import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import random
import math
import time
from sklearn.metrics import mean_squared_error,mean_absolute_error
import datetime
import operator
plt.style.use('fivethirtyeight')
%matplotlib inline
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVR
confirmed_cases = pd.read_csv('confirmed_cases.csv')
confirmed_cases.head()
Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 9/21/20 | 9/22/20 | 9/23/20 | 9/24/20 | 9/25/20 | 9/26/20 | 9/27/20 | 9/28/20 | 9/29/20 | 9/30/20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | NaN | Afghanistan | 33.93911 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 39074 | 39096 | 39145 | 39170 | 39186 | 39192 | 39227 | 39233 | 39254 | 39268 |
1 | NaN | Albania | 41.15330 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 12535 | 12666 | 12787 | 12921 | 13045 | 13153 | 13259 | 13391 | 13518 | 13649 |
2 | NaN | Algeria | 28.03390 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 50023 | 50214 | 50400 | 50579 | 50754 | 50914 | 51067 | 51213 | 51368 | 51530 |
3 | NaN | Andorra | 42.50630 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1681 | 1681 | 1753 | 1753 | 1836 | 1836 | 1836 | 1966 | 1966 | 2050 |
4 | NaN | Angola | -11.20270 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 4117 | 4236 | 4363 | 4475 | 4590 | 4672 | 4718 | 4797 | 4905 | 4972 |
5 rows × 257 columns
deaths_reported= pd.read_csv('deaths_cases.csv')
deaths_reported.head()
Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 9/21/20 | 9/22/20 | 9/23/20 | 9/24/20 | 9/25/20 | 9/26/20 | 9/27/20 | 9/28/20 | 9/29/20 | 9/30/20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | NaN | Afghanistan | 33.93911 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1444 | 1445 | 1446 | 1451 | 1451 | 1453 | 1453 | 1455 | 1458 | 1458 |
1 | NaN | Albania | 41.15330 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 364 | 367 | 370 | 370 | 373 | 375 | 377 | 380 | 384 | 387 |
2 | NaN | Algeria | 28.03390 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1679 | 1689 | 1698 | 1703 | 1707 | 1711 | 1714 | 1719 | 1726 | 1736 |
3 | NaN | Andorra | 42.50630 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 53 | 53 | 53 | 53 | 53 | 53 | 53 | 53 | 53 | 53 |
4 | NaN | Angola | -11.20270 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 154 | 155 | 159 | 162 | 167 | 171 | 174 | 176 | 179 | 183 |
5 rows × 257 columns
recoveries_cases = pd.read_csv('recovered_cases.csv')
recoveries_cases.head()
Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 9/21/20 | 9/22/20 | 9/23/20 | 9/24/20 | 9/25/20 | 9/26/20 | 9/27/20 | 9/28/20 | 9/29/20 | 9/30/20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | NaN | Afghanistan | 33.93911 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 32576 | 32576 | 32610 | 32619 | 32619 | 32635 | 32642 | 32642 | 32746 | 32789 |
1 | NaN | Albania | 41.15330 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 6995 | 7042 | 7139 | 7239 | 7309 | 7397 | 7397 | 7629 | 7732 | 7847 |
2 | NaN | Algeria | 28.03390 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 35180 | 35307 | 35428 | 35544 | 35654 | 35756 | 35860 | 35962 | 36063 | 36174 |
3 | NaN | Andorra | 42.50630 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1199 | 1199 | 1203 | 1203 | 1263 | 1263 | 1263 | 1265 | 1265 | 1432 |
4 | NaN | Angola | -11.20270 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1449 | 1462 | 1473 | 1503 | 1554 | 1639 | 1707 | 1813 | 1833 | 1941 |
5 rows × 257 columns
latest_data = pd.read_csv('latest_cases.csv')
latest_data.tail(200)
Province_State | Country_Region | Last_Update | Lat | Long_ | Confirmed | Deaths | Recovered | Active | Admin2 | FIPS | Combined_Key | Incident_Rate | People_Tested | People_Hospitalized | UID | ISO3 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3755 | West Virginia | US | 2020-10-02 05:23:31 | 38.843154 | -81.119349 | 25 | 0 | 0 | 25.0 | Calhoun | 54013.0 | Calhoun, West Virginia, US | 351.666901 | NaN | NaN | 84054013 | USA |
3756 | West Virginia | US | 2020-10-02 05:23:31 | 38.462311 | -81.076522 | 37 | 2 | 0 | 35.0 | Clay | 54015.0 | Clay, West Virginia, US | 434.884814 | NaN | NaN | 84054015 | USA |
3757 | West Virginia | US | 2020-10-02 05:23:31 | 39.270572 | -80.706715 | 28 | 1 | 0 | 27.0 | Doddridge | 54017.0 | Doddridge, West Virginia, US | 331.439394 | NaN | NaN | 84054017 | USA |
3758 | West Virginia | US | 2020-10-02 05:23:31 | 38.029749 | -81.082866 | 634 | 14 | 0 | 620.0 | Fayette | 54019.0 | Fayette, West Virginia, US | 1495.071452 | NaN | NaN | 84054019 | USA |
3759 | West Virginia | US | 2020-10-02 05:23:31 | 38.925128 | -80.859527 | 48 | 0 | 0 | 48.0 | Gilmer | 54021.0 | Gilmer, West Virginia, US | 613.575355 | NaN | NaN | 84054021 | USA |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
3950 | NaN | West Bank and Gaza | 2020-10-02 05:23:31 | 31.952200 | 35.233200 | 40322 | 318 | 32577 | 7427.0 | NaN | NaN | West Bank and Gaza | 790.407997 | NaN | NaN | 275 | PSE |
3951 | NaN | Western Sahara | 2020-10-02 05:23:31 | 24.215500 | -12.885800 | 10 | 1 | 8 | 1.0 | NaN | NaN | Western Sahara | 1.674116 | NaN | NaN | 732 | ESH |
3952 | NaN | Yemen | 2020-10-02 05:23:31 | 15.552727 | 48.516388 | 2039 | 587 | 1297 | 155.0 | NaN | NaN | Yemen | 6.836325 | NaN | NaN | 887 | YEM |
3953 | NaN | Zambia | 2020-10-02 05:23:31 | -13.133897 | 27.849332 | 14802 | 333 | 13961 | 508.0 | NaN | NaN | Zambia | 80.515859 | NaN | NaN | 894 | ZMB |
3954 | NaN | Zimbabwe | 2020-10-02 05:23:31 | -19.015438 | 29.154857 | 7850 | 228 | 6312 | 1310.0 | NaN | NaN | Zimbabwe | 52.815976 | NaN | NaN | 716 | ZWE |
200 rows × 17 columns
#Fetching all the columns from the confirmed dataset
colsc = confirmed_cases.keys()
colsd = deaths_reported.keys()
colsr = recoveries_cases.keys()
#Extracting the date columns
confirmed = confirmed_cases.loc[:,colsc[4]:colsc[-1]]
deaths = deaths_reported.loc[:,colsd[4]:colsd[-1]]
recoveries = recoveries_cases.loc[:,colsr[4]:colsr[-1]]
confirmed
1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | 1/28/20 | 1/29/20 | 1/30/20 | 1/31/20 | ... | 9/21/20 | 9/22/20 | 9/23/20 | 9/24/20 | 9/25/20 | 9/26/20 | 9/27/20 | 9/28/20 | 9/29/20 | 9/30/20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 39074 | 39096 | 39145 | 39170 | 39186 | 39192 | 39227 | 39233 | 39254 | 39268 |
1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 12535 | 12666 | 12787 | 12921 | 13045 | 13153 | 13259 | 13391 | 13518 | 13649 |
2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 50023 | 50214 | 50400 | 50579 | 50754 | 50914 | 51067 | 51213 | 51368 | 51530 |
3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1681 | 1681 | 1753 | 1753 | 1836 | 1836 | 1836 | 1966 | 1966 | 2050 |
4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 4117 | 4236 | 4363 | 4475 | 4590 | 4672 | 4718 | 4797 | 4905 | 4972 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
261 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 36151 | 36580 | 37083 | 37591 | 37963 | 38253 | 38703 | 39121 | 39541 | 39899 |
262 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 |
263 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 2028 | 2028 | 2029 | 2029 | 2029 | 2030 | 2030 | 2031 | 2031 | 2034 |
264 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 14175 | 14389 | 14443 | 14491 | 14515 | 14612 | 14641 | 14660 | 14715 | 14759 |
265 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 7683 | 7711 | 7725 | 7752 | 7787 | 7803 | 7812 | 7816 | 7837 | 7838 |
266 rows × 253 columns
deaths
1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | 1/28/20 | 1/29/20 | 1/30/20 | 1/31/20 | ... | 9/21/20 | 9/22/20 | 9/23/20 | 9/24/20 | 9/25/20 | 9/26/20 | 9/27/20 | 9/28/20 | 9/29/20 | 9/30/20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1444 | 1445 | 1446 | 1451 | 1451 | 1453 | 1453 | 1455 | 1458 | 1458 |
1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 364 | 367 | 370 | 370 | 373 | 375 | 377 | 380 | 384 | 387 |
2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1679 | 1689 | 1698 | 1703 | 1707 | 1711 | 1714 | 1719 | 1726 | 1736 |
3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 53 | 53 | 53 | 53 | 53 | 53 | 53 | 53 | 53 | 53 |
4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 154 | 155 | 159 | 162 | 167 | 171 | 174 | 176 | 179 | 183 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
261 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 265 | 269 | 272 | 274 | 278 | 285 | 291 | 299 | 306 | 311 |
262 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
263 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 586 | 586 | 586 | 586 | 587 | 587 | 587 | 587 | 587 | 587 |
264 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 331 | 331 | 332 | 332 | 332 | 332 | 332 | 332 | 332 | 332 |
265 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 225 | 226 | 227 | 227 | 227 | 227 | 227 | 228 | 228 | 228 |
266 rows × 253 columns
recoveries
1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | 1/28/20 | 1/29/20 | 1/30/20 | 1/31/20 | ... | 9/21/20 | 9/22/20 | 9/23/20 | 9/24/20 | 9/25/20 | 9/26/20 | 9/27/20 | 9/28/20 | 9/29/20 | 9/30/20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 32576 | 32576 | 32610 | 32619 | 32619 | 32635 | 32642 | 32642 | 32746 | 32789 |
1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 6995 | 7042 | 7139 | 7239 | 7309 | 7397 | 7397 | 7629 | 7732 | 7847 |
2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 35180 | 35307 | 35428 | 35544 | 35654 | 35756 | 35860 | 35962 | 36063 | 36174 |
3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1199 | 1199 | 1203 | 1203 | 1263 | 1263 | 1263 | 1265 | 1265 | 1432 |
4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1449 | 1462 | 1473 | 1503 | 1554 | 1639 | 1707 | 1813 | 1833 | 1941 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
248 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 24428 | 25469 | 26288 | 26934 | 27183 | 27704 | 29068 | 30220 | 31047 | 31743 |
249 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 |
250 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1235 | 1240 | 1245 | 1250 | 1255 | 1260 | 1266 | 1266 | 1275 | 1286 |
251 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 13629 | 13629 | 13629 | 13643 | 13643 | 13727 | 13784 | 13821 | 13937 | 13959 |
252 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 5924 | 5979 | 6007 | 6043 | 6057 | 6067 | 6106 | 6112 | 6122 | 6303 |
253 rows × 253 columns
dates = confirmed.keys()
dates
Index(['1/22/20', '1/23/20', '1/24/20', '1/25/20', '1/26/20', '1/27/20', '1/28/20', '1/29/20', '1/30/20', '1/31/20', ... '9/21/20', '9/22/20', '9/23/20', '9/24/20', '9/25/20', '9/26/20', '9/27/20', '9/28/20', '9/29/20', '9/30/20'], dtype='object', length=253)
world_cases = []
total_deaths = []
mortality_rate = []
recovery_rate = []
total_recoveries = []
total_active = []
china_cases = []
italy_cases = []
us_cases = []
spain_cases = []
france_cases = []
germany_cases = []
uk_cases = []
russia_cases = []
india_cases = []
china_deaths = []
italy_deaths = []
us_deaths = []
spain_deaths = []
france_deaths = []
germany_deaths = []
uk_deaths = []
russia_deaths = []
india_deaths = []
china_recoveries = []
italy_recoveries = []
us_recoveries = []
spain_recoveries = []
france_recoveries = []
germany_recoveries= []
uk_recoveries = []
russia_recoveries = []
india_recoveries = []
for i in dates:
confirmed_sum = confirmed[i].sum()
deaths_sum = deaths[i].sum()
recoveries_sum = recoveries[i].sum()
world_cases.append(confirmed_sum)
total_deaths.append(deaths_sum)
total_recoveries.append(recoveries_sum)
total_active.append(confirmed_sum-deaths_sum-recoveries_sum)
mortality_rate.append(deaths_sum/confirmed_sum)
recovery_rate.append(recoveries_sum/confirmed_sum)
china_cases.append(confirmed_cases[confirmed_cases['Country/Region']== 'China'][i].sum())
italy_cases.append(confirmed_cases[confirmed_cases['Country/Region']== 'Italy'][i].sum())
us_cases.append(confirmed_cases[confirmed_cases['Country/Region']== 'US'][i].sum())
spain_cases.append(confirmed_cases[confirmed_cases['Country/Region']== 'Spain'][i].sum())
france_cases.append(confirmed_cases[confirmed_cases['Country/Region']== 'France'][i].sum())
germany_cases.append(confirmed_cases[confirmed_cases['Country/Region']== 'Germany'][i].sum())
uk_cases.append(confirmed_cases[confirmed_cases['Country/Region']== 'United Kingdom'][i].sum())
russia_cases.append(confirmed_cases[confirmed_cases['Country/Region']== 'Russia'][i].sum())
india_cases.append(confirmed_cases[confirmed_cases['Country/Region']== 'India'][i].sum())
china_deaths.append(deaths_reported[deaths_reported['Country/Region'] == 'China'][i].sum())
russia_deaths.append(deaths_reported[deaths_reported['Country/Region'] == 'Russia'][i].sum())
us_deaths.append(deaths_reported[deaths_reported['Country/Region'] == 'US'][i].sum())
italy_deaths.append(deaths_reported[deaths_reported['Country/Region'] == 'Italy'][i].sum())
spain_deaths.append(deaths_reported[deaths_reported['Country/Region'] == 'Spain'][i].sum())
france_deaths.append(deaths_reported[deaths_reported['Country/Region'] == 'France'][i].sum())
germany_deaths.append(deaths_reported[deaths_reported['Country/Region'] == 'Germany'][i].sum())
uk_deaths.append(deaths_reported[deaths_reported['Country/Region'] == 'United Kingdom'][i].sum())
india_deaths.append(deaths_reported[deaths_reported['Country/Region'] == 'India'][i].sum())
china_recoveries.append(recoveries_cases[recoveries_cases['Country/Region'] == 'China'][i].sum())
russia_recoveries.append(recoveries_cases[recoveries_cases['Country/Region'] == 'Russia'][i].sum())
us_recoveries.append(recoveries_cases[recoveries_cases['Country/Region'] == 'US'][i].sum())
spain_recoveries.append(recoveries_cases[recoveries_cases['Country/Region'] == 'Spain'][i].sum())
france_recoveries.append(recoveries_cases[recoveries_cases['Country/Region'] == 'France'][i].sum())
germany_recoveries.append(recoveries_cases[recoveries_cases['Country/Region'] == 'Germany'][i].sum())
uk_recoveries.append(recoveries_cases[recoveries_cases['Country/Region'] == 'United Kingdom'][i].sum())
india_recoveries.append(recoveries_cases[recoveries_cases['Country/Region'] == 'India'][i].sum())
italy_recoveries.append(recoveries_cases[recoveries_cases['Country/Region'] == 'Italy'][i].sum())
world_cases
