import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

worldometer_df = pd.read_csv('worldometer_snapshots_April18_to_May18.csv')
worldometer_df = pd.read_csv('worldometers_snapshots_April18_to_September20.csv')
worldometer_df = pd.read_csv('worldometers_snapshots_April18_to_August1.csv')
worldometer_df = pd.read_csv('worldometers_snapshots_April18_to_July30.csv')
worldometer_df = pd.read_csv('worldometers_snapshots_April18_to_July3.csv')
worldometer_df = pd.read_csv('worldometers_snapshots_October11_to_October12.csv')

worldometer_df
Date Country Population Total Tests Total Cases Total Deaths Total Recovered Serious or Critical Active Cases
0 2020-10-11 USA 331552784 118486898.0 7991998 219695.0 5128162.0 14741.0 2644141.0
1 2020-10-11 India 1383826697 86877242.0 7119300 109184.0 6146427.0 8944.0 863689.0
2 2020-10-11 Brazil 212986866 17900000.0 5094979 150506.0 4470165.0 8318.0 474308.0
3 2020-10-11 Russia 145952340 50781349.0 1298718 22597.0 1020442.0 2300.0 255679.0
4 2020-10-11 Colombia 51035485 4173863.0 911316 27834.0 789787.0 2220.0 93695.0
... ... ... ... ... ... ... ... ... ...
423 2020-10-12 Montserrat 4993 483.0 13 1.0 12.0 NaN 0.0
424 2020-10-12 Falkland Islands 3508 2682.0 13 NaN 13.0 NaN 0.0
425 2020-10-12 Western Sahara 601379 NaN 10 1.0 8.0 NaN 1.0
426 2020-10-12 Anguilla 15041 1329.0 3 NaN 3.0 NaN 0.0
427 2020-10-12 Solomon Islands 691518 96.0 2 NaN NaN NaN 2.0

428 rows × 9 columns

country_name = 'Bangladesh'

country_df = worldometer_df.loc[worldometer_df['Country'] == country_name, :].reset_index(drop=True)
country_df
Date Country Population Total Tests Total Cases Total Deaths Total Recovered Serious or Critical Active Cases
0 2020-10-11 Bangladesh 165151528 2070995.0 378266 5524.0 292860.0 NaN 79882.0
1 2020-10-12 Bangladesh 165151528 2084222.0 379738 5555.0 294391.0 NaN 79792.0 
selected_date = datetime.strptime('10/11/2020', '%d/%m/%Y')

selected_date_df = worldometer_df.loc[worldometer_df['Date'] == selected_date.strftime('%Y-%m-%d'), :].reset_index(drop=True)
selected_date_df
Date Country Population Total Tests Total Cases Total Deaths Total Recovered Serious or Critical Active Cases 
last_date = datetime.strptime('12/10/2020', '%d/%m/%Y')

last_date_df = worldometer_df.loc[worldometer_df['Date'] == last_date.strftime('%Y-%m-%d'), :].reset_index(drop=True)
last_date_df
Date Country Population Total Tests Total Cases Total Deaths Total Recovered Serious or Critical Active Cases
0 2020-10-12 USA 331552784 119497624.0 8037789 220011.0 5184615.0 14914.0 2633163.0
1 2020-10-12 India 1383826697 87872093.0 7173565 109894.0 6224792.0 8944.0 838879.0
2 2020-10-12 Brazil 212986866 17900000.0 5103408 150709.0 4495269.0 8318.0 457430.0
3 2020-10-12 Russia 145952340 51191309.0 1312310 22722.0 1024235.0 2300.0 265353.0
4 2020-10-12 Colombia 51035485 4202181.0 919083 27985.0 798396.0 2220.0 92702.0
... ... ... ... ... ... ... ... ... ...
209 2020-10-12 Montserrat 4993 483.0 13 1.0 12.0 NaN 0.0
210 2020-10-12 Falkland Islands 3508 2682.0 13 NaN 13.0 NaN 0.0
211 2020-10-12 Western Sahara 601379 NaN 10 1.0 8.0 NaN 1.0
212 2020-10-12 Anguilla 15041 1329.0 3 NaN 3.0 NaN 0.0
213 2020-10-12 Solomon Islands 691518 96.0 2 NaN NaN NaN 2.0

