Corona virus is a disease that is reported as an illness caused by a novel coronavirus . It's outbreak was first reported in the year 2019 in Wuhan China. It later spread to other parts of the globe. It has really affected the entire world negatively in many areas. As a data analyst, I want to analyse the data about this dangerous desease from 2019 to 28th Feb of 2022. To get more information about corona virus disease, click click here
To recommend on one of the best ways of controling and overcoming the disease.
To achieve this goal, I'm going to use python libraries like
pandas
,numpy
,andmatplotlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display
# reading the dataset
data = pd.read_csv("covid_19 (2).xls")
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 221 entries, 0 to 220 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Countries 221 non-null object 1 Total_cases 221 non-null int64 2 New cases 221 non-null float64 3 Total_deaths 221 non-null int64 4 New_death 221 non-null float64 5 Total_recovered 221 non-null int64 6 New_recovered 221 non-null float64 7 Active_cases 221 non-null int64 8 Serious/Critical 221 non-null int64 9 Total_cases/1M 221 non-null float64 10 Total_deaths/1M 221 non-null float64 11 Total_tests 221 non-null int64 12 Test/1M 221 non-null int64 13 Popultion 221 non-null int64 14 Continent 221 non-null object dtypes: float64(5), int64(8), object(2) memory usage: 26.0+ KB
From the above output,we can clearely see that Countries
and Continent
is of type object which is okay because they they are string names.We can also see that New cases
,New_deaths
,New_recovered
,Total_cases/1M
and Total_deaths/1M
are of type float which is also in order.Lastly, we can see that Total_cases
,Total_deaths
,Total_recovered
,Active_cases
,Serious/Critical
,Total_tests
,Test/1M
and Population
are of type integer hence my data is clean.
# displaying the shape of the data
data.shape
(221, 15)
data.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
1 | India | 42951556 | 6396.0 | 514620 | 201.0 | 42367070 | 13450.0 | 69866 | 8944 | 30623.0 | 367.0 | 770050005 | 549014 | 1402606131 | Asia |
2 | Brazil | 28906214 | 64054.0 | 650646 | 594.0 | 26810286 | 142276.0 | 1445282 | 8318 | 134400.0 | 3025.0 | 63776166 | 296528 | 215076719 | South America |
3 | France | 22900531 | 60225.0 | 138942 | 180.0 | 21364892 | 131190.0 | 1396697 | 2484 | 349551.0 | 2121.0 | 246629975 | 3764529 | 65514171 | Europe |
4 | UK | 19074696 | 45656.0 | 161898 | 194.0 | 17537214 | -1.0 | 1375584 | 279 | 278543.0 | 2364.0 | 484240712 | 7071256 | 68480157 | Europe |
north_america = data[data["Continent"] == "North America"].copy()
north_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
16 | Mexico | 5534086 | 12342.0 | 318835 | 304.0 | 4809799 | 13721.0 | 405452 | 4798 | 42184.0 | 2430.0 | 15147396 | 115462 | 131189291 | North America |
28 | Canada | 3310385 | 6600.0 | 36843 | 114.0 | 3161973 | 6566.0 | 111569 | 600 | 86448.0 | 962.0 | 58319533 | 1522964 | 38293437 | North America |
53 | Cuba | 1071326 | 596.0 | 8498 | 2.0 | 1060502 | 602.0 | 2326 | 34 | 94681.0 | 751.0 | 12920253 | 1141860 | 11315096 | North America |
64 | Costa Rica | 812812 | 1772.0 | 8073 | 16.0 | 682991 | 3845.0 | 121748 | 110 | 157168.0 | 1561.0 | 3712867 | 717932 | 5171611 | North America |
66 | Guatemala | 786869 | 2845.0 | 17029 | 11.0 | 730907 | 4085.0 | 38933 | 5 | 42609.0 | 922.0 | 3922624 | 212408 | 18467383 | North America |
68 | Panama | 756539 | 454.0 | 8101 | 3.0 | 743902 | 518.0 | 4536 | 26 | 170858.0 | 1830.0 | 5534045 | 1249818 | 4427882 | North America |
77 | Dominican Republic | 575437 | 280.0 | 4370 | -1.0 | 569294 | 347.0 | 1773 | 33 | 52174.0 | 396.0 | 3129690 | 283766 | 11029126 | North America |
87 | Honduras | 413699 | 966.0 | 10785 | 7.0 | 128408 | 420.0 | 274506 | 105 | 40691.0 | 1061.0 | 1263329 | 124258 | 10166946 | North America |
113 | El Salvador | 156364 | -1.0 | 4081 | 4.0 | 132410 | -1.0 | 19873 | 153 | 23906.0 | 624.0 | 1758939 | 268921 | 6540733 | North America |
118 | Trinidad and Tobago | 128976 | 285.0 | 3642 | 5.0 | 102736 | -1.0 | 22598 | 18 | 91665.0 | 2588.0 | 634632 | 451041 | 1407039 | North America |
119 | Jamaica | 128108 | 29.0 | 2815 | 1.0 | 77860 | 267.0 | 47433 | 25 | 42950.0 | 944.0 | 857650 | 287541 | 2982701 | North America |
120 | Guadeloupe | 126642 | -1.0 | 837 | -1.0 | 2250 | -1.0 | 123555 | 19 | 316417.0 | 2091.0 | 938039 | 2343703 | 400238 | North America |
122 | Martinique | 115951 | -1.0 | 895 | -1.0 | 104 | -1.0 | 114952 | 10 | 309384.0 | 2388.0 | 707420 | 1887561 | 374780 | North America |
140 | Belize | 56842 | 26.0 | 651 | 1.0 | 55107 | 115.0 | 1084 | 6 | 138758.0 | 1589.0 | 504813 | 1232312 | 409647 | North America |
143 | Barbados | 55705 | 162.0 | 316 | -1.0 | 53644 | 23.0 | 1745 | -1 | 193446.0 | 1097.0 | 588782 | 2044659 | 287961 | North America |
149 | Curaçao | 39082 | 66.0 | 262 | 1.0 | 38507 | 40.0 | 313 | 3 | 236558.0 | 1586.0 | 493647 | 2987979 | 165211 | North America |
156 | Aruba | 33684 | -1.0 | 211 | -1.0 | 33418 | -1.0 | 55 | 6 | 313278.0 | 1962.0 | 177885 | 1654421 | 107521 | North America |
158 | Bahamas | 33152 | 2.0 | 771 | -1.0 | 31947 | 38.0 | 434 | 9 | 82983.0 | 1930.0 | 220353 | 551566 | 399504 | North America |
162 | Haiti | 30353 | 3.0 | 820 | -1.0 | 25258 | 28.0 | 4275 | -1 | 2609.0 | 70.0 | 132422 | 11382 | 11634062 | North America |
168 | Saint Lucia | 22733 | 4.0 | 360 | -1.0 | 22280 | 23.0 | 93 | 3 | 122864.0 | 1946.0 | 136343 | 736890 | 185025 | North America |
171 | Cayman Islands | 19373 | -1.0 | 17 | -1.0 | 8553 | -1.0 | 10803 | 2 | 289115.0 | 254.0 | 222773 | 3324573 | 67008 | North America |
172 | Nicaragua | 18105 | -1.0 | 221 | -1.0 | 4225 | -1.0 | 13659 | -1 | 2680.0 | 33.0 | -1 | -1 | 6755837 | North America |
181 | Grenada | 13690 | -1.0 | 216 | -1.0 | 13336 | -1.0 | 138 | 4 | 120732.0 | 1905.0 | 136411 | 1203004 | 113392 | North America |
184 | Greenland | 11778 | 19.0 | 18 | -1.0 | 2761 | -1.0 | 8999 | 7 | 206871.0 | 316.0 | 164926 | 2896793 | 56934 | North America |
186 | Bermuda | 11634 | 73.0 | 123 | -1.0 | 11338 | 67.0 | 173 | -1 | 187963.0 | 1987.0 | 805308 | 13010873 | 61895 | North America |
187 | Dominica | 11148 | 40.0 | 61 | 4.0 | 10881 | 169.0 | 206 | -1 | 154225.0 | 844.0 | 168239 | 2327472 | 72284 | North America |
188 | Saint Martin | 9912 | 35.0 | 63 | -1.0 | 1399 | -1.0 | 8450 | 7 | 249271.0 | 1584.0 | 112382 | 2826225 | 39764 | North America |
190 | Sint Maarten | 9570 | 1.0 | 85 | -1.0 | 9438 | 2.0 | 47 | 10 | 219058.0 | 1946.0 | 62056 | 1420468 | 43687 | North America |
196 | Caribbean Netherlands | 7605 | 6.0 | 31 | -1.0 | 7545 | -1.0 | 29 | -1 | 285591.0 | 1164.0 | 30126 | 1131323 | 26629 | North America |
197 | Antigua and Barbuda | 7449 | -1.0 | 135 | -1.0 | 7284 | -1.0 | 30 | 1 | 75030.0 | 1360.0 | 18901 | 190381 | 99280 | North America |
201 | St. Vincent Grenadines | 6742 | 1.0 | 106 | -1.0 | 6627 | 4.0 | 9 | -1 | 60452.0 | 950.0 | 98236 | 880827 | 111527 | North America |
202 | British Virgin Islands | 6091 | 6.0 | 62 | -1.0 | 0 | 0.0 | 0 | 1 | 199274.0 | 2028.0 | 100749 | 3296113 | 30566 | North America |
204 | Turks and Caicos | 5867 | -1.0 | 36 | -1.0 | 5787 | -1.0 | 44 | 4 | 148198.0 | 909.0 | 419792 | 10603754 | 39589 | North America |
205 | Saint Kitts and Nevis | 5531 | 1.0 | 42 | -1.0 | 5484 | -1.0 | 5 | 1 | 102757.0 | 780.0 | 65141 | 1210214 | 53826 | North America |
207 | St. Barth | 3780 | 22.0 | 6 | -1.0 | 0 | 0.0 | 0 | -1 | 380780.0 | 604.0 | 78646 | 7922434 | 9927 | North America |
209 | Anguilla | 2555 | -1.0 | 9 | -1.0 | 2528 | -1.0 | 18 | 4 | 167805.0 | 591.0 | 51382 | 3374622 | 15226 | North America |
210 | Saint Pierre Miquelon | 1087 | -1.0 | 1 | -1.0 | 1065 | -1.0 | 21 | 1 | 189142.0 | 174.0 | 17358 | 3020358 | 5747 | North America |
213 | Montserrat | 164 | -1.0 | 2 | -1.0 | 161 | -1.0 | 1 | -1 | 32820.0 | 400.0 | 8359 | 1672804 | 4997 | North America |
# sorting dataframe to help identify the country with the most number of death cases
north_america.sort_values("Total_cases",ascending = False,inplace = True)
# Displaying the data
north_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
16 | Mexico | 5534086 | 12342.0 | 318835 | 304.0 | 4809799 | 13721.0 | 405452 | 4798 | 42184.0 | 2430.0 | 15147396 | 115462 | 131189291 | North America |
28 | Canada | 3310385 | 6600.0 | 36843 | 114.0 | 3161973 | 6566.0 | 111569 | 600 | 86448.0 | 962.0 | 58319533 | 1522964 | 38293437 | North America |
53 | Cuba | 1071326 | 596.0 | 8498 | 2.0 | 1060502 | 602.0 | 2326 | 34 | 94681.0 | 751.0 | 12920253 | 1141860 | 11315096 | North America |
64 | Costa Rica | 812812 | 1772.0 | 8073 | 16.0 | 682991 | 3845.0 | 121748 | 110 | 157168.0 | 1561.0 | 3712867 | 717932 | 5171611 | North America |
66 | Guatemala | 786869 | 2845.0 | 17029 | 11.0 | 730907 | 4085.0 | 38933 | 5 | 42609.0 | 922.0 | 3922624 | 212408 | 18467383 | North America |
68 | Panama | 756539 | 454.0 | 8101 | 3.0 | 743902 | 518.0 | 4536 | 26 | 170858.0 | 1830.0 | 5534045 | 1249818 | 4427882 | North America |
77 | Dominican Republic | 575437 | 280.0 | 4370 | -1.0 | 569294 | 347.0 | 1773 | 33 | 52174.0 | 396.0 | 3129690 | 283766 | 11029126 | North America |
87 | Honduras | 413699 | 966.0 | 10785 | 7.0 | 128408 | 420.0 | 274506 | 105 | 40691.0 | 1061.0 | 1263329 | 124258 | 10166946 | North America |
113 | El Salvador | 156364 | -1.0 | 4081 | 4.0 | 132410 | -1.0 | 19873 | 153 | 23906.0 | 624.0 | 1758939 | 268921 | 6540733 | North America |
118 | Trinidad and Tobago | 128976 | 285.0 | 3642 | 5.0 | 102736 | -1.0 | 22598 | 18 | 91665.0 | 2588.0 | 634632 | 451041 | 1407039 | North America |
119 | Jamaica | 128108 | 29.0 | 2815 | 1.0 | 77860 | 267.0 | 47433 | 25 | 42950.0 | 944.0 | 857650 | 287541 | 2982701 | North America |
120 | Guadeloupe | 126642 | -1.0 | 837 | -1.0 | 2250 | -1.0 | 123555 | 19 | 316417.0 | 2091.0 | 938039 | 2343703 | 400238 | North America |
122 | Martinique | 115951 | -1.0 | 895 | -1.0 | 104 | -1.0 | 114952 | 10 | 309384.0 | 2388.0 | 707420 | 1887561 | 374780 | North America |
140 | Belize | 56842 | 26.0 | 651 | 1.0 | 55107 | 115.0 | 1084 | 6 | 138758.0 | 1589.0 | 504813 | 1232312 | 409647 | North America |
143 | Barbados | 55705 | 162.0 | 316 | -1.0 | 53644 | 23.0 | 1745 | -1 | 193446.0 | 1097.0 | 588782 | 2044659 | 287961 | North America |
149 | Curaçao | 39082 | 66.0 | 262 | 1.0 | 38507 | 40.0 | 313 | 3 | 236558.0 | 1586.0 | 493647 | 2987979 | 165211 | North America |
156 | Aruba | 33684 | -1.0 | 211 | -1.0 | 33418 | -1.0 | 55 | 6 | 313278.0 | 1962.0 | 177885 | 1654421 | 107521 | North America |
158 | Bahamas | 33152 | 2.0 | 771 | -1.0 | 31947 | 38.0 | 434 | 9 | 82983.0 | 1930.0 | 220353 | 551566 | 399504 | North America |
162 | Haiti | 30353 | 3.0 | 820 | -1.0 | 25258 | 28.0 | 4275 | -1 | 2609.0 | 70.0 | 132422 | 11382 | 11634062 | North America |
168 | Saint Lucia | 22733 | 4.0 | 360 | -1.0 | 22280 | 23.0 | 93 | 3 | 122864.0 | 1946.0 | 136343 | 736890 | 185025 | North America |
171 | Cayman Islands | 19373 | -1.0 | 17 | -1.0 | 8553 | -1.0 | 10803 | 2 | 289115.0 | 254.0 | 222773 | 3324573 | 67008 | North America |
172 | Nicaragua | 18105 | -1.0 | 221 | -1.0 | 4225 | -1.0 | 13659 | -1 | 2680.0 | 33.0 | -1 | -1 | 6755837 | North America |
181 | Grenada | 13690 | -1.0 | 216 | -1.0 | 13336 | -1.0 | 138 | 4 | 120732.0 | 1905.0 | 136411 | 1203004 | 113392 | North America |
184 | Greenland | 11778 | 19.0 | 18 | -1.0 | 2761 | -1.0 | 8999 | 7 | 206871.0 | 316.0 | 164926 | 2896793 | 56934 | North America |
186 | Bermuda | 11634 | 73.0 | 123 | -1.0 | 11338 | 67.0 | 173 | -1 | 187963.0 | 1987.0 | 805308 | 13010873 | 61895 | North America |
187 | Dominica | 11148 | 40.0 | 61 | 4.0 | 10881 | 169.0 | 206 | -1 | 154225.0 | 844.0 | 168239 | 2327472 | 72284 | North America |
188 | Saint Martin | 9912 | 35.0 | 63 | -1.0 | 1399 | -1.0 | 8450 | 7 | 249271.0 | 1584.0 | 112382 | 2826225 | 39764 | North America |
190 | Sint Maarten | 9570 | 1.0 | 85 | -1.0 | 9438 | 2.0 | 47 | 10 | 219058.0 | 1946.0 | 62056 | 1420468 | 43687 | North America |
196 | Caribbean Netherlands | 7605 | 6.0 | 31 | -1.0 | 7545 | -1.0 | 29 | -1 | 285591.0 | 1164.0 | 30126 | 1131323 | 26629 | North America |
197 | Antigua and Barbuda | 7449 | -1.0 | 135 | -1.0 | 7284 | -1.0 | 30 | 1 | 75030.0 | 1360.0 | 18901 | 190381 | 99280 | North America |
201 | St. Vincent Grenadines | 6742 | 1.0 | 106 | -1.0 | 6627 | 4.0 | 9 | -1 | 60452.0 | 950.0 | 98236 | 880827 | 111527 | North America |
202 | British Virgin Islands | 6091 | 6.0 | 62 | -1.0 | 0 | 0.0 | 0 | 1 | 199274.0 | 2028.0 | 100749 | 3296113 | 30566 | North America |
204 | Turks and Caicos | 5867 | -1.0 | 36 | -1.0 | 5787 | -1.0 | 44 | 4 | 148198.0 | 909.0 | 419792 | 10603754 | 39589 | North America |
205 | Saint Kitts and Nevis | 5531 | 1.0 | 42 | -1.0 | 5484 | -1.0 | 5 | 1 | 102757.0 | 780.0 | 65141 | 1210214 | 53826 | North America |
207 | St. Barth | 3780 | 22.0 | 6 | -1.0 | 0 | 0.0 | 0 | -1 | 380780.0 | 604.0 | 78646 | 7922434 | 9927 | North America |
209 | Anguilla | 2555 | -1.0 | 9 | -1.0 | 2528 | -1.0 | 18 | 4 | 167805.0 | 591.0 | 51382 | 3374622 | 15226 | North America |
210 | Saint Pierre Miquelon | 1087 | -1.0 | 1 | -1.0 | 1065 | -1.0 | 21 | 1 | 189142.0 | 174.0 | 17358 | 3020358 | 5747 | North America |
213 | Montserrat | 164 | -1.0 | 2 | -1.0 | 161 | -1.0 | 1 | -1 | 32820.0 | 400.0 | 8359 | 1672804 | 4997 | North America |
# Drowing visualization graph
plt.bar(north_america["Countries"].head(10),north_america["Total_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("North America Total Cases")
plt.xlabel("Countries")
plt.ylabel("Total cases(in ten million)")
Text(0, 0.5, 'Total cases(in ten million)')
# sorting the dataframe to help identify the country with highest covid 19 death cases
north_america.sort_values("Total_deaths",ascending = False,inplace = True)
north_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
16 | Mexico | 5534086 | 12342.0 | 318835 | 304.0 | 4809799 | 13721.0 | 405452 | 4798 | 42184.0 | 2430.0 | 15147396 | 115462 | 131189291 | North America |
28 | Canada | 3310385 | 6600.0 | 36843 | 114.0 | 3161973 | 6566.0 | 111569 | 600 | 86448.0 | 962.0 | 58319533 | 1522964 | 38293437 | North America |
66 | Guatemala | 786869 | 2845.0 | 17029 | 11.0 | 730907 | 4085.0 | 38933 | 5 | 42609.0 | 922.0 | 3922624 | 212408 | 18467383 | North America |
87 | Honduras | 413699 | 966.0 | 10785 | 7.0 | 128408 | 420.0 | 274506 | 105 | 40691.0 | 1061.0 | 1263329 | 124258 | 10166946 | North America |
53 | Cuba | 1071326 | 596.0 | 8498 | 2.0 | 1060502 | 602.0 | 2326 | 34 | 94681.0 | 751.0 | 12920253 | 1141860 | 11315096 | North America |
68 | Panama | 756539 | 454.0 | 8101 | 3.0 | 743902 | 518.0 | 4536 | 26 | 170858.0 | 1830.0 | 5534045 | 1249818 | 4427882 | North America |
64 | Costa Rica | 812812 | 1772.0 | 8073 | 16.0 | 682991 | 3845.0 | 121748 | 110 | 157168.0 | 1561.0 | 3712867 | 717932 | 5171611 | North America |
77 | Dominican Republic | 575437 | 280.0 | 4370 | -1.0 | 569294 | 347.0 | 1773 | 33 | 52174.0 | 396.0 | 3129690 | 283766 | 11029126 | North America |
113 | El Salvador | 156364 | -1.0 | 4081 | 4.0 | 132410 | -1.0 | 19873 | 153 | 23906.0 | 624.0 | 1758939 | 268921 | 6540733 | North America |
118 | Trinidad and Tobago | 128976 | 285.0 | 3642 | 5.0 | 102736 | -1.0 | 22598 | 18 | 91665.0 | 2588.0 | 634632 | 451041 | 1407039 | North America |
119 | Jamaica | 128108 | 29.0 | 2815 | 1.0 | 77860 | 267.0 | 47433 | 25 | 42950.0 | 944.0 | 857650 | 287541 | 2982701 | North America |
122 | Martinique | 115951 | -1.0 | 895 | -1.0 | 104 | -1.0 | 114952 | 10 | 309384.0 | 2388.0 | 707420 | 1887561 | 374780 | North America |
120 | Guadeloupe | 126642 | -1.0 | 837 | -1.0 | 2250 | -1.0 | 123555 | 19 | 316417.0 | 2091.0 | 938039 | 2343703 | 400238 | North America |
162 | Haiti | 30353 | 3.0 | 820 | -1.0 | 25258 | 28.0 | 4275 | -1 | 2609.0 | 70.0 | 132422 | 11382 | 11634062 | North America |
158 | Bahamas | 33152 | 2.0 | 771 | -1.0 | 31947 | 38.0 | 434 | 9 | 82983.0 | 1930.0 | 220353 | 551566 | 399504 | North America |
140 | Belize | 56842 | 26.0 | 651 | 1.0 | 55107 | 115.0 | 1084 | 6 | 138758.0 | 1589.0 | 504813 | 1232312 | 409647 | North America |
168 | Saint Lucia | 22733 | 4.0 | 360 | -1.0 | 22280 | 23.0 | 93 | 3 | 122864.0 | 1946.0 | 136343 | 736890 | 185025 | North America |
143 | Barbados | 55705 | 162.0 | 316 | -1.0 | 53644 | 23.0 | 1745 | -1 | 193446.0 | 1097.0 | 588782 | 2044659 | 287961 | North America |
149 | Curaçao | 39082 | 66.0 | 262 | 1.0 | 38507 | 40.0 | 313 | 3 | 236558.0 | 1586.0 | 493647 | 2987979 | 165211 | North America |
172 | Nicaragua | 18105 | -1.0 | 221 | -1.0 | 4225 | -1.0 | 13659 | -1 | 2680.0 | 33.0 | -1 | -1 | 6755837 | North America |
181 | Grenada | 13690 | -1.0 | 216 | -1.0 | 13336 | -1.0 | 138 | 4 | 120732.0 | 1905.0 | 136411 | 1203004 | 113392 | North America |
156 | Aruba | 33684 | -1.0 | 211 | -1.0 | 33418 | -1.0 | 55 | 6 | 313278.0 | 1962.0 | 177885 | 1654421 | 107521 | North America |
197 | Antigua and Barbuda | 7449 | -1.0 | 135 | -1.0 | 7284 | -1.0 | 30 | 1 | 75030.0 | 1360.0 | 18901 | 190381 | 99280 | North America |
186 | Bermuda | 11634 | 73.0 | 123 | -1.0 | 11338 | 67.0 | 173 | -1 | 187963.0 | 1987.0 | 805308 | 13010873 | 61895 | North America |
201 | St. Vincent Grenadines | 6742 | 1.0 | 106 | -1.0 | 6627 | 4.0 | 9 | -1 | 60452.0 | 950.0 | 98236 | 880827 | 111527 | North America |
190 | Sint Maarten | 9570 | 1.0 | 85 | -1.0 | 9438 | 2.0 | 47 | 10 | 219058.0 | 1946.0 | 62056 | 1420468 | 43687 | North America |
188 | Saint Martin | 9912 | 35.0 | 63 | -1.0 | 1399 | -1.0 | 8450 | 7 | 249271.0 | 1584.0 | 112382 | 2826225 | 39764 | North America |
202 | British Virgin Islands | 6091 | 6.0 | 62 | -1.0 | 0 | 0.0 | 0 | 1 | 199274.0 | 2028.0 | 100749 | 3296113 | 30566 | North America |
187 | Dominica | 11148 | 40.0 | 61 | 4.0 | 10881 | 169.0 | 206 | -1 | 154225.0 | 844.0 | 168239 | 2327472 | 72284 | North America |
205 | Saint Kitts and Nevis | 5531 | 1.0 | 42 | -1.0 | 5484 | -1.0 | 5 | 1 | 102757.0 | 780.0 | 65141 | 1210214 | 53826 | North America |
204 | Turks and Caicos | 5867 | -1.0 | 36 | -1.0 | 5787 | -1.0 | 44 | 4 | 148198.0 | 909.0 | 419792 | 10603754 | 39589 | North America |
196 | Caribbean Netherlands | 7605 | 6.0 | 31 | -1.0 | 7545 | -1.0 | 29 | -1 | 285591.0 | 1164.0 | 30126 | 1131323 | 26629 | North America |
184 | Greenland | 11778 | 19.0 | 18 | -1.0 | 2761 | -1.0 | 8999 | 7 | 206871.0 | 316.0 | 164926 | 2896793 | 56934 | North America |
171 | Cayman Islands | 19373 | -1.0 | 17 | -1.0 | 8553 | -1.0 | 10803 | 2 | 289115.0 | 254.0 | 222773 | 3324573 | 67008 | North America |
209 | Anguilla | 2555 | -1.0 | 9 | -1.0 | 2528 | -1.0 | 18 | 4 | 167805.0 | 591.0 | 51382 | 3374622 | 15226 | North America |
207 | St. Barth | 3780 | 22.0 | 6 | -1.0 | 0 | 0.0 | 0 | -1 | 380780.0 | 604.0 | 78646 | 7922434 | 9927 | North America |
213 | Montserrat | 164 | -1.0 | 2 | -1.0 | 161 | -1.0 | 1 | -1 | 32820.0 | 400.0 | 8359 | 1672804 | 4997 | North America |
210 | Saint Pierre Miquelon | 1087 | -1.0 | 1 | -1.0 | 1065 | -1.0 | 21 | 1 | 189142.0 | 174.0 | 17358 | 3020358 | 5747 | North America |
# Drowing visualization graph
plt.bar(north_america["Countries"].head(10),north_america["Total_deaths"].head(10))
plt.xticks(rotation = '45')
plt.title("North America Total Deaths")
plt.xlabel("Countries")
plt.ylabel("Total Deaths(in ten million)")
Text(0, 0.5, 'Total Deaths(in ten million)')
# sorting the dataframe to help identify the country with highest covid 19 active cases
north_america.sort_values("Active_cases",ascending = False,inplace = True)
north_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
16 | Mexico | 5534086 | 12342.0 | 318835 | 304.0 | 4809799 | 13721.0 | 405452 | 4798 | 42184.0 | 2430.0 | 15147396 | 115462 | 131189291 | North America |
87 | Honduras | 413699 | 966.0 | 10785 | 7.0 | 128408 | 420.0 | 274506 | 105 | 40691.0 | 1061.0 | 1263329 | 124258 | 10166946 | North America |
120 | Guadeloupe | 126642 | -1.0 | 837 | -1.0 | 2250 | -1.0 | 123555 | 19 | 316417.0 | 2091.0 | 938039 | 2343703 | 400238 | North America |
64 | Costa Rica | 812812 | 1772.0 | 8073 | 16.0 | 682991 | 3845.0 | 121748 | 110 | 157168.0 | 1561.0 | 3712867 | 717932 | 5171611 | North America |
122 | Martinique | 115951 | -1.0 | 895 | -1.0 | 104 | -1.0 | 114952 | 10 | 309384.0 | 2388.0 | 707420 | 1887561 | 374780 | North America |
28 | Canada | 3310385 | 6600.0 | 36843 | 114.0 | 3161973 | 6566.0 | 111569 | 600 | 86448.0 | 962.0 | 58319533 | 1522964 | 38293437 | North America |
119 | Jamaica | 128108 | 29.0 | 2815 | 1.0 | 77860 | 267.0 | 47433 | 25 | 42950.0 | 944.0 | 857650 | 287541 | 2982701 | North America |
66 | Guatemala | 786869 | 2845.0 | 17029 | 11.0 | 730907 | 4085.0 | 38933 | 5 | 42609.0 | 922.0 | 3922624 | 212408 | 18467383 | North America |
118 | Trinidad and Tobago | 128976 | 285.0 | 3642 | 5.0 | 102736 | -1.0 | 22598 | 18 | 91665.0 | 2588.0 | 634632 | 451041 | 1407039 | North America |
113 | El Salvador | 156364 | -1.0 | 4081 | 4.0 | 132410 | -1.0 | 19873 | 153 | 23906.0 | 624.0 | 1758939 | 268921 | 6540733 | North America |
172 | Nicaragua | 18105 | -1.0 | 221 | -1.0 | 4225 | -1.0 | 13659 | -1 | 2680.0 | 33.0 | -1 | -1 | 6755837 | North America |
171 | Cayman Islands | 19373 | -1.0 | 17 | -1.0 | 8553 | -1.0 | 10803 | 2 | 289115.0 | 254.0 | 222773 | 3324573 | 67008 | North America |
184 | Greenland | 11778 | 19.0 | 18 | -1.0 | 2761 | -1.0 | 8999 | 7 | 206871.0 | 316.0 | 164926 | 2896793 | 56934 | North America |
188 | Saint Martin | 9912 | 35.0 | 63 | -1.0 | 1399 | -1.0 | 8450 | 7 | 249271.0 | 1584.0 | 112382 | 2826225 | 39764 | North America |
68 | Panama | 756539 | 454.0 | 8101 | 3.0 | 743902 | 518.0 | 4536 | 26 | 170858.0 | 1830.0 | 5534045 | 1249818 | 4427882 | North America |
162 | Haiti | 30353 | 3.0 | 820 | -1.0 | 25258 | 28.0 | 4275 | -1 | 2609.0 | 70.0 | 132422 | 11382 | 11634062 | North America |