[555, 654, 941, 1434, 2118, 2927, 5578, 6167, 8235, 9927, 12038, 16787, 19887, 23898, 27643, 30803, 34396, 37130, 40160, 42769, 44811, 45229, 60382, 66909, 69051, 71235, 73270, 75152, 75652, 76212, 76841, 78602, 78982, 79546, 80399, 81376, 82736, 84121, 86014, 88397, 90374, 92959, 95276, 98040, 102040, 106102, 110062, 114020, 119036, 126715, 132504, 146882, 157958, 169257, 184002, 199934, 219450, 246602, 277234, 309192, 343436, 386753, 428246, 479208, 541953, 606912, 674295, 733734, 798905, 875799, 952188, 1033586, 1116829, 1197166, 1269049, 1342333, 1420111, 1504095, 1590519, 1678051, 1754350, 1849615, 1919891, 1991293, 2073104, 2169930, 2257855, 2331179, 2411130, 2485712, 2561532, 2639515, 2727611, 2811834, 2895698, 2967707, 3037293, 3112857, 3190204, 3273773, 3360841, 3441235, 3517738, 3594723, 3675349, 3765237, 3854289, 3945570, 4030490, 4105893, 4182819, 4267038, 4351907, 4448495, 4544707, 4639080, 4716640, 4805382, 4902142, 5005151, 5111218, 5217960, 5322828, 5417342, 5503968, 5597063, 5699923, 5819253, 5940303, 6077319, 6184188, 6279950, 6401009, 6519204, 6646030, 6776834, 6912199, 7023963, 7126556, 7251160, 7385702, 7523365, 7652299, 7787820, 7920905, 8040610, 8182435, 8324640, 8464650, 8644671, 8802303, 8930939, 9069007, 9233766, 9405255, 9582940, 9774286, 9952987, 10114982, 10271437, 10445390, 10662454, 10869980, 11073285, 11267193, 11450068, 11616107, 11826998, 12038790, 12266857, 12499279, 12715556, 12908220, 13100342, 13322055, 13553083, 13805656, 14047405, 14285016, 14499571, 14705751, 14939529, 15219970, 15502662, 15783799, 16039383, 16252479, 16478702, 16731323, 17020334, 17300908, 17590783, 17841522, 18070369, 18272611, 18531135, 18802272, 19087570, 19368389, 19627881, 19851904, 20079497, 20334239, 20611326, 20897221, 21201628, 21449685, 21661962, 21871191, 22127574, 22401098, 22668263, 22938796, 23204330, 23410383, 23636482, 23878519, 24166445, 24445652, 24727072, 24988837, 25215048, 25477252, 25742176, 26024054, 26304856, 26617971, 26881547, 27103845, 27337760, 27570742, 27862680, 28161434, 28481322, 28758945, 28995373, 29274650, 29558184, 29862502, 30176240, 30499534, 30780755, 31021972, 31320880, 31600134, 31866343, 32227277, 32562075, 32840012, 33077724, 33353615, 33641553, 33968093]
total_deaths
[17, 18, 26, 42, 56, 82, 131, 133, 171, 213, 259, 362, 426, 492, 564, 634, 719, 806, 906, 1013, 1113, 1118, 1371, 1523, 1666, 1770, 1868, 2008, 2123, 2248, 2252, 2459, 2470, 2630, 2710, 2771, 2814, 2873, 2942, 2996, 3085, 3160, 3255, 3348, 3460, 3559, 3803, 3987, 4267, 4611, 4917, 5414, 5834, 6475, 7153, 7964, 8867, 9981, 11460, 13180, 14854, 16797, 19079, 21877, 24893, 28388, 32102, 35572, 39620, 44365, 49863, 56101, 62205, 68351, 73540, 79481, 87712, 94464, 102237, 109629, 115854, 121671, 127587, 134567, 142918, 150221, 158588, 164601, 169862, 175726, 182917, 189644, 196485, 203142, 208774, 212708, 217382, 223929, 230657, 236559, 241722, 247205, 250694, 254808, 260714, 267277, 272579, 278054, 282287, 285874, 289354, 294889, 299981, 305171, 310304, 314424, 317712, 321412, 326158, 330926, 335657, 340851, 344742, 347870, 349062, 353180, 358326, 362944, 367536, 371610, 374478, 377554, 382296, 387745, 392879, 397453, 401265, 404006, 407707, 412562, 417623, 422322, 426545, 430722, 434129, 437559, 444294, 449396, 454386, 460545, 464746, 468747, 472311, 477539, 482712, 487411, 492109, 496574, 499712, 503435, 508361, 513291, 518361, 523297, 527661, 531133, 534954, 540984, 546257, 551670, 556965, 561750, 565740, 569553, 575148, 580582, 586332, 592988, 598609, 602661, 606829, 612998, 619920, 629817, 635852, 641398, 645058, 650233, 656545, 663118, 669124, 675313, 680796, 685087, 689402, 696350, 703302, 709787, 716089, 721499, 726039, 730973, 737391, 743999, 750232, 760366, 765715, 769903, 774036, 780909, 787659, 793684, 799224, 804787, 808648, 812998, 819396, 825680, 831566, 837080, 842452, 846353, 850535, 857015, 863028, 868733, 874639, 879577, 883339, 892646, 897383, 903686, 909479, 915356, 920231, 923873, 928303, 934852, 940606, 946062, 951767, 956999, 960695, 964746, 970626, 976233, 982949, 988864, 994143, 997734, 1001646, 1007755, 1014161]
total_recoveries
[28, 30, 36, 39, 52, 61, 107, 126, 143, 222, 284, 472, 623, 852, 1124, 1487, 2011, 2616, 3244, 3946, 4683, 5150, 6295, 8058, 9395, 10865, 12583, 14352, 16121, 18177, 18890, 22886, 23394, 25227, 27905, 30384, 33277, 36711, 39782, 42716, 45602, 48228, 51170, 53796, 55865, 58359, 60694, 62493, 64404, 67002, 68324, 70251, 72622, 76032, 78086, 80838, 83321, 84958, 87403, 91670, 97885, 98351, 107992, 113775, 122145, 130921, 139424, 148891, 164337, 177825, 192918, 209967, 225415, 245832, 259672, 276252, 299643, 328359, 353707, 375509, 401766, 421180, 448347, 473436, 510106, 540926, 567049, 590968, 622623, 644613, 679456, 709881, 738661, 788712, 816491, 845096, 872865, 906136, 948318, 1013284, 1051537, 1092416, 1124732, 1158819, 1195359, 1241365, 1280833, 1317383, 1370933, 1404527, 1451521, 1488542, 1544398, 1584114, 1632122, 1688714, 1729621, 1782542, 1834647, 1893575, 1944840, 2053491, 2108462, 2163902, 2227625, 2282839, 2346232, 2413089, 2490435, 2560888, 2637208, 2692105, 2796228, 2875332, 2945385, 3014544, 3086748, 3141849, 3293412, 3375694, 3454832, 3540714, 3620438, 3706372, 3777157, 3857365, 3955205, 4073992, 4155134, 4250149, 4365974, 4434711, 4526337, 4630412, 4746172, 4839028, 4945742, 5052107, 5141227, 5235793, 5353115, 5469041, 5753918, 5863818, 6059651, 6178973, 6302585, 6447610, 6605559, 6740073, 6879465, 7005174, 7116853, 7257197, 7399397, 7559178, 7711548, 7894858, 8045815, 8133692, 8292688, 8467335, 8643722, 8813886, 9043203, 9262520, 9402996, 9572619, 9746473, 9948163, 10170650, 10369140, 10553585, 10690555, 10913000, 11134735, 11356275, 11545401, 11737927, 11939109, 12115825, 12280520, 12585473, 12826815, 12992176, 13276831, 13445842, 13676868, 13888301, 14116451, 14333914, 14541573, 14712252, 14922177, 15137610, 15337659, 15570598, 15794947, 15996510, 16196934, 16408559, 16616995, 16818436, 17073236, 17291874, 17512585, 17726336, 17927195, 18137310, 18336112, 18524391, 18776723, 18992383, 19215800, 19439157, 19624935, 19854392, 20078979, 20307938, 20527185, 20778990, 21016801, 21252815, 21496061, 21714180, 21977816, 22232480, 22472106, 22715380, 22925667, 23151081, 23387690, 23637164]
deaths_sum
1014161
confirmed_sum
33968093
recoveries_sum
23637164
us_cases
[1, 1, 2, 2, 5, 5, 5, 6, 6, 8, 8, 8, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 16, 16, 16, 16, 16, 16, 17, 17, 25, 32, 54, 74, 107, 184, 237, 403, 519, 589, 782, 1145, 1584, 2214, 2971, 3211, 4679, 6513, 9153, 13643, 20007, 25991, 34804, 45973, 56571, 68583, 86495, 105103, 124701, 143672, 165615, 192048, 217730, 248186, 280173, 313154, 341245, 371481, 402917, 434886, 469478, 503006, 532447, 559318, 585240, 613887, 643833, 675261, 708179, 735960, 761620, 790121, 816100, 845361, 878645, 912247, 944231, 971149, 994204, 1018950, 1046653, 1076323, 1110394, 1138570, 1163016, 1186303, 1210960, 1236037, 1263804, 1290687, 1315641, 1334685, 1354141, 1376823, 1397811, 1424941, 1450023, 1474187, 1492437, 1514460, 1535031, 1558734, 1583861, 1607949, 1629327, 1649389, 1667937, 1687244, 1705634, 1728330, 1752689, 1776531, 1795916, 1813448, 1834439, 1854437, 1875699, 1900769, 1923078, 1940659, 1958249, 1976464, 1997257, 2019935, 2045136, 2070461, 2090161, 2109973, 2133693, 2159245, 2187044, 2218476, 2251036, 2277433, 2308050, 2343205, 2378183, 2418044, 2463274, 2505748, 2545202, 2586113, 2631972, 2683112, 2737612, 2790743, 2836480, 2886342, 2931382, 2991455, 3050023, 3113250, 3181074, 3241100, 3300020, 3359307, 3426658, 3494087, 3571354, 3642859, 3706333, 3767978, 3829433, 3894014, 3964927, 4033597, 4107256, 4173596, 4228323, 4284833, 4350802, 4421427, 4489400, 4556331, 4614540, 4661823, 4707373, 4764691, 4817408, 4877251, 4935332, 4991268, 5037976, 5087617, 5134340, 5190563, 5242056, 5306406, 5354024, 5395799, 5431045, 5476283, 5522710, 5566781, 5615216, 5659837, 5694522, 5732164, 5770344, 5814518, 5860493, 5906565, 5953475, 5988770, 6023072, 6066374, 6106154, 6150016, 6200518, 6244970, 6276365, 6300622, 6327009, 6360212, 6396100, 6443652, 6485123, 6520122, 6553652, 6592584, 6630604, 6675338, 6723933, 6768119, 6804814, 6856884, 6895918, 6933548, 6977658, 7032712, 7078089, 7115008, 7148045, 7190230, 7233042]
sum = 0
for i in india_recoveries:
sum +=i
sum
209284281
print("India total recover {}".format(sum))
India total recover 209284281
sum = 0
for i in us_recoveries:
sum += i
sum
194314836
print("Us total recover {}".format(sum))
Us total recover 194314836
# daily increse cases, death cases, recover cases for world as wel as different country
def daily_increse(data):
d = []
for i in range(len(data)):
if i == 0:
d.append(i)
else:
d.append(data[i]-data[i-1])
return d
#increase cases
world_daily_increase = daily_increse(world_cases)
china_daily_increase = daily_increse(china_cases)
india_daily_increase = daily_increse(india_cases)
us_daily_increase = daily_increse(us_cases)
german_daily_increse = daily_increse(germany_cases)
china_daily_increase
[0, 95, 277, 486, 669, 802, 2632, 578, 2054, 1661, 2089, 4739, 3086, 3991, 3733, 3147, 3523, 2704, 3015, 2525, 2032, 373, 15136, 6463, 2055, 2100, 1921, 1777, 408, 458, 473, 1451, 21, 219, 513, 412, 434, 328, 428, 576, 204, 125, 125, 151, 153, 80, 53, 37, 27, 34, 11, 13, 32, 26, 30, 25, 44, 54, 94, 55, 130, 63, 93, 70, 121, 115, 102, 123, 76, 81, 82, 71, 79, 32, 59, 63, 53, 91, 74, 58, 73, 120, 79, 93, 50, 47, 357, 27, 18, 12, 36, 15, 16, 15, 10, 3, 6, 22, 4, 12, 3, 0, 5, 2, 2, 2, 5, 1, 14, 20, 1, 7, 6, 5, 9, 6, 10, 9, 0, 0, 0, 18, 3, 11, 7, 1, 3, 0, 17, 5, 18, 8, 7, -1, 11, 6, 9, 5, 4, 3, 11, 7, 12, 58, 49, 43, 44, 36, 36, 0, 59, 19, 52, 29, 20, 28, 24, 18, 14, 23, 5, 31, 14, 8, 19, 14, 18, 28, 33, 42, 0, 79, 46, 0, 109, 20, 81, 75, 16, 85, 119, 86, 198, 139, 157, 179, 189, 213, 207, 223, 276, 166, 172, 158, 114, 107, 122, 132, 120, 92, 121, 113, 52, 87, 99, 70, 65, 96, 66, 53, 33, 40, 49, 38, 41, 23, 34, 32, 30, 22, 27, 32, 19, 19, 20, 33, 22, 17, 33, 20, 9, 13, 27, 18, 23, 29, 22, 16, 18, 41, 17, 23, 35, 12, 18, 10, 15, 17, 15, 27, 22, 23, 17]
india_daily_increase
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 23, 2, 1, 3, 5, 4, 13, 6, 11, 9, 20, 11, 6, 23, 14, 38, 50, 86, 66, 103, 37, 121, 70, 160, 100, 37, 227, 146, 601, 545, 24, 515, 506, 1190, 533, 605, 809, 873, 848, 759, 1248, 1034, 835, 1108, 922, 1370, 1893, 924, 1541, 1290, 1707, 1453, 1753, 1607, 1561, 1873, 1738, 1801, 2394, 2442, 2806, 3932, 2963, 3587, 3364, 3344, 3113, 4353, 3607, 3524, 3763, 3942, 3787, 4864, 5050, 4630, 6147, 5553, 6198, 6568, 6629, 7113, 6414, 5843, 7293, 7300, 8105, 8336, 8782, 7761, 8821, 9633, 9889, 9471, 10438, 10864, 8442, 10218, 10459, 10930, 11458, 11929, 11502, 10667, 10974, 12881, 13586, 14516, 15403, 14831, 14933, 15968, 16922, 17296, 18552, 19906, 19459, 18522, 18641, 19160, 20903, 22771, 24850, 24248, 22251, 22753, 24879, 26506, 27114, 28606, 28732, 28498, 29429, 32676, 34975, 35252, 38697, 40425, 37132, 37740, 45720, 49310, 48916, 48611, 49981, 44457, 51596, 50294, 52783, 61242, 54735, 52972, 52050, 52509, 56282, 62538, 61537, 64399, 62064, 53601, 60963, 66999, 64553, 64732, 64030, 57711, 55018, 64572, 69672, 68900, 69876, 69239, 61408, 60975, 57224, 85687, 77266, 76472, 78761, 78512, 69921, 78357, 83883, 83341, 86432, 90632, 90802, 75809, 89706, 95735, 96551, 97570, 94372, 92071, 83809, 90123, 97894, 96424, 93337, 92605, 86961, 75083, 83347, 86508, 86052, 85362, 88600, 82170, 70589, 80472, 86821]
us_daily_increase
[0, 0, 1, 0, 3, 0, 0, 1, 0, 2, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 8, 7, 22, 20, 33, 77, 53, 166, 116, 70, 193, 363, 439, 630, 757, 240, 1468, 1834, 2640, 4490, 6364, 5984, 8813, 11169, 10598, 12012, 17912, 18608, 19598, 18971, 21943, 26433, 25682, 30456, 31987, 32981, 28091, 30236, 31436, 31969, 34592, 33528, 29441, 26871, 25922, 28647, 29946, 31428, 32918, 27781, 25660, 28501, 25979, 29261, 33284, 33602, 31984, 26918, 23055, 24746, 27703, 29670, 34071, 28176, 24446, 23287, 24657, 25077, 27767, 26883, 24954, 19044, 19456, 22682, 20988, 27130, 25082, 24164, 18250, 22023, 20571, 23703, 25127, 24088, 21378, 20062, 18548, 19307, 18390, 22696, 24359, 23842, 19385, 17532, 20991, 19998, 21262, 25070, 22309, 17581, 17590, 18215, 20793, 22678, 25201, 25325, 19700, 19812, 23720, 25552, 27799, 31432, 32560, 26397, 30617, 35155, 34978, 39861, 45230, 42474, 39454, 40911, 45859, 51140, 54500, 53131, 45737, 49862, 45040, 60073, 58568, 63227, 67824, 60026, 58920, 59287, 67351, 67429, 77267, 71505, 63474, 61645, 61455, 64581, 70913, 68670, 73659, 66340, 54727, 56510, 65969, 70625, 67973, 66931, 58209, 47283, 45550, 57318, 52717, 59843, 58081, 55936, 46708, 49641, 46723, 56223, 51493, 64350, 47618, 41775, 35246, 45238, 46427, 44071, 48435, 44621, 34685, 37642, 38180, 44174, 45975, 46072, 46910, 35295, 34302, 43302, 39780, 43862, 50502, 44452, 31395, 24257, 26387, 33203, 35888, 47552, 41471, 34999, 33530, 38932, 38020, 44734, 48595, 44186, 36695, 52070, 39034, 37630, 44110, 55054, 45377, 36919, 33037, 42185, 42812]
# daily death cases world and as wel as different country
world_daily_deaths = daily_increse(total_deaths)
china_daily_deaths = daily_increse(china_deaths)
india_daily_deaths = daily_increse(india_deaths)
us_daily_deaths = daily_increse(us_deaths)
german_daily_deaths = daily_increse(germany_deaths)
world_daily_deaths
[0, 1, 8, 16, 14, 26, 49, 2, 38, 42, 46, 103, 64, 66, 72, 70, 85, 87, 100, 107, 100, 5, 253, 152, 143, 104, 98, 140, 115, 125, 4, 207, 11, 160, 80, 61, 43, 59, 69, 54, 89, 75, 95, 93, 112, 99, 244, 184, 280, 344, 306, 497, 420, 641, 678, 811, 903, 1114, 1479, 1720, 1674, 1943, 2282, 2798, 3016, 3495, 3714, 3470, 4048, 4745, 5498, 6238, 6104, 6146, 5189, 5941, 8231, 6752, 7773, 7392, 6225, 5817, 5916, 6980, 8351, 7303, 8367, 6013, 5261, 5864, 7191, 6727, 6841, 6657, 5632, 3934, 4674, 6547, 6728, 5902, 5163, 5483, 3489, 4114, 5906, 6563, 5302, 5475, 4233, 3587, 3480, 5535, 5092, 5190, 5133, 4120, 3288, 3700, 4746, 4768, 4731, 5194, 3891, 3128, 1192, 4118, 5146, 4618, 4592, 4074, 2868, 3076, 4742, 5449, 5134, 4574, 3812, 2741, 3701, 4855, 5061, 4699, 4223, 4177, 3407, 3430, 6735, 5102, 4990, 6159, 4201, 4001, 3564, 5228, 5173, 4699, 4698, 4465, 3138, 3723, 4926, 4930, 5070, 4936, 4364, 3472, 3821, 6030, 5273, 5413, 5295, 4785, 3990, 3813, 5595, 5434, 5750, 6656, 5621, 4052, 4168, 6169, 6922, 9897, 6035, 5546, 3660, 5175, 6312, 6573, 6006, 6189, 5483, 4291, 4315, 6948, 6952, 6485, 6302, 5410, 4540, 4934, 6418, 6608, 6233, 10134, 5349, 4188, 4133, 6873, 6750, 6025, 5540, 5563, 3861, 4350, 6398, 6284, 5886, 5514, 5372, 3901, 4182, 6480, 6013, 5705, 5906, 4938, 3762, 9307, 4737, 6303, 5793, 5877, 4875, 3642, 4430, 6549, 5754, 5456, 5705, 5232, 3696, 4051, 5880, 5607, 6716, 5915, 5279, 3591, 3912, 6109, 6406]
china_daily_deaths
[0, 1, 8, 16, 14, 26, 49, 2, 38, 42, 46, 102, 64, 66, 72, 70, 85, 87, 100, 107, 100, 5, 252, 152, 142, 103, 98, 139, 113, 122, 0, 205, 2, 150, 70, 52, 29, 44, 47, 35, 42, 33, 36, 32, 29, 28, 28, 23, 16, 22, 11, 8, 13, 10, 14, 13, 11, 8, 4, 6, 15, 0, 7, 4, 6, 5, 3, 5, 4, 1, 7, 6, 4, 4, 3, 2, 0, 2, 2, 1, 3, 0, 2, 0, 1, 0, 1290, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 1, 0, 0, 2, 0, 1, 1, 2, 0, 4, 1, 1, 1, 2, 6, 2, 3, 4, 1, 3, 1, 0, 5, 3, 4, 4, 3, 1, 2, 0, 0, 2, 1, 3, 0, 2, 0, 0, 1, 1, 2, 3, 3, 1, 1, 1, 3, 1, 0, 0, 2, 2, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0]
india_daily_deaths
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, -1, 3, 3, 0, 2, 8, 0, 4, 3, 5, 3, 23, 14, 0, 14, 13, 37, 14, 28, 48, 20, 42, 43, 27, 35, 12, 43, 38, 35, 38, 33, 53, 36, 40, 59, 45, 56, 58, 69, 71, 75, 69, 100, 68, 175, 127, 92, 104, 96, 116, 111, 82, 121, 136, 98, 104, 118, 154, 131, 146, 132, 150, 142, 142, 156, 148, 172, 190, 177, 269, 205, 223, 200, 221, 259, 275, 286, 297, 261, 266, 277, 352, 396, 386, 311, 325, 380, 2003, 334, 336, 375, 306, 445, 312, 465, 418, 407, 384, 410, 380, 418, 507, 434, 379, 442, 613, 425, 466, 483, 487, 475, 519, 550, 501, 553, 582, 605, 688, 671, 543, 681, 585, 650, 1129, 740, 757, 702, 711, 637, 785, 762, 763, 793, 853, 771, 803, 857, 904, 886, 933, 861, 1007, 871, 834, 942, 1007, 996, 944, 941, 876, 1091, 978, 983, 945, 912, 836, 848, 967, 1115, 1057, 1021, 948, 971, 819, 1045, 1043, 1096, 1089, 1065, 1016, 1133, 1115, 1172, 1209, 1201, 1114, 1136, 1054, 1290, 1132, 1174, 1247, 1133, 1130, 1053, 1085, 1129, 1141, 1089, 1124, 1039, 776, 1179, 1181]
