIm4ci2OM5Js4GYz+7ekWsAsSe+FeQ+b2aDEhSW1BboDBwJ7A/+StG8Yt/1JoDcwHRgHdMXHbXcxMTNe+uwlbhh/Axu2bmBwl8Gcuu+pcYflXMqVeiIxs2XAsjC9XtJXQONCVjkTGGFmW4CFkuYDHSQtAmqb2TQASS8AZ+GJxMVg6pKpXD/uej797lOOaXYMT5/2NG0btI07LOdKRayX/0pqDhwGzAhF10v6TNKzknYPZY2BJQmrZYWyxmE6b3l+++ktKVNS5sqVK0vwFTgH7y14j84vdmbt5rU8c/ozTO452ZOIq1BiSySSagKjgBvN7AeiaqpWQDuiM5bBuYvms7oVUv7rQrOhZpZhZhkNGjQoduzOAeRsz+HuSXdz8ksn03L3lkzvNZ1eh/ei0i6V4g7NuVIVy1VbkqoQJZGXzeyfAGa2PGH+34G3wtMsoGnC6k2ApaG8ST7lzqXc8g3LufifF/P+wvfpeWhPHu/2ODWr1ow7LOdiEcdVWwL+AXxlZg8llDdKWOxs4IswPQboLqmapBZAG2BmaGtZL6lj2GYPYHSpvAhXoX34zYcc9vRhTF0ylWfPeJZhZw3zJOIqtDjOSH4DXAZ8Lml2KLsDuEhSO6LqqUXANQBmNkfSq8CXRFd89QlXbAFcBwwDdiNqZPeGdpcy2207g6YO4o6Jd9Bi9xaMv2Q8h+51aNxhORc7meXbrFBuZWRkWGZmZtxhuDJm+Ybl9HizB+8ueJfz2p7HM6c/Q51d68QdlnOlRtIsM8vIb57f2e5cEf71v39x6T8v5fst3/P0aU9z9eFX4/e+Ovcz7/3XuQLkDj7V5cUu1Ktej0+u/oTe7Xt7EnEuDz8jcS4fi79fTPfXuzMtaxpXH341j3R9xLs5ca4Ankicy2N61nTOHHEmm7M3M+LcEVx40IVxh+RcWvNE4lyCkV+MpOebPWlcuzEfXP4B+9ffP+6QnEt73kbiHFGHi/d+cC/dR3WnQ+MOzLhqhicR55LkZySuwtu0bRNXjr6SkXNG0uPQHgw9bSjVKleLOyznygxPJK5CW/z9Ys4acRazv5vNgJMGcMvRt/hVWc7tIE8krsL68JsPOe+189icvZm3Ln6Lbm26Fb2Sc+5XvI3EVTjZ27O59b1bOX7Y8dSpVocZV83wJOJcMfgZiatQNmzdwCX/vIQx88ZwTftr+Gvnv1KrWq24w3KuTPNE4iqM+Wvmc/bIs/ly5Zc8dspjXN/h+rhDcq5c8ETiKoTRc0dz+ejL2UW7MOGSCXRu1TnukJwrN7yNxJVra39cS483enDWyLNouXtLZvWe5UnEuRLmZySuXNqcvZm/z/o79314H6s2reLu4+7mzuPupGqlqnGH5ly544nElStbc7by7KfPct+H95H1QxbH7XMcb3d5m/Z7t487NOfKLU8krlxYv2U9L3/+MgM+HsCidYs4qslRDDtzGCe0OMFvMHQuxcp8IpHUFXgUqAQ8Y2YPxhySKyUrN65kyjdTmDB/AiPmjGDD1g1k7J3B37r9ja6tu3oCca6UlOlEIqkS8ATQGcgCPpE0xsy+jDcyVxJytuewcdtGNm7dyJof1/D1mq+Zt2oe81bPY8a3M/hyZfQ216hSgwsOvIDe7XtzZOMjPYE4V8rKdCIBOgDzzex/AJJGAGcCJZ5Inv30WQZNHfTTc+OXY92bWVLzdnR+cdYtybhK8zVtt+1s2raJrTlbyU/DGg05rNFhXHbIZXRq3on2jdpTpVKVfJd1zqVeWU8kjYElCc+zgCPzLiSpN9AboFmzZju1o/rV63PQngfl3e4vn6Ok5u3o/OKs+6u40iWOIuZVr1Kd6lWqU6NKDWpUrUGdanVoU68NbfZoQ51d6+CcSx9lPZHkV4dhvyowGwoMBcjIyPjV/GScsd8ZnLHfGTuzqnPOlWtl/YbELKBpwvMmwNKYYnHOuQqprCeST4A2klpIqgp0B8bEHJNzzlUoZbpqy8yyJV0PvEN0+e+zZjYn5rCcc65CKdOJBMDMxgHj4o7DOecqqrJeteWccy5mnkicc84ViycS55xzxeKJxDnnXLEob3cV5Z2klcA3xdhEfWBVCYVTktIprnSKJVe6xZRu8UD6xZRu8eRKx7hKI6Z9zKxBfjMqXCIpLkmZZpYRdxx5pVNc6RRLrnSLKd3igfSLKd3iyZWOccUdk1dtOeecKxZPJM4554rFE8mOGxp3AAVIp7jSKZZc6RZTusUD6RdTusWTKx3jijUmbyNxzjlXLH5G4pxzrlg8kTjnnCseMyvXD6LxSiYBXwFzgL6hfA/gPeDr8Hf3UN4ZmAV8Hv6ekLCt9qF8PjCEUDWYzz7zXQ44Dvg3kA1ckw5xhePzNbAF2Az8F9gnpuNzbSifDcwEZsR9fPJ8jr4gGjhtQcyfo8uBleG4rAeWpckxug7YGD5L62I+Rg+Hz9Fs4H/AtnT4LAHNiP7vvwA2AIvTIKZ9gInAZ8BkoMkOf8+W1hd6XA+gEXB4mK5F9EXZFhgI3BbKbwMGhOnDgL3D9EHAtwnbmgkcRfQFPB44pYB95rsc0Bw4BHgBuCod4grHpzdQPcSxPMyL4/jUTlimJzA17uOTMK810Y+A6UQ/COL8HF0OPE56fbbbEH1JHR+et4j7PUtY5k5gdJocp6FECbcRcC6wKA1ieg3oGaZPAF7c4e/ZHV2hrD+A0UTZfR7QKJQ1Aubls6yA1UC1sMzchHkXAU/ns06RywHDgPPSLa5QPpno11LccVwEjE+X4wM8ApwWjk9GnPEQEkk6fbaJvgCvSpd48iw3FeicDnEBTwN/DNNH8fOPpThjmkM4Cwnb/iHv+kU9KlQbiaTmRJl9BtDQzJYBhL975rPKucCnZrYFaEw0tG+urFCWV7LLpV1cIY72wBtxxSGpj6QFRF9M/5cQV2zHR9JhQFMzeyvMaxRnPLnblPSZpNclNY37GAH7AvtK+ljSdEk9Y44HAEn7EJ0dvR+eN485rn7ApZKyiMZRuiENYvpP2CbA2UAtSfXy2UaByvzAVsmSVBMYBdxoZj9IKmr5A4EBQJfconwWs/xWTXK5tIorxDGJaMz7/sANccRhZk8AT0i6GPiTpD7EeHwk7UJU3355KKtElOTifL/GAsPNbIuka4GXgJoxx1SZqHqrE1FS+RS4Mg3+17oDr5tZTpr8r10EDDOzwZKOInrvNsUc0x+AxyVdDkwBviVqx01ahUgkkqoQfYBeNrN/huLlkhqZ2TJJjYAVCcs3IfpV3sPMFoTiLKBJwmabAEslVSJq/IJovPgn81suneMKcUwiaic5MnxBxX18RoRl94r5+NQiqpOeHP7Jm/JzI2kc8WBmqxPKnwMeI6ouifOznUXUfgTwEFHd/7wY48nVHeiTLv9rQC+ga5jOJGrofjDOmMxsKXBO2EdN4Fwz+54dsaN1YWXtQZSJXwAeyVP+V37ZqDUwTNclnOrls61PgI783FjVrYB9FrocoY0kHeIK02OJrrJpE+fxybP/04l6M02L9y3hc5QFZMR8nBolxDQJWBb3MSL6cnw+HKOngCVAvZjfs/2IElrafAeE6ctD+WiiHyWKOab6wC5h+j7gL/mtX9gj9i/6VD+AY4hO4T7j58sBuwH1iC55+zr83SMs/yeiSxhnJzz2DPMyiBqiFxBdNVPQZXb5LgccQfRFtBH4Ph3iSjg+24Afw2N6TMfnUaKGv9lEV0jFfnzy+RxtIPqlHefn6IFwnOaHmOYR/2dbRGeRFj5Di9LgPesHPEh6fQe0BT5OeO/+lwYxnRf291/gGaDajn7PehcpzjnniqVCXbXlnHOu5Hkicc45VyyeSJxzzhWLJxLnnHPF4onEOedcsXgicS7FJOVImi1pjqT/SPp9uGO+sHWah7v7nUt7nkicS70fzaydmR1I1DFfN+CeItZpDngicWWC30fiXIpJ2mBmNROetyS6y7g+URcZLwI1wuzrzWyqpOnAAcBCojvGhxDdXNeJqNfXJ8zs6VJ7Ec4VwhOJcymWN5GEsrXA/kQDU203s82S2hB1xpghqRPwBzM7LSzfm+hO5v6SqhHdHX2+mS0s1RfjXD4qRKeNzqWh3N5YqxD1vNoOyCHqOTc/XYBDJJ0Xntch6m3XE4mLnScS50pZqNrKIerZ9R6iUSkPJWqz3FzQasANZvZOqQTp3A7wxnbnSpGkBkS94z5uUb1yHaLee7cDlxGNdwJRlVethFXfAa4L3aEjaV9JNXAuDfgZiXOpt5uk2UTVWNlEjesPhXl/A0ZJOp+oS/iNofwzIFvSf4iGHXiU6EqufysaGGUlcFZpvQDnCuON7c4554rFq7acc84ViycS55xzxeKJxDnnXLF4InHOOVcsnkicc84ViycS55xzxeKJxDnnXLH8Pz20Lvx8QnnRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            date       location NEW_CASE_TREND\n",
      "40107 2019-12-31  United States            NaN\n",
      "40108 2020-01-01  United States            NaN\n",
      "40109 2020-01-02  United States            NaN\n",
      "40110 2020-01-03  United States            NaN\n",
      "40111 2020-01-04  United States            NaN\n",
      "...          ...            ...            ...\n",
      "40356 2020-09-05  United States   GROWING_FAST\n",
      "40357 2020-09-06  United States   GROWING_FAST\n",
      "40358 2020-09-07  United States   GROWING_FAST\n",
      "40359 2020-09-08  United States   GROWING_FAST\n",
      "40360 2020-09-09  United States   GROWING_FAST\n",
      "\n",
      "[254 rows x 3 columns]\n",
      "            date        location NEW_CASE_TREND\n",
      "39853 2019-12-31  United Kingdom            NaN\n",
      "39854 2020-01-01  United Kingdom            NaN\n",
      "39855 2020-01-02  United Kingdom            NaN\n",
      "39856 2020-01-03  United Kingdom            NaN\n",
      "39857 2020-01-04  United Kingdom            NaN\n",
      "...          ...             ...            ...\n",
      "40102 2020-09-05  United Kingdom        GROWING\n",
      "40103 2020-09-06  United Kingdom        GROWING\n",
      "40104 2020-09-07  United Kingdom        GROWING\n",
      "40105 2020-09-08  United Kingdom        GROWING\n",
      "40106 2020-09-09  United Kingdom        GROWING\n",
      "\n",
      "[254 rows x 3 columns]\n",
      "            date location NEW_CASE_TREND\n",
      "42141 2019-12-31    World            NaN\n",
      "42142 2020-01-01    World            NaN\n",
      "42143 2020-01-02    World            NaN\n",
      "42144 2020-01-03    World            NaN\n",
      "42145 2020-01-04    World            NaN\n",
      "...          ...      ...            ...\n",
      "42390 2020-09-05    World        GROWING\n",
      "42391 2020-09-06    World        GROWING\n",
      "42392 2020-09-07    World        GROWING\n",