53 | Cuba | 1071326 | 596.0 | 8498 | 2.0 | 1060502 | 602.0 | 2326 | 34 | 94681.0 | 751.0 | 12920253 | 1141860 | 11315096 | North America |
77 | Dominican Republic | 575437 | 280.0 | 4370 | -1.0 | 569294 | 347.0 | 1773 | 33 | 52174.0 | 396.0 | 3129690 | 283766 | 11029126 | North America |
143 | Barbados | 55705 | 162.0 | 316 | -1.0 | 53644 | 23.0 | 1745 | -1 | 193446.0 | 1097.0 | 588782 | 2044659 | 287961 | North America |
140 | Belize | 56842 | 26.0 | 651 | 1.0 | 55107 | 115.0 | 1084 | 6 | 138758.0 | 1589.0 | 504813 | 1232312 | 409647 | North America |
158 | Bahamas | 33152 | 2.0 | 771 | -1.0 | 31947 | 38.0 | 434 | 9 | 82983.0 | 1930.0 | 220353 | 551566 | 399504 | North America |
149 | Curaçao | 39082 | 66.0 | 262 | 1.0 | 38507 | 40.0 | 313 | 3 | 236558.0 | 1586.0 | 493647 | 2987979 | 165211 | North America |
187 | Dominica | 11148 | 40.0 | 61 | 4.0 | 10881 | 169.0 | 206 | -1 | 154225.0 | 844.0 | 168239 | 2327472 | 72284 | North America |
186 | Bermuda | 11634 | 73.0 | 123 | -1.0 | 11338 | 67.0 | 173 | -1 | 187963.0 | 1987.0 | 805308 | 13010873 | 61895 | North America |
181 | Grenada | 13690 | -1.0 | 216 | -1.0 | 13336 | -1.0 | 138 | 4 | 120732.0 | 1905.0 | 136411 | 1203004 | 113392 | North America |
168 | Saint Lucia | 22733 | 4.0 | 360 | -1.0 | 22280 | 23.0 | 93 | 3 | 122864.0 | 1946.0 | 136343 | 736890 | 185025 | North America |
156 | Aruba | 33684 | -1.0 | 211 | -1.0 | 33418 | -1.0 | 55 | 6 | 313278.0 | 1962.0 | 177885 | 1654421 | 107521 | North America |
190 | Sint Maarten | 9570 | 1.0 | 85 | -1.0 | 9438 | 2.0 | 47 | 10 | 219058.0 | 1946.0 | 62056 | 1420468 | 43687 | North America |
204 | Turks and Caicos | 5867 | -1.0 | 36 | -1.0 | 5787 | -1.0 | 44 | 4 | 148198.0 | 909.0 | 419792 | 10603754 | 39589 | North America |
197 | Antigua and Barbuda | 7449 | -1.0 | 135 | -1.0 | 7284 | -1.0 | 30 | 1 | 75030.0 | 1360.0 | 18901 | 190381 | 99280 | North America |
196 | Caribbean Netherlands | 7605 | 6.0 | 31 | -1.0 | 7545 | -1.0 | 29 | -1 | 285591.0 | 1164.0 | 30126 | 1131323 | 26629 | North America |
210 | Saint Pierre Miquelon | 1087 | -1.0 | 1 | -1.0 | 1065 | -1.0 | 21 | 1 | 189142.0 | 174.0 | 17358 | 3020358 | 5747 | North America |
209 | Anguilla | 2555 | -1.0 | 9 | -1.0 | 2528 | -1.0 | 18 | 4 | 167805.0 | 591.0 | 51382 | 3374622 | 15226 | North America |
201 | St. Vincent Grenadines | 6742 | 1.0 | 106 | -1.0 | 6627 | 4.0 | 9 | -1 | 60452.0 | 950.0 | 98236 | 880827 | 111527 | North America |
205 | Saint Kitts and Nevis | 5531 | 1.0 | 42 | -1.0 | 5484 | -1.0 | 5 | 1 | 102757.0 | 780.0 | 65141 | 1210214 | 53826 | North America |
213 | Montserrat | 164 | -1.0 | 2 | -1.0 | 161 | -1.0 | 1 | -1 | 32820.0 | 400.0 | 8359 | 1672804 | 4997 | North America |
202 | British Virgin Islands | 6091 | 6.0 | 62 | -1.0 | 0 | 0.0 | 0 | 1 | 199274.0 | 2028.0 | 100749 | 3296113 | 30566 | North America |
207 | St. Barth | 3780 | 22.0 | 6 | -1.0 | 0 | 0.0 | 0 | -1 | 380780.0 | 604.0 | 78646 | 7922434 | 9927 | North America |
# Drowing visualization graph
plt.bar(north_america["Countries"].head(10),north_america["Active_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("North America Active Cases")
plt.xlabel("Countries")
plt.ylabel("Active cases(in ten million)")
Text(0, 0.5, 'Active cases(in ten million)')
# sorting the dataframe to help identify the country with highest covid 19 New cases
north_america.sort_values("New cases",ascending = False,inplace = True)
north_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
16 | Mexico | 5534086 | 12342.0 | 318835 | 304.0 | 4809799 | 13721.0 | 405452 | 4798 | 42184.0 | 2430.0 | 15147396 | 115462 | 131189291 | North America |
28 | Canada | 3310385 | 6600.0 | 36843 | 114.0 | 3161973 | 6566.0 | 111569 | 600 | 86448.0 | 962.0 | 58319533 | 1522964 | 38293437 | North America |
66 | Guatemala | 786869 | 2845.0 | 17029 | 11.0 | 730907 | 4085.0 | 38933 | 5 | 42609.0 | 922.0 | 3922624 | 212408 | 18467383 | North America |
64 | Costa Rica | 812812 | 1772.0 | 8073 | 16.0 | 682991 | 3845.0 | 121748 | 110 | 157168.0 | 1561.0 | 3712867 | 717932 | 5171611 | North America |
87 | Honduras | 413699 | 966.0 | 10785 | 7.0 | 128408 | 420.0 | 274506 | 105 | 40691.0 | 1061.0 | 1263329 | 124258 | 10166946 | North America |
53 | Cuba | 1071326 | 596.0 | 8498 | 2.0 | 1060502 | 602.0 | 2326 | 34 | 94681.0 | 751.0 | 12920253 | 1141860 | 11315096 | North America |
68 | Panama | 756539 | 454.0 | 8101 | 3.0 | 743902 | 518.0 | 4536 | 26 | 170858.0 | 1830.0 | 5534045 | 1249818 | 4427882 | North America |
118 | Trinidad and Tobago | 128976 | 285.0 | 3642 | 5.0 | 102736 | -1.0 | 22598 | 18 | 91665.0 | 2588.0 | 634632 | 451041 | 1407039 | North America |
77 | Dominican Republic | 575437 | 280.0 | 4370 | -1.0 | 569294 | 347.0 | 1773 | 33 | 52174.0 | 396.0 | 3129690 | 283766 | 11029126 | North America |
143 | Barbados | 55705 | 162.0 | 316 | -1.0 | 53644 | 23.0 | 1745 | -1 | 193446.0 | 1097.0 | 588782 | 2044659 | 287961 | North America |
186 | Bermuda | 11634 | 73.0 | 123 | -1.0 | 11338 | 67.0 | 173 | -1 | 187963.0 | 1987.0 | 805308 | 13010873 | 61895 | North America |
149 | Curaçao | 39082 | 66.0 | 262 | 1.0 | 38507 | 40.0 | 313 | 3 | 236558.0 | 1586.0 | 493647 | 2987979 | 165211 | North America |
187 | Dominica | 11148 | 40.0 | 61 | 4.0 | 10881 | 169.0 | 206 | -1 | 154225.0 | 844.0 | 168239 | 2327472 | 72284 | North America |
188 | Saint Martin | 9912 | 35.0 | 63 | -1.0 | 1399 | -1.0 | 8450 | 7 | 249271.0 | 1584.0 | 112382 | 2826225 | 39764 | North America |
119 | Jamaica | 128108 | 29.0 | 2815 | 1.0 | 77860 | 267.0 | 47433 | 25 | 42950.0 | 944.0 | 857650 | 287541 | 2982701 | North America |
140 | Belize | 56842 | 26.0 | 651 | 1.0 | 55107 | 115.0 | 1084 | 6 | 138758.0 | 1589.0 | 504813 | 1232312 | 409647 | North America |
207 | St. Barth | 3780 | 22.0 | 6 | -1.0 | 0 | 0.0 | 0 | -1 | 380780.0 | 604.0 | 78646 | 7922434 | 9927 | North America |
184 | Greenland | 11778 | 19.0 | 18 | -1.0 | 2761 | -1.0 | 8999 | 7 | 206871.0 | 316.0 | 164926 | 2896793 | 56934 | North America |
196 | Caribbean Netherlands | 7605 | 6.0 | 31 | -1.0 | 7545 | -1.0 | 29 | -1 | 285591.0 | 1164.0 | 30126 | 1131323 | 26629 | North America |
202 | British Virgin Islands | 6091 | 6.0 | 62 | -1.0 | 0 | 0.0 | 0 | 1 | 199274.0 | 2028.0 | 100749 | 3296113 | 30566 | North America |
168 | Saint Lucia | 22733 | 4.0 | 360 | -1.0 | 22280 | 23.0 | 93 | 3 | 122864.0 | 1946.0 | 136343 | 736890 | 185025 | North America |
162 | Haiti | 30353 | 3.0 | 820 | -1.0 | 25258 | 28.0 | 4275 | -1 | 2609.0 | 70.0 | 132422 | 11382 | 11634062 | North America |
158 | Bahamas | 33152 | 2.0 | 771 | -1.0 | 31947 | 38.0 | 434 | 9 | 82983.0 | 1930.0 | 220353 | 551566 | 399504 | North America |
205 | Saint Kitts and Nevis | 5531 | 1.0 | 42 | -1.0 | 5484 | -1.0 | 5 | 1 | 102757.0 | 780.0 | 65141 | 1210214 | 53826 | North America |
201 | St. Vincent Grenadines | 6742 | 1.0 | 106 | -1.0 | 6627 | 4.0 | 9 | -1 | 60452.0 | 950.0 | 98236 | 880827 | 111527 | North America |
190 | Sint Maarten | 9570 | 1.0 | 85 | -1.0 | 9438 | 2.0 | 47 | 10 | 219058.0 | 1946.0 | 62056 | 1420468 | 43687 | North America |
213 | Montserrat | 164 | -1.0 | 2 | -1.0 | 161 | -1.0 | 1 | -1 | 32820.0 | 400.0 | 8359 | 1672804 | 4997 | North America |
209 | Anguilla | 2555 | -1.0 | 9 | -1.0 | 2528 | -1.0 | 18 | 4 | 167805.0 | 591.0 | 51382 | 3374622 | 15226 | North America |
210 | Saint Pierre Miquelon | 1087 | -1.0 | 1 | -1.0 | 1065 | -1.0 | 21 | 1 | 189142.0 | 174.0 | 17358 | 3020358 | 5747 | North America |
172 | Nicaragua | 18105 | -1.0 | 221 | -1.0 | 4225 | -1.0 | 13659 | -1 | 2680.0 | 33.0 | -1 | -1 | 6755837 | North America |
197 | Antigua and Barbuda | 7449 | -1.0 | 135 | -1.0 | 7284 | -1.0 | 30 | 1 | 75030.0 | 1360.0 | 18901 | 190381 | 99280 | North America |
204 | Turks and Caicos | 5867 | -1.0 | 36 | -1.0 | 5787 | -1.0 | 44 | 4 | 148198.0 | 909.0 | 419792 | 10603754 | 39589 | North America |
156 | Aruba | 33684 | -1.0 | 211 | -1.0 | 33418 | -1.0 | 55 | 6 | 313278.0 | 1962.0 | 177885 | 1654421 | 107521 | North America |
171 | Cayman Islands | 19373 | -1.0 | 17 | -1.0 | 8553 | -1.0 | 10803 | 2 | 289115.0 | 254.0 | 222773 | 3324573 | 67008 | North America |
120 | Guadeloupe | 126642 | -1.0 | 837 | -1.0 | 2250 | -1.0 | 123555 | 19 | 316417.0 | 2091.0 | 938039 | 2343703 | 400238 | North America |
122 | Martinique | 115951 | -1.0 | 895 | -1.0 | 104 | -1.0 | 114952 | 10 | 309384.0 | 2388.0 | 707420 | 1887561 | 374780 | North America |
113 | El Salvador | 156364 | -1.0 | 4081 | 4.0 | 132410 | -1.0 | 19873 | 153 | 23906.0 | 624.0 | 1758939 | 268921 | 6540733 | North America |
181 | Grenada | 13690 | -1.0 | 216 | -1.0 | 13336 | -1.0 | 138 | 4 | 120732.0 | 1905.0 | 136411 | 1203004 | 113392 | North America |
# Drowing visualization graph
plt.bar(north_america["Countries"].head(10),north_america["New cases"].head(10))
plt.xticks(rotation = '45')
plt.title("North America New Cases")
plt.xlabel("Countries")
plt.ylabel("New cases(in ten million)")
Text(0, 0.5, 'New cases(in ten million)')
# sorting the dataframe to help identify the country with highest covid 19 New death cases
north_america.sort_values("New_death",ascending = False,inplace = True)
north_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
16 | Mexico | 5534086 | 12342.0 | 318835 | 304.0 | 4809799 | 13721.0 | 405452 | 4798 | 42184.0 | 2430.0 | 15147396 | 115462 | 131189291 | North America |
28 | Canada | 3310385 | 6600.0 | 36843 | 114.0 | 3161973 | 6566.0 | 111569 | 600 | 86448.0 | 962.0 | 58319533 | 1522964 | 38293437 | North America |
64 | Costa Rica | 812812 | 1772.0 | 8073 | 16.0 | 682991 | 3845.0 | 121748 | 110 | 157168.0 | 1561.0 | 3712867 | 717932 | 5171611 | North America |
66 | Guatemala | 786869 | 2845.0 | 17029 | 11.0 | 730907 | 4085.0 | 38933 | 5 | 42609.0 | 922.0 | 3922624 | 212408 | 18467383 | North America |
87 | Honduras | 413699 | 966.0 | 10785 | 7.0 | 128408 | 420.0 | 274506 | 105 | 40691.0 | 1061.0 | 1263329 | 124258 | 10166946 | North America |
118 | Trinidad and Tobago | 128976 | 285.0 | 3642 | 5.0 | 102736 | -1.0 | 22598 | 18 | 91665.0 | 2588.0 | 634632 | 451041 | 1407039 | North America |
187 | Dominica | 11148 | 40.0 | 61 | 4.0 | 10881 | 169.0 | 206 | -1 | 154225.0 | 844.0 | 168239 | 2327472 | 72284 | North America |
113 | El Salvador | 156364 | -1.0 | 4081 | 4.0 | 132410 | -1.0 | 19873 | 153 | 23906.0 | 624.0 | 1758939 | 268921 | 6540733 | North America |
68 | Panama | 756539 | 454.0 | 8101 | 3.0 | 743902 | 518.0 | 4536 | 26 | 170858.0 | 1830.0 | 5534045 | 1249818 | 4427882 | North America |
53 | Cuba | 1071326 | 596.0 | 8498 | 2.0 | 1060502 | 602.0 | 2326 | 34 | 94681.0 | 751.0 | 12920253 | 1141860 | 11315096 | North America |
140 | Belize | 56842 | 26.0 | 651 | 1.0 | 55107 | 115.0 | 1084 | 6 | 138758.0 | 1589.0 | 504813 | 1232312 | 409647 | North America |
119 | Jamaica | 128108 | 29.0 | 2815 | 1.0 | 77860 | 267.0 | 47433 | 25 | 42950.0 | 944.0 | 857650 | 287541 | 2982701 | North America |
149 | Curaçao | 39082 | 66.0 | 262 | 1.0 | 38507 | 40.0 | 313 | 3 | 236558.0 | 1586.0 | 493647 | 2987979 | 165211 | North America |
171 | Cayman Islands | 19373 | -1.0 | 17 | -1.0 | 8553 | -1.0 | 10803 | 2 | 289115.0 | 254.0 | 222773 | 3324573 | 67008 | North America |
190 | Sint Maarten | 9570 | 1.0 | 85 | -1.0 | 9438 | 2.0 | 47 | 10 | 219058.0 | 1946.0 | 62056 | 1420468 | 43687 | North America |
120 | Guadeloupe | 126642 | -1.0 | 837 | -1.0 | 2250 | -1.0 | 123555 | 19 | 316417.0 | 2091.0 | 938039 | 2343703 | 400238 | North America |
156 | Aruba | 33684 | -1.0 | 211 | -1.0 | 33418 | -1.0 | 55 | 6 | 313278.0 | 1962.0 | 177885 | 1654421 | 107521 | North America |
204 | Turks and Caicos | 5867 | -1.0 | 36 | -1.0 | 5787 | -1.0 | 44 | 4 | 148198.0 | 909.0 | 419792 | 10603754 | 39589 | North America |
122 | Martinique | 115951 | -1.0 | 895 | -1.0 | 104 | -1.0 | 114952 | 10 | 309384.0 | 2388.0 | 707420 | 1887561 | 374780 | North America |
197 | Antigua and Barbuda | 7449 | -1.0 | 135 | -1.0 | 7284 | -1.0 | 30 | 1 | 75030.0 | 1360.0 | 18901 | 190381 | 99280 | North America |
172 | Nicaragua | 18105 | -1.0 | 221 | -1.0 | 4225 | -1.0 | 13659 | -1 | 2680.0 | 33.0 | -1 | -1 | 6755837 | North America |
210 | Saint Pierre Miquelon | 1087 | -1.0 | 1 | -1.0 | 1065 | -1.0 | 21 | 1 | 189142.0 | 174.0 | 17358 | 3020358 | 5747 | North America |
209 | Anguilla | 2555 | -1.0 | 9 | -1.0 | 2528 | -1.0 | 18 | 4 | 167805.0 | 591.0 | 51382 | 3374622 | 15226 | North America |
213 | Montserrat | 164 | -1.0 | 2 | -1.0 | 161 | -1.0 | 1 | -1 | 32820.0 | 400.0 | 8359 | 1672804 | 4997 | North America |
196 | Caribbean Netherlands | 7605 | 6.0 | 31 | -1.0 | 7545 | -1.0 | 29 | -1 | 285591.0 | 1164.0 | 30126 | 1131323 | 26629 | North America |
201 | St. Vincent Grenadines | 6742 | 1.0 | 106 | -1.0 | 6627 | 4.0 | 9 | -1 | 60452.0 | 950.0 | 98236 | 880827 | 111527 | North America |
205 | Saint Kitts and Nevis | 5531 | 1.0 | 42 | -1.0 | 5484 | -1.0 | 5 | 1 | 102757.0 | 780.0 | 65141 | 1210214 | 53826 | North America |
158 | Bahamas | 33152 | 2.0 | 771 | -1.0 | 31947 | 38.0 | 434 | 9 | 82983.0 | 1930.0 | 220353 | 551566 | 399504 | North America |
162 | Haiti | 30353 | 3.0 | 820 | -1.0 | 25258 | 28.0 | 4275 | -1 | 2609.0 | 70.0 | 132422 | 11382 | 11634062 | North America |
168 | Saint Lucia | 22733 | 4.0 | 360 | -1.0 | 22280 | 23.0 | 93 | 3 | 122864.0 | 1946.0 | 136343 | 736890 | 185025 | North America |
202 | British Virgin Islands | 6091 | 6.0 | 62 | -1.0 | 0 | 0.0 | 0 | 1 | 199274.0 | 2028.0 | 100749 | 3296113 | 30566 | North America |
184 | Greenland | 11778 | 19.0 | 18 | -1.0 | 2761 | -1.0 | 8999 | 7 | 206871.0 | 316.0 | 164926 | 2896793 | 56934 | North America |
207 | St. Barth | 3780 | 22.0 | 6 | -1.0 | 0 | 0.0 | 0 | -1 | 380780.0 | 604.0 | 78646 | 7922434 | 9927 | North America |
188 | Saint Martin | 9912 | 35.0 | 63 | -1.0 | 1399 | -1.0 | 8450 | 7 | 249271.0 | 1584.0 | 112382 | 2826225 | 39764 | North America |
186 | Bermuda | 11634 | 73.0 | 123 | -1.0 | 11338 | 67.0 | 173 | -1 | 187963.0 | 1987.0 | 805308 | 13010873 | 61895 | North America |
143 | Barbados | 55705 | 162.0 | 316 | -1.0 | 53644 | 23.0 | 1745 | -1 | 193446.0 | 1097.0 | 588782 | 2044659 | 287961 | North America |
77 | Dominican Republic | 575437 | 280.0 | 4370 | -1.0 | 569294 | 347.0 | 1773 | 33 | 52174.0 | 396.0 | 3129690 | 283766 | 11029126 | North America |
181 | Grenada | 13690 | -1.0 | 216 | -1.0 | 13336 | -1.0 | 138 | 4 | 120732.0 | 1905.0 | 136411 | 1203004 | 113392 | North America |
# Drowing visualization graph
plt.bar(north_america["Countries"].head(10),north_america["New_death"].head(10))
plt.xticks(rotation = '45')
plt.title("North America New Deaths")
plt.xlabel("Countries")
plt.ylabel("New Deaths(in ten million)")
Text(0, 0.5, 'New Deaths(in ten million)')
# sorting the dataframe to help identify the country with highest covid 19 total recovered cases
north_america.sort_values("Total_recovered",ascending = False,inplace = True)
north_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
16 | Mexico | 5534086 | 12342.0 | 318835 | 304.0 | 4809799 | 13721.0 | 405452 | 4798 | 42184.0 | 2430.0 | 15147396 | 115462 | 131189291 | North America |
28 | Canada | 3310385 | 6600.0 | 36843 | 114.0 | 3161973 | 6566.0 | 111569 | 600 | 86448.0 | 962.0 | 58319533 | 1522964 | 38293437 | North America |
53 | Cuba | 1071326 | 596.0 | 8498 | 2.0 | 1060502 | 602.0 | 2326 | 34 | 94681.0 | 751.0 | 12920253 | 1141860 | 11315096 | North America |
68 | Panama | 756539 | 454.0 | 8101 | 3.0 | 743902 | 518.0 | 4536 | 26 | 170858.0 | 1830.0 | 5534045 | 1249818 | 4427882 | North America |
66 | Guatemala | 786869 | 2845.0 | 17029 | 11.0 | 730907 | 4085.0 | 38933 | 5 | 42609.0 | 922.0 | 3922624 | 212408 | 18467383 | North America |
64 | Costa Rica | 812812 | 1772.0 | 8073 | 16.0 | 682991 | 3845.0 | 121748 | 110 | 157168.0 | 1561.0 | 3712867 | 717932 | 5171611 | North America |
77 | Dominican Republic | 575437 | 280.0 | 4370 | -1.0 | 569294 | 347.0 | 1773 | 33 | 52174.0 | 396.0 | 3129690 | 283766 | 11029126 | North America |
113 | El Salvador | 156364 | -1.0 | 4081 | 4.0 | 132410 | -1.0 | 19873 | 153 | 23906.0 | 624.0 | 1758939 | 268921 | 6540733 | North America |
87 | Honduras | 413699 | 966.0 | 10785 | 7.0 | 128408 | 420.0 | 274506 | 105 | 40691.0 | 1061.0 | 1263329 | 124258 | 10166946 | North America |
118 | Trinidad and Tobago | 128976 | 285.0 | 3642 | 5.0 | 102736 | -1.0 | 22598 | 18 | 91665.0 | 2588.0 | 634632 | 451041 | 1407039 | North America |
119 | Jamaica | 128108 | 29.0 | 2815 | 1.0 | 77860 | 267.0 | 47433 | 25 | 42950.0 | 944.0 | 857650 | 287541 | 2982701 | North America |
140 | Belize | 56842 | 26.0 | 651 | 1.0 | 55107 | 115.0 | 1084 | 6 | 138758.0 | 1589.0 | 504813 | 1232312 | 409647 | North America |
143 | Barbados | 55705 | 162.0 | 316 | -1.0 | 53644 | 23.0 | 1745 | -1 | 193446.0 | 1097.0 | 588782 | 2044659 | 287961 | North America |
149 | Curaçao | 39082 | 66.0 | 262 | 1.0 | 38507 | 40.0 | 313 | 3 | 236558.0 | 1586.0 | 493647 | 2987979 | 165211 | North America |
156 | Aruba | 33684 | -1.0 | 211 | -1.0 | 33418 | -1.0 | 55 | 6 | 313278.0 | 1962.0 | 177885 | 1654421 | 107521 | North America |
158 | Bahamas | 33152 | 2.0 | 771 | -1.0 | 31947 | 38.0 | 434 | 9 | 82983.0 | 1930.0 | 220353 | 551566 | 399504 | North America |
162 | Haiti | 30353 | 3.0 | 820 | -1.0 | 25258 | 28.0 | 4275 | -1 | 2609.0 | 70.0 | 132422 | 11382 | 11634062 | North America |
168 | Saint Lucia | 22733 | 4.0 | 360 | -1.0 | 22280 | 23.0 | 93 | 3 | 122864.0 | 1946.0 | 136343 | 736890 | 185025 | North America |
181 | Grenada | 13690 | -1.0 | 216 | -1.0 | 13336 | -1.0 | 138 | 4 | 120732.0 | 1905.0 | 136411 | 1203004 | 113392 | North America |
186 | Bermuda | 11634 | 73.0 | 123 | -1.0 | 11338 | 67.0 | 173 | -1 | 187963.0 | 1987.0 | 805308 | 13010873 | 61895 | North America |
187 | Dominica | 11148 | 40.0 | 61 | 4.0 | 10881 | 169.0 | 206 | -1 | 154225.0 | 844.0 | 168239 | 2327472 | 72284 | North America |
190 | Sint Maarten | 9570 | 1.0 | 85 | -1.0 | 9438 | 2.0 | 47 | 10 | 219058.0 | 1946.0 | 62056 | 1420468 | 43687 | North America |
171 | Cayman Islands | 19373 | -1.0 | 17 | -1.0 | 8553 | -1.0 | 10803 | 2 | 289115.0 | 254.0 | 222773 | 3324573 | 67008 | North America |
196 | Caribbean Netherlands | 7605 | 6.0 | 31 | -1.0 | 7545 | -1.0 | 29 | -1 | 285591.0 | 1164.0 | 30126 | 1131323 | 26629 | North America |
197 | Antigua and Barbuda | 7449 | -1.0 | 135 | -1.0 | 7284 | -1.0 | 30 | 1 | 75030.0 | 1360.0 | 18901 | 190381 | 99280 | North America |
201 | St. Vincent Grenadines | 6742 | 1.0 | 106 | -1.0 | 6627 | 4.0 | 9 | -1 | 60452.0 | 950.0 | 98236 | 880827 | 111527 | North America |
204 | Turks and Caicos | 5867 | -1.0 | 36 | -1.0 | 5787 | -1.0 | 44 | 4 | 148198.0 | 909.0 | 419792 | 10603754 | 39589 | North America |
205 | Saint Kitts and Nevis | 5531 | 1.0 | 42 | -1.0 | 5484 | -1.0 | 5 | 1 | 102757.0 | 780.0 | 65141 | 1210214 | 53826 | North America |
172 | Nicaragua | 18105 | -1.0 | 221 | -1.0 | 4225 | -1.0 | 13659 | -1 | 2680.0 | 33.0 | -1 | -1 | 6755837 | North America |
184 | Greenland | 11778 | 19.0 | 18 | -1.0 | 2761 | -1.0 | 8999 | 7 | 206871.0 | 316.0 | 164926 | 2896793 | 56934 | North America |
209 | Anguilla | 2555 | -1.0 | 9 | -1.0 | 2528 | -1.0 | 18 | 4 | 167805.0 | 591.0 | 51382 | 3374622 | 15226 | North America |
120 | Guadeloupe | 126642 | -1.0 | 837 | -1.0 | 2250 | -1.0 | 123555 | 19 | 316417.0 | 2091.0 | 938039 | 2343703 | 400238 | North America |
188 | Saint Martin | 9912 | 35.0 | 63 | -1.0 | 1399 | -1.0 | 8450 | 7 | 249271.0 | 1584.0 | 112382 | 2826225 | 39764 | North America |
210 | Saint Pierre Miquelon | 1087 | -1.0 | 1 | -1.0 | 1065 | -1.0 | 21 | 1 | 189142.0 | 174.0 | 17358 | 3020358 | 5747 | North America |
213 | Montserrat | 164 | -1.0 | 2 | -1.0 | 161 | -1.0 | 1 | -1 | 32820.0 | 400.0 | 8359 | 1672804 | 4997 | North America |
122 | Martinique | 115951 | -1.0 | 895 | -1.0 | 104 | -1.0 | 114952 | 10 | 309384.0 | 2388.0 | 707420 | 1887561 | 374780 | North America |
207 | St. Barth | 3780 | 22.0 | 6 | -1.0 | 0 | 0.0 | 0 | -1 | 380780.0 | 604.0 | 78646 | 7922434 | 9927 | North America |
202 | British Virgin Islands | 6091 | 6.0 | 62 | -1.0 | 0 | 0.0 | 0 | 1 | 199274.0 | 2028.0 | 100749 | 3296113 | 30566 | North America |
# Drowing visualization graph
plt.bar(north_america["Countries"].head(10),north_america["Total_recovered"].head(10))
plt.xticks(rotation = '45')
plt.title("North America Total Recovered Cases")
plt.xlabel("Countries")
plt.ylabel("Total Recovered(in ten million)")
Text(0, 0.5, 'Total Recovered(in ten million)')
# sorting the dataframe to help identify the country with highest covid 19 New recovered cases
north_america.sort_values("New_recovered",ascending = False,inplace = True)
north_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
16 | Mexico | 5534086 | 12342.0 | 318835 | 304.0 | 4809799 | 13721.0 | 405452 | 4798 | 42184.0 | 2430.0 | 15147396 | 115462 | 131189291 | North America |
28 | Canada | 3310385 | 6600.0 | 36843 | 114.0 | 3161973 | 6566.0 | 111569 | 600 | 86448.0 | 962.0 | 58319533 | 1522964 | 38293437 | North America |
66 | Guatemala | 786869 | 2845.0 | 17029 | 11.0 | 730907 | 4085.0 | 38933 | 5 | 42609.0 | 922.0 | 3922624 | 212408 | 18467383 | North America |
64 | Costa Rica | 812812 | 1772.0 | 8073 | 16.0 | 682991 | 3845.0 | 121748 | 110 | 157168.0 | 1561.0 | 3712867 | 717932 | 5171611 | North America |
53 | Cuba | 1071326 | 596.0 | 8498 | 2.0 | 1060502 | 602.0 | 2326 | 34 | 94681.0 | 751.0 | 12920253 | 1141860 | 11315096 | North America |
68 | Panama | 756539 | 454.0 | 8101 | 3.0 | 743902 | 518.0 | 4536 | 26 | 170858.0 | 1830.0 | 5534045 | 1249818 | 4427882 | North America |
87 | Honduras | 413699 | 966.0 | 10785 | 7.0 | 128408 | 420.0 | 274506 | 105 | 40691.0 | 1061.0 | 1263329 | 124258 | 10166946 | North America |
77 | Dominican Republic | 575437 | 280.0 | 4370 | -1.0 | 569294 | 347.0 | 1773 | 33 | 52174.0 | 396.0 | 3129690 | 283766 | 11029126 | North America |
119 | Jamaica | 128108 | 29.0 | 2815 | 1.0 | 77860 | 267.0 | 47433 | 25 | 42950.0 | 944.0 | 857650 | 287541 | 2982701 | North America |