us_daily_deaths
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 1, 4, 1, 2, 3, 4, 1, 6, 5, 10, 8, 7, 12, 27, 35, 59, 74, 99, 99, 110, 189, 239, 324, 408, 520, 633, 586, 692, 1085, 1172, 1503, 1392, 1523, 1590, 1770, 2571, 2146, 2228, 2218, 2119, 1813, 1946, 2455, 2609, 2176, 2093, 1960, 1931, 2186, 2548, 2434, 2469, 2154, 1698, 1329, 1466, 2241, 2521, 2322, 1891, 1692, 1120, 1333, 2318, 2364, 1934, 1747, 1488, 893, 1014, 1627, 1764, 1783, 1681, 1216, 758, 1172, 1552, 1526, 1217, 1244, 1109, 622, 548, 662, 1518, 1178, 1160, 963, 581, 776, 1068, 1007, 1023, 914, 659, 438, 519, 950, 901, 871, 828, 759, 308, 394, 838, 744, 718, 670, 604, 285, 389, 847, 759, 551, 617, 504, 273, 377, 625, 694, 727, 660, 265, 288, 355, 1222, 863, 1003, 824, 690, 459, 367, 926, 968, 943, 930, 868, 447, 514, 1107, 1216, 1094, 1132, 899, 472, 1120, 1374, 1432, 1209, 1243, 1115, 403, 530, 1378, 1376, 1253, 1242, 1077, 511, 527, 1064, 1505, 1070, 1338, 1034, 571, 446, 1325, 1353, 1079, 1105, 982, 450, 442, 1239, 1225, 1111, 971, 958, 310, 573, 1067, 1056, 1070, 965, 783, 403, 267, 445, 1206, 907, 1213, 714, 378, 422, 1288, 983, 870, 936, 712, 227, 356, 921, 1098, 914, 952, 740, 266, 316, 914, 946]
# daily recoveries form world and as wel as different countries
world_daily_recoveries = daily_increse(total_recoveries)
china_daily_recoveries = daily_increse(china_recoveries)
india_daily_recoveries = daily_increse(india_recoveries)
us_daily_recoveries = daily_increse(us_recoveries)
german_daily_recoveries = daily_increse(germany_recoveries)
world_daily_recoveries
[0, 2, 6, 3, 13, 9, 46, 19, 17, 79, 62, 188, 151, 229, 272, 363, 524, 605, 628, 702, 737, 467, 1145, 1763, 1337, 1470, 1718, 1769, 1769, 2056, 713, 3996, 508, 1833, 2678, 2479, 2893, 3434, 3071, 2934, 2886, 2626, 2942, 2626, 2069, 2494, 2335, 1799, 1911, 2598, 1322, 1927, 2371, 3410, 2054, 2752, 2483, 1637, 2445, 4267, 6215, 466, 9641, 5783, 8370, 8776, 8503, 9467, 15446, 13488, 15093, 17049, 15448, 20417, 13840, 16580, 23391, 28716, 25348, 21802, 26257, 19414, 27167, 25089, 36670, 30820, 26123, 23919, 31655, 21990, 34843, 30425, 28780, 50051, 27779, 28605, 27769, 33271, 42182, 64966, 38253, 40879, 32316, 34087, 36540, 46006, 39468, 36550, 53550, 33594, 46994, 37021, 55856, 39716, 48008, 56592, 40907, 52921, 52105, 58928, 51265, 108651, 54971, 55440, 63723, 55214, 63393, 66857, 77346, 70453, 76320, 54897, 104123, 79104, 70053, 69159, 72204, 55101, 151563, 82282, 79138, 85882, 79724, 85934, 70785, 80208, 97840, 118787, 81142, 95015, 115825, 68737, 91626, 104075, 115760, 92856, 106714, 106365, 89120, 94566, 117322, 115926, 284877, 109900, 195833, 119322, 123612, 145025, 157949, 134514, 139392, 125709, 111679, 140344, 142200, 159781, 152370, 183310, 150957, 87877, 158996, 174647, 176387, 170164, 229317, 219317, 140476, 169623, 173854, 201690, 222487, 198490, 184445, 136970, 222445, 221735, 221540, 189126, 192526, 201182, 176716, 164695, 304953, 241342, 165361, 284655, 169011, 231026, 211433, 228150, 217463, 207659, 170679, 209925, 215433, 200049, 232939, 224349, 201563, 200424, 211625, 208436, 201441, 254800, 218638, 220711, 213751, 200859, 210115, 198802, 188279, 252332, 215660, 223417, 223357, 185778, 229457, 224587, 228959, 219247, 251805, 237811, 236014, 243246, 218119, 263636, 254664, 239626, 243274, 210287, 225414, 236609, 249474]
india_daily_recoveries
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 9, 0, 1, 0, 1, 5, 3, 4, 0, 13, 3, 2, 28, 11, 11, 7, 21, 25, 43, 1, 37, 0, 146, 46, 85, 114, 154, 195, 111, 101, 178, 73, 336, 273, 422, 391, 419, 702, 395, 642, 486, 441, 584, 614, 610, 690, 631, 939, 812, 956, 1072, 1295, 1189, 1445, 1111, 1414, 1668, 1580, 1871, 1980, 1569, 2289, 3966, 2571, 2438, 3076, 3113, 3131, 3271, 2561, 3307, 3014, 3571, 3472, 3171, 11707, 4309, 4916, 3902, 4531, 3786, 4379, 4783, 5462, 5153, 5247, 5575, 536, 11989, 7135, 8049, 7419, 10215, 6922, 7390, 10386, 9120, 13897, 9468, 10994, 10495, 13012, 13940, 10244, 13832, 12010, 13099, 13090, 11948, 20032, 14335, 14856, 15350, 15501, 16897, 19547, 19135, 19873, 19232, 18853, 17989, 20572, 20736, 22989, 17994, 23672, 22664, 24491, 28472, 29557, 34602, 32223, 36141, 31995, 33598, 36863, 31706, 35613, 39026, 51255, 40574, 44306, 51706, 46121, 49769, 48900, 53879, 54859, 47746, 56110, 56383, 55573, 57381, 53322, 57584, 57829, 60145, 58848, 62282, 63631, 57989, 57469, 66550, 53754, 65432, 60177, 65050, 64935, 60868, 65081, 62026, 68584, 66659, 70072, 73642, 69564, 73521, 74894, 72939, 70880, 81533, 78399, 77512, 79292, 82961, 82719, 87472, 95880, 94612, 93356, 101468, 89746, 87374, 81177, 93420, 92043, 74893, 84877, 86428, 85376]
china_daily_recoveries
[0, 2, 6, 3, 10, 9, 43, 19, 15, 79, 61, 188, 151, 229, 272, 362, 522, 597, 623, 699, 718, 446, 1135, 1760, 1321, 1457, 1707, 1744, 1756, 2052, 690, 3995, 488, 1828, 2661, 2408, 2846, 3399, 2991, 2842, 2692, 2596, 2551, 2291, 1652, 1595, 1849, 1416, 1377, 1463, 1257, 1295, 1464, 1357, 893, 888, 957, 780, 731, 591, 505, 452, 466, 493, 408, 539, 380, 482, 341, 283, 199, 160, 195, 186, 261, 103, 100, 157, 112, 112, 86, 79, 83, 161, 111, 90, -849, 62, 76, 55, 54, 62, 122, 126, 66, 102, 97, 48, 52, 49, 50, 13, 98, 108, 78, 59, 48, 16, 134, 40, 31, 24, 24, 15, 20, 12, 13, 4, 0, 0, 0, 22, 3, 8, 9, 6, 9, 4, 11, 4, 3, 9, 2, 4, 11, 5, 4, 10, 10, 7, 5, 8, 8, 3, 7, 7, 4, 17, 5, 0, 19, 1, 12, 8, 7, 10, 8, 11, 18, 10, 13, 18, 15, 15, 26, 12, 7, 29, 48, 0, 0, 74, 31, 0, 60, 38, 33, 26, 28, 45, 25, 42, 40, 53, 44, 68, 50, 7, 51, 77, 86, 107, 120, 111, 95, 121, 183, 175, 196, 181, 156, 122, 193, 193, 171, 152, 152, 92, 83, 125, 158, 97, 130, 131, 64, 70, 110, 83, 84, 84, 65, 62, 48, 58, 43, 42, 45, 55, 36, 27, 32, 25, 48, 20, 29, 32, 15, 28, 26, 26, 23, 22, 13, 18, 18, 29, 19, 20, 17, 21, 17, 25, 37]
latest_data.head()
Province_State | Country_Region | Last_Update | Lat | Long_ | Confirmed | Deaths | Recovered | Active | Admin2 | FIPS | Combined_Key | Incident_Rate | People_Tested | People_Hospitalized | UID | ISO3 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | NaN | Afghanistan | 2020-10-02 05:23:31 | 33.93911 | 67.709953 | 39285 | 1458 | 32842 | 4985.0 | NaN | NaN | Afghanistan | 100.916194 | NaN | NaN | 4 | AFG |
1 | NaN | Albania | 2020-10-02 05:23:31 | 41.15330 | 20.168300 | 13806 | 388 | 8077 | 5341.0 | NaN | NaN | Albania | 479.741469 | NaN | NaN | 8 | ALB |
2 | NaN | Algeria | 2020-10-02 05:23:31 | 28.03390 | 1.659600 | 51690 | 1741 | 36282 | 13667.0 | NaN | NaN | Algeria | 117.876330 | NaN | NaN | 12 | DZA |
3 | NaN | Andorra | 2020-10-02 05:23:31 | 42.50630 | 1.521800 | 2050 | 53 | 1432 | 565.0 | NaN | NaN | Andorra | 2653.206497 | NaN | NaN | 20 | AND |
4 | NaN | Angola | 2020-10-02 05:23:31 | -11.20270 | 17.873900 | 5114 | 185 | 2082 | 2847.0 | NaN | NaN | Angola | 15.560026 | NaN | NaN | 24 | AGO |
unique_countries = list(latest_data['Country_Region'].unique())
unique_countries
['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burma', 'Burundi', 'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada', 'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Costa Rica', "Cote d'Ivoire", 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark', 'Diamond Princess', 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Holy See', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Korea, South', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'MS Zaandam', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'Mauritania', 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Namibia', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'North Macedonia', 'Norway', 'Oman', 'Pakistan', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia', 'Saint Vincent and the Grenadines', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia', 'Somalia', 'South Africa', 'South Sudan', 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan*', 'Tajikistan', 'Tanzania', 'Thailand', 'Timor-Leste', 'Togo', 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'US', 'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'Uruguay', 'Uzbekistan', 'Venezuela', 'Vietnam', 'West Bank and Gaza', 'Western Sahara', 'Yemen', 'Zambia', 'Zimbabwe']
len(unique_countries)
188
country_confirm_cases = []
country_death_cases = []
country_active_cases = []
country_recovery_cases = []
country_mortality_cases = []
no_cases = []
for i in unique_countries:
cases = latest_data[latest_data['Country_Region']==i]['Confirmed'].sum()
if cases>0:
country_confirm_cases.append(cases)
else:
no_cases.append(i) # append all the no_cases county for remove
for i in no_cases:
unique_countries.remove(i)
#sort countries by the number of confirmed cases
unique_countries = [k for k,v in sorted(zip(unique_countries,country_confirm_cases),key=operator.itemgetter(1),reverse=True)]
for i in range(len(unique_countries)):
country_confirm_cases[i] = latest_data[latest_data['Country_Region']==unique_countries[i]]['Confirmed'].sum()
country_death_cases.append(latest_data[latest_data['Country_Region']==unique_countries[i]]['Deaths'].sum())
country_recovery_cases.append(latest_data[latest_data['Country_Region']==unique_countries[i]]['Recovered'].sum())
country_active_cases.append(country_confirm_cases[i]-country_death_cases[i]-country_recovery_cases[i])
country_mortality_cases.append(country_death_cases[i]/country_confirm_cases[i])
len(unique_countries)
188
country_active_cases
[4209356, 942217, 402753, 198109, 57452, 91838, 596258, 141574, 40607, 49634, 485798, 13780, 418100, 51296, 62361, 82637, 10557, 30657, 52647, 54294, 8877, 27810, 61839, 70661, 116381, 15014, 14855, 31550, 20802, 118702, 19679, 2834, 91515, 21052, 22415, 3291, 7867, 516, 9122, 10024, 87722, 20537, 8348, 349, 6583, 21830, 3403, 46701, 36068, 25482, 43463, 9149, 39391, 5570, 7437, 245, 2863, 13229, 6458, 13667, 5668, 2793, 413, 8408, 15763, 22405, 7427, 1648, 4985, 13094, 11427, 14755, 32912, 4397, 6479, 5941, 20410, 1399, 1803, 980, 5374, 315, 17146, 13116, 2327, 1325, 792, 2170, 508, 9906, 2685, 5341, 6053, 8280, 3407, 1334, 2167, 3420, 6270, 248, 590, 1133, 1659, 1117, 3253, 1645, 707, 1190, 3782, 1310, 206, 3180, 4453, 727, 1807, 1086, 626, 389, 10, 2106, 2847, 1113, 83, 84, 1633, 2853, 2268, 1865, 2928, 1821, 493, 1255, 132, 742, 136, 2446, 540, 455, 1087, 582, 1365, 343, 736, 471, 667, 204, 565, 155, 727, 524, 43, 408, 381, 727, 40, 51, 14, 50, 10, 10, 48, 13, 24, 37, 305, 11, 28, 27, 6, 57, 3, 30, 7, 1, 1, 4, 6, 0, 2, 7, 0, 0, 0, 1, 2, 0, 1, 7]
country_active_cases
[4209356, 942217, 402753, 198109, 57452, 91838, 596258, 141574, 40607, 49634, 485798, 13780, 418100, 51296, 62361, 82637, 10557, 30657, 52647, 54294, 8877, 27810, 61839, 70661, 116381, 15014, 14855, 31550, 20802, 118702, 19679, 2834, 91515, 21052, 22415, 3291, 7867, 516, 9122, 10024, 87722, 20537, 8348, 349, 6583, 21830, 3403, 46701, 36068, 25482, 43463, 9149, 39391, 5570, 7437, 245, 2863, 13229, 6458, 13667, 5668, 2793, 413, 8408, 15763, 22405, 7427, 1648, 4985, 13094, 11427, 14755, 32912, 4397, 6479, 5941, 20410, 1399, 1803, 980, 5374, 315, 17146, 13116, 2327, 1325, 792, 2170, 508, 9906, 2685, 5341, 6053, 8280, 3407, 1334, 2167, 3420, 6270, 248, 590, 1133, 1659, 1117, 3253, 1645, 707, 1190, 3782, 1310, 206, 3180, 4453, 727, 1807, 1086, 626, 389, 10, 2106, 2847, 1113, 83, 84, 1633, 2853, 2268, 1865, 2928, 1821, 493, 1255, 132, 742, 136, 2446, 540, 455, 1087, 582, 1365, 343, 736, 471, 667, 204, 565, 155, 727, 524, 43, 408, 381, 727, 40, 51, 14, 50, 10, 10, 48, 13, 24, 37, 305, 11, 28, 27, 6, 57, 3, 30, 7, 1, 1, 4, 6, 0, 2, 7, 0, 0, 0, 1, 2, 0, 1, 7]
country_death_cases
[207808, 99773, 144680, 20796, 26196, 32463, 31973, 20288, 78078, 16866, 32034, 12822, 42292, 26380, 9231, 5272, 4794, 8262, 35918, 5562, 6499, 9509, 10856, 1622, 4288, 9368, 11433, 8001, 4862, 6470, 2229, 214, 10023, 2387, 2108, 1725, 612, 5946, 935, 421, 5893, 2543, 3261, 4739, 1580, 509, 839, 2380, 917, 1977, 1205, 635, 678, 254, 1112, 27, 472, 1336, 2074, 1741, 963, 1065, 301, 802, 869, 374, 318, 593, 1458, 711, 1806, 559, 750, 848, 651, 861, 781, 890, 416, 418, 825, 120, 393, 265, 743, 284, 232, 311, 333, 321, 274, 388, 836, 69, 488, 136, 123, 170, 48, 272, 66, 34, 344, 77, 62, 229, 54, 125, 75, 228, 161, 41, 111, 61, 152, 179, 122, 111, 61, 151, 185, 89, 83, 105, 29, 62, 92, 76, 202, 96, 99, 113, 59, 65, 13, 16, 131, 35, 82, 10, 49, 41, 39, 72, 58, 48, 53, 587, 27, 37, 25, 48, 22, 38, 82, 85, 69, 35, 15, 42, 13, 7, 7, 1, 21, 7, 0, 10, 0, 0, 0, 2, 7, 3, 0, 1, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 2]
country_active_cases
[4209356, 942217, 402753, 198109, 57452, 91838, 596258, 141574, 40607, 49634, 485798, 13780, 418100, 51296, 62361, 82637, 10557, 30657, 52647, 54294, 8877, 27810, 61839, 70661, 116381, 15014, 14855, 31550, 20802, 118702, 19679, 2834, 91515, 21052, 22415, 3291, 7867, 516, 9122, 10024, 87722, 20537, 8348, 349, 6583, 21830, 3403, 46701, 36068, 25482, 43463, 9149, 39391, 5570, 7437, 245, 2863, 13229, 6458, 13667, 5668, 2793, 413, 8408, 15763, 22405, 7427, 1648, 4985, 13094, 11427, 14755, 32912, 4397, 6479, 5941, 20410, 1399, 1803, 980, 5374, 315, 17146, 13116, 2327, 1325, 792, 2170, 508, 9906, 2685, 5341, 6053, 8280, 3407, 1334, 2167, 3420, 6270, 248, 590, 1133, 1659, 1117, 3253, 1645, 707, 1190, 3782, 1310, 206, 3180, 4453, 727, 1807, 1086, 626, 389, 10, 2106, 2847, 1113, 83, 84, 1633, 2853, 2268, 1865, 2928, 1821, 493, 1255, 132, 742, 136, 2446, 540, 455, 1087, 582, 1365, 343, 736, 471, 667, 204, 565, 155, 727, 524, 43, 408, 381, 727, 40, 51, 14, 50, 10, 10, 48, 13, 24, 37, 305, 11, 28, 27, 6, 57, 3, 30, 7, 1, 1, 4, 6, 0, 2, 7, 0, 0, 0, 1, 2, 0, 1, 7]
country_df = pd.DataFrame({'Country Name':unique_countries,
'Number of Confirmed Cases':country_confirm_cases,
'Number of Deaths':country_death_cases,
'Number of Recoveries':country_recovery_cases,
'Number fo Active Cases':country_active_cases,
'Mortality Rate':country_mortality_cases
})
country_df.style.background_gradient(cmap='Oranges')
Country Name | Number of Confirmed Cases | Number of Deaths | Number of Recoveries | Number fo Active Cases | Mortality Rate | |
---|---|---|---|---|---|---|
0 | US | 7277814 | 207808 | 2860650 | 4209356 | 0.028554 |
1 | India | 6394068 | 99773 | 5352078 | 942217 | 0.015604 |
2 | Brazil | 4847092 | 144680 | 4299659 | 402753 | 0.029849 |
3 | Russia | 1179634 | 20796 | 960729 | 198109 | 0.017629 |
4 | Colombia | 835339 | 26196 | 751691 | 57452 | 0.031360 |
5 | Peru | 814829 | 32463 | 690528 | 91838 | 0.039840 |
6 | Spain | 778607 | 31973 | 150376 | 596258 | 0.041064 |
7 | Argentina | 765002 | 20288 | 603140 | 141574 | 0.026520 |
8 | Mexico | 748315 | 78078 | 629630 | 40607 | 0.104338 |
9 | South Africa | 676084 | 16866 | 609584 | 49634 | 0.024947 |
10 | France | 616986 | 32034 | 99154 | 485798 | 0.051920 |
11 | Chile | 464750 | 12822 | 438148 | 13780 | 0.027589 |
12 | United Kingdom | 462775 | 42292 | 2383 | 418100 | 0.091388 |
13 | Iran | 461044 | 26380 | 383368 | 51296 | 0.057218 |