      "42393 2020-09-08    World        GROWING\n",
      "42394 2020-09-09    World        GROWING\n",
      "\n",
      "[254 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "#total_cases_per_million\n",
    "index_total_cases_per_million = np.argwhere(attributeName == 'total_cases_per_million')\n",
    "index_date = np.argwhere(attributeName == 'date')\n",
    "\n",
    "uk_total_cases_per_million = ukAttr[:,index_total_cases_per_million[0,0]]\n",
    "uk_date = ukAttr[:,index_date[0,0]]\n",
    "\n",
    "us_total_cases_per_million = usAttr[:,index_total_cases_per_million[0,0]]\n",
    "us_date = usAttr[:,index_date[0,0]]\n",
    "\n",
    "world_total_cases_per_million = worldAttr[:,index_total_cases_per_million[0,0]]\n",
    "world_date = worldAttr[:,index_date[0,0]]\n",
    "\n",
    "\n",
    "l1=plt.plot(uk_date,uk_total_cases_per_million,'r',label='UK')\n",
    "plt.title('total cases per million in the UK')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('total cases per million')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "l2=plt.plot(us_date,us_total_cases_per_million,'g',label='US')\n",
    "plt.title('total cases per million in the US')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('total cases per million')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "l3=plt.plot(world_date,world_total_cases_per_million,'b',label='World')\n",
    "plt.title('total cases per million in the World')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('total cases per million')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#NEW_CASE_TREND\n",
    "df_temp = df.loc[:,[\"date\", \"location\", \"NEW_CASE_TREND\"]]\n",
    "\n",
    "#drop null values\n",
    "#df_temp.dropna(axis=0, how='any', inplace=True)\n",
    "tem_df_uk = df_temp[df_temp['location'].isin(['United Kingdom'])]\n",
    "tem_df_us = df_temp[df_temp['location'].isin(['United States'])]\n",
    "tem_df_world = df_temp[df_temp['location'].isin(['World'])]\n",
    "print(tem_df_us)\n",
    "print(tem_df_uk)\n",
    "print(tem_df_world)\n",
    "\n",
    "del df_temp\n",
    "del tem_df_world\n",
    "del tem_df_uk\n",
    "del tem_df_us"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# Section: 3 - Data Processing\n",
    "In this section, I will justify what attributes I decide to remove, implement attribute transforms, creat new features of the dataset, and implement data cleaning steps.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section: 3.1 - What attributes do you decide to keep or remove? Please justify.\n",
    "I’d like to remove RECID, which already be removed in section 2.4. RECID assigns a unique number to each row of data, even when two rows of data have the same values, which keeps us from finding duplicated data and drop duplicated data. And I also decide to remove total_cases, new_cases, new_cases_smoothed, new_cases_smoothed_per_million, total_deaths, new_deaths, new_deaths_smoothed, new_deaths_smoothed_per_million because I think those attributes are kindly redundant and new_cases_per_million, total_cases_per_million, new_deaths_per_million, and total_deaths_per_million are enough in most cases. Besides, I will also remove total_tests, new_tests, new_tests_smoothed, new_tests_smoothed_per_thousand because those attributes are not only redundant but also contain lots of missing data (the rate of missing data are more than 60%).\n",