187 | Dominica | 11148 | 40.0 | 61 | 4.0 | 10881 | 169.0 | 206 | -1 | 154225.0 | 844.0 | 168239 | 2327472 | 72284 | North America |
140 | Belize | 56842 | 26.0 | 651 | 1.0 | 55107 | 115.0 | 1084 | 6 | 138758.0 | 1589.0 | 504813 | 1232312 | 409647 | North America |
186 | Bermuda | 11634 | 73.0 | 123 | -1.0 | 11338 | 67.0 | 173 | -1 | 187963.0 | 1987.0 | 805308 | 13010873 | 61895 | North America |
149 | Curaçao | 39082 | 66.0 | 262 | 1.0 | 38507 | 40.0 | 313 | 3 | 236558.0 | 1586.0 | 493647 | 2987979 | 165211 | North America |
158 | Bahamas | 33152 | 2.0 | 771 | -1.0 | 31947 | 38.0 | 434 | 9 | 82983.0 | 1930.0 | 220353 | 551566 | 399504 | North America |
162 | Haiti | 30353 | 3.0 | 820 | -1.0 | 25258 | 28.0 | 4275 | -1 | 2609.0 | 70.0 | 132422 | 11382 | 11634062 | North America |
143 | Barbados | 55705 | 162.0 | 316 | -1.0 | 53644 | 23.0 | 1745 | -1 | 193446.0 | 1097.0 | 588782 | 2044659 | 287961 | North America |
168 | Saint Lucia | 22733 | 4.0 | 360 | -1.0 | 22280 | 23.0 | 93 | 3 | 122864.0 | 1946.0 | 136343 | 736890 | 185025 | North America |
201 | St. Vincent Grenadines | 6742 | 1.0 | 106 | -1.0 | 6627 | 4.0 | 9 | -1 | 60452.0 | 950.0 | 98236 | 880827 | 111527 | North America |
190 | Sint Maarten | 9570 | 1.0 | 85 | -1.0 | 9438 | 2.0 | 47 | 10 | 219058.0 | 1946.0 | 62056 | 1420468 | 43687 | North America |
207 | St. Barth | 3780 | 22.0 | 6 | -1.0 | 0 | 0.0 | 0 | -1 | 380780.0 | 604.0 | 78646 | 7922434 | 9927 | North America |
202 | British Virgin Islands | 6091 | 6.0 | 62 | -1.0 | 0 | 0.0 | 0 | 1 | 199274.0 | 2028.0 | 100749 | 3296113 | 30566 | North America |
156 | Aruba | 33684 | -1.0 | 211 | -1.0 | 33418 | -1.0 | 55 | 6 | 313278.0 | 1962.0 | 177885 | 1654421 | 107521 | North America |
209 | Anguilla | 2555 | -1.0 | 9 | -1.0 | 2528 | -1.0 | 18 | 4 | 167805.0 | 591.0 | 51382 | 3374622 | 15226 | North America |
113 | El Salvador | 156364 | -1.0 | 4081 | 4.0 | 132410 | -1.0 | 19873 | 153 | 23906.0 | 624.0 | 1758939 | 268921 | 6540733 | North America |
122 | Martinique | 115951 | -1.0 | 895 | -1.0 | 104 | -1.0 | 114952 | 10 | 309384.0 | 2388.0 | 707420 | 1887561 | 374780 | North America |
213 | Montserrat | 164 | -1.0 | 2 | -1.0 | 161 | -1.0 | 1 | -1 | 32820.0 | 400.0 | 8359 | 1672804 | 4997 | North America |
210 | Saint Pierre Miquelon | 1087 | -1.0 | 1 | -1.0 | 1065 | -1.0 | 21 | 1 | 189142.0 | 174.0 | 17358 | 3020358 | 5747 | North America |
188 | Saint Martin | 9912 | 35.0 | 63 | -1.0 | 1399 | -1.0 | 8450 | 7 | 249271.0 | 1584.0 | 112382 | 2826225 | 39764 | North America |
120 | Guadeloupe | 126642 | -1.0 | 837 | -1.0 | 2250 | -1.0 | 123555 | 19 | 316417.0 | 2091.0 | 938039 | 2343703 | 400238 | North America |
184 | Greenland | 11778 | 19.0 | 18 | -1.0 | 2761 | -1.0 | 8999 | 7 | 206871.0 | 316.0 | 164926 | 2896793 | 56934 | North America |
171 | Cayman Islands | 19373 | -1.0 | 17 | -1.0 | 8553 | -1.0 | 10803 | 2 | 289115.0 | 254.0 | 222773 | 3324573 | 67008 | North America |
172 | Nicaragua | 18105 | -1.0 | 221 | -1.0 | 4225 | -1.0 | 13659 | -1 | 2680.0 | 33.0 | -1 | -1 | 6755837 | North America |
205 | Saint Kitts and Nevis | 5531 | 1.0 | 42 | -1.0 | 5484 | -1.0 | 5 | 1 | 102757.0 | 780.0 | 65141 | 1210214 | 53826 | North America |
204 | Turks and Caicos | 5867 | -1.0 | 36 | -1.0 | 5787 | -1.0 | 44 | 4 | 148198.0 | 909.0 | 419792 | 10603754 | 39589 | North America |
118 | Trinidad and Tobago | 128976 | 285.0 | 3642 | 5.0 | 102736 | -1.0 | 22598 | 18 | 91665.0 | 2588.0 | 634632 | 451041 | 1407039 | North America |
197 | Antigua and Barbuda | 7449 | -1.0 | 135 | -1.0 | 7284 | -1.0 | 30 | 1 | 75030.0 | 1360.0 | 18901 | 190381 | 99280 | North America |
196 | Caribbean Netherlands | 7605 | 6.0 | 31 | -1.0 | 7545 | -1.0 | 29 | -1 | 285591.0 | 1164.0 | 30126 | 1131323 | 26629 | North America |
181 | Grenada | 13690 | -1.0 | 216 | -1.0 | 13336 | -1.0 | 138 | 4 | 120732.0 | 1905.0 | 136411 | 1203004 | 113392 | North America |
# Drowing visualization graph
plt.bar(north_america["Countries"].head(10),north_america["New_recovered"].head(10))
plt.xticks(rotation = '45')
plt.title("North America New Recovered Cases")
plt.xlabel("Countries")
plt.ylabel("New Recovered(in ten million)")
Text(0, 0.5, 'New Recovered(in ten million)')
# sorting the dataframe to help identify the country with highest covid 19 Critical condition cases
north_america.sort_values("Serious/Critical",ascending = False,inplace = True)
north_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
16 | Mexico | 5534086 | 12342.0 | 318835 | 304.0 | 4809799 | 13721.0 | 405452 | 4798 | 42184.0 | 2430.0 | 15147396 | 115462 | 131189291 | North America |
28 | Canada | 3310385 | 6600.0 | 36843 | 114.0 | 3161973 | 6566.0 | 111569 | 600 | 86448.0 | 962.0 | 58319533 | 1522964 | 38293437 | North America |
113 | El Salvador | 156364 | -1.0 | 4081 | 4.0 | 132410 | -1.0 | 19873 | 153 | 23906.0 | 624.0 | 1758939 | 268921 | 6540733 | North America |
64 | Costa Rica | 812812 | 1772.0 | 8073 | 16.0 | 682991 | 3845.0 | 121748 | 110 | 157168.0 | 1561.0 | 3712867 | 717932 | 5171611 | North America |
87 | Honduras | 413699 | 966.0 | 10785 | 7.0 | 128408 | 420.0 | 274506 | 105 | 40691.0 | 1061.0 | 1263329 | 124258 | 10166946 | North America |
53 | Cuba | 1071326 | 596.0 | 8498 | 2.0 | 1060502 | 602.0 | 2326 | 34 | 94681.0 | 751.0 | 12920253 | 1141860 | 11315096 | North America |
77 | Dominican Republic | 575437 | 280.0 | 4370 | -1.0 | 569294 | 347.0 | 1773 | 33 | 52174.0 | 396.0 | 3129690 | 283766 | 11029126 | North America |
68 | Panama | 756539 | 454.0 | 8101 | 3.0 | 743902 | 518.0 | 4536 | 26 | 170858.0 | 1830.0 | 5534045 | 1249818 | 4427882 | North America |
119 | Jamaica | 128108 | 29.0 | 2815 | 1.0 | 77860 | 267.0 | 47433 | 25 | 42950.0 | 944.0 | 857650 | 287541 | 2982701 | North America |
120 | Guadeloupe | 126642 | -1.0 | 837 | -1.0 | 2250 | -1.0 | 123555 | 19 | 316417.0 | 2091.0 | 938039 | 2343703 | 400238 | North America |
118 | Trinidad and Tobago | 128976 | 285.0 | 3642 | 5.0 | 102736 | -1.0 | 22598 | 18 | 91665.0 | 2588.0 | 634632 | 451041 | 1407039 | North America |
122 | Martinique | 115951 | -1.0 | 895 | -1.0 | 104 | -1.0 | 114952 | 10 | 309384.0 | 2388.0 | 707420 | 1887561 | 374780 | North America |
190 | Sint Maarten | 9570 | 1.0 | 85 | -1.0 | 9438 | 2.0 | 47 | 10 | 219058.0 | 1946.0 | 62056 | 1420468 | 43687 | North America |
158 | Bahamas | 33152 | 2.0 | 771 | -1.0 | 31947 | 38.0 | 434 | 9 | 82983.0 | 1930.0 | 220353 | 551566 | 399504 | North America |
184 | Greenland | 11778 | 19.0 | 18 | -1.0 | 2761 | -1.0 | 8999 | 7 | 206871.0 | 316.0 | 164926 | 2896793 | 56934 | North America |
188 | Saint Martin | 9912 | 35.0 | 63 | -1.0 | 1399 | -1.0 | 8450 | 7 | 249271.0 | 1584.0 | 112382 | 2826225 | 39764 | North America |
156 | Aruba | 33684 | -1.0 | 211 | -1.0 | 33418 | -1.0 | 55 | 6 | 313278.0 | 1962.0 | 177885 | 1654421 | 107521 | North America |
140 | Belize | 56842 | 26.0 | 651 | 1.0 | 55107 | 115.0 | 1084 | 6 | 138758.0 | 1589.0 | 504813 | 1232312 | 409647 | North America |
66 | Guatemala | 786869 | 2845.0 | 17029 | 11.0 | 730907 | 4085.0 | 38933 | 5 | 42609.0 | 922.0 | 3922624 | 212408 | 18467383 | North America |
204 | Turks and Caicos | 5867 | -1.0 | 36 | -1.0 | 5787 | -1.0 | 44 | 4 | 148198.0 | 909.0 | 419792 | 10603754 | 39589 | North America |
209 | Anguilla | 2555 | -1.0 | 9 | -1.0 | 2528 | -1.0 | 18 | 4 | 167805.0 | 591.0 | 51382 | 3374622 | 15226 | North America |
181 | Grenada | 13690 | -1.0 | 216 | -1.0 | 13336 | -1.0 | 138 | 4 | 120732.0 | 1905.0 | 136411 | 1203004 | 113392 | North America |
149 | Curaçao | 39082 | 66.0 | 262 | 1.0 | 38507 | 40.0 | 313 | 3 | 236558.0 | 1586.0 | 493647 | 2987979 | 165211 | North America |
168 | Saint Lucia | 22733 | 4.0 | 360 | -1.0 | 22280 | 23.0 | 93 | 3 | 122864.0 | 1946.0 | 136343 | 736890 | 185025 | North America |
171 | Cayman Islands | 19373 | -1.0 | 17 | -1.0 | 8553 | -1.0 | 10803 | 2 | 289115.0 | 254.0 | 222773 | 3324573 | 67008 | North America |
202 | British Virgin Islands | 6091 | 6.0 | 62 | -1.0 | 0 | 0.0 | 0 | 1 | 199274.0 | 2028.0 | 100749 | 3296113 | 30566 | North America |
210 | Saint Pierre Miquelon | 1087 | -1.0 | 1 | -1.0 | 1065 | -1.0 | 21 | 1 | 189142.0 | 174.0 | 17358 | 3020358 | 5747 | North America |
205 | Saint Kitts and Nevis | 5531 | 1.0 | 42 | -1.0 | 5484 | -1.0 | 5 | 1 | 102757.0 | 780.0 | 65141 | 1210214 | 53826 | North America |
197 | Antigua and Barbuda | 7449 | -1.0 | 135 | -1.0 | 7284 | -1.0 | 30 | 1 | 75030.0 | 1360.0 | 18901 | 190381 | 99280 | North America |
186 | Bermuda | 11634 | 73.0 | 123 | -1.0 | 11338 | 67.0 | 173 | -1 | 187963.0 | 1987.0 | 805308 | 13010873 | 61895 | North America |
207 | St. Barth | 3780 | 22.0 | 6 | -1.0 | 0 | 0.0 | 0 | -1 | 380780.0 | 604.0 | 78646 | 7922434 | 9927 | North America |
162 | Haiti | 30353 | 3.0 | 820 | -1.0 | 25258 | 28.0 | 4275 | -1 | 2609.0 | 70.0 | 132422 | 11382 | 11634062 | North America |
187 | Dominica | 11148 | 40.0 | 61 | 4.0 | 10881 | 169.0 | 206 | -1 | 154225.0 | 844.0 | 168239 | 2327472 | 72284 | North America |
201 | St. Vincent Grenadines | 6742 | 1.0 | 106 | -1.0 | 6627 | 4.0 | 9 | -1 | 60452.0 | 950.0 | 98236 | 880827 | 111527 | North America |
172 | Nicaragua | 18105 | -1.0 | 221 | -1.0 | 4225 | -1.0 | 13659 | -1 | 2680.0 | 33.0 | -1 | -1 | 6755837 | North America |
143 | Barbados | 55705 | 162.0 | 316 | -1.0 | 53644 | 23.0 | 1745 | -1 | 193446.0 | 1097.0 | 588782 | 2044659 | 287961 | North America |
196 | Caribbean Netherlands | 7605 | 6.0 | 31 | -1.0 | 7545 | -1.0 | 29 | -1 | 285591.0 | 1164.0 | 30126 | 1131323 | 26629 | North America |
213 | Montserrat | 164 | -1.0 | 2 | -1.0 | 161 | -1.0 | 1 | -1 | 32820.0 | 400.0 | 8359 | 1672804 | 4997 | North America |
# Drowing visualization graph
plt.bar(north_america["Countries"].head(10),north_america["Serious/Critical"].head(10))
plt.xticks(rotation = '45')
plt.title("North America Critical Condition cases")
plt.xlabel("Countries")
plt.ylabel("Critical condition(in ten million)")
Text(0, 0.5, 'Critical condition(in ten million)')
# sorting the dataframe to help identify the country that is highly populated
north_america.sort_values("Popultion",ascending = False,inplace = True)
north_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
16 | Mexico | 5534086 | 12342.0 | 318835 | 304.0 | 4809799 | 13721.0 | 405452 | 4798 | 42184.0 | 2430.0 | 15147396 | 115462 | 131189291 | North America |
28 | Canada | 3310385 | 6600.0 | 36843 | 114.0 | 3161973 | 6566.0 | 111569 | 600 | 86448.0 | 962.0 | 58319533 | 1522964 | 38293437 | North America |
66 | Guatemala | 786869 | 2845.0 | 17029 | 11.0 | 730907 | 4085.0 | 38933 | 5 | 42609.0 | 922.0 | 3922624 | 212408 | 18467383 | North America |
162 | Haiti | 30353 | 3.0 | 820 | -1.0 | 25258 | 28.0 | 4275 | -1 | 2609.0 | 70.0 | 132422 | 11382 | 11634062 | North America |
53 | Cuba | 1071326 | 596.0 | 8498 | 2.0 | 1060502 | 602.0 | 2326 | 34 | 94681.0 | 751.0 | 12920253 | 1141860 | 11315096 | North America |
77 | Dominican Republic | 575437 | 280.0 | 4370 | -1.0 | 569294 | 347.0 | 1773 | 33 | 52174.0 | 396.0 | 3129690 | 283766 | 11029126 | North America |
87 | Honduras | 413699 | 966.0 | 10785 | 7.0 | 128408 | 420.0 | 274506 | 105 | 40691.0 | 1061.0 | 1263329 | 124258 | 10166946 | North America |
172 | Nicaragua | 18105 | -1.0 | 221 | -1.0 | 4225 | -1.0 | 13659 | -1 | 2680.0 | 33.0 | -1 | -1 | 6755837 | North America |
113 | El Salvador | 156364 | -1.0 | 4081 | 4.0 | 132410 | -1.0 | 19873 | 153 | 23906.0 | 624.0 | 1758939 | 268921 | 6540733 | North America |
64 | Costa Rica | 812812 | 1772.0 | 8073 | 16.0 | 682991 | 3845.0 | 121748 | 110 | 157168.0 | 1561.0 | 3712867 | 717932 | 5171611 | North America |
68 | Panama | 756539 | 454.0 | 8101 | 3.0 | 743902 | 518.0 | 4536 | 26 | 170858.0 | 1830.0 | 5534045 | 1249818 | 4427882 | North America |
119 | Jamaica | 128108 | 29.0 | 2815 | 1.0 | 77860 | 267.0 | 47433 | 25 | 42950.0 | 944.0 | 857650 | 287541 | 2982701 | North America |
118 | Trinidad and Tobago | 128976 | 285.0 | 3642 | 5.0 | 102736 | -1.0 | 22598 | 18 | 91665.0 | 2588.0 | 634632 | 451041 | 1407039 | North America |
140 | Belize | 56842 | 26.0 | 651 | 1.0 | 55107 | 115.0 | 1084 | 6 | 138758.0 | 1589.0 | 504813 | 1232312 | 409647 | North America |
120 | Guadeloupe | 126642 | -1.0 | 837 | -1.0 | 2250 | -1.0 | 123555 | 19 | 316417.0 | 2091.0 | 938039 | 2343703 | 400238 | North America |
158 | Bahamas | 33152 | 2.0 | 771 | -1.0 | 31947 | 38.0 | 434 | 9 | 82983.0 | 1930.0 | 220353 | 551566 | 399504 | North America |
122 | Martinique | 115951 | -1.0 | 895 | -1.0 | 104 | -1.0 | 114952 | 10 | 309384.0 | 2388.0 | 707420 | 1887561 | 374780 | North America |
143 | Barbados | 55705 | 162.0 | 316 | -1.0 | 53644 | 23.0 | 1745 | -1 | 193446.0 | 1097.0 | 588782 | 2044659 | 287961 | North America |
168 | Saint Lucia | 22733 | 4.0 | 360 | -1.0 | 22280 | 23.0 | 93 | 3 | 122864.0 | 1946.0 | 136343 | 736890 | 185025 | North America |
149 | Curaçao | 39082 | 66.0 | 262 | 1.0 | 38507 | 40.0 | 313 | 3 | 236558.0 | 1586.0 | 493647 | 2987979 | 165211 | North America |
181 | Grenada | 13690 | -1.0 | 216 | -1.0 | 13336 | -1.0 | 138 | 4 | 120732.0 | 1905.0 | 136411 | 1203004 | 113392 | North America |
201 | St. Vincent Grenadines | 6742 | 1.0 | 106 | -1.0 | 6627 | 4.0 | 9 | -1 | 60452.0 | 950.0 | 98236 | 880827 | 111527 | North America |
156 | Aruba | 33684 | -1.0 | 211 | -1.0 | 33418 | -1.0 | 55 | 6 | 313278.0 | 1962.0 | 177885 | 1654421 | 107521 | North America |
197 | Antigua and Barbuda | 7449 | -1.0 | 135 | -1.0 | 7284 | -1.0 | 30 | 1 | 75030.0 | 1360.0 | 18901 | 190381 | 99280 | North America |
187 | Dominica | 11148 | 40.0 | 61 | 4.0 | 10881 | 169.0 | 206 | -1 | 154225.0 | 844.0 | 168239 | 2327472 | 72284 | North America |
171 | Cayman Islands | 19373 | -1.0 | 17 | -1.0 | 8553 | -1.0 | 10803 | 2 | 289115.0 | 254.0 | 222773 | 3324573 | 67008 | North America |
186 | Bermuda | 11634 | 73.0 | 123 | -1.0 | 11338 | 67.0 | 173 | -1 | 187963.0 | 1987.0 | 805308 | 13010873 | 61895 | North America |
184 | Greenland | 11778 | 19.0 | 18 | -1.0 | 2761 | -1.0 | 8999 | 7 | 206871.0 | 316.0 | 164926 | 2896793 | 56934 | North America |
205 | Saint Kitts and Nevis | 5531 | 1.0 | 42 | -1.0 | 5484 | -1.0 | 5 | 1 | 102757.0 | 780.0 | 65141 | 1210214 | 53826 | North America |
190 | Sint Maarten | 9570 | 1.0 | 85 | -1.0 | 9438 | 2.0 | 47 | 10 | 219058.0 | 1946.0 | 62056 | 1420468 | 43687 | North America |
188 | Saint Martin | 9912 | 35.0 | 63 | -1.0 | 1399 | -1.0 | 8450 | 7 | 249271.0 | 1584.0 | 112382 | 2826225 | 39764 | North America |
204 | Turks and Caicos | 5867 | -1.0 | 36 | -1.0 | 5787 | -1.0 | 44 | 4 | 148198.0 | 909.0 | 419792 | 10603754 | 39589 | North America |
202 | British Virgin Islands | 6091 | 6.0 | 62 | -1.0 | 0 | 0.0 | 0 | 1 | 199274.0 | 2028.0 | 100749 | 3296113 | 30566 | North America |
196 | Caribbean Netherlands | 7605 | 6.0 | 31 | -1.0 | 7545 | -1.0 | 29 | -1 | 285591.0 | 1164.0 | 30126 | 1131323 | 26629 | North America |
209 | Anguilla | 2555 | -1.0 | 9 | -1.0 | 2528 | -1.0 | 18 | 4 | 167805.0 | 591.0 | 51382 | 3374622 | 15226 | North America |
207 | St. Barth | 3780 | 22.0 | 6 | -1.0 | 0 | 0.0 | 0 | -1 | 380780.0 | 604.0 | 78646 | 7922434 | 9927 | North America |
210 | Saint Pierre Miquelon | 1087 | -1.0 | 1 | -1.0 | 1065 | -1.0 | 21 | 1 | 189142.0 | 174.0 | 17358 | 3020358 | 5747 | North America |
213 | Montserrat | 164 | -1.0 | 2 | -1.0 | 161 | -1.0 | 1 | -1 | 32820.0 | 400.0 | 8359 | 1672804 | 4997 | North America |
# sorting the dataframe to help identify the country with highest covid 19 total tests
north_america.sort_values("Total_tests",ascending = False,inplace = True)
north_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | USA | 80843570 | 49091.0 | 981729 | 1258.0 | 54136964 | 191175.0 | 25724877 | 7025 | 241875.0 | 2937.0 | 954367702 | 2855366 | 334236503 | North America |
28 | Canada | 3310385 | 6600.0 | 36843 | 114.0 | 3161973 | 6566.0 | 111569 | 600 | 86448.0 | 962.0 | 58319533 | 1522964 | 38293437 | North America |
16 | Mexico | 5534086 | 12342.0 | 318835 | 304.0 | 4809799 | 13721.0 | 405452 | 4798 | 42184.0 | 2430.0 | 15147396 | 115462 | 131189291 | North America |
53 | Cuba | 1071326 | 596.0 | 8498 | 2.0 | 1060502 | 602.0 | 2326 | 34 | 94681.0 | 751.0 | 12920253 | 1141860 | 11315096 | North America |
68 | Panama | 756539 | 454.0 | 8101 | 3.0 | 743902 | 518.0 | 4536 | 26 | 170858.0 | 1830.0 | 5534045 | 1249818 | 4427882 | North America |
66 | Guatemala | 786869 | 2845.0 | 17029 | 11.0 | 730907 | 4085.0 | 38933 | 5 | 42609.0 | 922.0 | 3922624 | 212408 | 18467383 | North America |
64 | Costa Rica | 812812 | 1772.0 | 8073 | 16.0 | 682991 | 3845.0 | 121748 | 110 | 157168.0 | 1561.0 | 3712867 | 717932 | 5171611 | North America |
77 | Dominican Republic | 575437 | 280.0 | 4370 | -1.0 | 569294 | 347.0 | 1773 | 33 | 52174.0 | 396.0 | 3129690 | 283766 | 11029126 | North America |
113 | El Salvador | 156364 | -1.0 | 4081 | 4.0 | 132410 | -1.0 | 19873 | 153 | 23906.0 | 624.0 | 1758939 | 268921 | 6540733 | North America |
87 | Honduras | 413699 | 966.0 | 10785 | 7.0 | 128408 | 420.0 | 274506 | 105 | 40691.0 | 1061.0 | 1263329 | 124258 | 10166946 | North America |
120 | Guadeloupe | 126642 | -1.0 | 837 | -1.0 | 2250 | -1.0 | 123555 | 19 | 316417.0 | 2091.0 | 938039 | 2343703 | 400238 | North America |
119 | Jamaica | 128108 | 29.0 | 2815 | 1.0 | 77860 | 267.0 | 47433 | 25 | 42950.0 | 944.0 | 857650 | 287541 | 2982701 | North America |
186 | Bermuda | 11634 | 73.0 | 123 | -1.0 | 11338 | 67.0 | 173 | -1 | 187963.0 | 1987.0 | 805308 | 13010873 | 61895 | North America |
122 | Martinique | 115951 | -1.0 | 895 | -1.0 | 104 | -1.0 | 114952 | 10 | 309384.0 | 2388.0 | 707420 | 1887561 | 374780 | North America |
118 | Trinidad and Tobago | 128976 | 285.0 | 3642 | 5.0 | 102736 | -1.0 | 22598 | 18 | 91665.0 | 2588.0 | 634632 | 451041 | 1407039 | North America |
143 | Barbados | 55705 | 162.0 | 316 | -1.0 | 53644 | 23.0 | 1745 | -1 | 193446.0 | 1097.0 | 588782 | 2044659 | 287961 | North America |
140 | Belize | 56842 | 26.0 | 651 | 1.0 | 55107 | 115.0 | 1084 | 6 | 138758.0 | 1589.0 | 504813 | 1232312 | 409647 | North America |
149 | Curaçao | 39082 | 66.0 | 262 | 1.0 | 38507 | 40.0 | 313 | 3 | 236558.0 | 1586.0 | 493647 | 2987979 | 165211 | North America |
204 | Turks and Caicos | 5867 | -1.0 | 36 | -1.0 | 5787 | -1.0 | 44 | 4 | 148198.0 | 909.0 | 419792 | 10603754 | 39589 | North America |
171 | Cayman Islands | 19373 | -1.0 | 17 | -1.0 | 8553 | -1.0 | 10803 | 2 | 289115.0 | 254.0 | 222773 | 3324573 | 67008 | North America |
158 | Bahamas | 33152 | 2.0 | 771 | -1.0 | 31947 | 38.0 | 434 | 9 | 82983.0 | 1930.0 | 220353 | 551566 | 399504 | North America |
156 | Aruba | 33684 | -1.0 | 211 | -1.0 | 33418 | -1.0 | 55 | 6 | 313278.0 | 1962.0 | 177885 | 1654421 | 107521 | North America |
187 | Dominica | 11148 | 40.0 | 61 | 4.0 | 10881 | 169.0 | 206 | -1 | 154225.0 | 844.0 | 168239 | 2327472 | 72284 | North America |
184 | Greenland | 11778 | 19.0 | 18 | -1.0 | 2761 | -1.0 | 8999 | 7 | 206871.0 | 316.0 | 164926 | 2896793 | 56934 | North America |
181 | Grenada | 13690 | -1.0 | 216 | -1.0 | 13336 | -1.0 | 138 | 4 | 120732.0 | 1905.0 | 136411 | 1203004 | 113392 | North America |
168 | Saint Lucia | 22733 | 4.0 | 360 | -1.0 | 22280 | 23.0 | 93 | 3 | 122864.0 | 1946.0 | 136343 | 736890 | 185025 | North America |
162 | Haiti | 30353 | 3.0 | 820 | -1.0 | 25258 | 28.0 | 4275 | -1 | 2609.0 | 70.0 | 132422 | 11382 | 11634062 | North America |
188 | Saint Martin | 9912 | 35.0 | 63 | -1.0 | 1399 | -1.0 | 8450 | 7 | 249271.0 | 1584.0 | 112382 | 2826225 | 39764 | North America |
202 | British Virgin Islands | 6091 | 6.0 | 62 | -1.0 | 0 | 0.0 | 0 | 1 | 199274.0 | 2028.0 | 100749 | 3296113 | 30566 | North America |
201 | St. Vincent Grenadines | 6742 | 1.0 | 106 | -1.0 | 6627 | 4.0 | 9 | -1 | 60452.0 | 950.0 | 98236 | 880827 | 111527 | North America |
207 | St. Barth | 3780 | 22.0 | 6 | -1.0 | 0 | 0.0 | 0 | -1 | 380780.0 | 604.0 | 78646 | 7922434 | 9927 | North America |
205 | Saint Kitts and Nevis | 5531 | 1.0 | 42 | -1.0 | 5484 | -1.0 | 5 | 1 | 102757.0 | 780.0 | 65141 | 1210214 | 53826 | North America |
190 | Sint Maarten | 9570 | 1.0 | 85 | -1.0 | 9438 | 2.0 | 47 | 10 | 219058.0 | 1946.0 | 62056 | 1420468 | 43687 | North America |
209 | Anguilla | 2555 | -1.0 | 9 | -1.0 | 2528 | -1.0 | 18 | 4 | 167805.0 | 591.0 | 51382 | 3374622 | 15226 | North America |
196 | Caribbean Netherlands | 7605 | 6.0 | 31 | -1.0 | 7545 | -1.0 | 29 | -1 | 285591.0 | 1164.0 | 30126 | 1131323 | 26629 | North America |
197 | Antigua and Barbuda | 7449 | -1.0 | 135 | -1.0 | 7284 | -1.0 | 30 | 1 | 75030.0 | 1360.0 | 18901 | 190381 | 99280 | North America |
210 | Saint Pierre Miquelon | 1087 | -1.0 | 1 | -1.0 | 1065 | -1.0 | 21 | 1 | 189142.0 | 174.0 | 17358 | 3020358 | 5747 | North America |
213 | Montserrat | 164 | -1.0 | 2 | -1.0 | 161 | -1.0 | 1 | -1 | 32820.0 | 400.0 | 8359 | 1672804 | 4997 | North America |
172 | Nicaragua | 18105 | -1.0 | 221 | -1.0 | 4225 | -1.0 | 13659 | -1 | 2680.0 | 33.0 | -1 | -1 | 6755837 | North America |
From the above outputs, we can clearly see that USA has the highest number of cases in North America. From the same outputs we can also see that Montserrat has the lowest number of confirmed covid 19 cases in North America.
USA is leading in the number of covid 19 total number of confirmed cases mainly because of the following three reasons:
Montserrat has confirmed low number of covid 19 cases mainly because of the following three reasons:
We can also see that USA is also leading in the total people who have died from covid 19,with Montserrat recording the lowest number of the same hence doing well in the control measures.
From this, we can see that population is also a determing factor with the countries having the highest number of population finding it abit hard to control the spread of the disease as compared to those having lower population density.From these, we can ague that in as much as Montserrat having lower population density, it's doing well in the control measures.