14 | Iraq | 367474 | 9231 | 295882 | 62361 | 0.025120 |
15 | Bangladesh | 364987 | 5272 | 277078 | 82637 | 0.014444 |
16 | Saudi Arabia | 335097 | 4794 | 319746 | 10557 | 0.014306 |
17 | Turkey | 320070 | 8262 | 281151 | 30657 | 0.025813 |
18 | Italy | 317409 | 35918 | 228844 | 52647 | 0.113160 |
19 | Philippines | 314079 | 5562 | 254223 | 54294 | 0.017709 |
20 | Pakistan | 313431 | 6499 | 298055 | 8877 | 0.020735 |
21 | Germany | 295539 | 9509 | 258220 | 27810 | 0.032175 |
22 | Indonesia | 291182 | 10856 | 218487 | 61839 | 0.037283 |
23 | Israel | 255771 | 1622 | 183488 | 70661 | 0.006342 |
24 | Ukraine | 218625 | 4288 | 97956 | 116381 | 0.019613 |
25 | Canada | 162930 | 9368 | 138548 | 15014 | 0.057497 |
26 | Ecuador | 138584 | 11433 | 112296 | 14855 | 0.082499 |
27 | Bolivia | 135716 | 8001 | 96165 | 31550 | 0.058954 |
28 | Romania | 129658 | 4862 | 103994 | 20802 | 0.037499 |
29 | Netherlands | 129283 | 6470 | 4111 | 118702 | 0.050045 |
30 | Morocco | 126044 | 2229 | 104136 | 19679 | 0.017684 |
31 | Qatar | 125959 | 214 | 122911 | 2834 | 0.001699 |
32 | Belgium | 121059 | 10023 | 19521 | 91515 | 0.082794 |
33 | Panama | 113342 | 2387 | 89903 | 21052 | 0.021060 |
34 | Dominican Republic | 112728 | 2108 | 88205 | 22415 | 0.018700 |
35 | Kazakhstan | 108044 | 1725 | 103028 | 3291 | 0.015966 |
36 | Kuwait | 105676 | 612 | 97197 | 7867 | 0.005791 |
37 | Egypt | 103317 | 5946 | 96855 | 516 | 0.057551 |
38 | Oman | 98585 | 935 | 88528 | 9122 | 0.009484 |
39 | United Arab Emirates | 95348 | 421 | 84903 | 10024 | 0.004415 |
40 | Sweden | 93615 | 5893 | 0 | 87722 | 0.062949 |
41 | Poland | 93481 | 2543 | 70401 | 20537 | 0.027203 |
42 | Guatemala | 92409 | 3261 | 80800 | 8348 | 0.035289 |
43 | China | 90567 | 4739 | 85479 | 349 | 0.052326 |
44 | Japan | 84244 | 1580 | 76081 | 6583 | 0.018755 |
45 | Nepal | 79728 | 509 | 57389 | 21830 | 0.006384 |
46 | Belarus | 79019 | 839 | 74777 | 3403 | 0.010618 |
47 | Honduras | 77598 | 2380 | 28517 | 46701 | 0.030671 |
48 | Costa Rica | 76828 | 917 | 39843 | 36068 | 0.011936 |
49 | Portugal | 76396 | 1977 | 48937 | 25482 | 0.025878 |
50 | Ethiopia | 76098 | 1205 | 31430 | 43463 | 0.015835 |
51 | Venezuela | 76029 | 635 | 66245 | 9149 | 0.008352 |
52 | Czechia | 74255 | 678 | 34186 | 39391 | 0.009131 |
53 | Bahrain | 71374 | 254 | 65550 | 5570 | 0.003559 |
54 | Nigeria | 59001 | 1112 | 50452 | 7437 | 0.018847 |
55 | Singapore | 57784 | 27 | 57512 | 245 | 0.000467 |
56 | Uzbekistan | 57290 | 472 | 53955 | 2863 | 0.008239 |
57 | Moldova | 54064 | 1336 | 39499 | 13229 | 0.024711 |
58 | Switzerland | 53832 | 2074 | 45300 | 6458 | 0.038527 |
59 | Algeria | 51690 | 1741 | 36282 | 13667 | 0.033682 |
60 | Armenia | 50850 | 963 | 44219 | 5668 | 0.018938 |
61 | Kyrgyzstan | 46841 | 1065 | 42983 | 2793 | 0.022736 |
62 | Ghana | 46656 | 301 | 45942 | 413 | 0.006451 |
63 | Austria | 45686 | 802 | 36476 | 8408 | 0.017555 |
64 | Paraguay | 41799 | 869 | 25167 | 15763 | 0.020790 |
65 | Lebanon | 40882 | 374 | 18103 | 22405 | 0.009148 |
66 | West Bank and Gaza | 40322 | 318 | 32577 | 7427 | 0.007887 |
67 | Azerbaijan | 40309 | 593 | 38068 | 1648 | 0.014711 |
68 | Afghanistan | 39285 | 1458 | 32842 | 4985 | 0.037113 |
69 | Kenya | 38713 | 711 | 24908 | 13094 | 0.018366 |
70 | Ireland | 36597 | 1806 | 23364 | 11427 | 0.049348 |
71 | Libya | 35208 | 559 | 19894 | 14755 | 0.015877 |
72 | Serbia | 33662 | 750 | 0 | 32912 | 0.022280 |
73 | El Salvador | 29175 | 848 | 23930 | 4397 | 0.029066 |
74 | Denmark | 28882 | 651 | 21752 | 6479 | 0.022540 |
75 | Bosnia and Herzegovina | 27749 | 861 | 20947 | 5941 | 0.031028 |
76 | Hungary | 27309 | 781 | 6118 | 20410 | 0.028599 |
77 | Australia | 27109 | 890 | 24820 | 1399 | 0.032830 |
78 | Korea, South | 23952 | 416 | 21733 | 1803 | 0.017368 |
79 | Cameroon | 20838 | 418 | 19440 | 980 | 0.020060 |
80 | Bulgaria | 20833 | 825 | 14634 | 5374 | 0.039601 |
81 | Cote d'Ivoire | 19755 | 120 | 19320 | 315 | 0.006074 |
82 | Greece | 18886 | 393 | 1347 | 17146 | 0.020809 |
83 | Tunisia | 18413 | 265 | 5032 | 13116 | 0.014392 |
84 | North Macedonia | 18138 | 743 | 15068 | 2327 | 0.040964 |
85 | Croatia | 16827 | 284 | 15218 | 1325 | 0.016878 |
86 | Madagascar | 16454 | 232 | 15430 | 792 | 0.014100 |
87 | Senegal | 15019 | 311 | 12538 | 2170 | 0.020707 |
88 | Zambia | 14802 | 333 | 13961 | 508 | 0.022497 |
89 | Burma | 14383 | 321 | 4156 | 9906 | 0.022318 |
90 | Norway | 14149 | 274 | 11190 | 2685 | 0.019365 |
91 | Albania | 13806 | 388 | 8077 | 5341 | 0.028104 |
92 | Sudan | 13653 | 836 | 6764 | 6053 | 0.061232 |
93 | Jordan | 13101 | 69 | 4752 | 8280 | 0.005267 |
94 | Kosovo | 12683 | 488 | 8788 | 3407 | 0.038477 |
95 | Malaysia | 11484 | 136 | 10014 | 1334 | 0.011843 |
96 | Namibia | 11373 | 123 | 9083 | 2167 | 0.010815 |
97 | Montenegro | 10987 | 170 | 7397 | 3420 | 0.015473 |
98 | Slovakia | 10938 | 48 | 4620 | 6270 | 0.004388 |
99 | Congo (Kinshasa) | 10685 | 272 | 10165 | 248 | 0.025456 |
100 | Guinea | 10652 | 66 | 9996 | 590 | 0.006196 |
101 | Maldives | 10354 | 34 | 9187 | 1133 | 0.003284 |
102 | Finland | 10103 | 344 | 8100 | 1659 | 0.034049 |
103 | Tajikistan | 9811 | 77 | 8617 | 1117 | 0.007848 |
104 | Mozambique | 8888 | 62 | 5573 | 3253 | 0.006976 |
105 | Haiti | 8781 | 229 | 6907 | 1645 | 0.026079 |
106 | Gabon | 8766 | 54 | 8005 | 707 | 0.006160 |
107 | Luxembourg | 8595 | 125 | 7280 | 1190 | 0.014543 |
108 | Uganda | 8287 | 75 | 4430 | 3782 | 0.009050 |
109 | Zimbabwe | 7850 | 228 | 6312 | 1310 | 0.029045 |
110 | Mauritania | 7505 | 161 | 7138 | 206 | 0.021452 |
111 | Georgia | 6640 | 41 | 3419 | 3180 | 0.006175 |
112 | Jamaica | 6555 | 111 | 1991 | 4453 | 0.016934 |
113 | Cabo Verde | 6126 | 61 | 5338 | 727 | 0.009958 |
114 | Slovenia | 5865 | 152 | 3906 | 1807 | 0.025916 |
115 | Malawi | 5779 | 179 | 4514 | 1086 | 0.030974 |
116 | Cuba | 5670 | 122 | 4922 | 626 | 0.021517 |
117 | Eswatini | 5500 | 111 | 5000 | 389 | 0.020182 |
118 | Djibouti | 5417 | 61 | 5346 | 10 | 0.011261 |
119 | Nicaragua | 5170 | 151 | 2913 | 2106 | 0.029207 |
120 | Angola | 5114 | 185 | 2082 | 2847 | 0.036175 |
121 | Congo (Brazzaville) | 5089 | 89 | 3887 | 1113 | 0.017489 |
122 | Equatorial Guinea | 5045 | 83 | 4879 | 83 | 0.016452 |
123 | Suriname | 4891 | 105 | 4702 | 84 | 0.021468 |
124 | Rwanda | 4843 | 29 | 3181 | 1633 | 0.005988 |
125 | Central African Republic | 4829 | 62 | 1914 | 2853 | 0.012839 |
126 | Lithuania | 4784 | 92 | 2424 | 2268 | 0.019231 |
127 | Trinidad and Tobago | 4570 | 76 | 2629 | 1865 | 0.016630 |
128 | Syria | 4247 | 202 | 1117 | 2928 | 0.047563 |
129 | Bahamas | 4123 | 96 | 2206 | 1821 | 0.023284 |
130 | Somalia | 3593 | 99 | 3001 | 493 | 0.027554 |
131 | Gambia | 3584 | 113 | 2216 | 1255 | 0.031529 |
132 | Thailand | 3575 | 59 | 3384 | 132 | 0.016503 |
133 | Estonia | 3450 | 65 | 2643 | 742 | 0.018841 |
134 | Sri Lanka | 3382 | 13 | 3233 | 136 | 0.003844 |
135 | Botswana | 3172 | 16 | 710 | 2446 | 0.005044 |
136 | Mali | 3131 | 131 | 2460 | 540 | 0.041840 |
137 | Malta | 3095 | 35 | 2605 | 455 | 0.011309 |
138 | Guyana | 2929 | 82 | 1760 | 1087 | 0.027996 |
139 | Iceland | 2769 | 10 | 2177 | 582 | 0.003611 |
140 | South Sudan | 2704 | 49 | 1290 | 1365 | 0.018121 |
141 | Benin | 2357 | 41 | 1973 | 343 | 0.017395 |
142 | Guinea-Bissau | 2324 | 39 | 1549 | 736 | 0.016781 |
143 | Sierra Leone | 2238 | 72 | 1695 | 471 | 0.032172 |
144 | Burkina Faso | 2088 | 58 | 1363 | 667 | 0.027778 |
145 | Uruguay | 2061 | 48 | 1809 | 204 | 0.023290 |
146 | Andorra | 2050 | 53 | 1432 | 565 | 0.025854 |
147 | Yemen | 2039 | 587 | 1297 | 155 | 0.287886 |
148 | Belize | 2026 | 27 | 1272 | 727 | 0.013327 |
149 | Latvia | 1868 | 37 | 1307 | 524 | 0.019807 |
150 | New Zealand | 1848 | 25 | 1780 | 43 | 0.013528 |
151 | Togo | 1809 | 48 | 1353 | 408 | 0.026534 |
152 | Cyprus | 1772 | 22 | 1369 | 381 | 0.012415 |
153 | Lesotho | 1639 | 38 | 874 | 727 | 0.023185 |
154 | Liberia | 1343 | 82 | 1221 | 40 | 0.061057 |
155 | Chad | 1203 | 85 | 1067 | 51 | 0.070657 |
156 | Niger | 1197 | 69 | 1114 | 14 | 0.057644 |
157 | Vietnam | 1095 | 35 | 1010 | 50 | 0.031963 |
158 | Sao Tome and Principe | 911 | 15 | 886 | 10 | 0.016465 |
159 | San Marino | 732 | 42 | 680 | 10 | 0.057377 |
160 | Diamond Princess | 712 | 13 | 651 | 48 | 0.018258 |
161 | Papua New Guinea | 539 | 7 | 519 | 13 | 0.012987 |
162 | Taiwan* | 515 | 7 | 484 | 24 | 0.013592 |
163 | Burundi | 510 | 1 | 472 | 37 | 0.001961 |
164 | Tanzania | 509 | 21 | 183 | 305 | 0.041257 |
165 | Comoros | 484 | 7 | 466 | 11 | 0.014463 |
166 | Eritrea | 381 | 0 | 353 | 28 | 0.000000 |
167 | Mauritius | 381 | 10 | 344 | 27 | 0.026247 |
168 | Mongolia | 313 | 0 | 307 | 6 | 0.000000 |
169 | Bhutan | 282 | 0 | 225 | 57 | 0.000000 |
170 | Cambodia | 278 | 0 | 275 | 3 | 0.000000 |
171 | Monaco | 219 | 2 | 187 | 30 | 0.009132 |
172 | Barbados | 193 | 7 | 179 | 7 | 0.036269 |
173 | Brunei | 146 | 3 | 142 | 1 | 0.020548 |
174 | Seychelles | 144 | 0 | 143 | 1 | 0.000000 |
175 | Liechtenstein | 119 | 1 | 114 | 4 | 0.008403 |
176 | Antigua and Barbuda | 101 | 3 | 92 | 6 | 0.029703 |
177 | Saint Vincent and the Grenadines | 64 | 0 | 64 | 0 | 0.000000 |
178 | Fiji | 32 | 2 | 28 | 2 | 0.062500 |
179 | Dominica | 31 | 0 | 24 | 7 | 0.000000 |
180 | Timor-Leste | 28 | 0 | 28 | 0 | 0.000000 |
181 | Saint Lucia | 27 | 0 | 27 | 0 | 0.000000 |
182 | Grenada | 24 | 0 | 24 | 0 | 0.000000 |
183 | Laos | 23 | 0 | 22 | 1 | 0.000000 |
184 | Saint Kitts and Nevis | 19 | 0 | 17 | 2 | 0.000000 |
185 | Holy See | 12 | 0 | 12 | 0 | 0.000000 |
186 | Western Sahara | 10 | 1 | 8 | 1 | 0.100000 |
187 | MS Zaandam | 9 | 2 | 0 | 7 | 0.222222 |
country_df.isnull().sum()
Country Name 0 Number of Confirmed Cases 0 Number of Deaths 0 Number of Recoveries 0 Number fo Active Cases 0 Mortality Rate 0 dtype: int64
unique_provinces = list(latest_data['Province_State'].unique())
unique_provinces
[nan, 'Australian Capital Territory', 'New South Wales', 'Northern Territory', 'Queensland', 'South Australia', 'Tasmania', 'Victoria', 'Western Australia', 'Acre', 'Alagoas', 'Amapa', 'Amazonas', 'Bahia', 'Ceara', 'Distrito Federal', 'Espirito Santo', 'Goias', 'Maranhao', 'Mato Grosso', 'Mato Grosso do Sul', 'Minas Gerais', 'Para', 'Paraiba', 'Parana', 'Pernambuco', 'Piaui', 'Rio Grande do Norte', 'Rio Grande do Sul', 'Rio de Janeiro', 'Rondonia', 'Roraima', 'Santa Catarina', 'Sao Paulo', 'Sergipe', 'Tocantins', 'Alberta', 'British Columbia', 'Diamond Princess', 'Grand Princess', 'Manitoba', 'New Brunswick', 'Newfoundland and Labrador', 'Northwest Territories', 'Nova Scotia', 'Ontario', 'Prince Edward Island', 'Quebec', 'Saskatchewan', 'Yukon', 'Antofagasta', 'Araucania', 'Arica y Parinacota', 'Atacama', 'Aysen', 'Biobio', 'Coquimbo', 'Los Lagos', 'Los Rios', 'Magallanes', 'Maule', 'Metropolitana', 'Nuble', 'OHiggins', 'Tarapaca', 'Unknown', 'Valparaiso', 'Anhui', 'Beijing', 'Chongqing', 'Fujian', 'Gansu', 'Guangdong', 'Guangxi', 'Guizhou', 'Hainan', 'Hebei', 'Heilongjiang', 'Henan', 'Hong Kong', 'Hubei', 'Hunan', 'Inner Mongolia', 'Jiangsu', 'Jiangxi', 'Jilin', 'Liaoning', 'Macau', 'Ningxia', 'Qinghai', 'Shaanxi', 'Shandong', 'Shanghai', 'Shanxi', 'Sichuan', 'Tianjin', 'Tibet', 'Xinjiang', 'Yunnan', 'Zhejiang', 'Antioquia', 'Arauca', 'Atlantico', 'Bolivar', 'Boyaca', 'Caldas', 'Capital District', 'Caqueta', 'Casanare', 'Cauca', 'Cesar', 'Choco', 'Cordoba', 'Cundinamarca', 'Guainia', 'Guaviare', 'Huila', 'La Guajira', 'Magdalena', 'Meta', 'Narino', 'Norte de Santander', 'Putumayo', 'Quindio', 'Risaralda', 'San Andres y Providencia', 'Santander', 'Sucre', 'Tolima', 'Valle del Cauca', 'Vaupes', 'Vichada', 'Faroe Islands', 'Greenland', 'French Guiana', 'French Polynesia', 'Guadeloupe', 'Martinique', 'Mayotte', 'New Caledonia', 'Reunion', 'Saint Barthelemy', 'Saint Pierre and Miquelon', 'St Martin', 'Baden-Wurttemberg', 'Bayern', 'Berlin', 'Brandenburg', 'Bremen', 'Hamburg', 'Hessen', 'Mecklenburg-Vorpommern', 'Niedersachsen', 'Nordrhein-Westfalen', 'Rheinland-Pfalz', 'Saarland', 'Sachsen', 'Sachsen-Anhalt', 'Schleswig-Holstein', 'Thuringen', 'Andaman and Nicobar Islands', 'Andhra Pradesh', 'Arunachal Pradesh', 'Assam', 'Bihar', 'Chandigarh', 'Chhattisgarh', 'Dadra and Nagar Haveli and Daman and Diu', 'Delhi', 'Goa', 'Gujarat', 'Haryana', 'Himachal Pradesh', 'Jammu and Kashmir', 'Jharkhand', 'Karnataka', 'Kerala', 'Ladakh', 'Lakshadweep', 'Madhya Pradesh', 'Maharashtra', 'Manipur', 'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha', 'Puducherry', 'Punjab', 'Rajasthan', 'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura', 'Uttar Pradesh', 'Uttarakhand', 'West Bengal', 'Abruzzo', 'Basilicata', 'Calabria', 'Campania', 'Emilia-Romagna', 'Friuli Venezia Giulia', 'Lazio', 'Liguria', 'Lombardia', 'Marche', 'Molise', 'P.A. Bolzano', 'P.A. Trento', 'Piemonte', 'Puglia', 'Sardegna', 'Sicilia', 'Toscana', 'Umbria', "Valle d'Aosta", 'Veneto', 'Aichi', 'Akita', 'Aomori', 'Chiba', 'Ehime', 'Fukui', 'Fukuoka', 'Fukushima', 'Gifu', 'Gunma', 'Hiroshima', 'Hokkaido', 'Hyogo', 'Ibaraki', 'Ishikawa', 'Iwate', 'Kagawa', 'Kagoshima', 'Kanagawa', 'Kochi', 'Kumamoto', 'Kyoto', 'Mie', 'Miyagi', 'Miyazaki', 'Nagano', 'Nagasaki', 'Nara', 'Niigata', 'Oita', 'Okayama', 'Okinawa', 'Osaka', 'Port Quarantine', 'Saga', 'Saitama', 'Shiga', 'Shimane', 'Shizuoka', 'Tochigi', 'Tokushima', 'Tokyo', 'Tottori', 'Toyama', 'Wakayama', 'Yamagata', 'Yamaguchi', 'Yamanashi', 'Aguascalientes', 'Baja California', 'Baja California Sur', 'Campeche', 'Chiapas', 'Chihuahua', 'Ciudad de Mexico', 'Coahuila', 'Colima', 'Durango', 'Guanajuato', 'Guerrero', 'Hidalgo', 'Jalisco', 'Mexico', 'Michoacan', 'Morelos', 'Nayarit', 'Nuevo Leon', 'Oaxaca', 'Puebla', 'Queretaro', 'Quintana Roo', 'San Luis Potosi', 'Sinaloa', 'Sonora', 'Tabasco', 'Tamaulipas', 'Tlaxcala', 'Veracruz', 'Yucatan', 'Zacatecas', 'Aruba', 'Bonaire, Sint Eustatius and Saba', 'Curacao', 'Drenthe', 'Flevoland', 'Friesland', 'Gelderland', 'Groningen', 'Limburg', 'Noord-Brabant', 'Noord-Holland', 'Overijssel', 'Sint Maarten', 'Utrecht', 'Zeeland', 'Zuid-Holland', 'Azad Jammu and Kashmir', 'Balochistan', 'Gilgit-Baltistan', 'Islamabad', 'Khyber Pakhtunkhwa', 'Sindh', 'Ancash', 'Apurimac', 'Arequipa', 'Ayacucho', 'Cajamarca', 'Callao', 'Cusco', 'Huancavelica', 'Huanuco', 'Ica', 'Junin', 'La Libertad', 'Lambayeque', 'Lima', 'Loreto', 'Madre de Dios', 'Moquegua', 'Pasco', 'Piura', 'Puno', 'San Martin', 'Tacna', 'Tumbes', 'Ucayali', 'Adygea Republic', 'Altai Krai', 'Altai Republic', 'Amur Oblast', 'Arkhangelsk Oblast', 'Astrakhan Oblast', 'Bashkortostan Republic', 'Belgorod Oblast', 'Bryansk Oblast', 'Buryatia Republic', 'Chechen Republic', 'Chelyabinsk Oblast', 'Chukotka Autonomous Okrug', 'Chuvashia Republic', 'Dagestan Republic', 'Ingushetia Republic', 'Irkutsk Oblast', 'Ivanovo Oblast', 'Jewish Autonomous Okrug', 'Kabardino-Balkarian Republic', 'Kaliningrad Oblast', 'Kalmykia Republic', 'Kaluga Oblast', 'Kamchatka Krai', 'Karachay-Cherkess Republic', 'Karelia Republic', 'Kemerovo Oblast', 'Khabarovsk Krai', 'Khakassia Republic', 'Khanty-Mansi Autonomous Okrug', 'Kirov Oblast', 'Komi Republic', 'Kostroma Oblast', 'Krasnodar Krai', 'Krasnoyarsk Krai', 'Kurgan Oblast', 'Kursk Oblast', 'Leningrad Oblast', 'Lipetsk Oblast', 'Magadan Oblast', 'Mari El Republic', 'Mordovia