    "\n",
    "I will keep all other attributes in the df DataFrame because I’d like to keep the diversity of the original dataset. \n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RECID is already been removed in section 2.4\n",
      "\n",
      "The attributes of the dataset:\n",
      "Index(['iso_code', 'continent', 'location', 'date', 'total_cases', 'new_cases',\n",
      "       'new_cases_smoothed', 'total_deaths', 'new_deaths',\n",
      "       'new_deaths_smoothed', 'total_cases_per_million',\n",
      "       'new_cases_per_million', 'new_cases_smoothed_per_million',\n",
      "       'total_deaths_per_million', 'new_deaths_per_million',\n",
      "       'new_deaths_smoothed_per_million', 'new_tests', 'total_tests',\n",
      "       'total_tests_per_thousand', 'new_tests_per_thousand',\n",
      "       'new_tests_smoothed', 'new_tests_smoothed_per_thousand',\n",
      "       'tests_per_case', 'positive_rate', 'tests_units', 'stringency_index',\n",
      "       'population', 'population_density', 'median_age', 'aged_65_older',\n",
      "       'aged_70_older', 'gdp_per_capita', 'extreme_poverty',\n",
      "       'cardiovasc_death_rate', 'diabetes_prevalence', 'female_smokers',\n",
      "       'male_smokers', 'handwashing_facilities', 'hospital_beds_per_thousand',\n",
      "       'life_expectancy', 'NEW_CASE_TREND'],\n",
      "      dtype='object')\n",
      "\n",
      "The shape of the dataset:\n",
      "(42649, 41)\n",
      "\n",
      "\n",
      "The attributes of new dataset:\n",
      "Index(['iso_code', 'continent', 'location', 'date', 'total_cases_per_million',\n",
      "       'new_cases_per_million', 'total_deaths_per_million',\n",
      "       'new_deaths_per_million', 'total_tests_per_thousand',\n",
      "       'new_tests_per_thousand', 'tests_per_case', 'positive_rate',\n",
      "       'tests_units', 'stringency_index', 'population', 'population_density',\n",
      "       'median_age', 'aged_65_older', 'aged_70_older', 'gdp_per_capita',\n",
      "       'extreme_poverty', 'cardiovasc_death_rate', 'diabetes_prevalence',\n",
      "       'female_smokers', 'male_smokers', 'handwashing_facilities',\n",
      "       'hospital_beds_per_thousand', 'life_expectancy', 'NEW_CASE_TREND'],\n",
      "      dtype='object')\n",
      "\n",
      "The shape of the dataset after removing 12 attributes:\n",
      "(42649, 29)\n"
     ]
    }
   ],
   "source": [
    "#df = df.drop(columns=['RECID'])\n",
    "print(\"RECID is already been removed in section 2.4\")\n",
    "print(\"\\nThe attributes of the dataset:\")\n",
    "print(df.columns)\n",
    "print(\"\\nThe shape of the dataset:\")\n",
    "print(df.shape)\n",
    "df = df.drop(columns=['total_cases',\n",
    "                      'new_cases', \n",
    "                      'new_cases_smoothed', \n",
    "                      'new_cases_smoothed_per_million',\n",
    "                      'total_deaths',\n",