214 rows × 9 columns

last_date_df['Case Fatality Ratio'] = last_date_df['Total Deaths'] / last_date_df['Total Cases']

plt.figure(figsize=(12,8))
plt.hist(100 * np.array(last_date_df['Case Fatality Ratio']), bins=np.arange(35))
plt.xlabel('Death Rate (%)', fontsize=16)
plt.ylabel('Number of Countries', fontsize=16)
plt.title('Histogram of Death Rates for various Countries', fontsize=18)
plt.show()

min_number_of_cases = 1000

greatly_affected_df = last_date_df.loc[last_date_df['Total Cases'] > min_number_of_cases,:]

plt.figure(figsize=(12,8))
plt.hist(100 * np.array(greatly_affected_df['Case Fatality Ratio']), bins=np.arange(35))
plt.xlabel('Death Rate (%)', fontsize=16)
plt.ylabel('Number of Countries', fontsize=16)
plt.title('Histogram of Death Rates for various Countries', fontsize=18)
plt.show()

last_date_df['Num Tests per Positive Case'] = last_date_df['Total Tests'] / last_date_df['Total Cases']

min_number_of_cases = 1000
greatly_affected_df = last_date_df.loc[last_date_df['Total Cases'] > min_number_of_cases,:]

x_axis_limit = 80

death_rate_percent = 100 * np.array(greatly_affected_df['Case Fatality Ratio'])
num_test_per_positive = np.array(greatly_affected_df['Num Tests per Positive Case'])
num_test_per_positive[num_test_per_positive > x_axis_limit] = x_axis_limit
total_num_deaths = np.array(greatly_affected_df['Total Deaths'])
population = np.array(greatly_affected_df['Population'])

plt.figure(figsize=(16,12))
plt.scatter(x=num_test_per_positive, y=death_rate_percent, 
            s=0.5*np.power(np.log(1+population),2), 
            c=np.log10(1+total_num_deaths))
plt.colorbar()
plt.ylabel('Death Rate (%)', fontsize=16)
plt.xlabel('Number of Tests per Positive Case', fontsize=16)
plt.title('Death Rate as function of Testing Quality', fontsize=18)
plt.xlim(-1, x_axis_limit + 12)
plt.ylim(-0.2,17)

# plot on top of the figure the names of the
#countries_to_display = greatly_affected_df['Country'].unique().tolist()
countries_to_display = ['USA', 'Russia', 'Spain', 'Bangladesh', 'Brazil', 'UK', 'Italy', 'France', 
                        'Germany', 'India', 'Canada', 'Belgium', 'Mexico', 'Netherlands', 
                        'Sweden', 'Portugal', 'UAE', 'Poland', 'Indonesia', 'Romania', 
                        'Israel','Thailand','Kyrgyzstan','El Salvador', 'S. Korea', 
                        'Denmark', 'Serbia', 'Norway', 'Algeria', 'Bahrain','Slovenia',
                        'Greece','Cuba','Hong Kong','Lithuania', 'Australia', 'Morocco', 
                        'Malaysia', 'Nigeria', 'Moldova', 'Ghana', 'Armenia', 'Bolivia', 
                        'Iraq', 'Hungary', 'Cameroon', 'Azerbaijan']

for country_name in countries_to_display:
    country_index = greatly_affected_df.index[greatly_affected_df['Country'] == country_name]
    plt.text(x=num_test_per_positive[country_index] + 0.5,
             y=death_rate_percent[country_index] + 0.2,
             s=country_name, fontsize=10)
plt.show()

posx and posy should be finite values
posx and posy should be finite values

good_testing_threshold = 300
good_testing_df = greatly_affected_df.loc[greatly_affected_df['Num Tests per Positive Case'] > good_testing_threshold,:]
good_testing_df
Date Country Population Total Tests Total Cases Total Deaths Total Recovered Serious or Critical Active Cases Case Fatality Ratio Num Tests per Positive Case
48 2020-10-12 China 1439323776 160000000.0 85578 4634.0 80714.0 NaN 230.0 0.054149 1869.639393
123 2020-10-12 Hong Kong 7514161 3473928.0 5194 105.0 4921.0 8.0 168.0 0.020216 668.834809
158 2020-10-12 New Zealand 5002100 1002790.0 1871 25.0 1801.0 NaN 45.0 0.013362 535.964725
164 2020-10-12 Vietnam 97585632 1246480.0 1110 35.0 1025.0 NaN 50.0 0.031532 1122.954955 
estimated_death_rate_percent = 100 * good_testing_df['Total Deaths'].sum() / good_testing_df['Total Cases'].sum()

print('Death Rate only for "good testing countries" is %.2f%s' %(estimated_death_rate_percent,'%'))

Death Rate only for "good testing countries" is 5.12%