Republic', 'Moscow', 'Moscow Oblast', 'Murmansk Oblast', 'Nenets Autonomous Okrug', 'Nizhny Novgorod Oblast', 'North Ossetia - Alania Republic', 'Novgorod Oblast', 'Novosibirsk Oblast', 'Omsk Oblast', 'Orel Oblast', 'Orenburg Oblast', 'Penza Oblast', 'Perm Krai', 'Primorsky Krai', 'Pskov Oblast', 'Rostov Oblast', 'Ryazan Oblast', 'Saint Petersburg', 'Sakha (Yakutiya) Republic', 'Sakhalin Oblast', 'Samara Oblast', 'Saratov Oblast', 'Smolensk Oblast', 'Stavropol Krai', 'Sverdlovsk Oblast', 'Tambov Oblast', 'Tatarstan Republic', 'Tomsk Oblast', 'Tula Oblast', 'Tver Oblast', 'Tyumen Oblast', 'Tyva Republic', 'Udmurt Republic', 'Ulyanovsk Oblast', 'Vladimir Oblast', 'Volgograd Oblast', 'Vologda Oblast', 'Voronezh Oblast', 'Yamalo-Nenets Autonomous Okrug', 'Yaroslavl Oblast', 'Zabaykalsky Krai', 'Andalusia', 'Aragon', 'Asturias', 'Baleares', 'C. Valenciana', 'Canarias', 'Cantabria', 'Castilla - La Mancha', 'Castilla y Leon', 'Catalonia', 'Ceuta', 'Extremadura', 'Galicia', 'La Rioja', 'Madrid', 'Melilla', 'Murcia', 'Navarra', 'Pais Vasco', 'Blekinge', 'Dalarna', 'Gavleborg', 'Gotland', 'Halland', 'Jamtland Harjedalen', 'Jonkoping', 'Kalmar', 'Kronoberg', 'Norrbotten', 'Orebro', 'Ostergotland', 'Skane', 'Sormland', 'Stockholm', 'Uppsala', 'Varmland', 'Vasterbotten', 'Vasternorrland', 'Vastmanland', 'Vastra Gotaland', 'Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'District of Columbia', 'Florida', 'Georgia', 'Guam', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 'Northern Mariana Islands', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Puerto Rico', 'Recovered', 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virgin Islands', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming', 'Cherkasy Oblast', 'Chernihiv Oblast', 'Chernivtsi Oblast', 'Crimea Republic*', 'Dnipropetrovsk Oblast', 'Donetsk Oblast', 'Ivano-Frankivsk Oblast', 'Kharkiv Oblast', 'Kherson Oblast', 'Khmelnytskyi Oblast', 'Kiev', 'Kiev Oblast', 'Kirovohrad Oblast', 'Luhansk Oblast', 'Lviv Oblast', 'Mykolaiv Oblast', 'Odessa Oblast', 'Poltava Oblast', 'Rivne Oblast', 'Sevastopol*', 'Sumy Oblast', 'Ternopil Oblast', 'Vinnytsia Oblast', 'Volyn Oblast', 'Zakarpattia Oblast', 'Zaporizhia Oblast', 'Zhytomyr Oblast', 'Anguilla', 'Bermuda', 'British Virgin Islands', 'Cayman Islands', 'Channel Islands', 'England', 'Falkland Islands (Malvinas)', 'Gibraltar', 'Isle of Man', 'Montserrat', 'Northern Ireland', 'Scotland', 'Turks and Caicos Islands', 'Wales']
len(unique_provinces)
563
province_confirmed_cases = []
province_country = []
province_death_cases = []
province_recovery_cases = []
province_mortality = []
no_cases = []
for i in unique_provinces:
cases = latest_data[latest_data['Province_State']==i]['Confirmed'].sum()
if cases>0:
province_confirmed_cases.append(cases)
else:
no_cases.append(i)
for i in no_cases:
unique_provinces.remove(i)
unique_provinces = [k for k,v in sorted(zip(unique_provinces,province_confirmed_cases),key=operator.itemgetter(1),reverse=True)]
for i in range(len(unique_provinces)):
province_confirmed_cases[i] = latest_data[latest_data['Province_State']==unique_provinces[i]]['Confirmed'].sum()
province_death_cases.append(latest_data[latest_data['Province_State']==unique_provinces[i]]['Deaths'].sum())
province_recovery_cases.append(latest_data[latest_data['Province_State']==unique_provinces[i]]['Recovered'].sum())
province_country.append(latest_data[latest_data['Province_State']==unique_provinces[i]]['Country_Region'].unique()[0])
province_mortality.append(province_death_cases[i]/province_confirmed_cases[i])
len(unique_provinces)
560
len(province_confirmed_cases)
560
len(province_country)
560
province_df = pd.DataFrame({'Provinces Name':unique_provinces,
'Country/Rigion':province_country,
'Province Confirmed Casaes':province_confirmed_cases,
'Province Deaths Cases':province_death_cases,
'Province Recovery Cases':province_recovery_cases,
'Mortality Rate':province_mortality
})
province_df.style.background_gradient(cmap='Purples')
Provinces Name | Country/Rigion | Province Confirmed Casaes | Province Deaths Cases | Province Recovery Cases | Mortality Rate | |
---|---|---|---|---|---|---|
0 | Maharashtra | India | 1400922 | 37056 | 1104426 | 0.026451 |
1 | Sao Paulo | Brazil | 991725 | 35804 | 856453 | 0.036103 |
2 | California | US | 822205 | 15992 | 0 | 0.019450 |
3 | Texas | US | 776736 | 16130 | 0 | 0.020766 |
4 | Florida | US | 709144 | 14444 | 0 | 0.020368 |
5 | Andhra Pradesh | India | 700235 | 5869 | 636508 | 0.008381 |
6 | Karnataka | India | 611837 | 8994 | 492412 | 0.014700 |
7 | Tamil Nadu | India | 603290 | 9586 | 547335 | 0.015890 |
8 | New York | US | 460031 | 33159 | 0 | 0.072080 |
9 | Uttar Pradesh | India | 403101 | 5864 | 346859 | 0.014547 |
10 | England | United Kingdom | 393931 | 37477 | 0 | 0.095136 |
11 | Lima | Peru | 371065 | 14577 | 0 | 0.039284 |
12 | Georgia | US | 319359 | 7063 | 0 | 0.022116 |
13 | Bahia | Brazil | 312050 | 6795 | 298001 | 0.021775 |
14 | Minas Gerais | Brazil | 298607 | 7436 | 263599 | 0.024902 |
15 | Illinois | US | 297884 | 8940 | 0 | 0.030012 |
16 | Moscow | Russia | 295025 | 5254 | 249861 | 0.017809 |
17 | Metropolitana | Chile | 287116 | 9262 | 274501 | 0.032259 |
18 | Delhi | India | 282752 | 5401 | 250613 | 0.019102 |
19 | Capital District | Colombia | 270515 | 6825 | 238368 | 0.025230 |
20 | Rio de Janeiro | Brazil | 266235 | 18567 | 242937 | 0.069739 |
21 | West Bengal | India | 260324 | 5017 | 228755 | 0.019272 |
22 | Ceara | Brazil | 241684 | 9023 | 214797 | 0.037334 |
23 | Madrid | Spain | 238423 | 9450 | 40736 | 0.039635 |
24 | Para | Brazil | 231743 | 6582 | 215527 | 0.028402 |
25 | Odisha | India | 222734 | 859 | 190080 | 0.003857 |
26 | Arizona | US | 219212 | 5674 | 0 | 0.025884 |
27 | Santa Catarina | Brazil | 216624 | 2821 | 205075 | 0.013023 |
28 | Punjab | India | 214756 | 5688 | 191624 | 0.026486 |
29 | North Carolina | US | 212898 | 3579 | 0 | 0.016811 |
30 | Goias | Brazil | 212186 | 4723 | 201689 | 0.022259 |
31 | New Jersey | US | 205889 | 16140 | 0 | 0.078392 |
32 | Kerala | India | 204241 | 771 | 131052 | 0.003775 |
33 | Tennessee | US | 197432 | 2501 | 0 | 0.012668 |
34 | Rio Grande do Sul | Brazil | 197186 | 4815 | 182572 | 0.024419 |
35 | Telangana | India | 195609 | 1145 | 165844 | 0.005854 |
36 | Distrito Federal | Brazil | 193127 | 3276 | 182287 | 0.016963 |
37 | Bihar | India | 184038 | 904 | 171048 | 0.004912 |
38 | Assam | India | 182396 | 711 | 147522 | 0.003898 |
39 | Parana | Brazil | 180805 | 4527 | 132065 | 0.025038 |
40 | Maranhao | Brazil | 174195 | 3766 | 163892 | 0.021619 |
41 | Louisiana | US | 166584 | 5519 | 0 | 0.033130 |
42 | Pennsylvania | US | 165044 | 8144 | 0 | 0.049344 |
43 | Amazonas | Brazil | 157788 | 4489 | 118734 | 0.028450 |
44 | Alabama | US | 155744 | 2548 | 0 | 0.016360 |
45 | Ohio | US | 155314 | 4817 | 0 | 0.031015 |
46 | Virginia | US | 148536 | 3225 | 0 | 0.021712 |
47 | South Carolina | US | 148323 | 3400 | 0 | 0.022923 |
48 | Pernambuco | Brazil | 147872 | 8279 | 127590 | 0.055988 |
49 | Catalonia | Spain | 140416 | 5847 | 26203 | 0.041641 |
50 | Michigan | US | 139012 | 7102 | 0 | 0.051089 |
51 | Gujarat | India | 138583 | 3460 | 118433 | 0.024967 |
52 | Rajasthan | India | 137485 | 1500 | 115178 | 0.010910 |
53 | Sindh | Pakistan | 137467 | 2512 | 130510 | 0.018273 |
54 | Massachusetts | US | 132870 | 9480 | 0 | 0.071348 |
55 | Espirito Santo | Brazil | 132350 | 3551 | 121514 | 0.026830 |
56 | Madhya Pradesh | India | 130088 | 2336 | 107279 | 0.017957 |
57 | Haryana | India | 129912 | 1402 | 115038 | 0.010792 |
58 | Ciudad de Mexico | Mexico | 127463 | 12164 | 107489 | 0.095432 |
59 | Missouri | US | 126906 | 2074 | 0 | 0.016343 |
60 | Maryland | US | 125510 | 3949 | 0 | 0.031464 |
61 | Wisconsin | US | 125161 | 1348 | 0 | 0.010770 |
62 | Mato Grosso | Brazil | 124370 | 3428 | 105557 | 0.027563 |
63 | Paraiba | Brazil | 121809 | 2835 | 96725 | 0.023274 |
64 | Indiana | US | 121176 | 3645 | 0 | 0.030080 |
65 | Chhattisgarh | India | 116153 | 986 | 84699 | 0.008489 |
66 | Antioquia | Colombia | 116110 | 2440 | 107506 | 0.021015 |
67 | Lombardia | Italy | 107051 | 16960 | 80924 | 0.158429 |
68 | Minnesota | US | 100200 | 2102 | 0 | 0.020978 |
69 | Mississippi | US | 98886 | 2979 | 0 | 0.030126 |
70 | Piaui | Brazil | 96849 | 2127 | 94884 | 0.021962 |
71 | Iowa | US | 90483 | 1366 | 0 | 0.015097 |
72 | Oklahoma | US | 88369 | 1035 | 0 | 0.011712 |
73 | Washington | US | 88116 | 2132 | 0 | 0.024195 |
74 | Alagoas | Brazil | 87504 | 2078 | 84304 | 0.023747 |
75 | Arkansas | US | 84821 | 1384 | 0 | 0.016317 |
76 | Jharkhand | India | 84664 | 721 | 72461 | 0.008516 |
77 | Mexico | Mexico | 81532 | 9468 | 67759 | 0.116126 |
78 | Nevada | US | 80410 | 1603 | 0 | 0.019935 |
79 | Sergipe | Brazil | 77635 | 2040 | 71213 | 0.026277 |
80 | Jammu and Kashmir | India | 76163 | 1198 | 58552 | 0.015729 |
81 | Quebec | Canada | 75221 | 5850 | 63144 | 0.077771 |
82 | Utah | US | 74050 | 459 | 0 | 0.006199 |
83 | Moscow Oblast | Russia | 73832 | 1344 | 56423 | 0.018203 |
84 | Colorado | US | 71197 | 2054 | 0 | 0.028850 |
85 | Nordrhein-Westfalen | Germany | 70580 | 1880 | 62341 | 0.026636 |
86 | Mato Grosso do Sul | Brazil | 70239 | 1311 | 63802 | 0.018665 |
87 | Kentucky | US | 69728 | 1191 | 0 | 0.017081 |
88 | Rio Grande do Norte | Brazil | 69715 | 2395 | 40400 | 0.034354 |
89 | Tocantins | Brazil | 68606 | 948 | 52208 | 0.013818 |
90 | Bayern | Germany | 68534 | 2663 | 60946 | 0.038857 |
91 | Hubei | China | 68139 | 4512 | 63627 | 0.066218 |
92 | Atlantico | Colombia | 67666 | 3071 | 63796 | 0.045385 |
93 | Rondonia | Brazil | 66261 | 1367 | 57267 | 0.020631 |
94 | Andalusia | Spain | 63766 | 1845 | 10671 | 0.028934 |
95 | Valle del Cauca | Colombia | 63267 | 2306 | 56639 | 0.036449 |
96 | Kansas | US | 59514 | 672 | 0 | 0.011291 |
97 | Connecticut | US | 57742 | 4511 | 0 | 0.078123 |
98 | Ontario | Canada | 54643 | 2899 | 46591 | 0.053053 |
99 | Roraima | Brazil | 50681 | 654 | 46135 | 0.012904 |
100 | Baden-Wurttemberg | Germany | 50097 | 1887 | 44032 | 0.037667 |
101 | Uttarakhand | India | 49248 | 625 | 40079 | 0.012691 |
102 | Puerto Rico | US | 49067 | 665 | 0 | 0.013553 |
103 | Castilla y Leon | Spain | 48664 | 3064 | 8716 | 0.062962 |
104 | Amapa | Brazil | 48385 | 712 | 36342 | 0.014715 |
105 | Nebraska | US | 46185 | 493 | 0 | 0.010674 |
106 | Pais Vasco | Spain | 44878 | 1889 | 16160 | 0.042092 |
107 | Castilla - La Mancha | Spain | 43714 | 3196 | 6392 | 0.073112 |
108 | Saint Petersburg | Russia | 43306 | 2972 | 29217 | 0.068628 |
109 | Idaho | US | 42561 | 472 | 0 | 0.011090 |
110 | Arequipa | Peru | 41882 | 1355 | 0 | 0.032353 |
111 | C. Valenciana | Spain | 41248 | 1607 | 9970 | 0.038959 |
112 | Guanajuato | Mexico | 41063 | 2946 | 36352 | 0.071743 |
113 | Nuevo Leon | Mexico | 40495 | 3131 | 33482 | 0.077318 |
114 | Khyber Pakhtunkhwa | Pakistan | 37845 | 1260 | 36114 | 0.033294 |
115 | Zuid-Holland | Netherlands | 37162 | 1410 | 0 | 0.037942 |
116 | Aragon | Spain | 36704 | 1411 | 3772 | 0.038443 |
117 | Piemonte | Italy | 35512 | 4165 | 28440 | 0.117284 |
118 | Emilia-Romagna | Italy | 35414 | 4484 | 26276 | 0.126617 |
119 | Piura | Peru | 35195 | 1957 | 0 | 0.055604 |
120 | Goa | India | 33942 | 440 | 28525 | 0.012963 |
121 | Oregon | US | 33862 | 560 | 0 | 0.016538 |
122 | Cundinamarca | Colombia | 33668 | 1006 | 30881 | 0.029880 |
123 | Veracruz | Mexico | 33480 | 4265 | 27996 | 0.127389 |
124 | Nizhny Novgorod Oblast | Russia | 32585 | 581 | 28323 | 0.017830 |
125 | Callao | Peru | 32420 | 1749 | 0 | 0.053948 |
126 | Tabasco | Mexico | 32057 | 2834 | 28362 | 0.088405 |
127 | Santander | Colombia | 31559 | 1324 | 27496 | 0.041953 |
128 | Puebla | Mexico | 31380 | 4097 | 26285 | 0.130561 |
129 | Scotland | United Kingdom | 29912 | 2522 | 0 | 0.084314 |
130 | New Mexico | US | 29661 | 882 | 0 | 0.029736 |
131 | Sverdlovsk Oblast | Russia | 29311 | 605 | 21878 | 0.020641 |
132 | Bolivar | Colombia | 29137 | 776 | 27633 | 0.026633 |
133 | La Libertad | Peru | 29060 | 2208 | 0 | 0.075981 |
134 | Tamaulipas | Mexico | 29057 | 2257 | 25822 | 0.077675 |
135 | Acre | Brazil | 28409 | 661 | 26705 | 0.023267 |
136 | Puducherry | India | 28024 | 525 | 22505 | 0.018734 |
137 | Veneto | Italy | 27896 | 2183 | 21748 | 0.078255 |
138 | Ica | Peru | 27707 | 1599 | 0 | 0.057711 |
139 | Valparaiso | Chile | 27566 | 882 | 25806 | 0.031996 |
140 | Jalisco | Mexico | 27218 | 3317 | 21529 | 0.121868 |
141 | Coahuila | Mexico | 26568 | 1900 | 23195 | 0.071515 |
142 | Tripura | India | 26362 | 286 | 20596 | 0.010849 |
143 | Lambayeque | Peru | 26291 | 1688 | 0 | 0.064204 |
144 | Tokyo | Japan | 25986 | 409 | 23147 | 0.015739 |
145 | Noord-Holland | Netherlands | 25534 | 866 | 0 | 0.033916 |
146 | Stockholm | Sweden | 25459 | 2405 | 0 | 0.094466 |
147 | Rhode Island | US | 24914 | 1117 | 0 | 0.044834 |
148 | Sonora | Mexico | 24728 | 2904 | 21029 | 0.117438 |
149 | Wales | United Kingdom | 24383 | 1622 | 0 | 0.066522 |
150 | Cordoba | Colombia | 24037 | 1547 | 21504 | 0.064359 |
151 | Biobio | Chile | 23824 | 338 | 21754 | 0.014187 |
152 | San Luis Potosi | Mexico | 23219 | 1702 | 20609 | 0.073302 |
153 | Kiev | Ukraine | 23155 | 391 | 6941 | 0.016886 |
154 | South Dakota | US | 23136 | 236 | 0 | 0.010201 |
155 | Ancash | Peru | 22822 | 1293 | 0 | 0.056656 |
156 | Khanty-Mansi Autonomous Okrug | Russia | 22660 | 188 | 21193 | 0.008297 |
157 | North Dakota | US | 22218 | 256 | 0 | 0.011522 |
158 | Rostov Oblast | Russia | 22143 | 504 | 17633 | 0.022761 |
159 | Galicia | Spain | 21463 | 746 | 9204 | 0.034757 |
160 | Delaware | US | 20787 | 636 | 0 | 0.030596 |
161 | Lviv Oblast | Ukraine | 20590 | 565 | 7387 | 0.027441 |
162 | Michoacan | Mexico | 20563 | 1643 | 17742 | 0.079901 |
163 | Cusco | Peru | 20550 | 416 | 0 | 0.020243 |
164 | Niedersachsen | Germany | 20417 | 687 | 17821 | 0.033648 |
165 | Vastra Gotaland | Sweden | 20328 | 863 | 0 | 0.042454 |
166 | Cesar | Colombia | 20311 | 608 | 17729 | 0.029935 |
167 | Victoria | Australia | 20189 | 802 | 19068 | 0.039725 |
168 | Krasnoyarsk Krai | Russia | 20117 | 554 | 15396 | 0.027539 |
169 | Antofagasta | Chile | 20000 | 493 | 19036 | 0.024650 |
170 | Cajamarca | Peru | 19899 | 471 | 0 | 0.023670 |
171 | Junin | Peru | 19884 | 761 | 0 | 0.038272 |
172 | Murcia | Spain | 19736 | 215 | 2180 | 0.010894 |
173 | Baja California | Mexico | 19510 | 3539 | 15193 | 0.181394 |
174 | Loreto | Peru | 19208 | 949 | 0 | 0.049406 |
175 | Irkutsk Oblast | Russia | 19168 | 297 | 16175 | 0.015495 |
176 | Guerrero | Mexico | 19146 | 1896 | 16044 | 0.099029 |
177 | Kharkiv Oblast | Ukraine | 19128 | 354 | 4363 | 0.018507 |
178 | Hessen | Germany | 19089 | 551 | 16908 | 0.028865 |
179 | Sinaloa | Mexico | 18795 | 3196 | 14944 | 0.170045 |
180 | Narino | Colombia | 18531 | 687 | 16730 | 0.037073 |
181 | Yucatan | Mexico | 18451 | 1619 | 15711 | 0.087746 |
182 | Navarra | Spain | 18409 | 574 | 3905 | 0.031180 |
183 | Alberta | Canada | 18235 | 269 | 16370 | 0.014752 |
184 | Voronezh Oblast | Russia | 18177 | 184 | 16410 | 0.010123 |
185 | Noord-Brabant | Netherlands | 17891 | 1563 | 0 | 0.087362 |
186 | San Martin | Peru | 17886 | 701 | 0 | 0.039193 |
187 | Oaxaca | Mexico | 17127 | 1415 | 14638 | 0.082618 |