    "                      'new_deaths',\n",
    "                      'new_deaths_smoothed', \n",
    "                      'new_deaths_smoothed_per_million',\n",
    "                      'total_tests', \n",
    "                      'new_tests', \n",
    "                      'new_tests_smoothed', \n",
    "                      'new_tests_smoothed_per_thousand'])\n",
    "\n",
    "print(\"\\n\\nThe attributes of new dataset:\")\n",
    "print(df.columns)\n",
    "print(\"\\nThe shape of the dataset after removing 12 attributes:\")\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Section: 3.2 - Did you decide to implement any attribute transformations? If so, why?\n",
    "I‘d like to transform the attribute population to log(population + 1). The distribution of population is concentrated in a small range, which is not convenient for users to generate related graphs or analyze the relationship between population and other variables. After transforming population to log(population + 1), the distribution becomes much more smooth, which is convenient for users to deal with it. And since the values of population contain zero, I apply log(population + 1) instead of log(population). The following graphs are the distribution of the attribute before transformation and after transformation. We can see that the second graph, which is the distribution of the log(population + 1), is much more uniform than the first one.\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temp = df.loc[:,[\"location\", \"population\"]]\n",
    "\n",
    "#drop null values\n",
    "df_temp.dropna(axis=0, how='any', inplace=True)\n",
    "\n",
    "arr_temp = np.array(df_temp.groupby(\"location\")[\"population\"].mean())\n",
    "arr_temp = arr_temp[arr_temp.argsort()]\n",
    "\n",
    "x = np.arange(-106,105)\n",
    "\n",
    "l8=plt.plot(x,arr_temp,'r',label='population')\n",
    "plt.title('population distribution')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('populationn')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#z-score\n",
    "#df.population = (df.population-df.population.mean())/(df.population.std())\n",
    "\n",
    "#log(x+1)\n",
    "df[\"population\"] = df[\"population\"].apply(np.log1p)\n",
    "df = df.rename(columns={'population':'log_population'})\n",
    "\n",
    "df_temp = df.loc[:,[\"location\", \"log_population\"]]\n",
    "df_temp.dropna(axis=0, how='any', inplace=True)\n",
    "\n",
    "arr_temp = np.array(df_temp.groupby(\"location\")[\"log_population\"].mean())\n",
    "arr_temp = arr_temp[arr_temp.argsort()]\n",
    "\n",
    "l9=plt.plot(x,arr_temp,'r',label='log_population')\n",
    "plt.title('log_population distribution')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('log_populationn')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "del df_temp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Section: 3.3 - Did you decide to create any new features? If so, why?\n",
    "I’d like to create two new features which are showing below.\n",
    "    \n",
    "- total_deaths_cases_rate = total_deaths_per_million/ (total_cases_per_million + 1)\n",
    "- new_deaths_cases_rate = new_deaths_per_million/ (new_cases_per_million + 1) \n",