asia = data[data["Continent"] == "Asia"].copy()
asia.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | India | 42951556 | 6396.0 | 514620 | 201.0 | 42367070 | 13450.0 | 69866 | 8944 | 30623.0 | 367.0 | 770050005 | 549014 | 1402606131 | Asia |
7 | Turkey | 14255545 | 49424.0 | 95025 | 188.0 | 13572870 | 66873.0 | 587650 | 1011 | 166050.0 | 1107.0 | 145020155 | 1689211 | 85850820 | Asia |
11 | Iran | 7073747 | 6772.0 | 137439 | 172.0 | 6696494 | 18416.0 | 239814 | 4098 | 82460.0 | 1602.0 | 47494176 | 553651 | 85783612 | Asia |
15 | Indonesia | 5667355 | 37259.0 | 149268 | 232.0 | 4986391 | 42154.0 | 531696 | 2911 | 20361.0 | 536.0 | 86075507 | 309244 | 278341678 | Asia |
17 | Japan | 5139305 | 71570.0 | 24092 | 232.0 | 4419826 | 74909.0 | 695387 | 1418 | 40843.0 | 191.0 | 39385361 | 313001 | 125831319 | Asia |
# sorting dataframe to help identify the country with the most number of confirmed cases
asia.sort_values("Total_cases",ascending = False,inplace = True)
asia
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | India | 42951556 | 6396.0 | 514620 | 201.0 | 42367070 | 13450.0 | 69866 | 8944 | 30623.0 | 367.0 | 770050005 | 549014 | 1402606131 | Asia |
7 | Turkey | 14255545 | 49424.0 | 95025 | 188.0 | 13572870 | 66873.0 | 587650 | 1011 | 166050.0 | 1107.0 | 145020155 | 1689211 | 85850820 | Asia |
11 | Iran | 7073747 | 6772.0 | 137439 | 172.0 | 6696494 | 18416.0 | 239814 | 4098 | 82460.0 | 1602.0 | 47494176 | 553651 | 85783612 | Asia |
15 | Indonesia | 5667355 | 37259.0 | 149268 | 232.0 | 4986391 | 42154.0 | 531696 | 2911 | 20361.0 | 536.0 | 86075507 | 309244 | 278341678 | Asia |
17 | Japan | 5139305 | 71570.0 | 24092 | 232.0 | 4419826 | 74909.0 | 695387 | 1418 | 40843.0 | 191.0 | 39385361 | 313001 | 125831319 | Asia |
19 | Vietnam | 3885631 | 118790.0 | 40547 | 95.0 | 2550525 | 33740.0 | 1294559 | 3840 | 39329.0 | 410.0 | 79665130 | 806347 | 98797607 | Asia |
20 | S. Korea | 3691488 | 198802.0 | 8394 | 128.0 | 969524 | -1.0 | 2713570 | 766 | 71899.0 | 163.0 | 15804065 | 307815 | 51342704 | Asia |
22 | Philippines | 3664905 | 989.0 | 56538 | 34.0 | 3557909 | 1349.0 | 50458 | 297 | 32719.0 | 505.0 | 28203435 | 251793 | 112010322 | Asia |
23 | Israel | 3653608 | 2377.0 | 10239 | 2.0 | 3585292 | 7460.0 | 58077 | 510 | 391766.0 | 1098.0 | 41373364 | 4436346 | 9326000 | Asia |
26 | Malaysia | 3528557 | 32467.0 | 33028 | 86.0 | 3197298 | 27629.0 | 298231 | 355 | 106743.0 | 999.0 | 50211993 | 1518965 | 33056709 | Asia |
32 | Thailand | 2958162 | 23618.0 | 23073 | 49.0 | 2711678 | 18939.0 | 223411 | 687 | 42204.0 | 329.0 | 17270775 | 246402 | 70091808 | Asia |
39 | Iraq | 2306092 | 1009.0 | 25028 | 15.0 | 2250904 | 2088.0 | 30160 | 111 | 55269.0 | 600.0 | 18020396 | 431885 | 41724991 | Asia |
40 | Bangladesh | 1945765 | 657.0 | 29058 | 5.0 | 1831577 | 4628.0 | 85130 | 1275 | 11622.0 | 174.0 | 13470456 | 80458 | 167422637 | Asia |
43 | Jordan | 1638228 | -1.0 | 13849 | -1.0 | 1546429 | -1.0 | 77950 | 694 | 157949.0 | 1335.0 | 16319298 | 1573419 | 10371869 | Asia |
44 | Georgia | 1619274 | 3115.0 | 16274 | 43.0 | 1529623 | 11767.0 | 73377 | -1 | 407220.0 | 4093.0 | 15955552 | 4012554 | 3976408 | Asia |
45 | Pakistan | 1511754 | 768.0 | 30237 | 19.0 | 1449060 | 3815.0 | 32457 | 908 | 6630.0 | 133.0 | 26534095 | 116360 | 228033633 | Asia |
48 | Kazakhstan | 1303063 | 253.0 | 13620 | 4.0 | 1272470 | 2091.0 | 16973 | 528 | 68042.0 | 711.0 | 11575012 | 604414 | 19150815 | Asia |
52 | Lebanon | 1074372 | 1835.0 | 10125 | 10.0 | 682977 | -1.0 | 381270 | 186 | 158590.0 | 1495.0 | 4795578 | 707885 | 6774515 | Asia |
56 | Nepal | 977269 | 70.0 | 11944 | 3.0 | 957808 | 320.0 | 7517 | 100 | 32565.0 | 398.0 | 5433187 | 181049 | 30009423 | Asia |
61 | UAE | 881472 | 502.0 | 2301 | -1.0 | 838641 | 1502.0 | 40530 | -1 | 87366.0 | 228.0 | 139003973 | 13777165 | 10089447 | Asia |
65 | Azerbaijan | 787937 | 570.0 | 9473 | 19.0 | 771530 | 1784.0 | 6934 | -1 | 76562.0 | 920.0 | 6579163 | 639286 | 10291423 | Asia |
67 | Singapore | 785825 | 18162.0 | 1049 | 9.0 | 715419 | 14249.0 | 69357 | 28 | 132584.0 | 177.0 | 23230460 | 3919433 | 5926995 | Asia |
69 | Saudi Arabia | 746473 | 407.0 | 9004 | 2.0 | 725792 | 685.0 | 11677 | 501 | 20900.0 | 252.0 | 40887817 | 1144779 | 35716793 | Asia |
72 | Sri Lanka | 648410 | 711.0 | 16287 | 20.0 | 609485 | 193.0 | 22638 | -1 | 30071.0 | 755.0 | 6282076 | 291338 | 21562841 | Asia |
74 | Kuwait | 621466 | 486.0 | 2541 | 1.0 | 611381 | 1086.0 | 7544 | 44 | 142025.0 | 581.0 | 7691556 | 1757766 | 4375756 | Asia |
75 | Myanmar | 593958 | 1819.0 | 19379 | 4.0 | 541648 | 1407.0 | 32931 | -1 | 10796.0 | 352.0 | 7187337 | 130637 | 55017496 | Asia |
76 | Palestine | 577486 | 451.0 | 5267 | 14.0 | 561340 | 1472.0 | 10879 | 77 | 108984.0 | 994.0 | 3078533 | 580986 | 5298812 | Asia |
78 | Bahrain | 521613 | 2029.0 | 1456 | 1.0 | 494414 | 2418.0 | 25743 | 16 | 289792.0 | 809.0 | 9429334 | 5238644 | 1799957 | Asia |
85 | Mongolia | 465094 | 323.0 | 2172 | 1.0 | 313256 | -1.0 | 149666 | 192 | 138166.0 | 645.0 | 4030048 | 1197207 | 3366207 | Asia |
86 | Armenia | 420757 | 259.0 | 8505 | 12.0 | 404157 | 486.0 | 8095 | -1 | 141551.0 | 2861.0 | 2906066 | 977660 | 2972471 | Asia |
88 | Oman | 383874 | 485.0 | 4248 | 2.0 | 371874 | 1043.0 | 7752 | 55 | 72119.0 | 798.0 | 25000000 | 4696794 | 5322780 | Asia |
90 | Qatar | 357764 | 181.0 | 670 | -1.0 | 354367 | 337.0 | 2727 | 17 | 127418.0 | 239.0 | 3379862 | 1203738 | 2807805 | Asia |
91 | Hong Kong | 350557 | 56827.0 | 1366 | 198.0 | 13232 | -1.0 | 335959 | 16 | 46136.0 | 180.0 | 36260319 | 4772156 | 7598310 | Asia |
92 | Cyprus | 328657 | 2046.0 | 861 | 2.0 | 124370 | -1.0 | 203426 | 60 | 268950.0 | 705.0 | 9477138 | 7755419 | 1222002 | Asia |
102 | Uzbekistan | 236687 | 91.0 | 1637 | -1.0 | 233609 | 196.0 | 1441 | 23 | 6905.0 | 48.0 | 1377915 | 40199 | 34277055 | Asia |
105 | Kyrgyzstan | 200576 | 20.0 | 2960 | -1.0 | 195468 | 100.0 | 2148 | 131 | 29921.0 | 442.0 | 1907195 | 284510 | 6703444 | Asia |
107 | Afghanistan | 174214 | 141.0 | 7619 | 2.0 | 157199 | 306.0 | 9396 | 1124 | 4314.0 | 189.0 | 893513 | 22123 | 40387748 | Asia |
108 | Maldives | 171649 | 535.0 | 297 | -1.0 | 156452 | 479.0 | 14900 | 25 | 308501.0 | 534.0 | 2194571 | 3944254 | 556397 | Asia |
114 | Laos | 143486 | 246.0 | 626 | 3.0 | 7660 | -1.0 | 135200 | -1 | 19256.0 | 84.0 | 1002806 | 134580 | 7451392 | Asia |
116 | Cambodia | 131372 | 368.0 | 3033 | 1.0 | 125029 | 402.0 | 3310 | -1 | 7681.0 | 177.0 | 2866490 | 167588 | 17104339 | Asia |
123 | China | 109964 | 214.0 | 4636 | -1.0 | 102234 | 119.0 | 3094 | 20 | 76.0 | 3.0 | 160000000 | 111163 | 1439323776 | Asia |
131 | Brunei | 76534 | 4885.0 | 135 | 6.0 | 40782 | 2457.0 | 35617 | -1 | 172197.0 | 304.0 | 717784 | 1614968 | 444457 | Asia |
144 | Syria | 54822 | 78.0 | 3085 | 3.0 | 47715 | 190.0 | 4022 | -1 | 3011.0 | 169.0 | 146269 | 8033 | 18208268 | Asia |
167 | Timor-Leste | 22736 | 4.0 | 129 | -1.0 | 22291 | 59.0 | 316 | 9 | 16715.0 | 95.0 | 248770 | 182885 | 1360256 | Asia |
170 | Taiwan | 20653 | 71.0 | 853 | -1.0 | 18602 | 79.0 | 1198 | -1 | 865.0 | 36.0 | 11705186 | 489990 | 23888601 | Asia |
173 | Tajikistan | 17388 | -1.0 | 124 | -1.0 | 17263 | -1.0 | 1 | -1 | 1757.0 | 13.0 | -1 | -1 | 9894249 | Asia |
180 | Bhutan | 13846 | 311.0 | 6 | -1.0 | 9640 | 342.0 | 4200 | 3 | 17621.0 | 8.0 | 1999491 | 2544655 | 785761 | Asia |
185 | Yemen | 11772 | 1.0 | 2135 | -1.0 | 8696 | -1.0 | 941 | 23 | 381.0 | 69.0 | 265253 | 8579 | 30919491 | Asia |
216 | Macao | 82 | 1.0 | -1 | -1.0 | 79 | -1.0 | 3 | -1 | 123.0 | -1.0 | 5318 | 8008 | 664078 | Asia |
# Drowing visualization graph
plt.bar(asia["Countries"].head(10),asia["Total_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Asia Total Covid cases")
plt.xlabel("Countries")
plt.ylabel("Total cases(in ten million)")
Text(0, 0.5, 'Total cases(in ten million)')
# sorting dataframe to help identify the country with the most number of death cases
asia.sort_values("Total_deaths",ascending = False,inplace = True)
asia
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | India | 42951556 | 6396.0 | 514620 | 201.0 | 42367070 | 13450.0 | 69866 | 8944 | 30623.0 | 367.0 | 770050005 | 549014 | 1402606131 | Asia |
15 | Indonesia | 5667355 | 37259.0 | 149268 | 232.0 | 4986391 | 42154.0 | 531696 | 2911 | 20361.0 | 536.0 | 86075507 | 309244 | 278341678 | Asia |
11 | Iran | 7073747 | 6772.0 | 137439 | 172.0 | 6696494 | 18416.0 | 239814 | 4098 | 82460.0 | 1602.0 | 47494176 | 553651 | 85783612 | Asia |
7 | Turkey | 14255545 | 49424.0 | 95025 | 188.0 | 13572870 | 66873.0 | 587650 | 1011 | 166050.0 | 1107.0 | 145020155 | 1689211 | 85850820 | Asia |
22 | Philippines | 3664905 | 989.0 | 56538 | 34.0 | 3557909 | 1349.0 | 50458 | 297 | 32719.0 | 505.0 | 28203435 | 251793 | 112010322 | Asia |
19 | Vietnam | 3885631 | 118790.0 | 40547 | 95.0 | 2550525 | 33740.0 | 1294559 | 3840 | 39329.0 | 410.0 | 79665130 | 806347 | 98797607 | Asia |
26 | Malaysia | 3528557 | 32467.0 | 33028 | 86.0 | 3197298 | 27629.0 | 298231 | 355 | 106743.0 | 999.0 | 50211993 | 1518965 | 33056709 | Asia |
45 | Pakistan | 1511754 | 768.0 | 30237 | 19.0 | 1449060 | 3815.0 | 32457 | 908 | 6630.0 | 133.0 | 26534095 | 116360 | 228033633 | Asia |
40 | Bangladesh | 1945765 | 657.0 | 29058 | 5.0 | 1831577 | 4628.0 | 85130 | 1275 | 11622.0 | 174.0 | 13470456 | 80458 | 167422637 | Asia |
39 | Iraq | 2306092 | 1009.0 | 25028 | 15.0 | 2250904 | 2088.0 | 30160 | 111 | 55269.0 | 600.0 | 18020396 | 431885 | 41724991 | Asia |
17 | Japan | 5139305 | 71570.0 | 24092 | 232.0 | 4419826 | 74909.0 | 695387 | 1418 | 40843.0 | 191.0 | 39385361 | 313001 | 125831319 | Asia |
32 | Thailand | 2958162 | 23618.0 | 23073 | 49.0 | 2711678 | 18939.0 | 223411 | 687 | 42204.0 | 329.0 | 17270775 | 246402 | 70091808 | Asia |
75 | Myanmar | 593958 | 1819.0 | 19379 | 4.0 | 541648 | 1407.0 | 32931 | -1 | 10796.0 | 352.0 | 7187337 | 130637 | 55017496 | Asia |
72 | Sri Lanka | 648410 | 711.0 | 16287 | 20.0 | 609485 | 193.0 | 22638 | -1 | 30071.0 | 755.0 | 6282076 | 291338 | 21562841 | Asia |
44 | Georgia | 1619274 | 3115.0 | 16274 | 43.0 | 1529623 | 11767.0 | 73377 | -1 | 407220.0 | 4093.0 | 15955552 | 4012554 | 3976408 | Asia |
43 | Jordan | 1638228 | -1.0 | 13849 | -1.0 | 1546429 | -1.0 | 77950 | 694 | 157949.0 | 1335.0 | 16319298 | 1573419 | 10371869 | Asia |
48 | Kazakhstan | 1303063 | 253.0 | 13620 | 4.0 | 1272470 | 2091.0 | 16973 | 528 | 68042.0 | 711.0 | 11575012 | 604414 | 19150815 | Asia |
56 | Nepal | 977269 | 70.0 | 11944 | 3.0 | 957808 | 320.0 | 7517 | 100 | 32565.0 | 398.0 | 5433187 | 181049 | 30009423 | Asia |
23 | Israel | 3653608 | 2377.0 | 10239 | 2.0 | 3585292 | 7460.0 | 58077 | 510 | 391766.0 | 1098.0 | 41373364 | 4436346 | 9326000 | Asia |
52 | Lebanon | 1074372 | 1835.0 | 10125 | 10.0 | 682977 | -1.0 | 381270 | 186 | 158590.0 | 1495.0 | 4795578 | 707885 | 6774515 | Asia |
65 | Azerbaijan | 787937 | 570.0 | 9473 | 19.0 | 771530 | 1784.0 | 6934 | -1 | 76562.0 | 920.0 | 6579163 | 639286 | 10291423 | Asia |
69 | Saudi Arabia | 746473 | 407.0 | 9004 | 2.0 | 725792 | 685.0 | 11677 | 501 | 20900.0 | 252.0 | 40887817 | 1144779 | 35716793 | Asia |
86 | Armenia | 420757 | 259.0 | 8505 | 12.0 | 404157 | 486.0 | 8095 | -1 | 141551.0 | 2861.0 | 2906066 | 977660 | 2972471 | Asia |
20 | S. Korea | 3691488 | 198802.0 | 8394 | 128.0 | 969524 | -1.0 | 2713570 | 766 | 71899.0 | 163.0 | 15804065 | 307815 | 51342704 | Asia |
107 | Afghanistan | 174214 | 141.0 | 7619 | 2.0 | 157199 | 306.0 | 9396 | 1124 | 4314.0 | 189.0 | 893513 | 22123 | 40387748 | Asia |
76 | Palestine | 577486 | 451.0 | 5267 | 14.0 | 561340 | 1472.0 | 10879 | 77 | 108984.0 | 994.0 | 3078533 | 580986 | 5298812 | Asia |
123 | China | 109964 | 214.0 | 4636 | -1.0 | 102234 | 119.0 | 3094 | 20 | 76.0 | 3.0 | 160000000 | 111163 | 1439323776 | Asia |
88 | Oman | 383874 | 485.0 | 4248 | 2.0 | 371874 | 1043.0 | 7752 | 55 | 72119.0 | 798.0 | 25000000 | 4696794 | 5322780 | Asia |
144 | Syria | 54822 | 78.0 | 3085 | 3.0 | 47715 | 190.0 | 4022 | -1 | 3011.0 | 169.0 | 146269 | 8033 | 18208268 | Asia |
116 | Cambodia | 131372 | 368.0 | 3033 | 1.0 | 125029 | 402.0 | 3310 | -1 | 7681.0 | 177.0 | 2866490 | 167588 | 17104339 | Asia |
105 | Kyrgyzstan | 200576 | 20.0 | 2960 | -1.0 | 195468 | 100.0 | 2148 | 131 | 29921.0 | 442.0 | 1907195 | 284510 | 6703444 | Asia |
74 | Kuwait | 621466 | 486.0 | 2541 | 1.0 | 611381 | 1086.0 | 7544 | 44 | 142025.0 | 581.0 | 7691556 | 1757766 | 4375756 | Asia |
61 | UAE | 881472 | 502.0 | 2301 | -1.0 | 838641 | 1502.0 | 40530 | -1 | 87366.0 | 228.0 | 139003973 | 13777165 | 10089447 | Asia |
85 | Mongolia | 465094 | 323.0 | 2172 | 1.0 | 313256 | -1.0 | 149666 | 192 | 138166.0 | 645.0 | 4030048 | 1197207 | 3366207 | Asia |
185 | Yemen | 11772 | 1.0 | 2135 | -1.0 | 8696 | -1.0 | 941 | 23 | 381.0 | 69.0 | 265253 | 8579 | 30919491 | Asia |
102 | Uzbekistan | 236687 | 91.0 | 1637 | -1.0 | 233609 | 196.0 | 1441 | 23 | 6905.0 | 48.0 | 1377915 | 40199 | 34277055 | Asia |
78 | Bahrain | 521613 | 2029.0 | 1456 | 1.0 | 494414 | 2418.0 | 25743 | 16 | 289792.0 | 809.0 | 9429334 | 5238644 | 1799957 | Asia |
91 | Hong Kong | 350557 | 56827.0 | 1366 | 198.0 | 13232 | -1.0 | 335959 | 16 | 46136.0 | 180.0 | 36260319 | 4772156 | 7598310 | Asia |
67 | Singapore | 785825 | 18162.0 | 1049 | 9.0 | 715419 | 14249.0 | 69357 | 28 | 132584.0 | 177.0 | 23230460 | 3919433 | 5926995 | Asia |
92 | Cyprus | 328657 | 2046.0 | 861 | 2.0 | 124370 | -1.0 | 203426 | 60 | 268950.0 | 705.0 | 9477138 | 7755419 | 1222002 | Asia |
170 | Taiwan | 20653 | 71.0 | 853 | -1.0 | 18602 | 79.0 | 1198 | -1 | 865.0 | 36.0 | 11705186 | 489990 | 23888601 | Asia |
90 | Qatar | 357764 | 181.0 | 670 | -1.0 | 354367 | 337.0 | 2727 | 17 | 127418.0 | 239.0 | 3379862 | 1203738 | 2807805 | Asia |
114 | Laos | 143486 | 246.0 | 626 | 3.0 | 7660 | -1.0 | 135200 | -1 | 19256.0 | 84.0 | 1002806 | 134580 | 7451392 | Asia |
108 | Maldives | 171649 | 535.0 | 297 | -1.0 | 156452 | 479.0 | 14900 | 25 | 308501.0 | 534.0 | 2194571 | 3944254 | 556397 | Asia |
131 | Brunei | 76534 | 4885.0 | 135 | 6.0 | 40782 | 2457.0 | 35617 | -1 | 172197.0 | 304.0 | 717784 | 1614968 | 444457 | Asia |
167 | Timor-Leste | 22736 | 4.0 | 129 | -1.0 | 22291 | 59.0 | 316 | 9 | 16715.0 | 95.0 | 248770 | 182885 | 1360256 | Asia |
173 | Tajikistan | 17388 | -1.0 | 124 | -1.0 | 17263 | -1.0 | 1 | -1 | 1757.0 | 13.0 | -1 | -1 | 9894249 | Asia |
180 | Bhutan | 13846 | 311.0 | 6 | -1.0 | 9640 | 342.0 | 4200 | 3 | 17621.0 | 8.0 | 1999491 | 2544655 | 785761 | Asia |
216 | Macao | 82 | 1.0 | -1 | -1.0 | 79 | -1.0 | 3 | -1 | 123.0 | -1.0 | 5318 | 8008 | 664078 | Asia |
# Drowing visualization graph
plt.bar(asia["Countries"].head(10),asia["Total_deaths"].head(10))
plt.xticks(rotation = '45')
plt.title("Asia Total cases")
plt.xlabel("Countries")
plt.ylabel("Total deaths(in ten million)")
Text(0, 0.5, 'Total deaths(in ten million)')
# sorting dataframe to help identify the country with the most number of active cases
asia.sort_values("Active_cases",ascending = False,inplace = True)
asia.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
20 | S. Korea | 3691488 | 198802.0 | 8394 | 128.0 | 969524 | -1.0 | 2713570 | 766 | 71899.0 | 163.0 | 15804065 | 307815 | 51342704 | Asia |
19 | Vietnam | 3885631 | 118790.0 | 40547 | 95.0 | 2550525 | 33740.0 | 1294559 | 3840 | 39329.0 | 410.0 | 79665130 | 806347 | 98797607 | Asia |
17 | Japan | 5139305 | 71570.0 | 24092 | 232.0 | 4419826 | 74909.0 | 695387 | 1418 | 40843.0 | 191.0 | 39385361 | 313001 | 125831319 | Asia |
7 | Turkey | 14255545 | 49424.0 | 95025 | 188.0 | 13572870 | 66873.0 | 587650 | 1011 | 166050.0 | 1107.0 | 145020155 | 1689211 | 85850820 | Asia |
15 | Indonesia | 5667355 | 37259.0 | 149268 | 232.0 | 4986391 | 42154.0 | 531696 | 2911 | 20361.0 | 536.0 | 86075507 | 309244 | 278341678 | Asia |
# Drowing visualization graph
plt.bar(asia["Countries"].head(10),asia["Active_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Asia Active cases")
plt.xlabel("Countries")
plt.ylabel("Active cases(in ten million)")
Text(0, 0.5, 'Active cases(in ten million)')
# sorting dataframe to help identify the country with the most number of new cases
asia.sort_values("New cases",ascending = False,inplace = True)
asia.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
20 | S. Korea | 3691488 | 198802.0 | 8394 | 128.0 | 969524 | -1.0 | 2713570 | 766 | 71899.0 | 163.0 | 15804065 | 307815 | 51342704 | Asia |
19 | Vietnam | 3885631 | 118790.0 | 40547 | 95.0 | 2550525 | 33740.0 | 1294559 | 3840 | 39329.0 | 410.0 | 79665130 | 806347 | 98797607 | Asia |
17 | Japan | 5139305 | 71570.0 | 24092 | 232.0 | 4419826 | 74909.0 | 695387 | 1418 | 40843.0 | 191.0 | 39385361 | 313001 | 125831319 | Asia |
91 | Hong Kong | 350557 | 56827.0 | 1366 | 198.0 | 13232 | -1.0 | 335959 | 16 | 46136.0 | 180.0 | 36260319 | 4772156 | 7598310 | Asia |
7 | Turkey | 14255545 | 49424.0 | 95025 | 188.0 | 13572870 | 66873.0 | 587650 | 1011 | 166050.0 | 1107.0 | 145020155 | 1689211 | 85850820 | Asia |
# Drowing visualization graph
plt.bar(asia["Countries"].head(10),asia["New cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Asia New cases")
plt.xlabel("Countries")
plt.ylabel("New cases(in ten million)")
Text(0, 0.5, 'New cases(in ten million)')
# sorting dataframe to help identify the country with the most number of recovered cases
asia.sort_values("Total_recovered",ascending = False,inplace = True)
asia.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | India | 42951556 | 6396.0 | 514620 | 201.0 | 42367070 | 13450.0 | 69866 | 8944 | 30623.0 | 367.0 | 770050005 | 549014 | 1402606131 | Asia |
7 | Turkey | 14255545 | 49424.0 | 95025 | 188.0 | 13572870 | 66873.0 | 587650 | 1011 | 166050.0 | 1107.0 | 145020155 | 1689211 | 85850820 | Asia |
11 | Iran | 7073747 | 6772.0 | 137439 | 172.0 | 6696494 | 18416.0 | 239814 | 4098 | 82460.0 | 1602.0 | 47494176 | 553651 | 85783612 | Asia |
15 | Indonesia | 5667355 | 37259.0 | 149268 | 232.0 | 4986391 | 42154.0 | 531696 | 2911 | 20361.0 | 536.0 | 86075507 | 309244 | 278341678 | Asia |
17 | Japan | 5139305 | 71570.0 | 24092 | 232.0 | 4419826 | 74909.0 | 695387 | 1418 | 40843.0 | 191.0 | 39385361 | 313001 | 125831319 | Asia |
# Drowing visualization graph
plt.bar(asia["Countries"].head(10),asia["Total_recovered"].head(10))
plt.xticks(rotation = '45')
plt.title("Asia Recovered cases")
plt.xlabel("Countries")
plt.ylabel("Recovered cases(in ten million)")
Text(0, 0.5, 'Recovered cases(in ten million)')
# sorting dataframe to help identify the country with the most number of new death cases
asia.sort_values("New_death",ascending = False,inplace = True)
asia.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
15 | Indonesia | 5667355 | 37259.0 | 149268 | 232.0 | 4986391 | 42154.0 | 531696 | 2911 | 20361.0 | 536.0 | 86075507 | 309244 | 278341678 | Asia |
17 | Japan | 5139305 | 71570.0 | 24092 | 232.0 | 4419826 | 74909.0 | 695387 | 1418 | 40843.0 | 191.0 | 39385361 | 313001 | 125831319 | Asia |
1 | India | 42951556 | 6396.0 | 514620 | 201.0 | 42367070 | 13450.0 | 69866 | 8944 | 30623.0 | 367.0 | 770050005 | 549014 | 1402606131 | Asia |
91 | Hong Kong | 350557 | 56827.0 | 1366 | 198.0 | 13232 | -1.0 | 335959 | 16 | 46136.0 | 180.0 | 36260319 | 4772156 | 7598310 | Asia |
7 | Turkey | 14255545 | 49424.0 | 95025 | 188.0 | 13572870 | 66873.0 | 587650 | 1011 | 166050.0 | 1107.0 | 145020155 | 1689211 | 85850820 | Asia |
# Drowing visualization graph
plt.bar(asia["Countries"].head(10),asia["New_death"].head(10))
plt.xticks(rotation = '45')
plt.title("Asia New death cases")
plt.xlabel("Countries")
plt.ylabel("New death(in ten million)")
Text(0, 0.5, 'New death(in ten million)')
# sorting dataframe to help identify the country with the most number of new recovered cases
asia.sort_values("New_recovered",ascending = False,inplace = True)
asia.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
17 | Japan | 5139305 | 71570.0 | 24092 | 232.0 | 4419826 | 74909.0 | 695387 | 1418 | 40843.0 | 191.0 | 39385361 | 313001 | 125831319 | Asia |
7 | Turkey | 14255545 | 49424.0 | 95025 | 188.0 | 13572870 | 66873.0 | 587650 | 1011 | 166050.0 | 1107.0 | 145020155 | 1689211 | 85850820 | Asia |
15 | Indonesia | 5667355 | 37259.0 | 149268 | 232.0 | 4986391 | 42154.0 | 531696 | 2911 | 20361.0 | 536.0 | 86075507 | 309244 | 278341678 | Asia |
19 | Vietnam | 3885631 | 118790.0 | 40547 | 95.0 | 2550525 | 33740.0 | 1294559 | 3840 | 39329.0 | 410.0 | 79665130 | 806347 | 98797607 | Asia |
26 | Malaysia | 3528557 | 32467.0 | 33028 | 86.0 | 3197298 | 27629.0 | 298231 | 355 | 106743.0 | 999.0 | 50211993 | 1518965 | 33056709 | Asia |
# Drowing visualization graph
plt.bar(asia["Countries"].head(10),asia["New_recovered"].head(10))
plt.xticks(rotation = '45')
plt.title("Asia New Recovered cases")
plt.xlabel("Countries")
plt.ylabel("New recovered(in ten million)")
Text(0, 0.5, 'New recovered(in ten million)')
# sorting dataframe to help identify the country with the most number of critical cases
asia.sort_values("Serious/Critical",ascending = False,inplace = True)
asia.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | India | 42951556 | 6396.0 | 514620 | 201.0 | 42367070 | 13450.0 | 69866 | 8944 | 30623.0 | 367.0 | 770050005 | 549014 | 1402606131 | Asia |
11 | Iran | 7073747 | 6772.0 | 137439 | 172.0 | 6696494 | 18416.0 | 239814 | 4098 | 82460.0 | 1602.0 | 47494176 | 553651 | 85783612 | Asia |
19 | Vietnam | 3885631 | 118790.0 | 40547 | 95.0 | 2550525 | 33740.0 | 1294559 | 3840 | 39329.0 | 410.0 | 79665130 | 806347 | 98797607 | Asia |
15 | Indonesia | 5667355 | 37259.0 | 149268 | 232.0 | 4986391 | 42154.0 | 531696 | 2911 | 20361.0 | 536.0 | 86075507 | 309244 | 278341678 | Asia |
17 | Japan | 5139305 | 71570.0 | 24092 | 232.0 | 4419826 | 74909.0 | 695387 | 1418 | 40843.0 | 191.0 | 39385361 | 313001 | 125831319 | Asia |
# Drowing visualization graph
plt.bar(asia["Countries"].head(10),asia["Serious/Critical"].head(10))
plt.xticks(rotation = '45')
plt.title("Asia Critical condition cases")
plt.xlabel("Countries")
plt.ylabel("Critical condition(in ten million)")
Text(0, 0.5, 'Critical condition(in ten million)')
From the above outputs, we can clearly see that India has the highest number of cases in Asia. From the same outputs we can also see that Macao has the lowest number of confirmed covid 19 cases in Asia.
India is leading in the number of covid 19 total number of confirmed cases mainly because of the following three reasons:
Macao has confirmed low number of covid 19 cases mainly because of the following three reasons:
We can also see that India is also leading in the total people who have died from covid 19,with Macao recording the lowest number of the same hence doing well in the control measures. From this,just like in Noth America, we can see that population is also a determing factor with the countries having the highest number of population finding it abit hard to control the spread of the disease as compared to those having lower population density.From these, we can ague that in as much as Montserrat having lower population density, it's doing well in the control measures.