188 | Lazio | Italy | 16740 | 923 | 8474 | 0.055137 |
189 | Islamabad | Pakistan | 16650 | 182 | 15947 | 0.010931 |
190 | OHiggins | Chile | 16565 | 422 | 15506 | 0.025475 |
191 | Meta | Colombia | 16320 | 424 | 15037 | 0.025980 |
192 | Stavropol Krai | Russia | 16226 | 324 | 11544 | 0.019968 |
193 | Ucayali | Peru | 16119 | 319 | 0 | 0.019790 |
194 | Huanuco | Peru | 16070 | 379 | 0 | 0.023584 |
195 | West Virginia | US | 16026 | 359 | 0 | 0.022401 |
196 | Norte de Santander | Colombia | 16020 | 907 | 14136 | 0.056617 |
197 | Maule | Chile | 15915 | 327 | 14460 | 0.020547 |
198 | Chelyabinsk Oblast | Russia | 15768 | 130 | 11699 | 0.008245 |
199 | Saratov Oblast | Russia | 15613 | 105 | 10401 | 0.006725 |
200 | Ulyanovsk Oblast | Russia | 15612 | 171 | 13268 | 0.010953 |
201 | Yamalo-Nenets Autonomous Okrug | Russia | 15536 | 99 | 13466 | 0.006372 |
202 | Magdalena | Colombia | 15381 | 838 | 13885 | 0.054483 |
203 | District of Columbia | US | 15326 | 627 | 0 | 0.040911 |
204 | Balochistan | Pakistan | 15302 | 146 | 14016 | 0.009541 |
205 | Volgograd Oblast | Russia | 15231 | 151 | 13380 | 0.009914 |
206 | Himachal Pradesh | India | 15219 | 195 | 11608 | 0.012813 |
207 | Murmansk Oblast | Russia | 15082 | 218 | 12482 | 0.014454 |
208 | Berlin | Germany | 15031 | 229 | 12888 | 0.015235 |
209 | Puno | Peru | 15015 | 300 | 0 | 0.019980 |
210 | Toscana | Italy | 14971 | 1165 | 10338 | 0.077817 |
211 | Chernivtsi Oblast | Ukraine | 14605 | 364 | 8115 | 0.024923 |
212 | Sucre | Colombia | 14115 | 582 | 13151 | 0.041233 |
213 | Krasnodar Krai | Russia | 14083 | 282 | 10552 | 0.020024 |
214 | Baleares | Spain | 14005 | 299 | 1533 | 0.021350 |
215 | Altai Krai | Russia | 13921 | 209 | 12567 | 0.015013 |
216 | Ivano-Frankivsk Oblast | Ukraine | 13837 | 318 | 6536 | 0.022982 |
217 | Odessa Oblast | Ukraine | 13789 | 208 | 2550 | 0.015084 |
218 | Ternopil Oblast | Ukraine | 13767 | 170 | 8183 | 0.012348 |
219 | Canarias | Spain | 13676 | 232 | 1537 | 0.016964 |
220 | Arkhangelsk Oblast | Russia | 13589 | 260 | 10164 | 0.019133 |
221 | Dagestan Republic | Russia | 13514 | 629 | 11642 | 0.046544 |
222 | Montana | US | 13500 | 181 | 0 | 0.013407 |
223 | Liguria | Italy | 13446 | 1608 | 10067 | 0.119589 |
224 | Moquegua | Peru | 13277 | 263 | 0 | 0.019809 |
225 | Novosibirsk Oblast | Russia | 13229 | 454 | 12022 | 0.034319 |
226 | Campania | Italy | 13132 | 463 | 6273 | 0.035257 |
227 | Orenburg Oblast | Russia | 12963 | 85 | 11698 | 0.006557 |
228 | Hidalgo | Mexico | 12853 | 1988 | 10217 | 0.154672 |
229 | Rivne Oblast | Ukraine | 12554 | 167 | 10064 | 0.013303 |
230 | Hawaii | US | 12515 | 139 | 0 | 0.011107 |
231 | Tolima | Colombia | 12487 | 341 | 11238 | 0.027308 |
232 | Khabarovsk Krai | Russia | 12158 | 104 | 9769 | 0.008554 |
233 | Chandigarh | India | 12057 | 164 | 10009 | 0.013602 |
234 | Gelderland | Netherlands | 12050 | 702 | 0 | 0.058257 |
235 | Coquimbo | Chile | 11995 | 213 | 11435 | 0.017757 |
236 | Tacna | Peru | 11976 | 213 | 0 | 0.017786 |
237 | Northern Ireland | United Kingdom | 11952 | 581 | 0 | 0.048611 |
238 | Quintana Roo | Mexico | 11939 | 1652 | 9742 | 0.138370 |
239 | Huila | Colombia | 11918 | 376 | 10452 | 0.031549 |
240 | Samara Oblast | Russia | 11884 | 194 | 9253 | 0.016324 |
241 | Tarapaca | Chile | 11842 | 227 | 11332 | 0.019169 |
242 | Primorsky Krai | Russia | 11823 | 119 | 10239 | 0.010065 |
243 | Ayacucho | Peru | 11533 | 301 | 0 | 0.026099 |
244 | Risaralda | Colombia | 11370 | 252 | 10090 | 0.022164 |
245 | Chihuahua | Mexico | 11324 | 1391 | 9018 | 0.122836 |
246 | Omsk Oblast | Russia | 11117 | 297 | 9776 | 0.026716 |
247 | Manipur | India | 11111 | 68 | 8641 | 0.006120 |
248 | Penza Oblast | Russia | 10980 | 134 | 8962 | 0.012204 |
249 | Extremadura | Spain | 10853 | 581 | 2652 | 0.053534 |
250 | Rheinland-Pfalz | Germany | 10826 | 255 | 9742 | 0.023554 |
251 | Osaka | Japan | 10669 | 209 | 9853 | 0.019589 |
252 | Baja California Sur | Mexico | 10341 | 467 | 9082 | 0.045160 |
253 | Tula Oblast | Russia | 10196 | 374 | 9209 | 0.036681 |
254 | Arunachal Pradesh | India | 10020 | 16 | 7049 | 0.001597 |
255 | Kemerovo Oblast | Russia | 10004 | 106 | 7359 | 0.010596 |
256 | French Guiana | France | 9966 | 67 | 9613 | 0.006723 |
257 | Zakarpattia Oblast | Ukraine | 9918 | 308 | 4798 | 0.031055 |
258 | Perm Krai | Russia | 9908 | 282 | 6783 | 0.028462 |
259 | Utrecht | Netherlands | 9899 | 442 | 0 | 0.044651 |
260 | Cauca | Colombia | 9623 | 269 | 8348 | 0.027954 |
261 | Tyumen Oblast | Russia | 9587 | 38 | 7646 | 0.003964 |
262 | Los Lagos | Chile | 9491 | 96 | 8379 | 0.010115 |
263 | Bryansk Oblast | Russia | 9475 | 38 | 7680 | 0.004011 |
264 | Sakha (Yakutiya) Republic | Russia | 9467 | 96 | 7052 | 0.010140 |
265 | Kiev Oblast | Ukraine | 9279 | 178 | 5639 | 0.019183 |
266 | Kaluga Oblast | Russia | 9241 | 74 | 7670 | 0.008008 |
267 | British Columbia | Canada | 9220 | 235 | 7724 | 0.025488 |
268 | Queretaro | Mexico | 9216 | 997 | 7441 | 0.108181 |
269 | Durango | Mexico | 9051 | 635 | 7565 | 0.070158 |
270 | Belgorod Oblast | Russia | 8933 | 58 | 8035 | 0.006493 |
271 | Bashkortostan Republic | Russia | 8831 | 41 | 8544 | 0.004643 |
272 | Ivanovo Oblast | Russia | 8764 | 134 | 6991 | 0.015290 |
273 | Magallanes | Chile | 8656 | 83 | 7047 | 0.009589 |
274 | Yaroslavl Oblast | Russia | 8644 | 46 | 8084 | 0.005322 |
275 | Orel Oblast | Russia | 8642 | 129 | 7059 | 0.014927 |
276 | La Rioja | Spain | 8632 | 419 | 3107 | 0.048540 |
277 | Chuvashia Republic | Russia | 8623 | 93 | 7276 | 0.010785 |
278 | Kirov Oblast | Russia | 8593 | 132 | 5771 | 0.015361 |
279 | Caqueta | Colombia | 8500 | 304 | 7732 | 0.035765 |
280 | Komi Republic | Russia | 8435 | 114 | 6951 | 0.013515 |
281 | New Hampshire | US | 8317 | 441 | 0 | 0.053024 |
282 | Leningrad Oblast | Russia | 8249 | 98 | 5577 | 0.011880 |
283 | Ryazan Oblast | Russia | 8221 | 51 | 6743 | 0.006204 |
284 | Madre de Dios | Peru | 8219 | 138 | 0 | 0.016790 |
285 | Arica y Parinacota | Chile | 8207 | 150 | 7671 | 0.018277 |
286 | Volyn Oblast | Ukraine | 8194 | 170 | 5253 | 0.020747 |
287 | La Guajira | Colombia | 8161 | 309 | 7425 | 0.037863 |
288 | Kursk Oblast | Russia | 8159 | 57 | 6771 | 0.006986 |
289 | Hamburg | Germany | 7983 | 271 | 6635 | 0.033947 |
290 | Marche | Italy | 7983 | 990 | 6180 | 0.124014 |
291 | Alaska | US | 7948 | 57 | 0 | 0.007172 |
292 | Puglia | Italy | 7900 | 596 | 4697 | 0.075443 |
293 | Tyva Republic | Russia | 7796 | 84 | 6788 | 0.010775 |
294 | Tambov Oblast | Russia | 7771 | 46 | 6723 | 0.005919 |
295 | Kabardino-Balkarian Republic | Russia | 7712 | 93 | 6512 | 0.012059 |
296 | Araucania | Chile | 7585 | 117 | 6783 | 0.015425 |
297 | Tomsk Oblast | Russia | 7579 | 87 | 6275 | 0.011479 |
298 | Tlaxcala | Mexico | 7539 | 1027 | 6375 | 0.136225 |
299 | Zacatecas | Mexico | 7492 | 711 | 6153 | 0.094901 |
300 | Boyaca | Colombia | 7479 | 154 | 6405 | 0.020591 |
301 | Sachsen | Germany | 7338 | 236 | 6309 | 0.032161 |
302 | Vladimir Oblast | Russia | 7337 | 218 | 5932 | 0.029712 |
303 | Tumbes | Peru | 7333 | 301 | 0 | 0.041047 |
304 | Sicilia | Italy | 7274 | 312 | 4026 | 0.042892 |
305 | Aguascalientes | Mexico | 7270 | 656 | 5943 | 0.090234 |
306 | Tatarstan Republic | Russia | 7256 | 50 | 6113 | 0.006891 |
307 | Astrakhan Oblast | Russia | 7154 | 114 | 6395 | 0.015935 |
308 | Karachay-Cherkess Republic | Russia | 7115 | 29 | 4855 | 0.004076 |
309 | Atacama | Chile | 7066 | 67 | 6635 | 0.009482 |
310 | Kanagawa | Japan | 6983 | 141 | 6266 | 0.020192 |
311 | Buryatia Republic | Russia | 6963 | 71 | 5948 | 0.010197 |
312 | Cantabria | Spain | 6850 | 235 | 2287 | 0.034307 |
313 | Limburg | Netherlands | 6802 | 764 | 0 | 0.112320 |
314 | Huancavelica | Peru | 6713 | 112 | 0 | 0.016684 |
315 | Smolensk Oblast | Russia | 6634 | 140 | 5720 | 0.021103 |
316 | Chiapas | Mexico | 6552 | 1020 | 5412 | 0.155678 |
317 | Kalmykia Republic | Russia | 6503 | 73 | 3917 | 0.011226 |
318 | Mordovia Republic | Russia | 6477 | 41 | 5382 | 0.006330 |
319 | Nuble | Chile | 6433 | 123 | 5943 | 0.019120 |
320 | Tver Oblast | Russia | 6385 | 247 | 4557 | 0.038684 |
321 | Zabaykalsky Krai | Russia | 6342 | 69 | 5027 | 0.010880 |
322 | Vinnytsia Oblast | Ukraine | 6308 | 116 | 4041 | 0.018389 |
323 | Nagaland | India | 6244 | 17 | 5144 | 0.002723 |
324 | Khmelnytskyi Oblast | Ukraine | 6188 | 120 | 2645 | 0.019392 |
325 | Lipetsk Oblast | Russia | 6143 | 44 | 5455 | 0.007163 |
326 | Dnipropetrovsk Oblast | Ukraine | 6088 | 114 | 3221 | 0.018725 |
327 | Zhytomyr Oblast | Ukraine | 6088 | 111 | 3433 | 0.018233 |
328 | Wyoming | US | 6083 | 53 | 0 | 0.008713 |
329 | Caldas | Colombia | 6042 | 133 | 5188 | 0.022013 |
330 | P.A. Trento | Italy | 6040 | 406 | 5040 | 0.067219 |
331 | Nayarit | Mexico | 6000 | 741 | 4876 | 0.123500 |
332 | Campeche | Mexico | 5982 | 827 | 5078 | 0.138248 |
333 | Skane | Sweden | 5950 | 279 | 0 | 0.046891 |
334 | Morelos | Mexico | 5946 | 1100 | 4645 | 0.184998 |
335 | North Ossetia - Alania Republic | Russia | 5853 | 70 | 4745 | 0.011960 |
336 | Meghalaya | India | 5802 | 51 | 4001 | 0.008790 |
337 | Ingushetia Republic | Russia | 5727 | 83 | 4773 | 0.014493 |
338 | Sakhalin Oblast | Russia | 5639 | 0 | 3995 | 0.000000 |
339 | Guadeloupe | France | 5528 | 57 | 2199 | 0.010311 |
340 | Asturias | Spain | 5512 | 349 | 1063 | 0.063316 |
341 | Pskov Oblast | Russia | 5476 | 65 | 4079 | 0.011870 |
342 | Maine | US | 5428 | 141 | 0 | 0.025976 |
343 | Jonkoping | Sweden | 5393 | 183 | 0 | 0.033933 |
344 | Aichi | Japan | 5391 | 83 | 4903 | 0.015396 |
345 | Udmurt Republic | Russia | 5363 | 70 | 3791 | 0.013052 |
346 | Novgorod Oblast | Russia | 5303 | 86 | 3785 | 0.016217 |
347 | Overijssel | Netherlands | 5205 | 344 | 0 | 0.066090 |
348 | Pasco | Peru | 5118 | 109 | 0 | 0.021297 |
349 | Hong Kong | China | 5097 | 105 | 4837 | 0.020600 |
350 | Fukuoka | Japan | 5048 | 99 | 4842 | 0.019612 |
351 | Colima | Mexico | 4958 | 573 | 3902 | 0.115571 |
352 | Mari El Republic | Russia | 4881 | 50 | 4263 | 0.010244 |
353 | Schleswig-Holstein | Germany | 4821 | 162 | 4271 | 0.033603 |
354 | Kostroma Oblast | Russia | 4788 | 77 | 3475 | 0.016082 |
355 | Apurimac | Peru | 4783 | 91 | 0 | 0.019026 |
356 | Friuli Venezia Giulia | Italy | 4723 | 351 | 3613 | 0.074317 |
357 | Saitama | Japan | 4696 | 102 | 4286 | 0.021721 |
358 | Vologda Oblast | Russia | 4673 | 47 | 3789 | 0.010058 |
359 | Crimea Republic* | Ukraine | 4564 | 67 | 2863 | 0.014680 |
360 | Kamchatka Krai | Russia | 4519 | 67 | 3316 | 0.014826 |
361 | Abruzzo | Italy | 4449 | 481 | 3059 | 0.108114 |
362 | Kaliningrad Oblast | Russia | 4446 | 75 | 3335 | 0.016869 |
363 | Sumy Oblast | Ukraine | 4435 | 75 | 1798 | 0.016911 |
364 | Khakassia Republic | Russia | 4423 | 48 | 3770 | 0.010852 |
365 | Ladakh | India | 4360 | 61 | 3232 | 0.013991 |
366 | Brandenburg | Germany | 4343 | 173 | 3942 | 0.039834 |
367 | Amur Oblast | Russia | 4326 | 52 | 3858 | 0.012020 |
368 | Chernihiv Oblast | Ukraine | 4254 | 71 | 963 | 0.016690 |
369 | New South Wales | Australia | 4231 | 53 | 3129 | 0.012527 |
370 | Cherkasy Oblast | Ukraine | 4165 | 57 | 1740 | 0.013685 |
371 | Zaporizhia Oblast | Ukraine | 4128 | 64 | 1429 | 0.015504 |
372 | Ostergotland | Sweden | 4117 | 278 | 0 | 0.067525 |
373 | Adygea Republic | Russia | 4114 | 33 | 3512 | 0.008021 |
374 | Uppsala | Sweden | 4108 | 243 | 0 | 0.059153 |
375 | Thuringen | Germany | 4106 | 191 | 3757 | 0.046517 |
376 | Choco | Colombia | 4003 | 157 | 3773 | 0.039221 |
377 | Aruba | Netherlands | 3998 | 27 | 3327 | 0.006753 |
378 | Karelia Republic | Russia | 3997 | 32 | 2920 | 0.008006 |
379 | Sardegna | Italy | 3996 | 155 | 1705 | 0.038789 |
380 | Reunion | France | 3993 | 16 | 2819 | 0.004007 |
381 | Chiba | Japan | 3934 | 71 | 3554 | 0.018048 |
382 | Andaman and Nicobar Islands | India | 3848 | 53 | 3623 | 0.013773 |
383 | Kurgan Oblast | Russia | 3839 | 33 | 3249 | 0.008596 |
384 | Quindio | Colombia | 3814 | 105 | 3136 | 0.027530 |
385 | Gilgit-Baltistan | Pakistan | 3808 | 88 | 3399 | 0.023109 |
386 | Putumayo | Colombia | 3803 | 176 | 3344 | 0.046279 |
387 | Mayotte | France | 3779 | 42 | 2964 | 0.011114 |
388 | Unknown | Chile | 3657 | 19 | 690589 | 0.005196 |
389 | Altai Republic | Russia | 3598 | 12 | 2683 | 0.003335 |
390 | P.A. Bolzano | Italy | 3568 | 292 | 2694 | 0.081839 |
391 | Donetsk Oblast | Ukraine | 3557 | 56 | 1117 | 0.015744 |
392 | Saarland | Germany | 3417 | 177 | 3114 | 0.051800 |
393 | Gavleborg | Sweden | 3396 | 168 | 0 | 0.049470 |
394 | Mykolaiv Oblast | Ukraine | 3270 | 77 | 1086 | 0.023547 |
395 | Vastmanland | Sweden | 3138 | 183 | 0 | 0.058317 |
396 | Dadra and Nagar Haveli and Daman and Diu | India | 3054 | 2 | 2939 | 0.000655 |
397 | Sikkim | India | 3050 | 39 | 2375 | 0.012787 |
398 | Orebro | Sweden | 3014 | 172 | 0 | 0.057067 |
399 | Magadan Oblast | Russia | 2971 | 25 | 2318 | 0.008415 |
400 | Azad Jammu and Kashmir | Pakistan | 2754 | 74 | 2382 | 0.026870 |
401 | Hyogo | Japan | 2742 | 59 | 2544 | 0.021517 |
402 | Sachsen-Anhalt | Germany | 2694 | 68 | 2316 | 0.025241 |
403 | Halland | Sweden | 2606 | 87 | 0 | 0.033384 |
404 | Dalarna | Sweden | 2556 | 177 | 0 | 0.069249 |
405 | Sormland | Sweden | 2527 | 254 | 0 | 0.100514 |
406 | Okinawa | Japan | 2526 | 46 | 2251 | 0.018211 |
407 | Bremen | Germany | 2511 | 59 | 2126 | 0.023497 |
408 | Umbria | Italy | 2500 | 85 | 1853 | 0.034000 |
409 | Guam | US | 2488 | 49 | 0 | 0.019695 |
410 | Casanare | Colombia | 2457 | 48 | 1991 | 0.019536 |
411 | Chechen Republic | Russia | 2434 | 40 | 1883 | 0.016434 |
412 | Poltava Oblast | Ukraine | 2345 | 42 | 1072 | 0.017910 |
413 | Flevoland | Netherlands | 2295 | 100 | 0 | 0.043573 |
414 | Hokkaido | Japan | 2115 | 107 | 1848 | 0.050591 |
415 | Mizoram | India | 2049 | 0 | 1721 | 0.000000 |
416 | Manitoba | Canada | 2029 | 20 | 1388 | 0.009857 |
417 | Calabria | Italy | 2002 | 100 | 1365 | 0.049950 |
418 | Los Rios | Chile | 1957 | 20 | 1524 | 0.010220 |
419 | Saskatchewan | Canada | 1927 | 24 | 1759 | 0.012455 |
420 | Vasternorrland | Sweden | 1925 | 136 | 0 | 0.070649 |
421 | Groningen | Netherlands | 1871 | 20 | 0 | 0.010689 |
422 | French Polynesia | France | 1852 | 7 | 1504 | 0.003780 |
423 | Guangdong | China | 1831 | 8 | 1800 | 0.004369 |
424 | Arauca | Colombia | 1775 | 52 | 1576 | 0.029296 |
425 | Kyoto | Japan | 1767 | 26 | 1663 | 0.014714 |
426 | Norrbotten | Sweden | 1755 | 88 | 0 | 0.050142 |
427 | Vermont | US | 1755 | 58 | 0 | 0.033048 |
428 | Kronoberg | Sweden | 1730 | 122 | 0 | 0.070520 |
429 | Friesland | Netherlands | 1684 | 69 | 0 | 0.040974 |
430 | Martinique | France | 1543 | 21 | 98 | 0.013610 |
431 | San Andres y Providencia | Colombia | 1478 | 15 | 1131 | 0.010149 |
432 | Zeeland | Netherlands | 1406 | 72 | 0 | 0.051209 |
433 | Virgin Islands | US | 1326 | 20 | 0 | 0.015083 |
434 | Valle d'Aosta | Italy | 1315 | 146 | 1100 | 0.111027 |
435 | Jamtland Harjedalen | Sweden | 1292 | 64 | 0 | 0.049536 |
436 | Varmland | Sweden | 1288 | 73 | 0 | 0.056677 |
437 | Zhejiang | China | 1282 | 1 | 1272 | 0.000780 |
438 | Henan | China | 1281 | 22 | 1254 | 0.017174 |
439 | Drenthe | Netherlands | 1250 | 49 | 0 | 0.039200 |
440 | Mecklenburg-Vorpommern | Germany | 1204 | 20 | 1072 | 0.016611 |