    "    \n",
    "The two attributes can reflect the progress people made in the treatment of COVID-19, and the original dataset doesn’t contain similar attributes which directly reflect this kind of changes. That's the reason why I decide to create those two features.\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the dataset before adding features:\n",
      "(42649, 29)\n",
      "\n",
      "The attributes of the dataset before adding features:\n",
      "Index(['iso_code', 'continent', 'location', 'date', 'total_cases_per_million',\n",
      "       'new_cases_per_million', 'total_deaths_per_million',\n",
      "       'new_deaths_per_million', 'total_tests_per_thousand',\n",
      "       'new_tests_per_thousand', 'tests_per_case', 'positive_rate',\n",
      "       'tests_units', 'stringency_index', 'log_population',\n",
      "       'population_density', 'median_age', 'aged_65_older', 'aged_70_older',\n",
      "       'gdp_per_capita', 'extreme_poverty', 'cardiovasc_death_rate',\n",
      "       'diabetes_prevalence', 'female_smokers', 'male_smokers',\n",
      "       'handwashing_facilities', 'hospital_beds_per_thousand',\n",
      "       'life_expectancy', 'NEW_CASE_TREND'],\n",
      "      dtype='object')\n",
      "\n",
      "The shape of the dataset after adding features:\n",
      "(42649, 31)\n",
      "\n",
      "The attributes of the dataset after adding features:\n",
      "Index(['iso_code', 'continent', 'location', 'date', 'total_cases_per_million',\n",
      "       'new_cases_per_million', 'total_deaths_per_million',\n",
      "       'new_deaths_per_million', 'total_tests_per_thousand',\n",
      "       'new_tests_per_thousand', 'tests_per_case', 'positive_rate',\n",
      "       'tests_units', 'stringency_index', 'log_population',\n",
      "       'population_density', 'median_age', 'aged_65_older', 'aged_70_older',\n",
      "       'gdp_per_capita', 'extreme_poverty', 'cardiovasc_death_rate',\n",
      "       'diabetes_prevalence', 'female_smokers', 'male_smokers',\n",
      "       'handwashing_facilities', 'hospital_beds_per_thousand',\n",
      "       'life_expectancy', 'NEW_CASE_TREND', 'total_deaths_cases_rate',\n",
      "       'new_deaths_cases_rate'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"The shape of the dataset before adding features:\")\n",
    "print(df.shape)\n",
    "print(\"\\nThe attributes of the dataset before adding features:\")\n",
    "print(df.columns)\n",
    "\n",
    "df['total_deaths_cases_rate'] = df.apply(lambda x: x['total_deaths_per_million']/(x['total_cases_per_million']+1), axis=1)\n",
    "df['new_deaths_cases_rate'] = df.apply(lambda x: x['new_deaths_per_million']/(x['new_cases_per_million']+1), axis=1)\n",
    "\n",
    "print(\"\\nThe shape of the dataset after adding features:\")\n",
    "print(df.shape)\n",
    "print(\"\\nThe attributes of the dataset after adding features:\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Section: 3.4 - Implement any data cleaning steps previously identified.\n",