africa = data[data["Continent"] == "Africa"].copy()
africa
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
21 | South Africa | 3679539 | 1853.0 | 99499 | 41.0 | 3554282 | 1494.0 | 25758 | 546 | 60764.0 | 1643.0 | 23166727 | 382578 | 60554202 | Africa |
50 | Morocco | 1161392 | 102.0 | 16006 | 4.0 | 1142318 | 386.0 | 3068 | 293 | 30856.0 | 425.0 | 11237010 | 298545 | 37639296 | Africa |
55 | Tunisia | 1000518 | 1077.0 | 27857 | 33.0 | 958952 | 5365.0 | 13709 | 222 | 83206.0 | 2317.0 | 4355745 | 362235 | 12024624 | Africa |
82 | Libya | 496778 | 806.0 | 6284 | 8.0 | 471202 | 2334.0 | 19292 | 126 | 70696.0 | 894.0 | 2433311 | 346284 | 7026916 | Africa |
83 | Egypt | 487642 | 1261.0 | 24149 | 17.0 | 417479 | 1008.0 | 46014 | 122 | 4620.0 | 229.0 | 3693367 | 34992 | 105548117 | Africa |
84 | Ethiopia | 468850 | 64.0 | 7473 | 6.0 | 419993 | 484.0 | 41384 | 75 | 3917.0 | 62.0 | 4521046 | 37769 | 119703224 | Africa |
93 | Kenya | 323025 | 23.0 | 5640 | -1.0 | 303296 | -1.0 | 14089 | 4 | 5795.0 | 101.0 | 3381634 | 60665 | 55743011 | Africa |
94 | Zambia | 313394 | 191.0 | 3957 | 2.0 | 307667 | 335.0 | 1770 | 1 | 16288.0 | 206.0 | 3280022 | 170473 | 19240706 | Africa |
95 | Réunion | 302162 | -1.0 | 666 | -1.0 | 289069 | -1.0 | 12427 | 41 | 333528.0 | 735.0 | 1463269 | 1615164 | 905957 | Africa |
98 | Algeria | 265130 | 51.0 | 6848 | 5.0 | 177732 | 60.0 | 80550 | 15 | 5870.0 | 152.0 | 230861 | 5111 | 45169543 | Africa |
99 | Botswana | 263950 | -1.0 | 2619 | -1.0 | 259434 | -1.0 | 1897 | 1 | 108591.0 | 1077.0 | 2026898 | 833881 | 2430679 | Africa |
100 | Nigeria | 254606 | 8.0 | 3142 | -1.0 | 249154 | 16.0 | 2310 | 11 | 1186.0 | 15.0 | 4442964 | 20699 | 214643298 | Africa |
101 | Zimbabwe | 237503 | -1.0 | 5396 | -1.0 | 227008 | -1.0 | 5099 | 12 | 15601.0 | 354.0 | 2090217 | 137304 | 15223268 | Africa |
104 | Mozambique | 225100 | 20.0 | 2194 | 1.0 | 219863 | -1.0 | 3043 | 13 | 6881.0 | 67.0 | 1272590 | 38902 | 32712596 | Africa |
110 | Uganda | 163316 | 15.0 | 3588 | -1.0 | 100057 | 36.0 | 59671 | 35 | 3392.0 | 75.0 | 2468798 | 51279 | 48144278 | Africa |
111 | Ghana | 160028 | 137.0 | 1442 | -1.0 | 157999 | 217.0 | 587 | 1 | 4977.0 | 45.0 | 2321483 | 72202 | 32152763 | Africa |
112 | Namibia | 157284 | 9.0 | 4010 | 1.0 | 152806 | 19.0 | 468 | 5 | 60089.0 | 1532.0 | 958170 | 366059 | 2617526 | Africa |
117 | Rwanda | 129543 | 10.0 | 1458 | 1.0 | 45522 | -1.0 | 82563 | 1 | 9605.0 | 108.0 | 4876887 | 361610 | 13486604 | Africa |
121 | Cameroon | 119322 | 82.0 | 1926 | 3.0 | 117089 | -1.0 | 307 | 13 | 4316.0 | 70.0 | 1751774 | 63365 | 27645666 | Africa |
124 | Angola | 98746 | -1.0 | 1900 | -1.0 | 96680 | -1.0 | 166 | -1 | 2857.0 | 55.0 | 1429452 | 41353 | 34567212 | Africa |
125 | DRC | 86138 | 99.0 | 1335 | -1.0 | 50930 | -1.0 | 33873 | -1 | 915.0 | 14.0 | 846704 | 8999 | 94091682 | Africa |
126 | Senegal | 85729 | 17.0 | 1960 | -1.0 | 83683 | 19.0 | 86 | 4 | 4905.0 | 112.0 | 1018133 | 58251 | 17478250 | Africa |
127 | Malawi | 85383 | 21.0 | 2617 | -1.0 | 75914 | 33.0 | 6852 | 67 | 4279.0 | 131.0 | 549110 | 27521 | 19952646 | Africa |
128 | Ivory Coast | 81523 | 5.0 | 794 | -1.0 | 80132 | 69.0 | 597 | -1 | 2968.0 | 29.0 | 1423042 | 51812 | 27465460 | Africa |
133 | Eswatini | 69258 | 47.0 | 1391 | 1.0 | 67711 | 15.0 | 156 | 11 | 58685.0 | 1179.0 | 480011 | 406729 | 1180174 | Africa |
136 | Madagascar | 63659 | -1.0 | 1366 | -1.0 | 58677 | -1.0 | 3616 | 61 | 2205.0 | 47.0 | 398434 | 13798 | 28875793 | Africa |
138 | Sudan | 61554 | 42.0 | 3911 | 1.0 | 40329 | -1.0 | 17314 | -1 | 1351.0 | 86.0 | 562941 | 12358 | 45553439 | Africa |
139 | Mauritania | 58638 | -1.0 | 979 | -1.0 | 57618 | -1.0 | 41 | 30 | 12083.0 | 202.0 | 749041 | 154344 | 4853056 | Africa |
142 | Cabo Verde | 55889 | -1.0 | 401 | -1.0 | 55423 | 7.0 | 65 | 23 | 98736.0 | 708.0 | 400982 | 708391 | 566046 | Africa |
146 | Gabon | 47543 | -1.0 | 303 | -1.0 | 46561 | -1.0 | 679 | 1 | 20553.0 | 131.0 | 1571354 | 679313 | 2313152 | Africa |
148 | Seychelles | 39403 | -1.0 | 163 | -1.0 | 38923 | -1.0 | 317 | -1 | 396564.0 | 1640.0 | -1 | -1 | 99361 | Africa |
151 | Burundi | 38127 | -1.0 | 38 | -1.0 | 773 | -1.0 | 37316 | -1 | 3055.0 | 3.0 | 345742 | 27704 | 12480029 | Africa |
152 | Togo | 36812 | 4.0 | 272 | -1.0 | 36418 | 4.0 | 122 | -1 | 4280.0 | 32.0 | 692784 | 80540 | 8601694 | Africa |
153 | Mayotte | 36662 | -1.0 | 187 | -1.0 | 2964 | -1.0 | 33511 | -1 | 129196.0 | 659.0 | 176919 | 623457 | 283771 | Africa |
154 | Guinea | 36397 | -1.0 | 440 | -1.0 | 32939 | -1.0 | 3018 | 49 | 2652.0 | 32.0 | 587607 | 42811 | 13725600 | Africa |
157 | Tanzania | 33620 | -1.0 | 798 | -1.0 | 0 | 0.0 | 0 | 7 | 537.0 | 13.0 | -1 | -1 | 62565411 | Africa |
159 | Lesotho | 32707 | 95.0 | 697 | 1.0 | 23437 | 94.0 | 8573 | -1 | 15069.0 | 321.0 | 403174 | 185748 | 2170541 | Africa |
160 | Mauritius | 32154 | 158.0 | 786 | -1.0 | 28562 | 261.0 | 2806 | -1 | 25213.0 | 616.0 | 358675 | 281251 | 1275284 | Africa |
161 | Mali | 30392 | 1.0 | 724 | 2.0 | 29535 | 2.0 | 133 | -1 | 1432.0 | 34.0 | 624420 | 29424 | 21221400 | Africa |
163 | Benin | 26575 | 8.0 | 163 | -1.0 | 25506 | -1.0 | 906 | 5 | 2101.0 | 13.0 | 604310 | 47765 | 12651662 | Africa |
164 | Somalia | 26351 | -1.0 | 1348 | -1.0 | 13182 | -1.0 | 11821 | -1 | 1584.0 | 81.0 | 400466 | 24079 | 16631081 | Africa |
165 | Congo | 24020 | -1.0 | 378 | -1.0 | 20178 | -1.0 | 3464 | -1 | 4182.0 | 66.0 | 347815 | 60551 | 5744151 | Africa |
169 | Burkina Faso | 20751 | -1.0 | 375 | -1.0 | 20309 | -1.0 | 67 | -1 | 949.0 | 17.0 | 248995 | 11392 | 21857120 | Africa |
174 | South Sudan | 16993 | 4.0 | 137 | -1.0 | 13271 | -1.0 | 3585 | 1 | 1489.0 | 12.0 | 342501 | 30011 | 11412327 | Africa |
175 | Equatorial Guinea | 15885 | -1.0 | 183 | -1.0 | 15664 | -1.0 | 38 | 5 | 10736.0 | 124.0 | 273437 | 184797 | 1479663 | Africa |
177 | Djibouti | 15549 | 2.0 | 189 | -1.0 | 15357 | 3.0 | 3 | -1 | 15366.0 | 187.0 | 286380 | 283004 | 1011929 | Africa |
179 | CAR | 14225 | -1.0 | 113 | -1.0 | 6859 | -1.0 | 7253 | 2 | 2862.0 | 23.0 | 81294 | 16358 | 4969598 | Africa |
183 | Gambia | 11939 | -1.0 | 365 | -1.0 | 11559 | -1.0 | 15 | 4 | 4720.0 | 144.0 | 137657 | 54418 | 2529645 | Africa |
189 | Eritrea | 9707 | 2.0 | 103 | -1.0 | 9600 | 2.0 | 4 | -1 | 2675.0 | 28.0 | 23693 | 6530 | 3628170 | Africa |
192 | Niger | 8759 | -1.0 | 307 | -1.0 | 8433 | -1.0 | 19 | 1 | 341.0 | 12.0 | 229438 | 8940 | 25664043 | Africa |
193 | Comoros | 8033 | -1.0 | 160 | -1.0 | 7855 | -1.0 | 18 | -1 | 8921.0 | 178.0 | -1 | -1 | 900439 | Africa |
194 | Guinea-Bissau | 8027 | -1.0 | 167 | -1.0 | 7018 | 5.0 | 842 | 6 | 3924.0 | 82.0 | 124624 | 60928 | 2045424 | Africa |
195 | Sierra Leone | 7665 | -1.0 | 125 | -1.0 | 0 | 0.0 | 0 | -1 | 929.0 | 15.0 | 259958 | 31521 | 8247056 | Africa |
198 | Liberia | 7384 | -1.0 | 294 | -1.0 | 5747 | -1.0 | 1343 | 2 | 1405.0 | 56.0 | 139824 | 26605 | 5255551 | Africa |
200 | Chad | 7255 | -1.0 | 190 | -1.0 | 4874 | -1.0 | 2191 | -1 | 422.0 | 11.0 | 191341 | 11118 | 17210162 | Africa |
203 | Sao Tome and Principe | 5934 | -1.0 | 72 | -1.0 | 5857 | 4.0 | 5 | -1 | 26264.0 | 319.0 | 29036 | 128515 | 225935 | Africa |
220 | Western Sahara | 10 | -1.0 | 1 | -1.0 | 8 | -1.0 | 1 | -1 | 16.0 | 2.0 | -1 | -1 | 621783 | Africa |
# sorting dataframe to help identify the country with the most number of confirmed cases
africa.sort_values("Total_cases",ascending = False,inplace = True)
africa
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
21 | South Africa | 3679539 | 1853.0 | 99499 | 41.0 | 3554282 | 1494.0 | 25758 | 546 | 60764.0 | 1643.0 | 23166727 | 382578 | 60554202 | Africa |
50 | Morocco | 1161392 | 102.0 | 16006 | 4.0 | 1142318 | 386.0 | 3068 | 293 | 30856.0 | 425.0 | 11237010 | 298545 | 37639296 | Africa |
55 | Tunisia | 1000518 | 1077.0 | 27857 | 33.0 | 958952 | 5365.0 | 13709 | 222 | 83206.0 | 2317.0 | 4355745 | 362235 | 12024624 | Africa |
82 | Libya | 496778 | 806.0 | 6284 | 8.0 | 471202 | 2334.0 | 19292 | 126 | 70696.0 | 894.0 | 2433311 | 346284 | 7026916 | Africa |
83 | Egypt | 487642 | 1261.0 | 24149 | 17.0 | 417479 | 1008.0 | 46014 | 122 | 4620.0 | 229.0 | 3693367 | 34992 | 105548117 | Africa |
84 | Ethiopia | 468850 | 64.0 | 7473 | 6.0 | 419993 | 484.0 | 41384 | 75 | 3917.0 | 62.0 | 4521046 | 37769 | 119703224 | Africa |
93 | Kenya | 323025 | 23.0 | 5640 | -1.0 | 303296 | -1.0 | 14089 | 4 | 5795.0 | 101.0 | 3381634 | 60665 | 55743011 | Africa |
94 | Zambia | 313394 | 191.0 | 3957 | 2.0 | 307667 | 335.0 | 1770 | 1 | 16288.0 | 206.0 | 3280022 | 170473 | 19240706 | Africa |
95 | Réunion | 302162 | -1.0 | 666 | -1.0 | 289069 | -1.0 | 12427 | 41 | 333528.0 | 735.0 | 1463269 | 1615164 | 905957 | Africa |
98 | Algeria | 265130 | 51.0 | 6848 | 5.0 | 177732 | 60.0 | 80550 | 15 | 5870.0 | 152.0 | 230861 | 5111 | 45169543 | Africa |
99 | Botswana | 263950 | -1.0 | 2619 | -1.0 | 259434 | -1.0 | 1897 | 1 | 108591.0 | 1077.0 | 2026898 | 833881 | 2430679 | Africa |
100 | Nigeria | 254606 | 8.0 | 3142 | -1.0 | 249154 | 16.0 | 2310 | 11 | 1186.0 | 15.0 | 4442964 | 20699 | 214643298 | Africa |
101 | Zimbabwe | 237503 | -1.0 | 5396 | -1.0 | 227008 | -1.0 | 5099 | 12 | 15601.0 | 354.0 | 2090217 | 137304 | 15223268 | Africa |
104 | Mozambique | 225100 | 20.0 | 2194 | 1.0 | 219863 | -1.0 | 3043 | 13 | 6881.0 | 67.0 | 1272590 | 38902 | 32712596 | Africa |
110 | Uganda | 163316 | 15.0 | 3588 | -1.0 | 100057 | 36.0 | 59671 | 35 | 3392.0 | 75.0 | 2468798 | 51279 | 48144278 | Africa |
111 | Ghana | 160028 | 137.0 | 1442 | -1.0 | 157999 | 217.0 | 587 | 1 | 4977.0 | 45.0 | 2321483 | 72202 | 32152763 | Africa |
112 | Namibia | 157284 | 9.0 | 4010 | 1.0 | 152806 | 19.0 | 468 | 5 | 60089.0 | 1532.0 | 958170 | 366059 | 2617526 | Africa |
117 | Rwanda | 129543 | 10.0 | 1458 | 1.0 | 45522 | -1.0 | 82563 | 1 | 9605.0 | 108.0 | 4876887 | 361610 | 13486604 | Africa |
121 | Cameroon | 119322 | 82.0 | 1926 | 3.0 | 117089 | -1.0 | 307 | 13 | 4316.0 | 70.0 | 1751774 | 63365 | 27645666 | Africa |
124 | Angola | 98746 | -1.0 | 1900 | -1.0 | 96680 | -1.0 | 166 | -1 | 2857.0 | 55.0 | 1429452 | 41353 | 34567212 | Africa |
125 | DRC | 86138 | 99.0 | 1335 | -1.0 | 50930 | -1.0 | 33873 | -1 | 915.0 | 14.0 | 846704 | 8999 | 94091682 | Africa |
126 | Senegal | 85729 | 17.0 | 1960 | -1.0 | 83683 | 19.0 | 86 | 4 | 4905.0 | 112.0 | 1018133 | 58251 | 17478250 | Africa |
127 | Malawi | 85383 | 21.0 | 2617 | -1.0 | 75914 | 33.0 | 6852 | 67 | 4279.0 | 131.0 | 549110 | 27521 | 19952646 | Africa |
128 | Ivory Coast | 81523 | 5.0 | 794 | -1.0 | 80132 | 69.0 | 597 | -1 | 2968.0 | 29.0 | 1423042 | 51812 | 27465460 | Africa |
133 | Eswatini | 69258 | 47.0 | 1391 | 1.0 | 67711 | 15.0 | 156 | 11 | 58685.0 | 1179.0 | 480011 | 406729 | 1180174 | Africa |
136 | Madagascar | 63659 | -1.0 | 1366 | -1.0 | 58677 | -1.0 | 3616 | 61 | 2205.0 | 47.0 | 398434 | 13798 | 28875793 | Africa |
138 | Sudan | 61554 | 42.0 | 3911 | 1.0 | 40329 | -1.0 | 17314 | -1 | 1351.0 | 86.0 | 562941 | 12358 | 45553439 | Africa |
139 | Mauritania | 58638 | -1.0 | 979 | -1.0 | 57618 | -1.0 | 41 | 30 | 12083.0 | 202.0 | 749041 | 154344 | 4853056 | Africa |
142 | Cabo Verde | 55889 | -1.0 | 401 | -1.0 | 55423 | 7.0 | 65 | 23 | 98736.0 | 708.0 | 400982 | 708391 | 566046 | Africa |
146 | Gabon | 47543 | -1.0 | 303 | -1.0 | 46561 | -1.0 | 679 | 1 | 20553.0 | 131.0 | 1571354 | 679313 | 2313152 | Africa |
148 | Seychelles | 39403 | -1.0 | 163 | -1.0 | 38923 | -1.0 | 317 | -1 | 396564.0 | 1640.0 | -1 | -1 | 99361 | Africa |
151 | Burundi | 38127 | -1.0 | 38 | -1.0 | 773 | -1.0 | 37316 | -1 | 3055.0 | 3.0 | 345742 | 27704 | 12480029 | Africa |
152 | Togo | 36812 | 4.0 | 272 | -1.0 | 36418 | 4.0 | 122 | -1 | 4280.0 | 32.0 | 692784 | 80540 | 8601694 | Africa |
153 | Mayotte | 36662 | -1.0 | 187 | -1.0 | 2964 | -1.0 | 33511 | -1 | 129196.0 | 659.0 | 176919 | 623457 | 283771 | Africa |
154 | Guinea | 36397 | -1.0 | 440 | -1.0 | 32939 | -1.0 | 3018 | 49 | 2652.0 | 32.0 | 587607 | 42811 | 13725600 | Africa |
157 | Tanzania | 33620 | -1.0 | 798 | -1.0 | 0 | 0.0 | 0 | 7 | 537.0 | 13.0 | -1 | -1 | 62565411 | Africa |
159 | Lesotho | 32707 | 95.0 | 697 | 1.0 | 23437 | 94.0 | 8573 | -1 | 15069.0 | 321.0 | 403174 | 185748 | 2170541 | Africa |
160 | Mauritius | 32154 | 158.0 | 786 | -1.0 | 28562 | 261.0 | 2806 | -1 | 25213.0 | 616.0 | 358675 | 281251 | 1275284 | Africa |
161 | Mali | 30392 | 1.0 | 724 | 2.0 | 29535 | 2.0 | 133 | -1 | 1432.0 | 34.0 | 624420 | 29424 | 21221400 | Africa |
163 | Benin | 26575 | 8.0 | 163 | -1.0 | 25506 | -1.0 | 906 | 5 | 2101.0 | 13.0 | 604310 | 47765 | 12651662 | Africa |
164 | Somalia | 26351 | -1.0 | 1348 | -1.0 | 13182 | -1.0 | 11821 | -1 | 1584.0 | 81.0 | 400466 | 24079 | 16631081 | Africa |
165 | Congo | 24020 | -1.0 | 378 | -1.0 | 20178 | -1.0 | 3464 | -1 | 4182.0 | 66.0 | 347815 | 60551 | 5744151 | Africa |
169 | Burkina Faso | 20751 | -1.0 | 375 | -1.0 | 20309 | -1.0 | 67 | -1 | 949.0 | 17.0 | 248995 | 11392 | 21857120 | Africa |
174 | South Sudan | 16993 | 4.0 | 137 | -1.0 | 13271 | -1.0 | 3585 | 1 | 1489.0 | 12.0 | 342501 | 30011 | 11412327 | Africa |
175 | Equatorial Guinea | 15885 | -1.0 | 183 | -1.0 | 15664 | -1.0 | 38 | 5 | 10736.0 | 124.0 | 273437 | 184797 | 1479663 | Africa |
177 | Djibouti | 15549 | 2.0 | 189 | -1.0 | 15357 | 3.0 | 3 | -1 | 15366.0 | 187.0 | 286380 | 283004 | 1011929 | Africa |
179 | CAR | 14225 | -1.0 | 113 | -1.0 | 6859 | -1.0 | 7253 | 2 | 2862.0 | 23.0 | 81294 | 16358 | 4969598 | Africa |
183 | Gambia | 11939 | -1.0 | 365 | -1.0 | 11559 | -1.0 | 15 | 4 | 4720.0 | 144.0 | 137657 | 54418 | 2529645 | Africa |
189 | Eritrea | 9707 | 2.0 | 103 | -1.0 | 9600 | 2.0 | 4 | -1 | 2675.0 | 28.0 | 23693 | 6530 | 3628170 | Africa |
192 | Niger | 8759 | -1.0 | 307 | -1.0 | 8433 | -1.0 | 19 | 1 | 341.0 | 12.0 | 229438 | 8940 | 25664043 | Africa |
193 | Comoros | 8033 | -1.0 | 160 | -1.0 | 7855 | -1.0 | 18 | -1 | 8921.0 | 178.0 | -1 | -1 | 900439 | Africa |
194 | Guinea-Bissau | 8027 | -1.0 | 167 | -1.0 | 7018 | 5.0 | 842 | 6 | 3924.0 | 82.0 | 124624 | 60928 | 2045424 | Africa |
195 | Sierra Leone | 7665 | -1.0 | 125 | -1.0 | 0 | 0.0 | 0 | -1 | 929.0 | 15.0 | 259958 | 31521 | 8247056 | Africa |
198 | Liberia | 7384 | -1.0 | 294 | -1.0 | 5747 | -1.0 | 1343 | 2 | 1405.0 | 56.0 | 139824 | 26605 | 5255551 | Africa |
200 | Chad | 7255 | -1.0 | 190 | -1.0 | 4874 | -1.0 | 2191 | -1 | 422.0 | 11.0 | 191341 | 11118 | 17210162 | Africa |
203 | Sao Tome and Principe | 5934 | -1.0 | 72 | -1.0 | 5857 | 4.0 | 5 | -1 | 26264.0 | 319.0 | 29036 | 128515 | 225935 | Africa |
220 | Western Sahara | 10 | -1.0 | 1 | -1.0 | 8 | -1.0 | 1 | -1 | 16.0 | 2.0 | -1 | -1 | 621783 | Africa |
# Drowing visualization graph
plt.bar(africa["Countries"].head(10),africa["Total_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Africa Total cases")
plt.xlabel("Countries")
plt.ylabel("Total Cases(in ten million)")
Text(0, 0.5, 'Total Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of death cases
africa.sort_values("Total_deaths",ascending = False,inplace = True)
africa
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
21 | South Africa | 3679539 | 1853.0 | 99499 | 41.0 | 3554282 | 1494.0 | 25758 | 546 | 60764.0 | 1643.0 | 23166727 | 382578 | 60554202 | Africa |
55 | Tunisia | 1000518 | 1077.0 | 27857 | 33.0 | 958952 | 5365.0 | 13709 | 222 | 83206.0 | 2317.0 | 4355745 | 362235 | 12024624 | Africa |
83 | Egypt | 487642 | 1261.0 | 24149 | 17.0 | 417479 | 1008.0 | 46014 | 122 | 4620.0 | 229.0 | 3693367 | 34992 | 105548117 | Africa |
50 | Morocco | 1161392 | 102.0 | 16006 | 4.0 | 1142318 | 386.0 | 3068 | 293 | 30856.0 | 425.0 | 11237010 | 298545 | 37639296 | Africa |
84 | Ethiopia | 468850 | 64.0 | 7473 | 6.0 | 419993 | 484.0 | 41384 | 75 | 3917.0 | 62.0 | 4521046 | 37769 | 119703224 | Africa |
98 | Algeria | 265130 | 51.0 | 6848 | 5.0 | 177732 | 60.0 | 80550 | 15 | 5870.0 | 152.0 | 230861 | 5111 | 45169543 | Africa |
82 | Libya | 496778 | 806.0 | 6284 | 8.0 | 471202 | 2334.0 | 19292 | 126 | 70696.0 | 894.0 | 2433311 | 346284 | 7026916 | Africa |
93 | Kenya | 323025 | 23.0 | 5640 | -1.0 | 303296 | -1.0 | 14089 | 4 | 5795.0 | 101.0 | 3381634 | 60665 | 55743011 | Africa |
101 | Zimbabwe | 237503 | -1.0 | 5396 | -1.0 | 227008 | -1.0 | 5099 | 12 | 15601.0 | 354.0 | 2090217 | 137304 | 15223268 | Africa |
112 | Namibia | 157284 | 9.0 | 4010 | 1.0 | 152806 | 19.0 | 468 | 5 | 60089.0 | 1532.0 | 958170 | 366059 | 2617526 | Africa |
94 | Zambia | 313394 | 191.0 | 3957 | 2.0 | 307667 | 335.0 | 1770 | 1 | 16288.0 | 206.0 | 3280022 | 170473 | 19240706 | Africa |
138 | Sudan | 61554 | 42.0 | 3911 | 1.0 | 40329 | -1.0 | 17314 | -1 | 1351.0 | 86.0 | 562941 | 12358 | 45553439 | Africa |
110 | Uganda | 163316 | 15.0 | 3588 | -1.0 | 100057 | 36.0 | 59671 | 35 | 3392.0 | 75.0 | 2468798 | 51279 | 48144278 | Africa |
100 | Nigeria | 254606 | 8.0 | 3142 | -1.0 | 249154 | 16.0 | 2310 | 11 | 1186.0 | 15.0 | 4442964 | 20699 | 214643298 | Africa |
99 | Botswana | 263950 | -1.0 | 2619 | -1.0 | 259434 | -1.0 | 1897 | 1 | 108591.0 | 1077.0 | 2026898 | 833881 | 2430679 | Africa |
127 | Malawi | 85383 | 21.0 | 2617 | -1.0 | 75914 | 33.0 | 6852 | 67 | 4279.0 | 131.0 | 549110 | 27521 | 19952646 | Africa |
104 | Mozambique | 225100 | 20.0 | 2194 | 1.0 | 219863 | -1.0 | 3043 | 13 | 6881.0 | 67.0 | 1272590 | 38902 | 32712596 | Africa |
126 | Senegal | 85729 | 17.0 | 1960 | -1.0 | 83683 | 19.0 | 86 | 4 | 4905.0 | 112.0 | 1018133 | 58251 | 17478250 | Africa |
121 | Cameroon | 119322 | 82.0 | 1926 | 3.0 | 117089 | -1.0 | 307 | 13 | 4316.0 | 70.0 | 1751774 | 63365 | 27645666 | Africa |
124 | Angola | 98746 | -1.0 | 1900 | -1.0 | 96680 | -1.0 | 166 | -1 | 2857.0 | 55.0 | 1429452 | 41353 | 34567212 | Africa |
117 | Rwanda | 129543 | 10.0 | 1458 | 1.0 | 45522 | -1.0 | 82563 | 1 | 9605.0 | 108.0 | 4876887 | 361610 | 13486604 | Africa |
111 | Ghana | 160028 | 137.0 | 1442 | -1.0 | 157999 | 217.0 | 587 | 1 | 4977.0 | 45.0 | 2321483 | 72202 | 32152763 | Africa |
133 | Eswatini | 69258 | 47.0 | 1391 | 1.0 | 67711 | 15.0 | 156 | 11 | 58685.0 | 1179.0 | 480011 | 406729 | 1180174 | Africa |
136 | Madagascar | 63659 | -1.0 | 1366 | -1.0 | 58677 | -1.0 | 3616 | 61 | 2205.0 | 47.0 | 398434 | 13798 | 28875793 | Africa |
164 | Somalia | 26351 | -1.0 | 1348 | -1.0 | 13182 | -1.0 | 11821 | -1 | 1584.0 | 81.0 | 400466 | 24079 | 16631081 | Africa |
125 | DRC | 86138 | 99.0 | 1335 | -1.0 | 50930 | -1.0 | 33873 | -1 | 915.0 | 14.0 | 846704 | 8999 | 94091682 | Africa |
139 | Mauritania | 58638 | -1.0 | 979 | -1.0 | 57618 | -1.0 | 41 | 30 | 12083.0 | 202.0 | 749041 | 154344 | 4853056 | Africa |
157 | Tanzania | 33620 | -1.0 | 798 | -1.0 | 0 | 0.0 | 0 | 7 | 537.0 | 13.0 | -1 | -1 | 62565411 | Africa |
128 | Ivory Coast | 81523 | 5.0 | 794 | -1.0 | 80132 | 69.0 | 597 | -1 | 2968.0 | 29.0 | 1423042 | 51812 | 27465460 | Africa |
160 | Mauritius | 32154 | 158.0 | 786 | -1.0 | 28562 | 261.0 | 2806 | -1 | 25213.0 | 616.0 | 358675 | 281251 | 1275284 | Africa |
161 | Mali | 30392 | 1.0 | 724 | 2.0 | 29535 | 2.0 | 133 | -1 | 1432.0 | 34.0 | 624420 | 29424 | 21221400 | Africa |
159 | Lesotho | 32707 | 95.0 | 697 | 1.0 | 23437 | 94.0 | 8573 | -1 | 15069.0 | 321.0 | 403174 | 185748 | 2170541 | Africa |
95 | Réunion | 302162 | -1.0 | 666 | -1.0 | 289069 | -1.0 | 12427 | 41 | 333528.0 | 735.0 | 1463269 | 1615164 | 905957 | Africa |
154 | Guinea | 36397 | -1.0 | 440 | -1.0 | 32939 | -1.0 | 3018 | 49 | 2652.0 | 32.0 | 587607 | 42811 | 13725600 | Africa |
142 | Cabo Verde | 55889 | -1.0 | 401 | -1.0 | 55423 | 7.0 | 65 | 23 | 98736.0 | 708.0 | 400982 | 708391 | 566046 | Africa |
165 | Congo | 24020 | -1.0 | 378 | -1.0 | 20178 | -1.0 | 3464 | -1 | 4182.0 | 66.0 | 347815 | 60551 | 5744151 | Africa |
169 | Burkina Faso | 20751 | -1.0 | 375 | -1.0 | 20309 | -1.0 | 67 | -1 | 949.0 | 17.0 | 248995 | 11392 | 21857120 | Africa |
183 | Gambia | 11939 | -1.0 | 365 | -1.0 | 11559 | -1.0 | 15 | 4 | 4720.0 | 144.0 | 137657 | 54418 | 2529645 | Africa |
192 | Niger | 8759 | -1.0 | 307 | -1.0 | 8433 | -1.0 | 19 | 1 | 341.0 | 12.0 | 229438 | 8940 | 25664043 | Africa |
146 | Gabon | 47543 | -1.0 | 303 | -1.0 | 46561 | -1.0 | 679 | 1 | 20553.0 | 131.0 | 1571354 | 679313 | 2313152 | Africa |
198 | Liberia | 7384 | -1.0 | 294 | -1.0 | 5747 | -1.0 | 1343 | 2 | 1405.0 | 56.0 | 139824 | 26605 | 5255551 | Africa |
152 | Togo | 36812 | 4.0 | 272 | -1.0 | 36418 | 4.0 | 122 | -1 | 4280.0 | 32.0 | 692784 | 80540 | 8601694 | Africa |
200 | Chad | 7255 | -1.0 | 190 | -1.0 | 4874 | -1.0 | 2191 | -1 | 422.0 | 11.0 | 191341 | 11118 | 17210162 | Africa |
177 | Djibouti | 15549 | 2.0 | 189 | -1.0 | 15357 | 3.0 | 3 | -1 | 15366.0 | 187.0 | 286380 | 283004 | 1011929 | Africa |
153 | Mayotte | 36662 | -1.0 | 187 | -1.0 | 2964 | -1.0 | 33511 | -1 | 129196.0 | 659.0 | 176919 | 623457 | 283771 | Africa |
175 | Equatorial Guinea | 15885 | -1.0 | 183 | -1.0 | 15664 | -1.0 | 38 | 5 | 10736.0 | 124.0 | 273437 | 184797 | 1479663 | Africa |
194 | Guinea-Bissau | 8027 | -1.0 | 167 | -1.0 | 7018 | 5.0 | 842 | 6 | 3924.0 | 82.0 | 124624 | 60928 | 2045424 | Africa |
163 | Benin | 26575 | 8.0 | 163 | -1.0 | 25506 | -1.0 | 906 | 5 | 2101.0 | 13.0 | 604310 | 47765 | 12651662 | Africa |
148 | Seychelles | 39403 | -1.0 | 163 | -1.0 | 38923 | -1.0 | 317 | -1 | 396564.0 | 1640.0 | -1 | -1 | 99361 | Africa |
193 | Comoros | 8033 | -1.0 | 160 | -1.0 | 7855 | -1.0 | 18 | -1 | 8921.0 | 178.0 | -1 | -1 | 900439 | Africa |
174 | South Sudan | 16993 | 4.0 | 137 | -1.0 | 13271 | -1.0 | 3585 | 1 | 1489.0 | 12.0 | 342501 | 30011 | 11412327 | Africa |
195 | Sierra Leone | 7665 | -1.0 | 125 | -1.0 | 0 | 0.0 | 0 | -1 | 929.0 | 15.0 | 259958 | 31521 | 8247056 | Africa |
179 | CAR | 14225 | -1.0 | 113 | -1.0 | 6859 | -1.0 | 7253 | 2 | 2862.0 | 23.0 | 81294 | 16358 | 4969598 | Africa |
189 | Eritrea | 9707 | 2.0 | 103 | -1.0 | 9600 | 2.0 | 4 | -1 | 2675.0 | 28.0 | 23693 | 6530 | 3628170 | Africa |
203 | Sao Tome and Principe | 5934 | -1.0 | 72 | -1.0 | 5857 | 4.0 | 5 | -1 | 26264.0 | 319.0 | 29036 | 128515 | 225935 | Africa |
151 | Burundi | 38127 | -1.0 | 38 | -1.0 | 773 | -1.0 | 37316 | -1 | 3055.0 | 3.0 | 345742 | 27704 | 12480029 | Africa |
220 | Western Sahara | 10 | -1.0 | 1 | -1.0 | 8 | -1.0 | 1 | -1 | 16.0 | 2.0 | -1 | -1 | 621783 | Africa |
# Drowing visualization graph
plt.bar(africa["Countries"].head(10),africa["Total_deaths"].head(10))
plt.xticks(rotation = '45')
plt.title("Africa Total Death cases")
plt.xlabel("Countries")
plt.ylabel("Death Cases(in ten million)")
Text(0, 0.5, 'Death Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of active cases
africa.sort_values("Active_cases",ascending = False,inplace = True)
africa
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
117 | Rwanda | 129543 | 10.0 | 1458 | 1.0 | 45522 | -1.0 | 82563 | 1 | 9605.0 | 108.0 | 4876887 | 361610 | 13486604 | Africa |
98 | Algeria | 265130 | 51.0 | 6848 | 5.0 | 177732 | 60.0 | 80550 | 15 | 5870.0 | 152.0 | 230861 | 5111 | 45169543 | Africa |
110 | Uganda | 163316 | 15.0 | 3588 | -1.0 | 100057 | 36.0 | 59671 | 35 | 3392.0 | 75.0 | 2468798 | 51279 | 48144278 | Africa |
83 | Egypt | 487642 | 1261.0 | 24149 | 17.0 | 417479 | 1008.0 | 46014 | 122 | 4620.0 | 229.0 | 3693367 | 34992 | 105548117 | Africa |
84 | Ethiopia | 468850 | 64.0 | 7473 | 6.0 | 419993 | 484.0 | 41384 | 75 | 3917.0 | 62.0 | 4521046 | 37769 | 119703224 | Africa |