441 | Kherson Oblast | Ukraine | 1168 | 26 | 616 | 0.022260 |
442 | Queensland | Australia | 1160 | 6 | 1147 | 0.005172 |
443 | Kirovohrad Oblast | Ukraine | 1127 | 57 | 877 | 0.050577 |
444 | Luhansk Oblast | Ukraine | 1091 | 14 | 576 | 0.012832 |
445 | Nova Scotia | Canada | 1088 | 65 | 1021 | 0.059743 |
446 | Vasterbotten | Sweden | 1043 | 31 | 0 | 0.029722 |
447 | Melilla | Spain | 1035 | 4 | 125 | 0.003865 |
448 | Sevastopol* | Ukraine | 1033 | 28 | 650 | 0.027106 |
449 | Hunan | China | 1019 | 4 | 1015 | 0.003925 |
450 | Shanghai | China | 1007 | 7 | 950 | 0.006951 |
451 | Anhui | China | 991 | 6 | 985 | 0.006054 |
452 | Port Quarantine | Japan | 971 | 1 | 514 | 0.001030 |
453 | Heilongjiang | China | 948 | 13 | 935 | 0.013713 |
454 | Jewish Autonomous Okrug | Russia | 945 | 14 | 683 | 0.014815 |
455 | Beijing | China | 936 | 9 | 926 | 0.009615 |
456 | Jiangxi | China | 935 | 1 | 934 | 0.001070 |
457 | Kalmar | Sweden | 931 | 64 | 0 | 0.068743 |
458 | Guainia | Colombia | 929 | 14 | 803 | 0.015070 |
459 | Xinjiang | China | 902 | 3 | 899 | 0.003326 |
460 | Basilicata | Italy | 841 | 29 | 474 | 0.034483 |
461 | Shandong | China | 832 | 7 | 823 | 0.008413 |
462 | Guaviare | Colombia | 816 | 17 | 719 | 0.020833 |
463 | Ishikawa | Japan | 777 | 47 | 686 | 0.060489 |
464 | Vaupes | Colombia | 770 | 11 | 733 | 0.014286 |
465 | Blekinge | Sweden | 728 | 17 | 0 | 0.023352 |
466 | Gunma | Japan | 706 | 19 | 627 | 0.026912 |
467 | Sichuan | China | 696 | 3 | 663 | 0.004310 |
468 | Turks and Caicos Islands | United Kingdom | 690 | 6 | 648 | 0.008696 |
469 | Western Australia | Australia | 685 | 9 | 654 | 0.013139 |
470 | Sint Maarten | Netherlands | 668 | 22 | 567 | 0.032934 |
471 | Jiangsu | China | 666 | 0 | 664 | 0.000000 |
472 | Channel Islands | United Kingdom | 665 | 48 | 600 | 0.072180 |
473 | Ibaraki | Japan | 659 | 17 | 603 | 0.025797 |
474 | Molise | Italy | 656 | 24 | 498 | 0.036585 |
475 | Gifu | Japan | 627 | 10 | 585 | 0.015949 |
476 | Ceuta | Spain | 623 | 10 | 163 | 0.016051 |
477 | Chongqing | China | 585 | 6 | 578 | 0.010256 |
478 | Hiroshima | Japan | 583 | 3 | 470 | 0.005146 |
479 | Kumamoto | Japan | 576 | 8 | 559 | 0.013889 |
480 | Nara | Japan | 574 | 9 | 545 | 0.015679 |
481 | Shizuoka | Japan | 548 | 2 | 516 | 0.003650 |
482 | Vichada | Colombia | 533 | 5 | 501 | 0.009381 |
483 | Mie | Japan | 524 | 6 | 413 | 0.011450 |
484 | Shiga | Japan | 503 | 8 | 466 | 0.015905 |
485 | Aysen | Chile | 479 | 1 | 283 | 0.002088 |
486 | Faroe Islands | Denmark | 472 | 0 | 429 | 0.000000 |
487 | South Australia | Australia | 468 | 4 | 464 | 0.008547 |
488 | Tochigi | Japan | 432 | 1 | 382 | 0.002315 |
489 | Kagoshima | Japan | 422 | 12 | 378 | 0.028436 |
490 | Toyama | Japan | 421 | 26 | 383 | 0.061758 |
491 | Miyagi | Japan | 413 | 2 | 359 | 0.004843 |
492 | Fujian | China | 411 | 1 | 390 | 0.002433 |
493 | Shaanxi | China | 410 | 3 | 383 | 0.007317 |
494 | Gibraltar | United Kingdom | 410 | 0 | 349 | 0.000000 |
495 | St Martin | France | 403 | 8 | 309 | 0.019851 |
496 | Curacao | Netherlands | 399 | 1 | 185 | 0.002506 |
497 | Hebei | China | 365 | 6 | 358 | 0.016438 |
498 | Miyazaki | Japan | 365 | 1 | 343 | 0.002740 |
499 | Isle of Man | United Kingdom | 340 | 24 | 315 | 0.070588 |
500 | Nenets Autonomous Okrug | Russia | 333 | 0 | 132 | 0.000000 |
501 | Gotland | Sweden | 331 | 6 | 0 | 0.018127 |
502 | Nagano | Japan | 309 | 1 | 306 | 0.003236 |
503 | Newfoundland and Labrador | Canada | 275 | 3 | 269 | 0.010909 |
504 | Liaoning | China | 271 | 2 | 263 | 0.007380 |
505 | Inner Mongolia | China | 266 | 1 | 260 | 0.003759 |
506 | Fukushima | Japan | 263 | 3 | 213 | 0.011407 |
507 | Guangxi | China | 259 | 2 | 255 | 0.007722 |
508 | Fukui | Japan | 244 | 11 | 229 | 0.045082 |
509 | Saga | Japan | 243 | 0 | 246 | 0.000000 |
510 | Wakayama | Japan | 242 | 4 | 229 | 0.016529 |
511 | Tianjin | China | 240 | 3 | 234 | 0.012500 |
512 | Nagasaki | Japan | 238 | 3 | 231 | 0.012605 |
513 | Tasmania | Australia | 230 | 13 | 217 | 0.056522 |
514 | Cayman Islands | United Kingdom | 211 | 1 | 209 | 0.004739 |
515 | Yunnan | China | 209 | 2 | 198 | 0.009569 |
516 | Shanxi | China | 204 | 0 | 203 | 0.000000 |
517 | Yamaguchi | Japan | 201 | 2 | 191 | 0.009950 |
518 | New Brunswick | Canada | 200 | 2 | 192 | 0.010000 |
519 | Chukotka Autonomous Okrug | Russia | 194 | 2 | 182 | 0.010309 |
520 | Yamanashi | Japan | 193 | 6 | 172 | 0.031088 |
521 | Bermuda | United Kingdom | 181 | 9 | 168 | 0.049724 |
522 | Hainan | China | 171 | 6 | 165 | 0.035088 |
523 | Gansu | China | 170 | 2 | 168 | 0.011765 |
524 | Niigata | Japan | 170 | 0 | 161 | 0.000000 |
525 | Oita | Japan | 158 | 2 | 153 | 0.012658 |
526 | Jilin | China | 157 | 2 | 155 | 0.012739 |
527 | Okayama | Japan | 157 | 1 | 147 | 0.006369 |
528 | Tokushima | Japan | 148 | 9 | 126 | 0.060811 |
529 | Guizhou | China | 147 | 2 | 145 | 0.013605 |
530 | Shimane | Japan | 140 | 0 | 137 | 0.000000 |
531 | Kochi | Japan | 138 | 4 | 133 | 0.028986 |
532 | Bonaire, Sint Eustatius and Saba | Netherlands | 121 | 1 | 32 | 0.008264 |
533 | Grand Princess | Canada | 116 | 3 | 13 | 0.025862 |
534 | Ehime | Japan | 114 | 6 | 108 | 0.052632 |
535 | Australian Capital Territory | Australia | 113 | 3 | 110 | 0.026549 |
536 | Kagawa | Japan | 94 | 2 | 88 | 0.021277 |
537 | Yamagata | Japan | 78 | 1 | 76 | 0.012821 |
538 | Ningxia | China | 75 | 0 | 75 | 0.000000 |
539 | Northern Mariana Islands | US | 73 | 2 | 0 | 0.027397 |
540 | British Virgin Islands | United Kingdom | 71 | 1 | 66 | 0.014085 |
541 | Prince Edward Island | Canada | 59 | 0 | 57 | 0.000000 |
542 | Saint Barthelemy | France | 54 | 0 | 37 | 0.000000 |
543 | Akita | Japan | 53 | 0 | 53 | 0.000000 |
544 | Diamond Princess | Canada | 49 | 1 | 0 | 0.020408 |
545 | Macau | China | 46 | 0 | 46 | 0.000000 |
546 | Aomori | Japan | 36 | 1 | 34 | 0.027778 |
547 | Tottori | Japan | 36 | 0 | 31 | 0.000000 |
548 | Northern Territory | Australia | 33 | 0 | 31 | 0.000000 |
549 | New Caledonia | France | 27 | 0 | 27 | 0.000000 |
550 | Iwate | Japan | 23 | 0 | 23 | 0.000000 |
551 | Qinghai | China | 18 | 0 | 18 | 0.000000 |
552 | Saint Pierre and Miquelon | France | 16 | 0 | 6 | 0.000000 |
553 | Yukon | Canada | 15 | 0 | 15 | 0.000000 |
554 | Greenland | Denmark | 14 | 0 | 14 | 0.000000 |
555 | Falkland Islands (Malvinas) | United Kingdom | 13 | 0 | 13 | 0.000000 |
556 | Montserrat | United Kingdom | 13 | 1 | 12 | 0.076923 |
557 | Northwest Territories | Canada | 5 | 0 | 5 | 0.000000 |
558 | Anguilla | United Kingdom | 3 | 0 | 3 | 0.000000 |
559 | Tibet | China | 1 | 0 | 1 | 0.000000 |
province_df.isnull().sum()
Provinces Name 0 Country/Rigion 0 Province Confirmed Casaes 0 Province Deaths Cases 0 Province Recovery Cases 0 Mortality Rate 0 dtype: int64
Bangladesh_confirmed = latest_data[latest_data['Country_Region']=='Bangladesh']['Confirmed'].sum()
outside_Bangadesh_confirmed = np.sum(country_confirm_cases)-Bangladesh_confirmed
plt.figure(figsize=(16,10))
plt.barh('BANGLADESH',Bangladesh_confirmed)
plt.barh("OUTSIDE BANGLADESH",outside_Bangadesh_confirmed)
plt.title('Number of Conronavirus Confirmed Cases',size=20)
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()
us_confirmed = latest_data[latest_data['Country_Region']=='US']['Confirmed'].sum()
outside_us_confirmed = np.sum(country_confirm_cases)-us_confirmed
plt.figure(figsize=(16,9))
plt.barh('US',us_confirmed)
plt.barh("OUTSIDE us",outside_us_confirmed)
plt.title('Number of Conronavirus Confirmed Cases',size=20)
plt.legend(labels = ['US','outside of us'])
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()
print('Outside Banglades : {} cases '.format(outside_Bangadesh_confirmed))
print('Bangladesh : {} cases'.format(Bangladesh_confirmed))
print("Total : {} cases".format(outside_Bangadesh_confirmed+Bangladesh_confirmed))
Outside Banglades : 33924722 cases Bangladesh : 364987 cases Total : 34289709 cases
# only show 10 countries with the most confirmed cases, the rest are grouped into other category
visual_unique_countries = []
visual_confirmed_cases = []
others = np.sum(country_confirm_cases[10:])
for i in range(len(country_confirm_cases[:10])):
visual_unique_countries.append(unique_countries[i])
visual_confirmed_cases.append(country_confirm_cases[i])
visual_unique_countries.append('others')
visual_confirmed_cases.append(others)
visual_unique_countries
['US', 'India', 'Brazil', 'Russia', 'Colombia', 'Peru', 'Spain', 'Argentina', 'Mexico', 'South Africa', 'others']
def plot_bar_graphs(x, y, title):
plt.figure(figsize=(16,9))
plt.barh(x, y)
plt.title(title,size=20)
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()
plot_bar_graphs(visual_unique_countries,visual_confirmed_cases,'Number of Covid-19 Confirm Cases in Coutries/Region')
def plot_pie_charts(x, y, title):
c = random.choices(list(mcolors.CSS4_COLORS.values()),k=len(unique_countries))
plt.figure(figsize=(20,15))
plt.pie(y, colors=c)
plt.title(title,size=20)
plt.legend(x,loc='best', fontsize=15)
plt.yticks(size=20)
plt.show()
plot_pie_charts(visual_unique_countries,visual_confirmed_cases,'Number of Covid-19 Confirm Cases in Coutries/Region')
# only show 10 provinces with the most confirmed cases, the rest are grouped into other category
visual_unique_province = []
visual_confirmed_cases2 = []
others = np.sum(province_confirmed_cases[10:])
for i in range(len(province_confirmed_cases[:10])):
visual_unique_province.append(unique_provinces[i])
visual_confirmed_cases2.append(province_confirmed_cases[i])
visual_unique_province.append('others')
visual_confirmed_cases2.append(others)
plot_bar_graphs(visual_unique_province,visual_confirmed_cases2,'Number of Covid-19 Confirm Cases in Provinces/States')
def plot_pie_country_with_regions(country_name, title):
region = list(latest_data[latest_data['Country_Region']==country_name]['Province_State'].unique())
confirmed_cases = []
no_cases = []
for i in region:
cases = latest_data[latest_data['Province_State']==i]['Confirmed'].sum()
if cases > 0:
confirmed_cases.append(cases)
else:
no_cases.append(i)
for i in no_cases:
region.remove(i)
region = [k for k,v in sorted(zip(region,confirmed_cases),key=operator.itemgetter(1),reverse=True)]
for i in range(len(region)):
confirmed_cases[i] = latest_data[latest_data['Province_State']==region[i]]['Confirmed'].sum()
#Additional province/state will be conciderd 'others'
if (len(region)>10):
region_10 = region[:10]
region_10.append('others')
confirmed_cases = confirmed_cases[:10]
confirmed_cases.append(np.sum(confirmed_cases[10:]))
plot_pie_charts(region, confirmed_cases, title)
else:
plot_pie_charts(region, confirmed_cases, title)
unique_provinces_country = []
for i in province_country:
if i not in unique_provinces_country:
unique_provinces_country.append(i)
unique_provinces_country
['India', 'Brazil', 'US', 'United Kingdom', 'Peru', 'Russia', 'Chile', 'Colombia', 'Spain', 'Pakistan', 'Mexico', 'Italy', 'Canada', 'Germany', 'China', 'Netherlands', 'Japan', 'Sweden', 'Ukraine', 'Australia', 'France', 'Denmark']
for i in unique_provinces_country:
plot_pie_country_with_regions(i,'Convid-19 confirmed cases in {}'.format(i))
days_since_1_22 = np.array([i for i in range(len(dates))]).reshape(-1,1)
world_cases = np.array(world_cases).reshape(-1,1)
total_deaths = np.array(total_deaths).reshape(-1,1)
total_deaths = np.array(total_deaths).reshape(-1,1)
total_recoveries = np.array(total_recoveries).reshape(-1,1)
days_in_future = 20
future_forecast = np.array([i for i in range(len(dates)+days_in_future
)]).reshape(-1,1)
adjusted_dates = future_forecast[:-20]
adjusted_dates
array([[ 0], [ 1], [ 2], [ 3], [ 4], [ 5], [ 6], [ 7], [ 8], [ 9], [ 10], [ 11], [ 12], [ 13], [ 14], [ 15], [ 16], [ 17], [ 18], [ 19], [ 20], [ 21], [ 22], [ 23], [ 24], [ 25], [ 26], [ 27], [ 28], [ 29], [ 30], [ 31], [ 32], [ 33], [ 34], [ 35], [ 36], [ 37], [ 38], [ 39], [ 40], [ 41], [ 42], [ 43], [ 44], [ 45], [ 46], [ 47], [ 48], [ 49], [ 50], [ 51], [ 52], [ 53], [ 54], [ 55], [ 56], [ 57], [ 58], [ 59], [ 60], [ 61], [ 62], [ 63], [ 64], [ 65], [ 66], [ 67], [ 68], [ 69], [ 70], [ 71], [ 72], [ 73], [ 74], [ 75], [ 76], [ 77], [ 78], [ 79], [ 80], [ 81], [ 82], [ 83], [ 84], [ 85], [ 86], [ 87], [ 88], [ 89], [ 90], [ 91], [ 92], [ 93], [ 94], [ 95], [ 96], [ 97], [ 98], [ 99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109], [110], [111], [112], [113], [114], [115], [116], [117], [118], [119], [120], [121], [122], [123], [124], [125], [126], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136], [137], [138], [139], [140], [141], [142], [143], [144], [145], [146], [147], [148], [149], [150], [151], [152], [153], [154], [155], [156], [157], [158], [159], [160], [161], [162], [163], [164], [165], [166], [167], [168], [169], [170], [171], [172], [173], [174], [175], [176], [177], [178], [179], [180], [181], [182], [183], [184], [185], [186], [187], [188], [189], [190], [191], [192], [193], [194], [195], [196], [197], [198], [199], [200], [201], [202], [203], [204], [205], [206], [207], [208], [209], [210], [211], [212], [213], [214], [215], [216], [217], [218], [219], [220], [221], [222], [223], [224], [225], [226], [227], [228], [229], [230], [231], [232], [233], [234], [235], [236], [237], [238], [239], [240], [241], [242], [243], [244], [245], [246], [247], [248], [249], [250], [251], [252]])
future_forecast
array([[ 0], [ 1], [ 2], [ 3], [ 4], [ 5], [ 6], [ 7], [ 8], [ 9], [ 10], [ 11], [ 12], [ 13], [ 14], [ 15], [ 16], [ 17], [ 18], [ 19], [ 20], [ 21], [ 22], [ 23], [ 24], [ 25], [ 26], [ 27], [ 28], [ 29], [ 30], [ 31], [ 32], [ 33], [ 34], [ 35], [ 36], [ 37], [ 38], [ 39], [ 40], [ 41], [ 42], [ 43], [ 44], [ 45], [ 46], [ 47], [ 48], [ 49], [ 50], [ 51], [ 52], [ 53], [ 54], [ 55], [ 56], [ 57], [ 58], [ 59], [ 60], [ 61], [ 62], [ 63], [ 64], [ 65], [ 66], [ 67], [ 68], [ 69], [ 70], [ 71], [ 72], [ 73], [ 74], [ 75], [ 76], [ 77], [ 78], [ 79], [ 80], [ 81], [ 82], [ 83], [ 84], [ 85], [ 86], [ 87], [ 88], [ 89], [ 90], [ 91], [ 92], [ 93], [ 94], [ 95], [ 96], [ 97], [ 98], [ 99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109], [110], [111], [112], [113], [114], [115], [116], [117], [118], [119], [120], [121], [122], [123], [124], [125], [126], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136], [137], [138], [139], [140], [141], [142], [143], [144], [145], [146], [147], [148], [149], [150], [151], [152], [153], [154], [155], [156], [157], [158], [159], [160], [161], [162], [163], [164], [165], [166], [167], [168], [169], [170], [171], [172], [173], [174], [175], [176], [177], [178], [179], [180], [181], [182], [183], [184], [185], [186], [187], [188], [189], [190], [191], [192], [193], [194], [195], [196], [197], [198], [199], [200], [201], [202], [203], [204], [205], [206], [207], [208], [209], [210], [211], [212], [213], [214], [215], [216], [217], [218], [219], [220], [221], [222], [223], [224], [225], [226], [227], [228], [229], [230], [231], [232], [233], [234], [235], [236], [237], [238], [239], [240], [241], [242], [243], [244], [245], [246], [247], [248], [249], [250], [251], [252], [253], [254], [255], [256], [257], [258], [259], [260], [261], [262], [263], [264], [265], [266], [267], [268], [269], [270], [271], [272]])