    "The data cleaning steps are already implemented in previous sections. In this section, I will show the effects of that cleaning, which include the shape of the new dataset, the attributes of the new dataset, the description of the new dataset, the distribution of log_population, total_deaths_cases_rate change, and new_deaths_cases_rate change. For the last two, it's impossible to show them for all the location in here, so I will choose the UK, the US, and the World as samples to show their total_deaths_cases_rate, and new_deaths_cases_rate.\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The unnecessary or redundant attributes are removed in section 2.4 and section 3.1\n",
      "The attribute 'population' was transformed to 'log_population' in section 3.2\n",
      "The new features 'total_deaths_cases_rate' and 'new_deaths_cases_rate' were implemented in section 3.3\n",
      "-------------------------------------------------------------------------------------------- \n",
      "\n",
      "The shape of the new dataset: \n",
      "(42649, 31)\n",
      "\n",
      "The attributes of the new dataset: \n",
      "Index(['iso_code', 'continent', 'location', 'date', 'total_cases_per_million',\n",
      "       'new_cases_per_million', 'total_deaths_per_million',\n",
      "       'new_deaths_per_million', 'total_tests_per_thousand',\n",
      "       'new_tests_per_thousand', 'tests_per_case', 'positive_rate',\n",
      "       'tests_units', 'stringency_index', 'log_population',\n",
      "       'population_density', 'median_age', 'aged_65_older', 'aged_70_older',\n",
      "       'gdp_per_capita', 'extreme_poverty', 'cardiovasc_death_rate',\n",
      "       'diabetes_prevalence', 'female_smokers', 'male_smokers',\n",
      "       'handwashing_facilities', 'hospital_beds_per_thousand',\n",
      "       'life_expectancy', 'NEW_CASE_TREND', 'total_deaths_cases_rate',\n",
      "       'new_deaths_cases_rate'],\n",
      "      dtype='object')\n",
      "\n",
      "The log_population distribution: \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The two new features display: \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"The unnecessary or redundant attributes are removed in section 2.4 and section 3.1\")\n",
    "print(\"The attribute 'population' was transformed to 'log_population' in section 3.2\")\n",
    "print(\"The new features 'total_deaths_cases_rate' and 'new_deaths_cases_rate' were implemented in section 3.3\")\n",
    "print(\"----------------------------------------------------------------------------------- \")\n",
    "print(\"\\nThe shape of the new dataset: \")\n",
    "print(df.shape)\n",
    "print(\"\\nThe attributes of the new dataset: \")\n",
    "print(df.columns)\n",
    "df.describe(include = 'all')\n",
    "\n",
    "# population after transformation\n",
    "print(\"\\nThe log_population distribution: \")\n",
    "x = np.arange(-106,105)\n",
    "df_temp = df.loc[:,[\"location\", \"log_population\"]]\n",
    "df_temp.dropna(axis=0, how='any', inplace=True)\n",
    "\n",
    "arr_temp = np.array(df_temp.groupby(\"location\")[\"log_population\"].mean())\n",
    "arr_temp = arr_temp[arr_temp.argsort()]\n",
    "\n",
    "l9=plt.plot(x,arr_temp,'r',label='log_population')\n",