151 | Burundi | 38127 | -1.0 | 38 | -1.0 | 773 | -1.0 | 37316 | -1 | 3055.0 | 3.0 | 345742 | 27704 | 12480029 | Africa |
125 | DRC | 86138 | 99.0 | 1335 | -1.0 | 50930 | -1.0 | 33873 | -1 | 915.0 | 14.0 | 846704 | 8999 | 94091682 | Africa |
153 | Mayotte | 36662 | -1.0 | 187 | -1.0 | 2964 | -1.0 | 33511 | -1 | 129196.0 | 659.0 | 176919 | 623457 | 283771 | Africa |
21 | South Africa | 3679539 | 1853.0 | 99499 | 41.0 | 3554282 | 1494.0 | 25758 | 546 | 60764.0 | 1643.0 | 23166727 | 382578 | 60554202 | Africa |
82 | Libya | 496778 | 806.0 | 6284 | 8.0 | 471202 | 2334.0 | 19292 | 126 | 70696.0 | 894.0 | 2433311 | 346284 | 7026916 | Africa |
138 | Sudan | 61554 | 42.0 | 3911 | 1.0 | 40329 | -1.0 | 17314 | -1 | 1351.0 | 86.0 | 562941 | 12358 | 45553439 | Africa |
93 | Kenya | 323025 | 23.0 | 5640 | -1.0 | 303296 | -1.0 | 14089 | 4 | 5795.0 | 101.0 | 3381634 | 60665 | 55743011 | Africa |
55 | Tunisia | 1000518 | 1077.0 | 27857 | 33.0 | 958952 | 5365.0 | 13709 | 222 | 83206.0 | 2317.0 | 4355745 | 362235 | 12024624 | Africa |
95 | Réunion | 302162 | -1.0 | 666 | -1.0 | 289069 | -1.0 | 12427 | 41 | 333528.0 | 735.0 | 1463269 | 1615164 | 905957 | Africa |
164 | Somalia | 26351 | -1.0 | 1348 | -1.0 | 13182 | -1.0 | 11821 | -1 | 1584.0 | 81.0 | 400466 | 24079 | 16631081 | Africa |
159 | Lesotho | 32707 | 95.0 | 697 | 1.0 | 23437 | 94.0 | 8573 | -1 | 15069.0 | 321.0 | 403174 | 185748 | 2170541 | Africa |
179 | CAR | 14225 | -1.0 | 113 | -1.0 | 6859 | -1.0 | 7253 | 2 | 2862.0 | 23.0 | 81294 | 16358 | 4969598 | Africa |
127 | Malawi | 85383 | 21.0 | 2617 | -1.0 | 75914 | 33.0 | 6852 | 67 | 4279.0 | 131.0 | 549110 | 27521 | 19952646 | Africa |
101 | Zimbabwe | 237503 | -1.0 | 5396 | -1.0 | 227008 | -1.0 | 5099 | 12 | 15601.0 | 354.0 | 2090217 | 137304 | 15223268 | Africa |
136 | Madagascar | 63659 | -1.0 | 1366 | -1.0 | 58677 | -1.0 | 3616 | 61 | 2205.0 | 47.0 | 398434 | 13798 | 28875793 | Africa |
174 | South Sudan | 16993 | 4.0 | 137 | -1.0 | 13271 | -1.0 | 3585 | 1 | 1489.0 | 12.0 | 342501 | 30011 | 11412327 | Africa |
165 | Congo | 24020 | -1.0 | 378 | -1.0 | 20178 | -1.0 | 3464 | -1 | 4182.0 | 66.0 | 347815 | 60551 | 5744151 | Africa |
50 | Morocco | 1161392 | 102.0 | 16006 | 4.0 | 1142318 | 386.0 | 3068 | 293 | 30856.0 | 425.0 | 11237010 | 298545 | 37639296 | Africa |
104 | Mozambique | 225100 | 20.0 | 2194 | 1.0 | 219863 | -1.0 | 3043 | 13 | 6881.0 | 67.0 | 1272590 | 38902 | 32712596 | Africa |
154 | Guinea | 36397 | -1.0 | 440 | -1.0 | 32939 | -1.0 | 3018 | 49 | 2652.0 | 32.0 | 587607 | 42811 | 13725600 | Africa |
160 | Mauritius | 32154 | 158.0 | 786 | -1.0 | 28562 | 261.0 | 2806 | -1 | 25213.0 | 616.0 | 358675 | 281251 | 1275284 | Africa |
100 | Nigeria | 254606 | 8.0 | 3142 | -1.0 | 249154 | 16.0 | 2310 | 11 | 1186.0 | 15.0 | 4442964 | 20699 | 214643298 | Africa |
200 | Chad | 7255 | -1.0 | 190 | -1.0 | 4874 | -1.0 | 2191 | -1 | 422.0 | 11.0 | 191341 | 11118 | 17210162 | Africa |
99 | Botswana | 263950 | -1.0 | 2619 | -1.0 | 259434 | -1.0 | 1897 | 1 | 108591.0 | 1077.0 | 2026898 | 833881 | 2430679 | Africa |
94 | Zambia | 313394 | 191.0 | 3957 | 2.0 | 307667 | 335.0 | 1770 | 1 | 16288.0 | 206.0 | 3280022 | 170473 | 19240706 | Africa |
198 | Liberia | 7384 | -1.0 | 294 | -1.0 | 5747 | -1.0 | 1343 | 2 | 1405.0 | 56.0 | 139824 | 26605 | 5255551 | Africa |
163 | Benin | 26575 | 8.0 | 163 | -1.0 | 25506 | -1.0 | 906 | 5 | 2101.0 | 13.0 | 604310 | 47765 | 12651662 | Africa |
194 | Guinea-Bissau | 8027 | -1.0 | 167 | -1.0 | 7018 | 5.0 | 842 | 6 | 3924.0 | 82.0 | 124624 | 60928 | 2045424 | Africa |
146 | Gabon | 47543 | -1.0 | 303 | -1.0 | 46561 | -1.0 | 679 | 1 | 20553.0 | 131.0 | 1571354 | 679313 | 2313152 | Africa |
128 | Ivory Coast | 81523 | 5.0 | 794 | -1.0 | 80132 | 69.0 | 597 | -1 | 2968.0 | 29.0 | 1423042 | 51812 | 27465460 | Africa |
111 | Ghana | 160028 | 137.0 | 1442 | -1.0 | 157999 | 217.0 | 587 | 1 | 4977.0 | 45.0 | 2321483 | 72202 | 32152763 | Africa |
112 | Namibia | 157284 | 9.0 | 4010 | 1.0 | 152806 | 19.0 | 468 | 5 | 60089.0 | 1532.0 | 958170 | 366059 | 2617526 | Africa |
148 | Seychelles | 39403 | -1.0 | 163 | -1.0 | 38923 | -1.0 | 317 | -1 | 396564.0 | 1640.0 | -1 | -1 | 99361 | Africa |
121 | Cameroon | 119322 | 82.0 | 1926 | 3.0 | 117089 | -1.0 | 307 | 13 | 4316.0 | 70.0 | 1751774 | 63365 | 27645666 | Africa |
124 | Angola | 98746 | -1.0 | 1900 | -1.0 | 96680 | -1.0 | 166 | -1 | 2857.0 | 55.0 | 1429452 | 41353 | 34567212 | Africa |
133 | Eswatini | 69258 | 47.0 | 1391 | 1.0 | 67711 | 15.0 | 156 | 11 | 58685.0 | 1179.0 | 480011 | 406729 | 1180174 | Africa |
161 | Mali | 30392 | 1.0 | 724 | 2.0 | 29535 | 2.0 | 133 | -1 | 1432.0 | 34.0 | 624420 | 29424 | 21221400 | Africa |
152 | Togo | 36812 | 4.0 | 272 | -1.0 | 36418 | 4.0 | 122 | -1 | 4280.0 | 32.0 | 692784 | 80540 | 8601694 | Africa |
126 | Senegal | 85729 | 17.0 | 1960 | -1.0 | 83683 | 19.0 | 86 | 4 | 4905.0 | 112.0 | 1018133 | 58251 | 17478250 | Africa |
169 | Burkina Faso | 20751 | -1.0 | 375 | -1.0 | 20309 | -1.0 | 67 | -1 | 949.0 | 17.0 | 248995 | 11392 | 21857120 | Africa |
142 | Cabo Verde | 55889 | -1.0 | 401 | -1.0 | 55423 | 7.0 | 65 | 23 | 98736.0 | 708.0 | 400982 | 708391 | 566046 | Africa |
139 | Mauritania | 58638 | -1.0 | 979 | -1.0 | 57618 | -1.0 | 41 | 30 | 12083.0 | 202.0 | 749041 | 154344 | 4853056 | Africa |
175 | Equatorial Guinea | 15885 | -1.0 | 183 | -1.0 | 15664 | -1.0 | 38 | 5 | 10736.0 | 124.0 | 273437 | 184797 | 1479663 | Africa |
192 | Niger | 8759 | -1.0 | 307 | -1.0 | 8433 | -1.0 | 19 | 1 | 341.0 | 12.0 | 229438 | 8940 | 25664043 | Africa |
193 | Comoros | 8033 | -1.0 | 160 | -1.0 | 7855 | -1.0 | 18 | -1 | 8921.0 | 178.0 | -1 | -1 | 900439 | Africa |
183 | Gambia | 11939 | -1.0 | 365 | -1.0 | 11559 | -1.0 | 15 | 4 | 4720.0 | 144.0 | 137657 | 54418 | 2529645 | Africa |
203 | Sao Tome and Principe | 5934 | -1.0 | 72 | -1.0 | 5857 | 4.0 | 5 | -1 | 26264.0 | 319.0 | 29036 | 128515 | 225935 | Africa |
189 | Eritrea | 9707 | 2.0 | 103 | -1.0 | 9600 | 2.0 | 4 | -1 | 2675.0 | 28.0 | 23693 | 6530 | 3628170 | Africa |
177 | Djibouti | 15549 | 2.0 | 189 | -1.0 | 15357 | 3.0 | 3 | -1 | 15366.0 | 187.0 | 286380 | 283004 | 1011929 | Africa |
220 | Western Sahara | 10 | -1.0 | 1 | -1.0 | 8 | -1.0 | 1 | -1 | 16.0 | 2.0 | -1 | -1 | 621783 | Africa |
157 | Tanzania | 33620 | -1.0 | 798 | -1.0 | 0 | 0.0 | 0 | 7 | 537.0 | 13.0 | -1 | -1 | 62565411 | Africa |
195 | Sierra Leone | 7665 | -1.0 | 125 | -1.0 | 0 | 0.0 | 0 | -1 | 929.0 | 15.0 | 259958 | 31521 | 8247056 | Africa |
# Drowing visualization graph
plt.bar(africa["Countries"].head(10),africa["Active_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Africa Active cases")
plt.xlabel("Countries")
plt.ylabel("Active Cases(in ten million)")
Text(0, 0.5, 'Active Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of new cases
africa.sort_values("New cases",ascending = False,inplace = True)
africa
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
21 | South Africa | 3679539 | 1853.0 | 99499 | 41.0 | 3554282 | 1494.0 | 25758 | 546 | 60764.0 | 1643.0 | 23166727 | 382578 | 60554202 | Africa |
83 | Egypt | 487642 | 1261.0 | 24149 | 17.0 | 417479 | 1008.0 | 46014 | 122 | 4620.0 | 229.0 | 3693367 | 34992 | 105548117 | Africa |
55 | Tunisia | 1000518 | 1077.0 | 27857 | 33.0 | 958952 | 5365.0 | 13709 | 222 | 83206.0 | 2317.0 | 4355745 | 362235 | 12024624 | Africa |
82 | Libya | 496778 | 806.0 | 6284 | 8.0 | 471202 | 2334.0 | 19292 | 126 | 70696.0 | 894.0 | 2433311 | 346284 | 7026916 | Africa |
94 | Zambia | 313394 | 191.0 | 3957 | 2.0 | 307667 | 335.0 | 1770 | 1 | 16288.0 | 206.0 | 3280022 | 170473 | 19240706 | Africa |
160 | Mauritius | 32154 | 158.0 | 786 | -1.0 | 28562 | 261.0 | 2806 | -1 | 25213.0 | 616.0 | 358675 | 281251 | 1275284 | Africa |
111 | Ghana | 160028 | 137.0 | 1442 | -1.0 | 157999 | 217.0 | 587 | 1 | 4977.0 | 45.0 | 2321483 | 72202 | 32152763 | Africa |
50 | Morocco | 1161392 | 102.0 | 16006 | 4.0 | 1142318 | 386.0 | 3068 | 293 | 30856.0 | 425.0 | 11237010 | 298545 | 37639296 | Africa |
125 | DRC | 86138 | 99.0 | 1335 | -1.0 | 50930 | -1.0 | 33873 | -1 | 915.0 | 14.0 | 846704 | 8999 | 94091682 | Africa |
159 | Lesotho | 32707 | 95.0 | 697 | 1.0 | 23437 | 94.0 | 8573 | -1 | 15069.0 | 321.0 | 403174 | 185748 | 2170541 | Africa |
121 | Cameroon | 119322 | 82.0 | 1926 | 3.0 | 117089 | -1.0 | 307 | 13 | 4316.0 | 70.0 | 1751774 | 63365 | 27645666 | Africa |
84 | Ethiopia | 468850 | 64.0 | 7473 | 6.0 | 419993 | 484.0 | 41384 | 75 | 3917.0 | 62.0 | 4521046 | 37769 | 119703224 | Africa |
98 | Algeria | 265130 | 51.0 | 6848 | 5.0 | 177732 | 60.0 | 80550 | 15 | 5870.0 | 152.0 | 230861 | 5111 | 45169543 | Africa |
133 | Eswatini | 69258 | 47.0 | 1391 | 1.0 | 67711 | 15.0 | 156 | 11 | 58685.0 | 1179.0 | 480011 | 406729 | 1180174 | Africa |
138 | Sudan | 61554 | 42.0 | 3911 | 1.0 | 40329 | -1.0 | 17314 | -1 | 1351.0 | 86.0 | 562941 | 12358 | 45553439 | Africa |
93 | Kenya | 323025 | 23.0 | 5640 | -1.0 | 303296 | -1.0 | 14089 | 4 | 5795.0 | 101.0 | 3381634 | 60665 | 55743011 | Africa |
127 | Malawi | 85383 | 21.0 | 2617 | -1.0 | 75914 | 33.0 | 6852 | 67 | 4279.0 | 131.0 | 549110 | 27521 | 19952646 | Africa |
104 | Mozambique | 225100 | 20.0 | 2194 | 1.0 | 219863 | -1.0 | 3043 | 13 | 6881.0 | 67.0 | 1272590 | 38902 | 32712596 | Africa |
126 | Senegal | 85729 | 17.0 | 1960 | -1.0 | 83683 | 19.0 | 86 | 4 | 4905.0 | 112.0 | 1018133 | 58251 | 17478250 | Africa |
110 | Uganda | 163316 | 15.0 | 3588 | -1.0 | 100057 | 36.0 | 59671 | 35 | 3392.0 | 75.0 | 2468798 | 51279 | 48144278 | Africa |
117 | Rwanda | 129543 | 10.0 | 1458 | 1.0 | 45522 | -1.0 | 82563 | 1 | 9605.0 | 108.0 | 4876887 | 361610 | 13486604 | Africa |
112 | Namibia | 157284 | 9.0 | 4010 | 1.0 | 152806 | 19.0 | 468 | 5 | 60089.0 | 1532.0 | 958170 | 366059 | 2617526 | Africa |
163 | Benin | 26575 | 8.0 | 163 | -1.0 | 25506 | -1.0 | 906 | 5 | 2101.0 | 13.0 | 604310 | 47765 | 12651662 | Africa |
100 | Nigeria | 254606 | 8.0 | 3142 | -1.0 | 249154 | 16.0 | 2310 | 11 | 1186.0 | 15.0 | 4442964 | 20699 | 214643298 | Africa |
128 | Ivory Coast | 81523 | 5.0 | 794 | -1.0 | 80132 | 69.0 | 597 | -1 | 2968.0 | 29.0 | 1423042 | 51812 | 27465460 | Africa |
152 | Togo | 36812 | 4.0 | 272 | -1.0 | 36418 | 4.0 | 122 | -1 | 4280.0 | 32.0 | 692784 | 80540 | 8601694 | Africa |
174 | South Sudan | 16993 | 4.0 | 137 | -1.0 | 13271 | -1.0 | 3585 | 1 | 1489.0 | 12.0 | 342501 | 30011 | 11412327 | Africa |
189 | Eritrea | 9707 | 2.0 | 103 | -1.0 | 9600 | 2.0 | 4 | -1 | 2675.0 | 28.0 | 23693 | 6530 | 3628170 | Africa |
177 | Djibouti | 15549 | 2.0 | 189 | -1.0 | 15357 | 3.0 | 3 | -1 | 15366.0 | 187.0 | 286380 | 283004 | 1011929 | Africa |
161 | Mali | 30392 | 1.0 | 724 | 2.0 | 29535 | 2.0 | 133 | -1 | 1432.0 | 34.0 | 624420 | 29424 | 21221400 | Africa |
139 | Mauritania | 58638 | -1.0 | 979 | -1.0 | 57618 | -1.0 | 41 | 30 | 12083.0 | 202.0 | 749041 | 154344 | 4853056 | Africa |
175 | Equatorial Guinea | 15885 | -1.0 | 183 | -1.0 | 15664 | -1.0 | 38 | 5 | 10736.0 | 124.0 | 273437 | 184797 | 1479663 | Africa |
192 | Niger | 8759 | -1.0 | 307 | -1.0 | 8433 | -1.0 | 19 | 1 | 341.0 | 12.0 | 229438 | 8940 | 25664043 | Africa |
142 | Cabo Verde | 55889 | -1.0 | 401 | -1.0 | 55423 | 7.0 | 65 | 23 | 98736.0 | 708.0 | 400982 | 708391 | 566046 | Africa |
193 | Comoros | 8033 | -1.0 | 160 | -1.0 | 7855 | -1.0 | 18 | -1 | 8921.0 | 178.0 | -1 | -1 | 900439 | Africa |
169 | Burkina Faso | 20751 | -1.0 | 375 | -1.0 | 20309 | -1.0 | 67 | -1 | 949.0 | 17.0 | 248995 | 11392 | 21857120 | Africa |
183 | Gambia | 11939 | -1.0 | 365 | -1.0 | 11559 | -1.0 | 15 | 4 | 4720.0 | 144.0 | 137657 | 54418 | 2529645 | Africa |
203 | Sao Tome and Principe | 5934 | -1.0 | 72 | -1.0 | 5857 | 4.0 | 5 | -1 | 26264.0 | 319.0 | 29036 | 128515 | 225935 | Africa |
220 | Western Sahara | 10 | -1.0 | 1 | -1.0 | 8 | -1.0 | 1 | -1 | 16.0 | 2.0 | -1 | -1 | 621783 | Africa |
157 | Tanzania | 33620 | -1.0 | 798 | -1.0 | 0 | 0.0 | 0 | 7 | 537.0 | 13.0 | -1 | -1 | 62565411 | Africa |
99 | Botswana | 263950 | -1.0 | 2619 | -1.0 | 259434 | -1.0 | 1897 | 1 | 108591.0 | 1077.0 | 2026898 | 833881 | 2430679 | Africa |
124 | Angola | 98746 | -1.0 | 1900 | -1.0 | 96680 | -1.0 | 166 | -1 | 2857.0 | 55.0 | 1429452 | 41353 | 34567212 | Africa |
148 | Seychelles | 39403 | -1.0 | 163 | -1.0 | 38923 | -1.0 | 317 | -1 | 396564.0 | 1640.0 | -1 | -1 | 99361 | Africa |
146 | Gabon | 47543 | -1.0 | 303 | -1.0 | 46561 | -1.0 | 679 | 1 | 20553.0 | 131.0 | 1571354 | 679313 | 2313152 | Africa |
194 | Guinea-Bissau | 8027 | -1.0 | 167 | -1.0 | 7018 | 5.0 | 842 | 6 | 3924.0 | 82.0 | 124624 | 60928 | 2045424 | Africa |
198 | Liberia | 7384 | -1.0 | 294 | -1.0 | 5747 | -1.0 | 1343 | 2 | 1405.0 | 56.0 | 139824 | 26605 | 5255551 | Africa |
200 | Chad | 7255 | -1.0 | 190 | -1.0 | 4874 | -1.0 | 2191 | -1 | 422.0 | 11.0 | 191341 | 11118 | 17210162 | Africa |
154 | Guinea | 36397 | -1.0 | 440 | -1.0 | 32939 | -1.0 | 3018 | 49 | 2652.0 | 32.0 | 587607 | 42811 | 13725600 | Africa |
165 | Congo | 24020 | -1.0 | 378 | -1.0 | 20178 | -1.0 | 3464 | -1 | 4182.0 | 66.0 | 347815 | 60551 | 5744151 | Africa |
136 | Madagascar | 63659 | -1.0 | 1366 | -1.0 | 58677 | -1.0 | 3616 | 61 | 2205.0 | 47.0 | 398434 | 13798 | 28875793 | Africa |
101 | Zimbabwe | 237503 | -1.0 | 5396 | -1.0 | 227008 | -1.0 | 5099 | 12 | 15601.0 | 354.0 | 2090217 | 137304 | 15223268 | Africa |
179 | CAR | 14225 | -1.0 | 113 | -1.0 | 6859 | -1.0 | 7253 | 2 | 2862.0 | 23.0 | 81294 | 16358 | 4969598 | Africa |
164 | Somalia | 26351 | -1.0 | 1348 | -1.0 | 13182 | -1.0 | 11821 | -1 | 1584.0 | 81.0 | 400466 | 24079 | 16631081 | Africa |
95 | Réunion | 302162 | -1.0 | 666 | -1.0 | 289069 | -1.0 | 12427 | 41 | 333528.0 | 735.0 | 1463269 | 1615164 | 905957 | Africa |
153 | Mayotte | 36662 | -1.0 | 187 | -1.0 | 2964 | -1.0 | 33511 | -1 | 129196.0 | 659.0 | 176919 | 623457 | 283771 | Africa |
151 | Burundi | 38127 | -1.0 | 38 | -1.0 | 773 | -1.0 | 37316 | -1 | 3055.0 | 3.0 | 345742 | 27704 | 12480029 | Africa |
195 | Sierra Leone | 7665 | -1.0 | 125 | -1.0 | 0 | 0.0 | 0 | -1 | 929.0 | 15.0 | 259958 | 31521 | 8247056 | Africa |
# Drowing visualization graph
plt.bar(africa["Countries"].head(10),africa["New cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Africa New cases")
plt.xlabel("Countries")
plt.ylabel("New Cases(in ten million)")
Text(0, 0.5, 'New Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of new death cases
africa.sort_values("New_death",ascending = False,inplace = True)
africa.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
21 | South Africa | 3679539 | 1853.0 | 99499 | 41.0 | 3554282 | 1494.0 | 25758 | 546 | 60764.0 | 1643.0 | 23166727 | 382578 | 60554202 | Africa |
55 | Tunisia | 1000518 | 1077.0 | 27857 | 33.0 | 958952 | 5365.0 | 13709 | 222 | 83206.0 | 2317.0 | 4355745 | 362235 | 12024624 | Africa |
83 | Egypt | 487642 | 1261.0 | 24149 | 17.0 | 417479 | 1008.0 | 46014 | 122 | 4620.0 | 229.0 | 3693367 | 34992 | 105548117 | Africa |
82 | Libya | 496778 | 806.0 | 6284 | 8.0 | 471202 | 2334.0 | 19292 | 126 | 70696.0 | 894.0 | 2433311 | 346284 | 7026916 | Africa |
84 | Ethiopia | 468850 | 64.0 | 7473 | 6.0 | 419993 | 484.0 | 41384 | 75 | 3917.0 | 62.0 | 4521046 | 37769 | 119703224 | Africa |
# Drowing visualization graph
plt.bar(africa["Countries"].head(10),africa["New_death"].head(10))
plt.xticks(rotation = '45')
plt.title("Africa New death cases")
plt.xlabel("Countries")
plt.ylabel("New death Cases(in ten million)")
Text(0, 0.5, 'New death Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of recovered cases
africa.sort_values("Total_recovered",ascending = False,inplace = True)
africa
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
21 | South Africa | 3679539 | 1853.0 | 99499 | 41.0 | 3554282 | 1494.0 | 25758 | 546 | 60764.0 | 1643.0 | 23166727 | 382578 | 60554202 | Africa |
50 | Morocco | 1161392 | 102.0 | 16006 | 4.0 | 1142318 | 386.0 | 3068 | 293 | 30856.0 | 425.0 | 11237010 | 298545 | 37639296 | Africa |
55 | Tunisia | 1000518 | 1077.0 | 27857 | 33.0 | 958952 | 5365.0 | 13709 | 222 | 83206.0 | 2317.0 | 4355745 | 362235 | 12024624 | Africa |
82 | Libya | 496778 | 806.0 | 6284 | 8.0 | 471202 | 2334.0 | 19292 | 126 | 70696.0 | 894.0 | 2433311 | 346284 | 7026916 | Africa |
84 | Ethiopia | 468850 | 64.0 | 7473 | 6.0 | 419993 | 484.0 | 41384 | 75 | 3917.0 | 62.0 | 4521046 | 37769 | 119703224 | Africa |
83 | Egypt | 487642 | 1261.0 | 24149 | 17.0 | 417479 | 1008.0 | 46014 | 122 | 4620.0 | 229.0 | 3693367 | 34992 | 105548117 | Africa |
94 | Zambia | 313394 | 191.0 | 3957 | 2.0 | 307667 | 335.0 | 1770 | 1 | 16288.0 | 206.0 | 3280022 | 170473 | 19240706 | Africa |
93 | Kenya | 323025 | 23.0 | 5640 | -1.0 | 303296 | -1.0 | 14089 | 4 | 5795.0 | 101.0 | 3381634 | 60665 | 55743011 | Africa |
95 | Réunion | 302162 | -1.0 | 666 | -1.0 | 289069 | -1.0 | 12427 | 41 | 333528.0 | 735.0 | 1463269 | 1615164 | 905957 | Africa |
99 | Botswana | 263950 | -1.0 | 2619 | -1.0 | 259434 | -1.0 | 1897 | 1 | 108591.0 | 1077.0 | 2026898 | 833881 | 2430679 | Africa |
100 | Nigeria | 254606 | 8.0 | 3142 | -1.0 | 249154 | 16.0 | 2310 | 11 | 1186.0 | 15.0 | 4442964 | 20699 | 214643298 | Africa |
101 | Zimbabwe | 237503 | -1.0 | 5396 | -1.0 | 227008 | -1.0 | 5099 | 12 | 15601.0 | 354.0 | 2090217 | 137304 | 15223268 | Africa |
104 | Mozambique | 225100 | 20.0 | 2194 | 1.0 | 219863 | -1.0 | 3043 | 13 | 6881.0 | 67.0 | 1272590 | 38902 | 32712596 | Africa |
98 | Algeria | 265130 | 51.0 | 6848 | 5.0 | 177732 | 60.0 | 80550 | 15 | 5870.0 | 152.0 | 230861 | 5111 | 45169543 | Africa |
111 | Ghana | 160028 | 137.0 | 1442 | -1.0 | 157999 | 217.0 | 587 | 1 | 4977.0 | 45.0 | 2321483 | 72202 | 32152763 | Africa |
112 | Namibia | 157284 | 9.0 | 4010 | 1.0 | 152806 | 19.0 | 468 | 5 | 60089.0 | 1532.0 | 958170 | 366059 | 2617526 | Africa |
121 | Cameroon | 119322 | 82.0 | 1926 | 3.0 | 117089 | -1.0 | 307 | 13 | 4316.0 | 70.0 | 1751774 | 63365 | 27645666 | Africa |
110 | Uganda | 163316 | 15.0 | 3588 | -1.0 | 100057 | 36.0 | 59671 | 35 | 3392.0 | 75.0 | 2468798 | 51279 | 48144278 | Africa |
124 | Angola | 98746 | -1.0 | 1900 | -1.0 | 96680 | -1.0 | 166 | -1 | 2857.0 | 55.0 | 1429452 | 41353 | 34567212 | Africa |
126 | Senegal | 85729 | 17.0 | 1960 | -1.0 | 83683 | 19.0 | 86 | 4 | 4905.0 | 112.0 | 1018133 | 58251 | 17478250 | Africa |
128 | Ivory Coast | 81523 | 5.0 | 794 | -1.0 | 80132 | 69.0 | 597 | -1 | 2968.0 | 29.0 | 1423042 | 51812 | 27465460 | Africa |
127 | Malawi | 85383 | 21.0 | 2617 | -1.0 | 75914 | 33.0 | 6852 | 67 | 4279.0 | 131.0 | 549110 | 27521 | 19952646 | Africa |
133 | Eswatini | 69258 | 47.0 | 1391 | 1.0 | 67711 | 15.0 | 156 | 11 | 58685.0 | 1179.0 | 480011 | 406729 | 1180174 | Africa |
136 | Madagascar | 63659 | -1.0 | 1366 | -1.0 | 58677 | -1.0 | 3616 | 61 | 2205.0 | 47.0 | 398434 | 13798 | 28875793 | Africa |
139 | Mauritania | 58638 | -1.0 | 979 | -1.0 | 57618 | -1.0 | 41 | 30 | 12083.0 | 202.0 | 749041 | 154344 | 4853056 | Africa |
142 | Cabo Verde | 55889 | -1.0 | 401 | -1.0 | 55423 | 7.0 | 65 | 23 | 98736.0 | 708.0 | 400982 | 708391 | 566046 | Africa |
125 | DRC | 86138 | 99.0 | 1335 | -1.0 | 50930 | -1.0 | 33873 | -1 | 915.0 | 14.0 | 846704 | 8999 | 94091682 | Africa |
146 | Gabon | 47543 | -1.0 | 303 | -1.0 | 46561 | -1.0 | 679 | 1 | 20553.0 | 131.0 | 1571354 | 679313 | 2313152 | Africa |
117 | Rwanda | 129543 | 10.0 | 1458 | 1.0 | 45522 | -1.0 | 82563 | 1 | 9605.0 | 108.0 | 4876887 | 361610 | 13486604 | Africa |
138 | Sudan | 61554 | 42.0 | 3911 | 1.0 | 40329 | -1.0 | 17314 | -1 | 1351.0 | 86.0 | 562941 | 12358 | 45553439 | Africa |
148 | Seychelles | 39403 | -1.0 | 163 | -1.0 | 38923 | -1.0 | 317 | -1 | 396564.0 | 1640.0 | -1 | -1 | 99361 | Africa |
152 | Togo | 36812 | 4.0 | 272 | -1.0 | 36418 | 4.0 | 122 | -1 | 4280.0 | 32.0 | 692784 | 80540 | 8601694 | Africa |
154 | Guinea | 36397 | -1.0 | 440 | -1.0 | 32939 | -1.0 | 3018 | 49 | 2652.0 | 32.0 | 587607 | 42811 | 13725600 | Africa |
161 | Mali | 30392 | 1.0 | 724 | 2.0 | 29535 | 2.0 | 133 | -1 | 1432.0 | 34.0 | 624420 | 29424 | 21221400 | Africa |
160 | Mauritius | 32154 | 158.0 | 786 | -1.0 | 28562 | 261.0 | 2806 | -1 | 25213.0 | 616.0 | 358675 | 281251 | 1275284 | Africa |
163 | Benin | 26575 | 8.0 | 163 | -1.0 | 25506 | -1.0 | 906 | 5 | 2101.0 | 13.0 | 604310 | 47765 | 12651662 | Africa |
159 | Lesotho | 32707 | 95.0 | 697 | 1.0 | 23437 | 94.0 | 8573 | -1 | 15069.0 | 321.0 | 403174 | 185748 | 2170541 | Africa |
169 | Burkina Faso | 20751 | -1.0 | 375 | -1.0 | 20309 | -1.0 | 67 | -1 | 949.0 | 17.0 | 248995 | 11392 | 21857120 | Africa |
165 | Congo | 24020 | -1.0 | 378 | -1.0 | 20178 | -1.0 | 3464 | -1 | 4182.0 | 66.0 | 347815 | 60551 | 5744151 | Africa |
175 | Equatorial Guinea | 15885 | -1.0 | 183 | -1.0 | 15664 | -1.0 | 38 | 5 | 10736.0 | 124.0 | 273437 | 184797 | 1479663 | Africa |
177 | Djibouti | 15549 | 2.0 | 189 | -1.0 | 15357 | 3.0 | 3 | -1 | 15366.0 | 187.0 | 286380 | 283004 | 1011929 | Africa |
174 | South Sudan | 16993 | 4.0 | 137 | -1.0 | 13271 | -1.0 | 3585 | 1 | 1489.0 | 12.0 | 342501 | 30011 | 11412327 | Africa |
164 | Somalia | 26351 | -1.0 | 1348 | -1.0 | 13182 | -1.0 | 11821 | -1 | 1584.0 | 81.0 | 400466 | 24079 | 16631081 | Africa |
183 | Gambia | 11939 | -1.0 | 365 | -1.0 | 11559 | -1.0 | 15 | 4 | 4720.0 | 144.0 | 137657 | 54418 | 2529645 | Africa |
189 | Eritrea | 9707 | 2.0 | 103 | -1.0 | 9600 | 2.0 | 4 | -1 | 2675.0 | 28.0 | 23693 | 6530 | 3628170 | Africa |
192 | Niger | 8759 | -1.0 | 307 | -1.0 | 8433 | -1.0 | 19 | 1 | 341.0 | 12.0 | 229438 | 8940 | 25664043 | Africa |
193 | Comoros | 8033 | -1.0 | 160 | -1.0 | 7855 | -1.0 | 18 | -1 | 8921.0 | 178.0 | -1 | -1 | 900439 | Africa |
194 | Guinea-Bissau | 8027 | -1.0 | 167 | -1.0 | 7018 | 5.0 | 842 | 6 | 3924.0 | 82.0 | 124624 | 60928 | 2045424 | Africa |
179 | CAR | 14225 | -1.0 | 113 | -1.0 | 6859 | -1.0 | 7253 | 2 | 2862.0 | 23.0 | 81294 | 16358 | 4969598 | Africa |
203 | Sao Tome and Principe | 5934 | -1.0 | 72 | -1.0 | 5857 | 4.0 | 5 | -1 | 26264.0 | 319.0 | 29036 | 128515 | 225935 | Africa |
198 | Liberia | 7384 | -1.0 | 294 | -1.0 | 5747 | -1.0 | 1343 | 2 | 1405.0 | 56.0 | 139824 | 26605 | 5255551 | Africa |
200 | Chad | 7255 | -1.0 | 190 | -1.0 | 4874 | -1.0 | 2191 | -1 | 422.0 | 11.0 | 191341 | 11118 | 17210162 | Africa |
153 | Mayotte | 36662 | -1.0 | 187 | -1.0 | 2964 | -1.0 | 33511 | -1 | 129196.0 | 659.0 | 176919 | 623457 | 283771 | Africa |
151 | Burundi | 38127 | -1.0 | 38 | -1.0 | 773 | -1.0 | 37316 | -1 | 3055.0 | 3.0 | 345742 | 27704 | 12480029 | Africa |
220 | Western Sahara | 10 | -1.0 | 1 | -1.0 | 8 | -1.0 | 1 | -1 | 16.0 | 2.0 | -1 | -1 | 621783 | Africa |
157 | Tanzania | 33620 | -1.0 | 798 | -1.0 | 0 | 0.0 | 0 | 7 | 537.0 | 13.0 | -1 | -1 | 62565411 | Africa |
195 | Sierra Leone | 7665 | -1.0 | 125 | -1.0 | 0 | 0.0 | 0 | -1 | 929.0 | 15.0 | 259958 | 31521 | 8247056 | Africa |
# sorting dataframe to help identify the country with the most number of critical cases
africa.sort_values("Serious/Critical",ascending = False,inplace = True)
africa
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
21 | South Africa | 3679539 | 1853.0 | 99499 | 41.0 | 3554282 | 1494.0 | 25758 | 546 | 60764.0 | 1643.0 | 23166727 | 382578 | 60554202 | Africa |