start = '1/22/2020'
start_dates = datetime.datetime.strptime(start, '%m/%d/%Y')
future_forecast_dates = []
for i in range(len(future_forecast)):
future_forecast_dates.append((start_dates+datetime.timedelta(days=i)).strftime('%m/%d/%Y'))
future_forecast_dates
['01/22/2020', '01/23/2020', '01/24/2020', '01/25/2020', '01/26/2020', '01/27/2020', '01/28/2020', '01/29/2020', '01/30/2020', '01/31/2020', '02/01/2020', '02/02/2020', '02/03/2020', '02/04/2020', '02/05/2020', '02/06/2020', '02/07/2020', '02/08/2020', '02/09/2020', '02/10/2020', '02/11/2020', '02/12/2020', '02/13/2020', '02/14/2020', '02/15/2020', '02/16/2020', '02/17/2020', '02/18/2020', '02/19/2020', '02/20/2020', '02/21/2020', '02/22/2020', '02/23/2020', '02/24/2020', '02/25/2020', '02/26/2020', '02/27/2020', '02/28/2020', '02/29/2020', '03/01/2020', '03/02/2020', '03/03/2020', '03/04/2020', '03/05/2020', '03/06/2020', '03/07/2020', '03/08/2020', '03/09/2020', '03/10/2020', '03/11/2020', '03/12/2020', '03/13/2020', '03/14/2020', '03/15/2020', '03/16/2020', '03/17/2020', '03/18/2020', '03/19/2020', '03/20/2020', '03/21/2020', '03/22/2020', '03/23/2020', '03/24/2020', '03/25/2020', '03/26/2020', '03/27/2020', '03/28/2020', '03/29/2020', '03/30/2020', '03/31/2020', '04/01/2020', '04/02/2020', '04/03/2020', '04/04/2020', '04/05/2020', '04/06/2020', '04/07/2020', '04/08/2020', '04/09/2020', '04/10/2020', '04/11/2020', '04/12/2020', '04/13/2020', '04/14/2020', '04/15/2020', '04/16/2020', '04/17/2020', '04/18/2020', '04/19/2020', '04/20/2020', '04/21/2020', '04/22/2020', '04/23/2020', '04/24/2020', '04/25/2020', '04/26/2020', '04/27/2020', '04/28/2020', '04/29/2020', '04/30/2020', '05/01/2020', '05/02/2020', '05/03/2020', '05/04/2020', '05/05/2020', '05/06/2020', '05/07/2020', '05/08/2020', '05/09/2020', '05/10/2020', '05/11/2020', '05/12/2020', '05/13/2020', '05/14/2020', '05/15/2020', '05/16/2020', '05/17/2020', '05/18/2020', '05/19/2020', '05/20/2020', '05/21/2020', '05/22/2020', '05/23/2020', '05/24/2020', '05/25/2020', '05/26/2020', '05/27/2020', '05/28/2020', '05/29/2020', '05/30/2020', '05/31/2020', '06/01/2020', '06/02/2020', '06/03/2020', '06/04/2020', '06/05/2020', '06/06/2020', '06/07/2020', '06/08/2020', '06/09/2020', '06/10/2020', '06/11/2020', '06/12/2020', '06/13/2020', '06/14/2020', '06/15/2020', '06/16/2020', '06/17/2020', '06/18/2020', '06/19/2020', '06/20/2020', '06/21/2020', '06/22/2020', '06/23/2020', '06/24/2020', '06/25/2020', '06/26/2020', '06/27/2020', '06/28/2020', '06/29/2020', '06/30/2020', '07/01/2020', '07/02/2020', '07/03/2020', '07/04/2020', '07/05/2020', '07/06/2020', '07/07/2020', '07/08/2020', '07/09/2020', '07/10/2020', '07/11/2020', '07/12/2020', '07/13/2020', '07/14/2020', '07/15/2020', '07/16/2020', '07/17/2020', '07/18/2020', '07/19/2020', '07/20/2020', '07/21/2020', '07/22/2020', '07/23/2020', '07/24/2020', '07/25/2020', '07/26/2020', '07/27/2020', '07/28/2020', '07/29/2020', '07/30/2020', '07/31/2020', '08/01/2020', '08/02/2020', '08/03/2020', '08/04/2020', '08/05/2020', '08/06/2020', '08/07/2020', '08/08/2020', '08/09/2020', '08/10/2020', '08/11/2020', '08/12/2020', '08/13/2020', '08/14/2020', '08/15/2020', '08/16/2020', '08/17/2020', '08/18/2020', '08/19/2020', '08/20/2020', '08/21/2020', '08/22/2020', '08/23/2020', '08/24/2020', '08/25/2020', '08/26/2020', '08/27/2020', '08/28/2020', '08/29/2020', '08/30/2020', '08/31/2020', '09/01/2020', '09/02/2020', '09/03/2020', '09/04/2020', '09/05/2020', '09/06/2020', '09/07/2020', '09/08/2020', '09/09/2020', '09/10/2020', '09/11/2020', '09/12/2020', '09/13/2020', '09/14/2020', '09/15/2020', '09/16/2020', '09/17/2020', '09/18/2020', '09/19/2020', '09/20/2020', '09/21/2020', '09/22/2020', '09/23/2020', '09/24/2020', '09/25/2020', '09/26/2020', '09/27/2020', '09/28/2020', '09/29/2020', '09/30/2020', '10/01/2020', '10/02/2020', '10/03/2020', '10/04/2020', '10/05/2020', '10/06/2020', '10/07/2020', '10/08/2020', '10/09/2020', '10/10/2020', '10/11/2020', '10/12/2020', '10/13/2020', '10/14/2020', '10/15/2020', '10/16/2020', '10/17/2020', '10/18/2020', '10/19/2020', '10/20/2020']
X_train_confirmed, X_test_confirmed, y_train_confirmed, y_test_confirmed = train_test_split(days_since_1_22,world_cases, test_size=0.25, shuffle=False)
#transform our data for polynomial regression
poly = PolynomialFeatures(degree=3)
poly_X_train_confirmed = poly.fit_transform(X_train_confirmed)
poly_X_test_confirmed = poly.fit_transform(X_test_confirmed)
poly_future_forecast = poly.fit_transform(future_forecast)
#polynomial regression
linear_model = LinearRegression(normalize=True, fit_intercept=False)
linear_model.fit(poly_X_train_confirmed, y_train_confirmed)
test_linear_pred = linear_model.predict(poly_X_test_confirmed)
linear_pred = linear_model.predict(poly_future_forecast)
print('MAF:',mean_absolute_error(test_linear_pred,y_test_confirmed))
print('MSE:',mean_squared_error(test_linear_pred,y_test_confirmed))
MAF: 532445.639227961 MSE: 377825446353.40576
plt.plot(y_test_confirmed)
plt.plot(test_linear_pred)
plt.legend(['Test Data','Polynomial Regression Prediction'])
<matplotlib.legend.Legend at 0x23ecfde0448>
adjusted_dates = adjusted_dates.reshape(1,-1)[0]
plt.figure(figsize=(16,9))
plt.plot(adjusted_dates, world_cases)
plt.title('Number of Coronavirus Cases Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Number of Cases', size=30)
plt.xticks(size=30)
plt.yticks(size=30)
plt.show()
plt.figure(figsize=(16,9))
plt.plot(adjusted_dates, total_deaths)
plt.title('Number of Coronavirus Cases Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Number of Deaths Cases', size=30)
plt.xticks(size=30)
plt.yticks(size=30)
plt.show()
plt.figure(figsize=(16,9))
plt.plot(adjusted_dates, total_recoveries)
plt.title('Number of Coronavirus Cases Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Number of Recovery Cases', size=30)
plt.xticks(size=30)
plt.yticks(size=30)
plt.show()
# Whoat is world daily increse positive cases
plt.figure(figsize=(16,9))
plt.bar(adjusted_dates, world_daily_increase)
plt.title('Number of Coronavirus Cases Increase Daily', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Number of Cases', size=30)
plt.xticks(size=30)
plt.yticks(size=30)
plt.show()
plt.figure(figsize=(16,9))
plt.bar(adjusted_dates, world_daily_deaths)
plt.title('Number of Coronavirus Cases Deaths Daily', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Number of Cases', size=30)
plt.xticks(size=30)
plt.yticks(size=30)
plt.show()
plt.figure(figsize=(16,9))
plt.bar(adjusted_dates, world_daily_recoveries)
plt.title('Number of Coronavirus Cases Recoveris Daily', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Number of Cases', size=30)
plt.xticks(size=30)
plt.yticks(size=30)
plt.show()
linear_pred
array([[ 2.56326900e+04], [ 1.57486759e+04], [ 6.43003127e+03], [-2.31567135e+03], [-1.04808595e+04], [-1.80579607e+04], [-2.50394024e+04], [-3.14176122e+04], [-3.71850176e+04], [-4.23340460e+04], [-4.68571251e+04], [-5.07466822e+04], [-5.39951450e+04], [-5.65949408e+04], [-5.85384973e+04], [-5.98182420e+04], [-6.04266023e+04], [-6.03560057e+04], [-5.95988798e+04], [-5.81476521e+04], [-5.59947501e+04], [-5.31326013e+04], [-4.95536333e+04], [-4.52502734e+04], [-4.02149493e+04], [-3.44400884e+04], [-2.79181182e+04], [-2.06414664e+04], [-1.26025602e+04], [-3.79382743e+03], [ 5.79230459e+03], [ 1.61634083e+04], [ 2.73270562e+04], [ 3.92908208e+04], [ 5.20622746e+04], [ 6.56489900e+04], [ 8.00585395e+04], [ 9.52984957e+04], [ 1.11376431e+05], [ 1.28299918e+05], [ 1.46076529e+05], [ 1.64713837e+05], [ 1.84219413e+05], [ 2.04600832e+05], [ 2.25865664e+05], [ 2.48021483e+05], [ 2.71075861e+05], [ 2.95036370e+05], [ 3.19910584e+05], [ 3.45706074e+05], [ 3.72430413e+05], [ 4.00091173e+05], [ 4.28695928e+05], [ 4.58252249e+05], [ 4.88767709e+05], [ 5.20249880e+05], [ 5.52706335e+05], [ 5.86144647e+05], [ 6.20572388e+05], [ 6.55997130e+05], [ 6.92426446e+05], [ 7.29867908e+05], [ 7.68329090e+05], [ 8.07817562e+05], [ 8.48340899e+05], [ 8.89906672e+05], [ 9.32522453e+05], [ 9.76195816e+05], [ 1.02093433e+06], [ 1.06674558e+06], [ 1.11363712e+06], [ 1.16161653e+06], [ 1.21069139e+06], [ 1.26086926e+06], [ 1.31215772e+06], [ 1.36456435e+06], [ 1.41809670e+06], [ 1.47276237e+06], [ 1.52856891e+06], [ 1.58552390e+06], [ 1.64363492e+06], [ 1.70290953e+06], [ 1.76335531e+06], [ 1.82497984e+06], [ 1.88779067e+06], [ 1.95179540e+06], [ 2.01700158e+06], [ 2.08341679e+06], [ 2.15104861e+06], [ 2.21990460e+06], [ 2.28999234e+06], [ 2.36131940e+06], [ 2.43389336e+06], [ 2.50772178e+06], [ 2.58281224e+06], [ 2.65917231e+06], [ 2.73680956e+06], [ 2.81573157e+06], [ 2.89594591e+06], [ 2.97746015e+06], [ 3.06028187e+06], [ 3.14441863e+06], [ 3.22987801e+06], [ 3.31666757e+06], [ 3.40479491e+06], [ 3.49426758e+06], [ 3.58509315e+06], [ 3.67727921e+06], [ 3.77083332e+06], [ 3.86576306e+06], [ 3.96207599e+06], [ 4.05977970e+06], [ 4.15888175e+06], [ 4.25938972e+06], [ 4.36131117e+06], [ 4.46465369e+06], [ 4.56942483e+06], [ 4.67563219e+06], [ 4.78328332e+06], [ 4.89238580e+06], [ 5.00294721e+06], [ 5.11497511e+06], [ 5.22847708e+06], [ 5.34346069e+06], [ 5.45993352e+06], [ 5.57790313e+06], [ 5.69737710e+06], [ 5.81836300e+06], [ 5.94086840e+06], [ 6.06490088e+06], [ 6.19046800e+06], [ 6.31757735e+06], [ 6.44623649e+06], [ 6.57645300e+06], [ 6.70823444e+06], [ 6.84158839e+06], [ 6.97652243e+06], [ 7.11304413e+06], [ 7.25116105e+06], [ 7.39088077e+06], [ 7.53221086e+06], [ 7.67515891e+06], [ 7.81973246e+06], [ 7.96593911e+06], [ 8.11378643e+06], [ 8.26328197e+06], [ 8.41443333e+06], [ 8.56724807e+06], [ 8.72173376e+06], [ 8.87789797e+06], [ 9.03574828e+06], [ 9.19529227e+06], [ 9.35653749e+06], [ 9.51949154e+06], [ 9.68416196e+06], [ 9.85055636e+06], [ 1.00186823e+07], [ 1.01885473e+07], [ 1.03601590e+07], [ 1.05335250e+07], [ 1.07086527e+07], [ 1.08855499e+07], [ 1.10642241e+07], [ 1.12446828e+07], [ 1.14269335e+07], [ 1.16109840e+07], [ 1.17968418e+07], [ 1.19845143e+07], [ 1.21740093e+07], [ 1.23653343e+07], [ 1.25584968e+07], [ 1.27535044e+07], [ 1.29503647e+07], [ 1.31490853e+07], [ 1.33496737e+07], [ 1.35521375e+07], [ 1.37564843e+07], [ 1.39627216e+07], [ 1.41708571e+07], [ 1.43808983e+07], [ 1.45928528e+07], [ 1.48067281e+07], [ 1.50225318e+07], [ 1.52402715e+07], [ 1.54599548e+07], [ 1.56815892e+07], [ 1.59051823e+07], [ 1.61307417e+07], [ 1.63582750e+07], [ 1.65877897e+07], [ 1.68192933e+07], [ 1.70527936e+07], [ 1.72882980e+07], [ 1.75258141e+07], [ 1.77653495e+07], [ 1.80069117e+07], [ 1.82505084e+07], [ 1.84961471e+07], [ 1.87438353e+07], [ 1.89935808e+07], [ 1.92453909e+07], [ 1.94992733e+07], [ 1.97552356e+07], [ 2.00132854e+07], [ 2.02734301e+07], [ 2.05356774e+07], [ 2.08000349e+07], [ 2.10665102e+07], [ 2.13351107e+07], [ 2.16058441e+07], [ 2.18787179e+07], [ 2.21537398e+07], [ 2.24309173e+07], [ 2.27102579e+07], [ 2.29917693e+07], [ 2.32754589e+07], [ 2.35613345e+07], [ 2.38494035e+07], [ 2.41396735e+07], [ 2.44321522e+07], [ 2.47268470e+07], [ 2.50237655e+07], [ 2.53229154e+07], [ 2.56243042e+07], [ 2.59279394e+07], [ 2.62338286e+07], [ 2.65419795e+07], [ 2.68523996e+07], [ 2.71650964e+07], [ 2.74800775e+07], [ 2.77973505e+07], [ 2.81169230e+07], [ 2.84388025e+07], [ 2.87629967e+07], [ 2.90895130e+07], [ 2.94183591e+07], [ 2.97495425e+07], [ 3.00830708e+07], [ 3.04189516e+07], [ 3.07571925e+07], [ 3.10978010e+07], [ 3.14407846e+07], [ 3.17861511e+07], [ 3.21339079e+07], [ 3.24840626e+07], [ 3.28366227e+07], [ 3.31915960e+07], [ 3.35489898e+07], [ 3.39088119e+07], [ 3.42710697e+07], [ 3.46357709e+07], [ 3.50029230e+07], [ 3.53725336e+07], [ 3.57446103e+07], [ 3.61191606e+07], [ 3.64961921e+07], [ 3.68757124e+07], [ 3.72577290e+07], [ 3.76422496e+07], [ 3.80292816e+07], [ 3.84188328e+07], [ 3.88109106e+07], [ 3.92055226e+07], [ 3.96026764e+07], [ 4.00023795e+07], [ 4.04046396e+07], [ 4.08094642e+07], [ 4.12168609e+07], [ 4.16268372e+07], [ 4.20394008e+07], [ 4.24545591e+07], [ 4.28723198e+07], [ 4.32926905e+07]])
# Future predictions using polynomial regression
linear_pred = linear_pred.reshape(1,-1)[0]
poly_df = pd.DataFrame({'Date':future_forecast_dates[-20:],'Predicted number of Confirmed Cases Worldwide':np.round(linear_pred[-20:])})
poly_df
Date | Predicted number of Confirmed Cases Worldwide | |
---|---|---|
0 | 10/01/2020 | 35744610.0 |
1 | 10/02/2020 | 36119161.0 |
2 | 10/03/2020 | 36496192.0 |
3 | 10/04/2020 | 36875712.0 |
4 | 10/05/2020 | 37257729.0 |
5 | 10/06/2020 | 37642250.0 |
6 | 10/07/2020 | 38029282.0 |
7 | 10/08/2020 | 38418833.0 |
8 | 10/09/2020 | 38810911.0 |
9 | 10/10/2020 | 39205523.0 |
10 | 10/11/2020 | 39602676.0 |
11 | 10/12/2020 | 40002380.0 |
12 | 10/13/2020 | 40404640.0 |
13 | 10/14/2020 | 40809464.0 |
14 | 10/15/2020 | 41216861.0 |
15 | 10/16/2020 | 41626837.0 |
16 | 10/17/2020 | 42039401.0 |
17 | 10/18/2020 | 42454559.0 |
18 | 10/19/2020 | 42872320.0 |
19 | 10/20/2020 | 43292690.0 |
plt.figure(figsize=(16,9))
plt.plot(adjusted_dates, total_deaths,color='r')
plt.plot(adjusted_dates, total_recoveries,color='green')
plt.title('Number of Death cases Vs Number of Recoveries Cases', size=30)
plt.legend(['death','recoveries'],loc='best',fontsize=20)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Number of Cases', size=30)
plt.xticks(size=30)
plt.yticks(size=30)
plt.show()
#Figureout the Graph for Prediction of Upcoming cases and Confirmed cases
plt.figure(figsize=(16,9))
plt.plot(adjusted_dates, world_cases,color='r')
plt.plot(future_forecast,linear_pred,linestyle='dashed',color='green')
plt.title('Number of Death cases Vs Number of Recoveries Cases', size=30)
plt.legend(['cofirmed cases','polynomial regression prediction'],loc='best',fontsize=20)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Number of Cases', size=30)
plt.xticks(size=30)
plt.yticks(size=30)
plt.show()
# Figure out total number of death and recoveries cases in Bangladesh
plt.figure(figsize=(16,9))
plt.plot(adjusted_dates, confirmed.loc[15],color='r')
plt.plot(adjusted_dates,deaths.loc[15],linestyle='dashed',color='green')
plt.plot(adjusted_dates,recoveries.loc[15],linestyle='dotted',color='purple')
plt.title('Number of Confirmed cases and Number of Death cases Vs Number of Recoveries Cases in Bangladesh', size=30)
plt.legend(['cofirmed cases','deadth cases','recoveries cases'],loc='best',fontsize=20)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Number of Cases', size=30)
plt.xticks(size=30)
plt.yticks(size=30)
plt.show()
unique_countries = unique_countries[:10]
unique_countries
['US', 'India', 'Brazil', 'Russia', 'Colombia', 'Peru', 'Spain', 'Argentina', 'Mexico', 'South Africa']
for i in range(len(unique_countries)):
plt.figure(figsize=(16,9))
plt.plot(adjusted_dates, confirmed.loc[i],color='r')
plt.plot(adjusted_dates,deaths.loc[i],linestyle='dashed',color='green')
plt.plot(adjusted_dates,recoveries.loc[i],linestyle='dotted',color='purple')
plt.title('Number of Confirmed cases and Number of Death cases Vs Number of Recoveries Cases in {}'.format(unique_countries[i]), size=30)
plt.legend(['cofirmed cases','deadth cases','recoveries cases'],loc='best',fontsize=20)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Number of Cases', size=30)
plt.xticks(size=30)
plt.yticks(size=30)
plt.show()