    "plt.title('log_population distribution')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('log_populationn')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "del df_temp\n",
    "\n",
    "# two new features\n",
    "print(\"\\nThe two new features display: \")\n",
    "\n",
    "attributeName = np.array(df.columns)\n",
    "#attribute = np.array(df)\n",
    "worldAttr = np.array(df[df['location'].isin(['World'])])\n",
    "ukAttr = np.array(df[df['location'].isin(['United Kingdom'])])\n",
    "usAttr = np.array(df[df['location'].isin(['United States'])])\n",
    "\n",
    "index_total_deaths_cases_rate = np.argwhere(attributeName == 'total_deaths_cases_rate')\n",
    "index_new_deaths_cases_rate = np.argwhere(attributeName == 'new_deaths_cases_rate')\n",
    "index_date = np.argwhere(attributeName == 'date')\n",
    "\n",
    "uk_total_deaths_cases_rate = ukAttr[:,index_total_deaths_cases_rate[0,0]]\n",
    "uk_new_deaths_cases_rate = ukAttr[:,index_new_deaths_cases_rate[0,0]]\n",
    "uk_date = ukAttr[:,index_date[0,0]]\n",
    "\n",
    "us_total_deaths_cases_rate = usAttr[:,index_total_deaths_cases_rate[0,0]]\n",
    "us_new_deaths_cases_rate = usAttr[:,index_new_deaths_cases_rate[0,0]]\n",
    "us_date = usAttr[:,index_date[0,0]]\n",
    "\n",
    "world_total_deaths_cases_rate = worldAttr[:,index_total_deaths_cases_rate[0,0]]\n",
    "world_new_deaths_cases_rate = worldAttr[:,index_new_deaths_cases_rate[0,0]]\n",
    "world_date = worldAttr[:,index_date[0,0]]\n",
    "\n",
    "\n",
    "l1=plt.plot(uk_date,uk_total_deaths_cases_rate,'r',label='total')\n",
    "l4=plt.plot(uk_date,uk_new_deaths_cases_rate,'g',label='new')\n",
    "plt.title('Deaths_cases_rate in the UK')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Deaths_cases_rate')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "l2=plt.plot(us_date,us_total_deaths_cases_rate,'r',label='total')\n",
    "l5=plt.plot(us_date,us_new_deaths_cases_rate,'g',label='new')\n",
    "plt.title('Deaths_cases_rate in the US')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Deaths_cases_rate')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "l3=plt.plot(world_date,world_total_deaths_cases_rate,'r',label='total')\n",
    "l6=plt.plot(world_date,world_new_deaths_cases_rate,'g',label='new')\n",
    "plt.title('Deaths_cases_rate in the World')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Deaths_cases_rate')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# Section: 4 - Conclusions\n",
    "The dataset contains data on confirmed cases, deaths, testing, ages, geographical locations, and other variables about COVID-19 or potential influencing factors about COVID-19.  Through analyzing this data, we visually see the trends of patient growth rates, the deaths rate, and some other related data, and so that we can learn about the overall trends of COVID-19, like the characteristics of the infection of COVID-19 -- especially deadly and highly infections. The patterns of the class attribute -- NEW_CASE_TREND -- reflect the trend of the total COVID-19 cases. \n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./output.csv')\n",
    "#df.to_excel('./output.xlsx')\n",
    "#df.to_html('./output.html')"
   ]
  }
 ],
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}