50 | Morocco | 1161392 | 102.0 | 16006 | 4.0 | 1142318 | 386.0 | 3068 | 293 | 30856.0 | 425.0 | 11237010 | 298545 | 37639296 | Africa |
55 | Tunisia | 1000518 | 1077.0 | 27857 | 33.0 | 958952 | 5365.0 | 13709 | 222 | 83206.0 | 2317.0 | 4355745 | 362235 | 12024624 | Africa |
82 | Libya | 496778 | 806.0 | 6284 | 8.0 | 471202 | 2334.0 | 19292 | 126 | 70696.0 | 894.0 | 2433311 | 346284 | 7026916 | Africa |
83 | Egypt | 487642 | 1261.0 | 24149 | 17.0 | 417479 | 1008.0 | 46014 | 122 | 4620.0 | 229.0 | 3693367 | 34992 | 105548117 | Africa |
84 | Ethiopia | 468850 | 64.0 | 7473 | 6.0 | 419993 | 484.0 | 41384 | 75 | 3917.0 | 62.0 | 4521046 | 37769 | 119703224 | Africa |
127 | Malawi | 85383 | 21.0 | 2617 | -1.0 | 75914 | 33.0 | 6852 | 67 | 4279.0 | 131.0 | 549110 | 27521 | 19952646 | Africa |
136 | Madagascar | 63659 | -1.0 | 1366 | -1.0 | 58677 | -1.0 | 3616 | 61 | 2205.0 | 47.0 | 398434 | 13798 | 28875793 | Africa |
154 | Guinea | 36397 | -1.0 | 440 | -1.0 | 32939 | -1.0 | 3018 | 49 | 2652.0 | 32.0 | 587607 | 42811 | 13725600 | Africa |
95 | Réunion | 302162 | -1.0 | 666 | -1.0 | 289069 | -1.0 | 12427 | 41 | 333528.0 | 735.0 | 1463269 | 1615164 | 905957 | Africa |
110 | Uganda | 163316 | 15.0 | 3588 | -1.0 | 100057 | 36.0 | 59671 | 35 | 3392.0 | 75.0 | 2468798 | 51279 | 48144278 | Africa |
139 | Mauritania | 58638 | -1.0 | 979 | -1.0 | 57618 | -1.0 | 41 | 30 | 12083.0 | 202.0 | 749041 | 154344 | 4853056 | Africa |
142 | Cabo Verde | 55889 | -1.0 | 401 | -1.0 | 55423 | 7.0 | 65 | 23 | 98736.0 | 708.0 | 400982 | 708391 | 566046 | Africa |
98 | Algeria | 265130 | 51.0 | 6848 | 5.0 | 177732 | 60.0 | 80550 | 15 | 5870.0 | 152.0 | 230861 | 5111 | 45169543 | Africa |
104 | Mozambique | 225100 | 20.0 | 2194 | 1.0 | 219863 | -1.0 | 3043 | 13 | 6881.0 | 67.0 | 1272590 | 38902 | 32712596 | Africa |
121 | Cameroon | 119322 | 82.0 | 1926 | 3.0 | 117089 | -1.0 | 307 | 13 | 4316.0 | 70.0 | 1751774 | 63365 | 27645666 | Africa |
101 | Zimbabwe | 237503 | -1.0 | 5396 | -1.0 | 227008 | -1.0 | 5099 | 12 | 15601.0 | 354.0 | 2090217 | 137304 | 15223268 | Africa |
100 | Nigeria | 254606 | 8.0 | 3142 | -1.0 | 249154 | 16.0 | 2310 | 11 | 1186.0 | 15.0 | 4442964 | 20699 | 214643298 | Africa |
133 | Eswatini | 69258 | 47.0 | 1391 | 1.0 | 67711 | 15.0 | 156 | 11 | 58685.0 | 1179.0 | 480011 | 406729 | 1180174 | Africa |
157 | Tanzania | 33620 | -1.0 | 798 | -1.0 | 0 | 0.0 | 0 | 7 | 537.0 | 13.0 | -1 | -1 | 62565411 | Africa |
194 | Guinea-Bissau | 8027 | -1.0 | 167 | -1.0 | 7018 | 5.0 | 842 | 6 | 3924.0 | 82.0 | 124624 | 60928 | 2045424 | Africa |
175 | Equatorial Guinea | 15885 | -1.0 | 183 | -1.0 | 15664 | -1.0 | 38 | 5 | 10736.0 | 124.0 | 273437 | 184797 | 1479663 | Africa |
112 | Namibia | 157284 | 9.0 | 4010 | 1.0 | 152806 | 19.0 | 468 | 5 | 60089.0 | 1532.0 | 958170 | 366059 | 2617526 | Africa |
163 | Benin | 26575 | 8.0 | 163 | -1.0 | 25506 | -1.0 | 906 | 5 | 2101.0 | 13.0 | 604310 | 47765 | 12651662 | Africa |
126 | Senegal | 85729 | 17.0 | 1960 | -1.0 | 83683 | 19.0 | 86 | 4 | 4905.0 | 112.0 | 1018133 | 58251 | 17478250 | Africa |
183 | Gambia | 11939 | -1.0 | 365 | -1.0 | 11559 | -1.0 | 15 | 4 | 4720.0 | 144.0 | 137657 | 54418 | 2529645 | Africa |
93 | Kenya | 323025 | 23.0 | 5640 | -1.0 | 303296 | -1.0 | 14089 | 4 | 5795.0 | 101.0 | 3381634 | 60665 | 55743011 | Africa |
179 | CAR | 14225 | -1.0 | 113 | -1.0 | 6859 | -1.0 | 7253 | 2 | 2862.0 | 23.0 | 81294 | 16358 | 4969598 | Africa |
198 | Liberia | 7384 | -1.0 | 294 | -1.0 | 5747 | -1.0 | 1343 | 2 | 1405.0 | 56.0 | 139824 | 26605 | 5255551 | Africa |
192 | Niger | 8759 | -1.0 | 307 | -1.0 | 8433 | -1.0 | 19 | 1 | 341.0 | 12.0 | 229438 | 8940 | 25664043 | Africa |
174 | South Sudan | 16993 | 4.0 | 137 | -1.0 | 13271 | -1.0 | 3585 | 1 | 1489.0 | 12.0 | 342501 | 30011 | 11412327 | Africa |
117 | Rwanda | 129543 | 10.0 | 1458 | 1.0 | 45522 | -1.0 | 82563 | 1 | 9605.0 | 108.0 | 4876887 | 361610 | 13486604 | Africa |
94 | Zambia | 313394 | 191.0 | 3957 | 2.0 | 307667 | 335.0 | 1770 | 1 | 16288.0 | 206.0 | 3280022 | 170473 | 19240706 | Africa |
99 | Botswana | 263950 | -1.0 | 2619 | -1.0 | 259434 | -1.0 | 1897 | 1 | 108591.0 | 1077.0 | 2026898 | 833881 | 2430679 | Africa |
146 | Gabon | 47543 | -1.0 | 303 | -1.0 | 46561 | -1.0 | 679 | 1 | 20553.0 | 131.0 | 1571354 | 679313 | 2313152 | Africa |
111 | Ghana | 160028 | 137.0 | 1442 | -1.0 | 157999 | 217.0 | 587 | 1 | 4977.0 | 45.0 | 2321483 | 72202 | 32152763 | Africa |
152 | Togo | 36812 | 4.0 | 272 | -1.0 | 36418 | 4.0 | 122 | -1 | 4280.0 | 32.0 | 692784 | 80540 | 8601694 | Africa |
128 | Ivory Coast | 81523 | 5.0 | 794 | -1.0 | 80132 | 69.0 | 597 | -1 | 2968.0 | 29.0 | 1423042 | 51812 | 27465460 | Africa |
220 | Western Sahara | 10 | -1.0 | 1 | -1.0 | 8 | -1.0 | 1 | -1 | 16.0 | 2.0 | -1 | -1 | 621783 | Africa |
151 | Burundi | 38127 | -1.0 | 38 | -1.0 | 773 | -1.0 | 37316 | -1 | 3055.0 | 3.0 | 345742 | 27704 | 12480029 | Africa |
153 | Mayotte | 36662 | -1.0 | 187 | -1.0 | 2964 | -1.0 | 33511 | -1 | 129196.0 | 659.0 | 176919 | 623457 | 283771 | Africa |
200 | Chad | 7255 | -1.0 | 190 | -1.0 | 4874 | -1.0 | 2191 | -1 | 422.0 | 11.0 | 191341 | 11118 | 17210162 | Africa |
203 | Sao Tome and Principe | 5934 | -1.0 | 72 | -1.0 | 5857 | 4.0 | 5 | -1 | 26264.0 | 319.0 | 29036 | 128515 | 225935 | Africa |
124 | Angola | 98746 | -1.0 | 1900 | -1.0 | 96680 | -1.0 | 166 | -1 | 2857.0 | 55.0 | 1429452 | 41353 | 34567212 | Africa |
193 | Comoros | 8033 | -1.0 | 160 | -1.0 | 7855 | -1.0 | 18 | -1 | 8921.0 | 178.0 | -1 | -1 | 900439 | Africa |
189 | Eritrea | 9707 | 2.0 | 103 | -1.0 | 9600 | 2.0 | 4 | -1 | 2675.0 | 28.0 | 23693 | 6530 | 3628170 | Africa |
161 | Mali | 30392 | 1.0 | 724 | 2.0 | 29535 | 2.0 | 133 | -1 | 1432.0 | 34.0 | 624420 | 29424 | 21221400 | Africa |
125 | DRC | 86138 | 99.0 | 1335 | -1.0 | 50930 | -1.0 | 33873 | -1 | 915.0 | 14.0 | 846704 | 8999 | 94091682 | Africa |
164 | Somalia | 26351 | -1.0 | 1348 | -1.0 | 13182 | -1.0 | 11821 | -1 | 1584.0 | 81.0 | 400466 | 24079 | 16631081 | Africa |
177 | Djibouti | 15549 | 2.0 | 189 | -1.0 | 15357 | 3.0 | 3 | -1 | 15366.0 | 187.0 | 286380 | 283004 | 1011929 | Africa |
138 | Sudan | 61554 | 42.0 | 3911 | 1.0 | 40329 | -1.0 | 17314 | -1 | 1351.0 | 86.0 | 562941 | 12358 | 45553439 | Africa |
165 | Congo | 24020 | -1.0 | 378 | -1.0 | 20178 | -1.0 | 3464 | -1 | 4182.0 | 66.0 | 347815 | 60551 | 5744151 | Africa |
169 | Burkina Faso | 20751 | -1.0 | 375 | -1.0 | 20309 | -1.0 | 67 | -1 | 949.0 | 17.0 | 248995 | 11392 | 21857120 | Africa |
159 | Lesotho | 32707 | 95.0 | 697 | 1.0 | 23437 | 94.0 | 8573 | -1 | 15069.0 | 321.0 | 403174 | 185748 | 2170541 | Africa |
148 | Seychelles | 39403 | -1.0 | 163 | -1.0 | 38923 | -1.0 | 317 | -1 | 396564.0 | 1640.0 | -1 | -1 | 99361 | Africa |
160 | Mauritius | 32154 | 158.0 | 786 | -1.0 | 28562 | 261.0 | 2806 | -1 | 25213.0 | 616.0 | 358675 | 281251 | 1275284 | Africa |
195 | Sierra Leone | 7665 | -1.0 | 125 | -1.0 | 0 | 0.0 | 0 | -1 | 929.0 | 15.0 | 259958 | 31521 | 8247056 | Africa |
# Drowing visualization graph
plt.bar(africa["Countries"].head(10),africa["Serious/Critical"].head(10))
plt.xticks(rotation = '45')
plt.title("Africa Critical condition cases")
plt.xlabel("Countries")
plt.ylabel("critical condition Cases(in ten million)")
Text(0, 0.5, 'critical condition Cases(in ten million)')
From the above outputs, we can clearly see that South A frica has the highest number of cases in Africa. From the same outputs we can also see that Western Sahara has the lowest number of confirmed covid 19 cases in Africa.
South Africa is leading in the number of covid 19 total number of confirmed cases mainly because of the following reasons:
Western Sahara has confirmed low number of covid 19 cases mainly because of the following reasons:
We can also see that South Africa is also leading in the total people who have died from covid 19,with Western Sahara recording the lowest number of the same hence doing well in the control measures. From this,just like in Noth America and Asia, we can see that population is also a determining factor with the countries having the highest number of population finding it abit hard to control the spread of the disease as compared to those having lower population density.From these, we can ague that in as much as Werstern Sahara having lower population density, it's doing well in the control measures.
It's also evident that the death cases in Sierra Leone may increase since there are many Active cases in the country than any other African country
south_america = data[data["Continent"] == "South America"].copy()
south_america.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | Brazil | 28906214 | 64054.0 | 650646 | 594.0 | 26810286 | 142276.0 | 1445282 | 8318 | 134400.0 | 3025.0 | 63776166 | 296528 | 215076719 | South America |
10 | Argentina | 8921536 | 9219.0 | 126531 | 141.0 | 8705178 | 10670.0 | 89827 | 1076 | 194425.0 | 2757.0 | 34379641 | 749229 | 45886668 | South America |
13 | Colombia | 6068074 | 1051.0 | 138939 | 40.0 | 5895880 | 1891.0 | 33255 | 342 | 117175.0 | 2683.0 | 33281725 | 642674 | 51786322 | South America |
27 | Peru | 3522484 | 1880.0 | 210907 | 56.0 | 0 | 0.0 | 0 | 871 | 104411.0 | 6252.0 | 27510769 | 815452 | 33736832 | South America |
31 | Chile | 3122802 | 23395.0 | 42683 | 272.0 | 2149021 | 23033.0 | 931098 | 1105 | 161056.0 | 2201.0 | 33241006 | 1714376 | 19389568 | South America |
# sorting dataframe to help identify the country with the most number of confirmed cases
south_america.sort_values("Total_cases",ascending = False,inplace = True)
south_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | Brazil | 28906214 | 64054.0 | 650646 | 594.0 | 26810286 | 142276.0 | 1445282 | 8318 | 134400.0 | 3025.0 | 63776166 | 296528 | 215076719 | South America |
10 | Argentina | 8921536 | 9219.0 | 126531 | 141.0 | 8705178 | 10670.0 | 89827 | 1076 | 194425.0 | 2757.0 | 34379641 | 749229 | 45886668 | South America |
13 | Colombia | 6068074 | 1051.0 | 138939 | 40.0 | 5895880 | 1891.0 | 33255 | 342 | 117175.0 | 2683.0 | 33281725 | 642674 | 51786322 | South America |
27 | Peru | 3522484 | 1880.0 | 210907 | 56.0 | 0 | 0.0 | 0 | 871 | 104411.0 | 6252.0 | 27510769 | 815452 | 33736832 | South America |
31 | Chile | 3122802 | 23395.0 | 42683 | 272.0 | 2149021 | 23033.0 | 931098 | 1105 | 161056.0 | 2201.0 | 33241006 | 1714376 | 19389568 | South America |
60 | Bolivia | 893775 | 263.0 | 21443 | 2.0 | 792419 | 1799.0 | 79913 | 220 | 74867.0 | 1796.0 | 2669468 | 223607 | 11938225 | South America |
62 | Uruguay | 849050 | 2182.0 | 7014 | 9.0 | 825045 | 3775.0 | 16991 | 126 | 243017.0 | 2008.0 | 5733295 | 1640997 | 3493788 | South America |
63 | Ecuador | 836216 | 2356.0 | 35264 | 14.0 | 0 | 0.0 | 0 | 759 | 46227.0 | 1949.0 | 2470170 | 136555 | 18089229 | South America |
73 | Paraguay | 642982 | 409.0 | 18440 | 18.0 | 612406 | 956.0 | 12136 | 130 | 88340.0 | 2533.0 | 2536555 | 348501 | 7278472 | South America |
79 | Venezuela | 515943 | 181.0 | 5643 | 2.0 | 504626 | 509.0 | 5674 | 230 | 18230.0 | 199.0 | 3359014 | 118686 | 28301690 | South America |
129 | Suriname | 78353 | 59.0 | 1317 | -1.0 | 49359 | 1.0 | 27677 | 3 | 131600.0 | 2212.0 | 229578 | 385594 | 595388 | South America |
130 | French Guiana | 77765 | 14.0 | 392 | 1.0 | 11254 | -1.0 | 66119 | 6 | 249594.0 | 1258.0 | 596857 | 1915668 | 311566 | South America |
137 | Guyana | 63002 | 31.0 | 1223 | 1.0 | 61445 | 118.0 | 334 | 7 | 79460.0 | 1542.0 | 543130 | 685013 | 792876 | South America |
214 | Falkland Islands | 114 | 5.0 | -1 | -1.0 | 0 | 0.0 | 0 | -1 | 31241.0 | -1.0 | 8632 | 2365580 | 3649 | South America |
# Drowing visualization graph
plt.bar(south_america["Countries"].head(10),south_america["Total_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("South America Total cases")
plt.xlabel("Countries")
plt.ylabel("Total Cases(in ten million)")
Text(0, 0.5, 'Total Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of death cases
south_america.sort_values("Total_deaths",ascending = False,inplace = True)
south_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | Brazil | 28906214 | 64054.0 | 650646 | 594.0 | 26810286 | 142276.0 | 1445282 | 8318 | 134400.0 | 3025.0 | 63776166 | 296528 | 215076719 | South America |
27 | Peru | 3522484 | 1880.0 | 210907 | 56.0 | 0 | 0.0 | 0 | 871 | 104411.0 | 6252.0 | 27510769 | 815452 | 33736832 | South America |
13 | Colombia | 6068074 | 1051.0 | 138939 | 40.0 | 5895880 | 1891.0 | 33255 | 342 | 117175.0 | 2683.0 | 33281725 | 642674 | 51786322 | South America |
10 | Argentina | 8921536 | 9219.0 | 126531 | 141.0 | 8705178 | 10670.0 | 89827 | 1076 | 194425.0 | 2757.0 | 34379641 | 749229 | 45886668 | South America |
31 | Chile | 3122802 | 23395.0 | 42683 | 272.0 | 2149021 | 23033.0 | 931098 | 1105 | 161056.0 | 2201.0 | 33241006 | 1714376 | 19389568 | South America |
63 | Ecuador | 836216 | 2356.0 | 35264 | 14.0 | 0 | 0.0 | 0 | 759 | 46227.0 | 1949.0 | 2470170 | 136555 | 18089229 | South America |
60 | Bolivia | 893775 | 263.0 | 21443 | 2.0 | 792419 | 1799.0 | 79913 | 220 | 74867.0 | 1796.0 | 2669468 | 223607 | 11938225 | South America |
73 | Paraguay | 642982 | 409.0 | 18440 | 18.0 | 612406 | 956.0 | 12136 | 130 | 88340.0 | 2533.0 | 2536555 | 348501 | 7278472 | South America |
62 | Uruguay | 849050 | 2182.0 | 7014 | 9.0 | 825045 | 3775.0 | 16991 | 126 | 243017.0 | 2008.0 | 5733295 | 1640997 | 3493788 | South America |
79 | Venezuela | 515943 | 181.0 | 5643 | 2.0 | 504626 | 509.0 | 5674 | 230 | 18230.0 | 199.0 | 3359014 | 118686 | 28301690 | South America |
129 | Suriname | 78353 | 59.0 | 1317 | -1.0 | 49359 | 1.0 | 27677 | 3 | 131600.0 | 2212.0 | 229578 | 385594 | 595388 | South America |
137 | Guyana | 63002 | 31.0 | 1223 | 1.0 | 61445 | 118.0 | 334 | 7 | 79460.0 | 1542.0 | 543130 | 685013 | 792876 | South America |
130 | French Guiana | 77765 | 14.0 | 392 | 1.0 | 11254 | -1.0 | 66119 | 6 | 249594.0 | 1258.0 | 596857 | 1915668 | 311566 | South America |
214 | Falkland Islands | 114 | 5.0 | -1 | -1.0 | 0 | 0.0 | 0 | -1 | 31241.0 | -1.0 | 8632 | 2365580 | 3649 | South America |
# Drowing visualization graph
plt.bar(south_america["Countries"].head(10),south_america["Total_deaths"].head(10))
plt.xticks(rotation = '45')
plt.title("South America Total Death cases")
plt.xlabel("Countries")
plt.ylabel("Total death Cases(in ten million)")
Text(0, 0.5, 'Total death Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of active cases
south_america.sort_values("Active_cases",ascending = False,inplace = True)
south_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | Brazil | 28906214 | 64054.0 | 650646 | 594.0 | 26810286 | 142276.0 | 1445282 | 8318 | 134400.0 | 3025.0 | 63776166 | 296528 | 215076719 | South America |
31 | Chile | 3122802 | 23395.0 | 42683 | 272.0 | 2149021 | 23033.0 | 931098 | 1105 | 161056.0 | 2201.0 | 33241006 | 1714376 | 19389568 | South America |
10 | Argentina | 8921536 | 9219.0 | 126531 | 141.0 | 8705178 | 10670.0 | 89827 | 1076 | 194425.0 | 2757.0 | 34379641 | 749229 | 45886668 | South America |
60 | Bolivia | 893775 | 263.0 | 21443 | 2.0 | 792419 | 1799.0 | 79913 | 220 | 74867.0 | 1796.0 | 2669468 | 223607 | 11938225 | South America |
130 | French Guiana | 77765 | 14.0 | 392 | 1.0 | 11254 | -1.0 | 66119 | 6 | 249594.0 | 1258.0 | 596857 | 1915668 | 311566 | South America |
13 | Colombia | 6068074 | 1051.0 | 138939 | 40.0 | 5895880 | 1891.0 | 33255 | 342 | 117175.0 | 2683.0 | 33281725 | 642674 | 51786322 | South America |
129 | Suriname | 78353 | 59.0 | 1317 | -1.0 | 49359 | 1.0 | 27677 | 3 | 131600.0 | 2212.0 | 229578 | 385594 | 595388 | South America |
62 | Uruguay | 849050 | 2182.0 | 7014 | 9.0 | 825045 | 3775.0 | 16991 | 126 | 243017.0 | 2008.0 | 5733295 | 1640997 | 3493788 | South America |
73 | Paraguay | 642982 | 409.0 | 18440 | 18.0 | 612406 | 956.0 | 12136 | 130 | 88340.0 | 2533.0 | 2536555 | 348501 | 7278472 | South America |
79 | Venezuela | 515943 | 181.0 | 5643 | 2.0 | 504626 | 509.0 | 5674 | 230 | 18230.0 | 199.0 | 3359014 | 118686 | 28301690 | South America |
137 | Guyana | 63002 | 31.0 | 1223 | 1.0 | 61445 | 118.0 | 334 | 7 | 79460.0 | 1542.0 | 543130 | 685013 | 792876 | South America |
27 | Peru | 3522484 | 1880.0 | 210907 | 56.0 | 0 | 0.0 | 0 | 871 | 104411.0 | 6252.0 | 27510769 | 815452 | 33736832 | South America |
63 | Ecuador | 836216 | 2356.0 | 35264 | 14.0 | 0 | 0.0 | 0 | 759 | 46227.0 | 1949.0 | 2470170 | 136555 | 18089229 | South America |
214 | Falkland Islands | 114 | 5.0 | -1 | -1.0 | 0 | 0.0 | 0 | -1 | 31241.0 | -1.0 | 8632 | 2365580 | 3649 | South America |
# Drowing visualization graph
plt.bar(south_america["Countries"].head(10),south_america["Active_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("South America Total Active cases")
plt.xlabel("Countries")
plt.ylabel("Total Active Cases(in ten million)")
Text(0, 0.5, 'Total Active Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of new cases
south_america.sort_values("New cases",ascending = False,inplace = True)
south_america.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | Brazil | 28906214 | 64054.0 | 650646 | 594.0 | 26810286 | 142276.0 | 1445282 | 8318 | 134400.0 | 3025.0 | 63776166 | 296528 | 215076719 | South America |
31 | Chile | 3122802 | 23395.0 | 42683 | 272.0 | 2149021 | 23033.0 | 931098 | 1105 | 161056.0 | 2201.0 | 33241006 | 1714376 | 19389568 | South America |
10 | Argentina | 8921536 | 9219.0 | 126531 | 141.0 | 8705178 | 10670.0 | 89827 | 1076 | 194425.0 | 2757.0 | 34379641 | 749229 | 45886668 | South America |
63 | Ecuador | 836216 | 2356.0 | 35264 | 14.0 | 0 | 0.0 | 0 | 759 | 46227.0 | 1949.0 | 2470170 | 136555 | 18089229 | South America |
62 | Uruguay | 849050 | 2182.0 | 7014 | 9.0 | 825045 | 3775.0 | 16991 | 126 | 243017.0 | 2008.0 | 5733295 | 1640997 | 3493788 | South America |
# Drowing visualization graph
plt.bar(south_america["Countries"].head(10),south_america["New cases"].head(10))
plt.xticks(rotation = '45')
plt.title("South America Total New cases")
plt.xlabel("Countries")
plt.ylabel("Total New Cases(in ten million)")
Text(0, 0.5, 'Total New Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of new death cases
south_america.sort_values("New_death",ascending = False,inplace = True)
south_america.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | Brazil | 28906214 | 64054.0 | 650646 | 594.0 | 26810286 | 142276.0 | 1445282 | 8318 | 134400.0 | 3025.0 | 63776166 | 296528 | 215076719 | South America |
31 | Chile | 3122802 | 23395.0 | 42683 | 272.0 | 2149021 | 23033.0 | 931098 | 1105 | 161056.0 | 2201.0 | 33241006 | 1714376 | 19389568 | South America |
10 | Argentina | 8921536 | 9219.0 | 126531 | 141.0 | 8705178 | 10670.0 | 89827 | 1076 | 194425.0 | 2757.0 | 34379641 | 749229 | 45886668 | South America |
27 | Peru | 3522484 | 1880.0 | 210907 | 56.0 | 0 | 0.0 | 0 | 871 | 104411.0 | 6252.0 | 27510769 | 815452 | 33736832 | South America |
13 | Colombia | 6068074 | 1051.0 | 138939 | 40.0 | 5895880 | 1891.0 | 33255 | 342 | 117175.0 | 2683.0 | 33281725 | 642674 | 51786322 | South America |
# Drowing visualization graph
plt.bar(south_america["Countries"].head(10),south_america["New_death"].head(10))
plt.xticks(rotation = '45')
plt.title("South America New Death cases")
plt.xlabel("Countries")
plt.ylabel("New Death Cases(in ten million)")
Text(0, 0.5, 'New Death Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of new cases
south_america.sort_values("Total_recovered",ascending = False,inplace = True)
south_america
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | Brazil | 28906214 | 64054.0 | 650646 | 594.0 | 26810286 | 142276.0 | 1445282 | 8318 | 134400.0 | 3025.0 | 63776166 | 296528 | 215076719 | South America |
10 | Argentina | 8921536 | 9219.0 | 126531 | 141.0 | 8705178 | 10670.0 | 89827 | 1076 | 194425.0 | 2757.0 | 34379641 | 749229 | 45886668 | South America |
13 | Colombia | 6068074 | 1051.0 | 138939 | 40.0 | 5895880 | 1891.0 | 33255 | 342 | 117175.0 | 2683.0 | 33281725 | 642674 | 51786322 | South America |
31 | Chile | 3122802 | 23395.0 | 42683 | 272.0 | 2149021 | 23033.0 | 931098 | 1105 | 161056.0 | 2201.0 | 33241006 | 1714376 | 19389568 | South America |
62 | Uruguay | 849050 | 2182.0 | 7014 | 9.0 | 825045 | 3775.0 | 16991 | 126 | 243017.0 | 2008.0 | 5733295 | 1640997 | 3493788 | South America |
60 | Bolivia | 893775 | 263.0 | 21443 | 2.0 | 792419 | 1799.0 | 79913 | 220 | 74867.0 | 1796.0 | 2669468 | 223607 | 11938225 | South America |
73 | Paraguay | 642982 | 409.0 | 18440 | 18.0 | 612406 | 956.0 | 12136 | 130 | 88340.0 | 2533.0 | 2536555 | 348501 | 7278472 | South America |
79 | Venezuela | 515943 | 181.0 | 5643 | 2.0 | 504626 | 509.0 | 5674 | 230 | 18230.0 | 199.0 | 3359014 | 118686 | 28301690 | South America |
137 | Guyana | 63002 | 31.0 | 1223 | 1.0 | 61445 | 118.0 | 334 | 7 | 79460.0 | 1542.0 | 543130 | 685013 | 792876 | South America |
129 | Suriname | 78353 | 59.0 | 1317 | -1.0 | 49359 | 1.0 | 27677 | 3 | 131600.0 | 2212.0 | 229578 | 385594 | 595388 | South America |
130 | French Guiana | 77765 | 14.0 | 392 | 1.0 | 11254 | -1.0 | 66119 | 6 | 249594.0 | 1258.0 | 596857 | 1915668 | 311566 | South America |
27 | Peru | 3522484 | 1880.0 | 210907 | 56.0 | 0 | 0.0 | 0 | 871 | 104411.0 | 6252.0 | 27510769 | 815452 | 33736832 | South America |
63 | Ecuador | 836216 | 2356.0 | 35264 | 14.0 | 0 | 0.0 | 0 | 759 | 46227.0 | 1949.0 | 2470170 | 136555 | 18089229 | South America |
214 | Falkland Islands | 114 | 5.0 | -1 | -1.0 | 0 | 0.0 | 0 | -1 | 31241.0 | -1.0 | 8632 | 2365580 | 3649 | South America |
# Drowing visualization graph
plt.bar(south_america["Countries"].head(10),south_america["Total_recovered"].head(10))
plt.xticks(rotation = '45')
plt.title("South America Total Recovered cases")
plt.xlabel("Countries")
plt.ylabel("Total Recovered Cases(in ten million)")
Text(0, 0.5, 'Total Recovered Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of critical condition cases
south_america.sort_values("Serious/Critical",ascending = False,inplace = True)
south_america.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | Brazil | 28906214 | 64054.0 | 650646 | 594.0 | 26810286 | 142276.0 | 1445282 | 8318 | 134400.0 | 3025.0 | 63776166 | 296528 | 215076719 | South America |
31 | Chile | 3122802 | 23395.0 | 42683 | 272.0 | 2149021 | 23033.0 | 931098 | 1105 | 161056.0 | 2201.0 | 33241006 | 1714376 | 19389568 | South America |
10 | Argentina | 8921536 | 9219.0 | 126531 | 141.0 | 8705178 | 10670.0 | 89827 | 1076 | 194425.0 | 2757.0 | 34379641 | 749229 | 45886668 | South America |
27 | Peru | 3522484 | 1880.0 | 210907 | 56.0 | 0 | 0.0 | 0 | 871 | 104411.0 | 6252.0 | 27510769 | 815452 | 33736832 | South America |
63 | Ecuador | 836216 | 2356.0 | 35264 | 14.0 | 0 | 0.0 | 0 | 759 | 46227.0 | 1949.0 | 2470170 | 136555 | 18089229 | South America |
# Drowing visualization graph
plt.bar(south_america["Countries"].head(10),south_america["Serious/Critical"].head(10))
plt.xticks(rotation = '45')
plt.title("South America Serious cases")
plt.xlabel("Countries")
plt.ylabel("Total serious Cases(in ten million)")
Text(0, 0.5, 'Total serious Cases(in ten million)')
From the above outputs, we can clearly see that Brazil has the highest number of cases in South America. From the same outputs we can also see that Falkland Islands has the lowest number of confirmed covid 19 cases in South America.
South America is leading in the number of covid 19 total number of confirmed cases mainly because of the following reasons:
Falkland Islands has confirmed low number of covid 19 cases mainly because of the following reasons:
We can also see that Brazil is also leading in the total people who have died from covid 19 and she also lead in the total number of patients who are in critical condition hence the death cases may increase in the Region,with Falkland Islands recording the lowest number of the same, hence doing well in the control measures. From this,just like in south America and Asia, we can see that population is also a determining factor with the countries having the highest number of population finding it abit hard to control the spread of the disease as compared to those having lower population density.From these, we can ague that in as much as Falkland Islands having lower population density, it's doing well in the control measures just as Brazil is doing well since it records high number of Recovery rate.
europe = data[data["Continent"] == "Europe"].copy()
europe.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | France | 22900531 | 60225.0 | 138942 | 180.0 | 21364892 | 131190.0 | 1396697 | 2484 | 349551.0 | 2121.0 | 246629975 | 3764529 | 65514171 | Europe |
4 | UK | 19074696 | 45656.0 | 161898 | 194.0 | 17537214 | -1.0 | 1375584 | 279 | 278543.0 | 2364.0 | 484240712 | 7071256 | 68480157 | Europe |
5 | Russia | 16685850 | 93026.0 | 354011 | 781.0 | 14102684 | 170689.0 | 2229155 | 2300 | 114256.0 | 2424.0 | 273400000 | 1872106 | 146038728 | Europe |
6 | Germany | 15375508 | 202338.0 | 124265 | 289.0 | 11696600 | 226300.0 | 3554643 | 2494 | 182541.0 | 1475.0 | 104701826 | 1243042 | 84230316 | Europe |
8 | Italy | 12910506 | 41500.0 | 155399 | 185.0 | 11713645 | 62551.0 | 1041462 | 654 | 214056.0 | 2577.0 | 188713837 | 3128873 | 60313686 | Europe |
# sorting dataframe to help identify the country with the most number of confirmed cases
europe.sort_values("Total_cases",ascending = False,inplace = True)
europe
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | France | 22900531 | 60225.0 | 138942 | 180.0 | 21364892 | 131190.0 | 1396697 | 2484 | 349551.0 | 2121.0 | 246629975 | 3764529 | 65514171 | Europe |
4 | UK | 19074696 | 45656.0 | 161898 | 194.0 | 17537214 | -1.0 | 1375584 | 279 | 278543.0 | 2364.0 | 484240712 | 7071256 | 68480157 | Europe |
5 | Russia | 16685850 | 93026.0 | 354011 | 781.0 | 14102684 | 170689.0 | 2229155 | 2300 | 114256.0 | 2424.0 | 273400000 | 1872106 | 146038728 | Europe |
6 | Germany | 15375508 | 202338.0 | 124265 | 289.0 | 11696600 | 226300.0 | 3554643 | 2494 | 182541.0 | 1475.0 | 104701826 | 1243042 | 84230316 | Europe |
8 | Italy | 12910506 | 41500.0 | 155399 | 185.0 | 11713645 | 62551.0 | 1041462 | 654 | 214056.0 | 2577.0 | 188713837 | 3128873 | 60313686 | Europe |
9 | Spain | 11078028 | 23140.0 | 100239 | 202.0 | 9800509 | 60554.0 | 1177280 | 890 | 236786.0 | 2143.0 | 471036328 | 10068116 | 46784953 | Europe |
12 | Netherlands | 6504886 | 58283.0 | 21589 | 7.0 | 4653122 | 114923.0 | 1830175 | 164 | 378234.0 | 1255.0 | 21107399 | 1227314 | 17198043 | Europe |
14 | Poland | 5708827 | 14068.0 | 112130 | 266.0 | 5099515 | 12389.0 | 497182 | 696 | 151117.0 | 2968.0 | 33881265 | 896863 | 37777543 | Europe |
18 | Ukraine | 4809624 | -1.0 | 105505 | -1.0 | 4058020 | -1.0 | 646099 | 177 | 111087.0 | 2437.0 | 19521252 | 450879 | 43295968 | Europe |
24 | Czechia | 3608535 | 8963.0 | 38787 | 16.0 | 3474694 | 5824.0 | 95054 | 197 | 335925.0 | 3611.0 | 52741015 | 4909756 | 10742084 | Europe |
25 | Belgium | 3571579 | 7738.0 | 30217 | 18.0 | 3125040 | 60907.0 | 416322 | 240 | 305952.0 | 2588.0 | 31794693 | 2723626 | 11673662 | Europe |
30 | Portugal | 3294691 | 12234.0 | 21141 | 30.0 | 2799531 | 11726.0 | 474019 | 90 | 324691.0 | 2083.0 | 37805639 | 3725733 | 10147169 | Europe |
33 | Switzerland | 2852838 | 14283.0 | 13252 | 3.0 | 2256659 | 43667.0 | 582927 | 139 | 325664.0 | 1513.0 | 18811814 | 2147451 | 8760067 | Europe |
34 | Austria | 2775589 | 31566.0 | 14924 | 36.0 | 2478168 | 24379.0 | 282497 | 189 | 305278.0 | 1641.0 | 161305125 | 17741404 | 9092016 | Europe |
35 | Romania | 2754730 | 5953.0 | 63782 | 105.0 | 2536050 | 11938.0 | 154898 | 848 | 144804.0 | 3353.0 | 21202396 | 1114516 | 19023859 | Europe |
36 | Denmark | 2683219 | 12595.0 | 4731 | 44.0 | 2362924 | 38404.0 | 315564 | 42 | 460545.0 | 812.0 | 125634988 | 21563840 | 5826188 | Europe |
37 | Greece | 2470212 | 15783.0 | 26036 | 64.0 | 2263358 | 15247.0 | 180818 | 393 | 238940.0 | 2518.0 | 66567338 | 6438955 | 10338221 | Europe |
38 | Sweden | 2453936 | -1.0 | 17391 | -1.0 | 2207320 | 40783.0 | 229225 | 53 | 240483.0 | 1704.0 | 18174812 | 1781114 | 10204183 | Europe |
41 | Serbia | 1919327 | 2620.0 | 15346 | 31.0 | 1825695 | 6001.0 | 78286 | 100 | 221153.0 | 1768.0 | 8800146 | 1013990 | 8678729 | Europe |
42 | Hungary | 1796982 | 3862.0 | 44211 | 77.0 | 1610315 | 4231.0 | 142456 | 126 | 186807.0 | 4596.0 | 10818978 | 1124695 | 9619478 | Europe |
46 | Slovakia | 1482354 | 11462.0 | 18611 | 44.0 | 1288400 | 18406.0 | 175343 | 232 | 271292.0 | 3406.0 | 6444955 | 1179520 | 5464048 | Europe |
47 | Ireland | 1311105 | 4043.0 | 6527 | 6.0 | 1180695 | 4195.0 | 123883 | 46 | 260674.0 | 1298.0 | 11402648 | 2267079 | 5029664 | Europe |
49 | Norway | 1289884 | 10715.0 | 1664 | -1.0 | 88952 | -1.0 | 1199268 | 25 | 234872.0 | 303.0 | 9852076 | 1793941 | 5491863 | Europe |
51 | Bulgaria | 1097298 | 1104.0 | 35716 | 20.0 | 848792 | 766.0 | 212790 | 436 | 159926.0 | 5205.0 | 9234039 | 1345817 | 6861291 | Europe |
54 | Croatia | 1060078 | 1625.0 | 15145 | 23.0 | 1032892 | 1800.0 | 12041 | 81 | 260905.0 | 3727.0 | 4533308 | 1115731 | 4063084 | Europe |
57 | Belarus | 925515 | 2083.0 | 6521 | 15.0 | 915723 | 2926.0 | 3271 | -1 | 97999.0 | 690.0 | 12488928 | 1322399 | 9444144 | Europe |
58 | Lithuania | 918569 | 5269.0 | 8484 | 19.0 | 828211 | 4770.0 | 81874 | 84 | 345456.0 | 3191.0 | 7787196 | 2928614 | 2659004 | Europe |
59 | Slovenia | 898947 | 1823.0 | 6337 | 10.0 | 862051 | 4563.0 | 30559 | 83 | 432308.0 | 3047.0 | 2575157 | 1238405 | 2079414 | Europe |
70 | Latvia | 682118 | 8900.0 | 5299 | 15.0 | 551818 | 22988.0 | 125001 | 63 | 368423.0 | 2862.0 | 6771858 | 3657593 | 1851452 | Europe |
71 | Finland | 664588 | 6029.0 | 2384 | 3.0 | 46000 | -1.0 | 616204 | 31 | 119636.0 | 429.0 | 9996285 | 1799491 | 5555062 | Europe |
80 | Estonia | 507711 | 3564.0 | 2267 | 8.0 | 390415 | 10571.0 | 115029 | 24 | 382306.0 | 1707.0 | 3123957 | 2352340 | 1328021 | Europe |
81 | Moldova | 503690 | 734.0 | 11254 | 12.0 | 480308 | 1413.0 | 12128 | 89 | 125348.0 | 2801.0 | 3043271 | 757345 | 4018341 | Europe |
89 | Bosnia and Herzegovina | 371846 | 293.0 | 15505 | 21.0 | 192218 | -1.0 | 164123 | -1 | 114526.0 | 4775.0 | 1704660 | 525025 | 3246816 | Europe |
96 | North Macedonia | 298661 | 466.0 | 9050 | 14.0 | 286378 | 471.0 | 3233 | -1 | 143364.0 | 4344.0 | 1898672 | 911407 | 2083231 | Europe |
97 | Albania | 271920 | 95.0 | 3476 | 2.0 | 266697 | 396.0 | 1747 | 13 | 94661.0 | 1210.0 | 1746906 | 608135 | 2872561 | Europe |
103 | Montenegro | 230631 | 119.0 | 2682 | -1.0 | 226684 | 199.0 | 1265 | 60 | 367131.0 | 4269.0 | 1174393 | 1869463 | 628198 | Europe |
106 | Luxembourg | 185707 | 845.0 | 995 | 2.0 | 175045 | 767.0 | 9667 | 6 | 288842.0 | 1548.0 | 4079174 | 6344594 | 642937 | Europe |
115 | Iceland | 138281 | 2534.0 | 68 | 3.0 | 75685 | -1.0 | 62528 | 3 | 400892.0 | 197.0 | 1766309 | 5120731 | 344933 | Europe |
132 | Malta | 71581 | 126.0 | 607 | 1.0 | 69788 | 88.0 | 1186 | 3 | 161399.0 | 1369.0 | 1211456 | 2731568 | 443502 | Europe |
145 | Channel Islands | 52914 | 321.0 | 144 | -1.0 | 49526 | 181.0 | 3244 | -1 | 299740.0 | 816.0 | 1209938 | 6853891 | 176533 | Europe |
150 | Andorra | 38342 | 93.0 | 151 | -1.0 | 37520 | -1.0 | 671 | 14 | 494921.0 | 1949.0 | 249838 | 3224923 | 77471 | Europe |
155 | Faeroe Islands | 34237 | -1.0 | 28 | -1.0 | 7693 | -1.0 | 26516 | 5 | 696270.0 | 569.0 | 778000 | 15822013 | 49172 | Europe |
166 | Isle of Man | 23231 | 132.0 | 79 | -1.0 | 22552 | 88.0 | 600 | -1 | 270811.0 | 921.0 | 150753 | 1757376 | 85783 | Europe |
176 | Gibraltar | 15573 | 47.0 | 101 | -1.0 | 14973 | 47.0 | 499 | 10 | 462464.0 | 2999.0 | 517168 | 15358080 | 33674 | Europe |
178 | San Marino | 14369 | -1.0 | 112 | -1.0 | 13994 | -1.0 | 263 | 4 | 421997.0 | 3289.0 | 136485 | 4008370 | 34050 | Europe |
182 | Liechtenstein | 12319 | 178.0 | 78 | -1.0 | 11770 | 92.0 | 471 | 7 | 321561.0 | 2036.0 | 80413 | 2099008 | 38310 | Europe |
191 | Monaco | 9500 | 27.0 | 51 | -1.0 | 9264 | 23.0 | 185 | 4 | 239265.0 | 1284.0 | 54960 | 1384209 | 39705 | Europe |
218 | Vatican City | 29 | -1.0 | -1 | -1.0 | 28 | -1.0 | 1 | -1 | 36070.0 | -1.0 | -1 | -1 | 804 | Europe |
# Drowing visualization graph
plt.bar(europe["Countries"].head(10),europe["Total_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Europe's Total cases")
plt.xlabel("Countries")
plt.ylabel("Total Cases(in ten million)")
Text(0, 0.5, 'Total Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of death cases
europe.sort_values("Total_deaths",ascending = False,inplace = True)
europe
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5 | Russia | 16685850 | 93026.0 | 354011 | 781.0 | 14102684 | 170689.0 | 2229155 | 2300 | 114256.0 | 2424.0 | 273400000 | 1872106 | 146038728 | Europe |
4 | UK | 19074696 | 45656.0 | 161898 | 194.0 | 17537214 | -1.0 | 1375584 | 279 | 278543.0 | 2364.0 | 484240712 | 7071256 | 68480157 | Europe |
8 | Italy | 12910506 | 41500.0 | 155399 | 185.0 | 11713645 | 62551.0 | 1041462 | 654 | 214056.0 | 2577.0 | 188713837 | 3128873 | 60313686 | Europe |
3 | France | 22900531 | 60225.0 | 138942 | 180.0 | 21364892 | 131190.0 | 1396697 | 2484 | 349551.0 | 2121.0 | 246629975 | 3764529 | 65514171 | Europe |
6 | Germany | 15375508 | 202338.0 | 124265 | 289.0 | 11696600 | 226300.0 | 3554643 | 2494 | 182541.0 | 1475.0 | 104701826 | 1243042 | 84230316 | Europe |
14 | Poland | 5708827 | 14068.0 | 112130 | 266.0 | 5099515 | 12389.0 | 497182 | 696 | 151117.0 | 2968.0 | 33881265 | 896863 | 37777543 | Europe |
18 | Ukraine | 4809624 | -1.0 | 105505 | -1.0 | 4058020 | -1.0 | 646099 | 177 | 111087.0 | 2437.0 | 19521252 | 450879 | 43295968 | Europe |
9 | Spain | 11078028 | 23140.0 | 100239 | 202.0 | 9800509 | 60554.0 | 1177280 | 890 | 236786.0 | 2143.0 | 471036328 | 10068116 | 46784953 | Europe |
35 | Romania | 2754730 | 5953.0 | 63782 | 105.0 | 2536050 | 11938.0 | 154898 | 848 | 144804.0 | 3353.0 | 21202396 | 1114516 | 19023859 | Europe |
42 | Hungary | 1796982 | 3862.0 | 44211 | 77.0 | 1610315 | 4231.0 | 142456 | 126 | 186807.0 | 4596.0 | 10818978 | 1124695 | 9619478 | Europe |
24 | Czechia | 3608535 | 8963.0 | 38787 | 16.0 | 3474694 | 5824.0 | 95054 | 197 | 335925.0 | 3611.0 | 52741015 | 4909756 | 10742084 | Europe |
51 | Bulgaria | 1097298 | 1104.0 | 35716 | 20.0 | 848792 | 766.0 | 212790 | 436 | 159926.0 | 5205.0 | 9234039 | 1345817 | 6861291 | Europe |
25 | Belgium | 3571579 | 7738.0 | 30217 | 18.0 | 3125040 | 60907.0 | 416322 | 240 | 305952.0 | 2588.0 | 31794693 | 2723626 | 11673662 | Europe |
37 | Greece | 2470212 | 15783.0 | 26036 | 64.0 | 2263358 | 15247.0 | 180818 | 393 | 238940.0 | 2518.0 | 66567338 | 6438955 | 10338221 | Europe |
12 | Netherlands | 6504886 | 58283.0 | 21589 | 7.0 | 4653122 | 114923.0 | 1830175 | 164 | 378234.0 | 1255.0 | 21107399 | 1227314 | 17198043 | Europe |
30 | Portugal | 3294691 | 12234.0 | 21141 | 30.0 | 2799531 | 11726.0 | 474019 | 90 | 324691.0 | 2083.0 | 37805639 | 3725733 | 10147169 | Europe |
46 | Slovakia | 1482354 | 11462.0 | 18611 | 44.0 | 1288400 | 18406.0 | 175343 | 232 | 271292.0 | 3406.0 | 6444955 | 1179520 | 5464048 | Europe |
38 | Sweden | 2453936 | -1.0 | 17391 | -1.0 | 2207320 | 40783.0 | 229225 | 53 | 240483.0 | 1704.0 | 18174812 | 1781114 | 10204183 | Europe |
89 | Bosnia and Herzegovina | 371846 | 293.0 | 15505 | 21.0 | 192218 | -1.0 | 164123 | -1 | 114526.0 | 4775.0 | 1704660 | 525025 | 3246816 | Europe |
41 | Serbia | 1919327 | 2620.0 | 15346 | 31.0 | 1825695 | 6001.0 | 78286 | 100 | 221153.0 | 1768.0 | 8800146 | 1013990 | 8678729 | Europe |
54 | Croatia | 1060078 | 1625.0 | 15145 | 23.0 | 1032892 | 1800.0 | 12041 | 81 | 260905.0 | 3727.0 | 4533308 | 1115731 | 4063084 | Europe |
34 | Austria | 2775589 | 31566.0 | 14924 | 36.0 | 2478168 | 24379.0 | 282497 | 189 | 305278.0 | 1641.0 | 161305125 | 17741404 | 9092016 | Europe |
33 | Switzerland | 2852838 | 14283.0 | 13252 | 3.0 | 2256659 | 43667.0 | 582927 | 139 | 325664.0 | 1513.0 | 18811814 | 2147451 | 8760067 | Europe |
81 | Moldova | 503690 | 734.0 | 11254 | 12.0 | 480308 | 1413.0 | 12128 | 89 | 125348.0 | 2801.0 | 3043271 | 757345 | 4018341 | Europe |
96 | North Macedonia | 298661 | 466.0 | 9050 | 14.0 | 286378 | 471.0 | 3233 | -1 | 143364.0 | 4344.0 | 1898672 | 911407 | 2083231 | Europe |
58 | Lithuania | 918569 | 5269.0 | 8484 | 19.0 | 828211 | 4770.0 | 81874 | 84 | 345456.0 | 3191.0 | 7787196 | 2928614 | 2659004 | Europe |
47 | Ireland | 1311105 | 4043.0 | 6527 | 6.0 | 1180695 | 4195.0 | 123883 | 46 | 260674.0 | 1298.0 | 11402648 | 2267079 | 5029664 | Europe |
57 | Belarus | 925515 | 2083.0 | 6521 | 15.0 | 915723 | 2926.0 | 3271 | -1 | 97999.0 | 690.0 | 12488928 | 1322399 | 9444144 | Europe |
59 | Slovenia | 898947 | 1823.0 | 6337 | 10.0 | 862051 | 4563.0 | 30559 | 83 | 432308.0 | 3047.0 | 2575157 | 1238405 | 2079414 | Europe |
70 | Latvia | 682118 | 8900.0 | 5299 | 15.0 | 551818 | 22988.0 | 125001 | 63 | 368423.0 | 2862.0 | 6771858 | 3657593 | 1851452 | Europe |
36 | Denmark | 2683219 | 12595.0 | 4731 | 44.0 | 2362924 | 38404.0 | 315564 | 42 | 460545.0 | 812.0 | 125634988 | 21563840 | 5826188 | Europe |
97 | Albania | 271920 | 95.0 | 3476 | 2.0 | 266697 | 396.0 | 1747 | 13 | 94661.0 | 1210.0 | 1746906 | 608135 | 2872561 | Europe |
103 | Montenegro | 230631 | 119.0 | 2682 | -1.0 | 226684 | 199.0 | 1265 | 60 | 367131.0 | 4269.0 | 1174393 | 1869463 | 628198 | Europe |
71 | Finland | 664588 | 6029.0 | 2384 | 3.0 | 46000 | -1.0 | 616204 | 31 | 119636.0 | 429.0 | 9996285 | 1799491 | 5555062 | Europe |
80 | Estonia | 507711 | 3564.0 | 2267 | 8.0 | 390415 | 10571.0 | 115029 | 24 | 382306.0 | 1707.0 | 3123957 | 2352340 | 1328021 | Europe |
49 | Norway | 1289884 | 10715.0 | 1664 | -1.0 | 88952 | -1.0 | 1199268 | 25 | 234872.0 | 303.0 | 9852076 | 1793941 | 5491863 | Europe |
106 | Luxembourg | 185707 | 845.0 | 995 | 2.0 | 175045 | 767.0 | 9667 | 6 | 288842.0 | 1548.0 | 4079174 | 6344594 | 642937 | Europe |
132 | Malta | 71581 | 126.0 | 607 | 1.0 | 69788 | 88.0 | 1186 | 3 | 161399.0 | 1369.0 | 1211456 | 2731568 | 443502 | Europe |
150 | Andorra | 38342 | 93.0 | 151 | -1.0 | 37520 | -1.0 | 671 | 14 | 494921.0 | 1949.0 | 249838 | 3224923 | 77471 | Europe |
145 | Channel Islands | 52914 | 321.0 | 144 | -1.0 | 49526 | 181.0 | 3244 | -1 | 299740.0 | 816.0 | 1209938 | 6853891 | 176533 | Europe |
178 | San Marino | 14369 | -1.0 | 112 | -1.0 | 13994 | -1.0 | 263 | 4 | 421997.0 | 3289.0 | 136485 | 4008370 | 34050 | Europe |
176 | Gibraltar | 15573 | 47.0 | 101 | -1.0 | 14973 | 47.0 | 499 | 10 | 462464.0 | 2999.0 | 517168 | 15358080 | 33674 | Europe |
166 | Isle of Man | 23231 | 132.0 | 79 | -1.0 | 22552 | 88.0 | 600 | -1 | 270811.0 | 921.0 | 150753 | 1757376 | 85783 | Europe |
182 | Liechtenstein | 12319 | 178.0 | 78 | -1.0 | 11770 | 92.0 | 471 | 7 | 321561.0 | 2036.0 | 80413 | 2099008 | 38310 | Europe |
115 | Iceland | 138281 | 2534.0 | 68 | 3.0 | 75685 | -1.0 | 62528 | 3 | 400892.0 | 197.0 | 1766309 | 5120731 | 344933 | Europe |
191 | Monaco | 9500 | 27.0 | 51 | -1.0 | 9264 | 23.0 | 185 | 4 | 239265.0 | 1284.0 | 54960 | 1384209 | 39705 | Europe |
155 | Faeroe Islands | 34237 | -1.0 | 28 | -1.0 | 7693 | -1.0 | 26516 | 5 | 696270.0 | 569.0 | 778000 | 15822013 | 49172 | Europe |
218 | Vatican City | 29 | -1.0 | -1 | -1.0 | 28 | -1.0 | 1 | -1 | 36070.0 | -1.0 | -1 | -1 | 804 | Europe |
# Drowing visualization graph
plt.bar(europe["Countries"].head(10),europe["Total_deaths"].head(10))
plt.xticks(rotation = '45')
plt.title("Europe's Total Death cases")
plt.xlabel("Countries")
plt.ylabel("Total Death Cases(in ten million)")
Text(0, 0.5, 'Total Death Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of active cases
europe.sort_values("Active_cases",ascending = False,inplace = True)
europe.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | Germany | 15375508 | 202338.0 | 124265 | 289.0 | 11696600 | 226300.0 | 3554643 | 2494 | 182541.0 | 1475.0 | 104701826 | 1243042 | 84230316 | Europe |
5 | Russia | 16685850 | 93026.0 | 354011 | 781.0 | 14102684 | 170689.0 | 2229155 | 2300 | 114256.0 | 2424.0 | 273400000 | 1872106 | 146038728 | Europe |
12 | Netherlands | 6504886 | 58283.0 | 21589 | 7.0 | 4653122 | 114923.0 | 1830175 | 164 | 378234.0 | 1255.0 | 21107399 | 1227314 | 17198043 | Europe |
3 | France | 22900531 | 60225.0 | 138942 | 180.0 | 21364892 | 131190.0 | 1396697 | 2484 | 349551.0 | 2121.0 | 246629975 | 3764529 | 65514171 | Europe |
4 | UK | 19074696 | 45656.0 | 161898 | 194.0 | 17537214 | -1.0 | 1375584 | 279 | 278543.0 | 2364.0 | 484240712 | 7071256 | 68480157 | Europe |
# Drowing visualization graph
plt.bar(europe["Countries"].head(10),europe["Active_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Europe's Total Active cases")
plt.xlabel("Countries")
plt.ylabel("Total Active Cases(in ten million)")
Text(0, 0.5, 'Total Active Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of new cases
europe.sort_values("New cases",ascending = False,inplace = True)
europe.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | Germany | 15375508 | 202338.0 | 124265 | 289.0 | 11696600 | 226300.0 | 3554643 | 2494 | 182541.0 | 1475.0 | 104701826 | 1243042 | 84230316 | Europe |
5 | Russia | 16685850 | 93026.0 | 354011 | 781.0 | 14102684 | 170689.0 | 2229155 | 2300 | 114256.0 | 2424.0 | 273400000 | 1872106 | 146038728 | Europe |
3 | France | 22900531 | 60225.0 | 138942 | 180.0 | 21364892 | 131190.0 | 1396697 | 2484 | 349551.0 | 2121.0 | 246629975 | 3764529 | 65514171 | Europe |
12 | Netherlands | 6504886 | 58283.0 | 21589 | 7.0 | 4653122 | 114923.0 | 1830175 | 164 | 378234.0 | 1255.0 | 21107399 | 1227314 | 17198043 | Europe |
4 | UK | 19074696 | 45656.0 | 161898 | 194.0 | 17537214 | -1.0 | 1375584 | 279 | 278543.0 | 2364.0 | 484240712 | 7071256 | 68480157 | Europe |
# Drowing visualization graph
plt.bar(europe["Countries"].head(10),europe["New cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Europe's New cases")
plt.xlabel("Countries")
plt.ylabel("New Cases(in ten million)")
Text(0, 0.5, 'New Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of new death cases
europe.sort_values("New_death",ascending = False,inplace = True)
europe.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5 | Russia | 16685850 | 93026.0 | 354011 | 781.0 | 14102684 | 170689.0 | 2229155 | 2300 | 114256.0 | 2424.0 | 273400000 | 1872106 | 146038728 | Europe |
6 | Germany | 15375508 | 202338.0 | 124265 | 289.0 | 11696600 | 226300.0 | 3554643 | 2494 | 182541.0 | 1475.0 | 104701826 | 1243042 | 84230316 | Europe |
14 | Poland | 5708827 | 14068.0 | 112130 | 266.0 | 5099515 | 12389.0 | 497182 | 696 | 151117.0 | 2968.0 | 33881265 | 896863 | 37777543 | Europe |
9 | Spain | 11078028 | 23140.0 | 100239 | 202.0 | 9800509 | 60554.0 | 1177280 | 890 | 236786.0 | 2143.0 | 471036328 | 10068116 | 46784953 | Europe |
4 | UK | 19074696 | 45656.0 | 161898 | 194.0 | 17537214 | -1.0 | 1375584 | 279 | 278543.0 | 2364.0 | 484240712 | 7071256 | 68480157 | Europe |
# Drowing visualization graph
plt.bar(europe["Countries"].head(10),europe["New_death"].head(10))
plt.xticks(rotation = '45')
plt.title("Europe's Total New Death cases")
plt.xlabel("Countries")
plt.ylabel("Total New Death Cases(in ten million)")
Text(0, 0.5, 'Total New Death Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of recovered cases
europe.sort_values("Total_recovered",ascending = False,inplace = True)
europe
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | France | 22900531 | 60225.0 | 138942 | 180.0 | 21364892 | 131190.0 | 1396697 | 2484 | 349551.0 | 2121.0 | 246629975 | 3764529 | 65514171 | Europe |
4 | UK | 19074696 | 45656.0 | 161898 | 194.0 | 17537214 | -1.0 | 1375584 | 279 | 278543.0 | 2364.0 | 484240712 | 7071256 | 68480157 | Europe |
5 | Russia | 16685850 | 93026.0 | 354011 | 781.0 | 14102684 | 170689.0 | 2229155 | 2300 | 114256.0 | 2424.0 | 273400000 | 1872106 | 146038728 | Europe |
8 | Italy | 12910506 | 41500.0 | 155399 | 185.0 | 11713645 | 62551.0 | 1041462 | 654 | 214056.0 | 2577.0 | 188713837 | 3128873 | 60313686 | Europe |
6 | Germany | 15375508 | 202338.0 | 124265 | 289.0 | 11696600 | 226300.0 | 3554643 | 2494 | 182541.0 | 1475.0 | 104701826 | 1243042 | 84230316 | Europe |
9 | Spain | 11078028 | 23140.0 | 100239 | 202.0 | 9800509 | 60554.0 | 1177280 | 890 | 236786.0 | 2143.0 | 471036328 | 10068116 | 46784953 | Europe |
14 | Poland | 5708827 | 14068.0 | 112130 | 266.0 | 5099515 | 12389.0 | 497182 | 696 | 151117.0 | 2968.0 | 33881265 | 896863 | 37777543 | Europe |
12 | Netherlands | 6504886 | 58283.0 | 21589 | 7.0 | 4653122 | 114923.0 | 1830175 | 164 | 378234.0 | 1255.0 | 21107399 | 1227314 | 17198043 | Europe |
18 | Ukraine | 4809624 | -1.0 | 105505 | -1.0 | 4058020 | -1.0 | 646099 | 177 | 111087.0 | 2437.0 | 19521252 | 450879 | 43295968 | Europe |
24 | Czechia | 3608535 | 8963.0 | 38787 | 16.0 | 3474694 | 5824.0 | 95054 | 197 | 335925.0 | 3611.0 | 52741015 | 4909756 | 10742084 | Europe |
25 | Belgium | 3571579 | 7738.0 | 30217 | 18.0 | 3125040 | 60907.0 | 416322 | 240 | 305952.0 | 2588.0 | 31794693 | 2723626 | 11673662 | Europe |
30 | Portugal | 3294691 | 12234.0 | 21141 | 30.0 | 2799531 | 11726.0 | 474019 | 90 | 324691.0 | 2083.0 | 37805639 | 3725733 | 10147169 | Europe |
35 | Romania | 2754730 | 5953.0 | 63782 | 105.0 | 2536050 | 11938.0 | 154898 | 848 | 144804.0 | 3353.0 | 21202396 | 1114516 | 19023859 | Europe |
34 | Austria | 2775589 | 31566.0 | 14924 | 36.0 | 2478168 | 24379.0 | 282497 | 189 | 305278.0 | 1641.0 | 161305125 | 17741404 | 9092016 | Europe |
36 | Denmark | 2683219 | 12595.0 | 4731 | 44.0 | 2362924 | 38404.0 | 315564 | 42 | 460545.0 | 812.0 | 125634988 | 21563840 | 5826188 | Europe |
37 | Greece | 2470212 | 15783.0 | 26036 | 64.0 | 2263358 | 15247.0 | 180818 | 393 | 238940.0 | 2518.0 | 66567338 | 6438955 | 10338221 | Europe |
33 | Switzerland | 2852838 | 14283.0 | 13252 | 3.0 | 2256659 | 43667.0 | 582927 | 139 | 325664.0 | 1513.0 | 18811814 | 2147451 | 8760067 | Europe |
38 | Sweden | 2453936 | -1.0 | 17391 | -1.0 | 2207320 | 40783.0 | 229225 | 53 | 240483.0 | 1704.0 | 18174812 | 1781114 | 10204183 | Europe |
41 | Serbia | 1919327 | 2620.0 | 15346 | 31.0 | 1825695 | 6001.0 | 78286 | 100 | 221153.0 | 1768.0 | 8800146 | 1013990 | 8678729 | Europe |
42 | Hungary | 1796982 | 3862.0 | 44211 | 77.0 | 1610315 | 4231.0 | 142456 | 126 | 186807.0 | 4596.0 | 10818978 | 1124695 | 9619478 | Europe |
46 | Slovakia | 1482354 | 11462.0 | 18611 | 44.0 | 1288400 | 18406.0 | 175343 | 232 | 271292.0 | 3406.0 | 6444955 | 1179520 | 5464048 | Europe |
47 | Ireland | 1311105 | 4043.0 | 6527 | 6.0 | 1180695 | 4195.0 | 123883 | 46 | 260674.0 | 1298.0 | 11402648 | 2267079 | 5029664 | Europe |
54 | Croatia | 1060078 | 1625.0 | 15145 | 23.0 | 1032892 | 1800.0 | 12041 | 81 | 260905.0 | 3727.0 | 4533308 | 1115731 | 4063084 | Europe |
57 | Belarus | 925515 | 2083.0 | 6521 | 15.0 | 915723 | 2926.0 | 3271 | -1 | 97999.0 | 690.0 | 12488928 | 1322399 | 9444144 | Europe |
59 | Slovenia | 898947 | 1823.0 | 6337 | 10.0 | 862051 | 4563.0 | 30559 | 83 | 432308.0 | 3047.0 | 2575157 | 1238405 | 2079414 | Europe |
51 | Bulgaria | 1097298 | 1104.0 | 35716 | 20.0 | 848792 | 766.0 | 212790 | 436 | 159926.0 | 5205.0 | 9234039 | 1345817 | 6861291 | Europe |
58 | Lithuania | 918569 | 5269.0 | 8484 | 19.0 | 828211 | 4770.0 | 81874 | 84 | 345456.0 | 3191.0 | 7787196 | 2928614 | 2659004 | Europe |
70 | Latvia | 682118 | 8900.0 | 5299 | 15.0 | 551818 | 22988.0 | 125001 | 63 | 368423.0 | 2862.0 | 6771858 | 3657593 | 1851452 | Europe |
81 | Moldova | 503690 | 734.0 | 11254 | 12.0 | 480308 | 1413.0 | 12128 | 89 | 125348.0 | 2801.0 | 3043271 | 757345 | 4018341 | Europe |
80 | Estonia | 507711 | 3564.0 | 2267 | 8.0 | 390415 | 10571.0 | 115029 | 24 | 382306.0 | 1707.0 | 3123957 | 2352340 | 1328021 | Europe |
96 | North Macedonia | 298661 | 466.0 | 9050 | 14.0 | 286378 | 471.0 | 3233 | -1 | 143364.0 | 4344.0 | 1898672 | 911407 | 2083231 | Europe |
97 | Albania | 271920 | 95.0 | 3476 | 2.0 | 266697 | 396.0 | 1747 | 13 | 94661.0 | 1210.0 | 1746906 | 608135 | 2872561 | Europe |
103 | Montenegro | 230631 | 119.0 | 2682 | -1.0 | 226684 | 199.0 | 1265 | 60 | 367131.0 | 4269.0 | 1174393 | 1869463 | 628198 | Europe |
89 | Bosnia and Herzegovina | 371846 | 293.0 | 15505 | 21.0 | 192218 | -1.0 | 164123 | -1 | 114526.0 | 4775.0 | 1704660 | 525025 | 3246816 | Europe |
106 | Luxembourg | 185707 | 845.0 | 995 | 2.0 | 175045 | 767.0 | 9667 | 6 | 288842.0 | 1548.0 | 4079174 | 6344594 | 642937 | Europe |
49 | Norway | 1289884 | 10715.0 | 1664 | -1.0 | 88952 | -1.0 | 1199268 | 25 | 234872.0 | 303.0 | 9852076 | 1793941 | 5491863 | Europe |
115 | Iceland | 138281 | 2534.0 | 68 | 3.0 | 75685 | -1.0 | 62528 | 3 | 400892.0 | 197.0 | 1766309 | 5120731 | 344933 | Europe |
132 | Malta | 71581 | 126.0 | 607 | 1.0 | 69788 | 88.0 | 1186 | 3 | 161399.0 | 1369.0 | 1211456 | 2731568 | 443502 | Europe |
145 | Channel Islands | 52914 | 321.0 | 144 | -1.0 | 49526 | 181.0 | 3244 | -1 | 299740.0 | 816.0 | 1209938 | 6853891 | 176533 | Europe |
71 | Finland | 664588 | 6029.0 | 2384 | 3.0 | 46000 | -1.0 | 616204 | 31 | 119636.0 | 429.0 | 9996285 | 1799491 | 5555062 | Europe |
150 | Andorra | 38342 | 93.0 | 151 | -1.0 | 37520 | -1.0 | 671 | 14 | 494921.0 | 1949.0 | 249838 | 3224923 | 77471 | Europe |
166 | Isle of Man | 23231 | 132.0 | 79 | -1.0 | 22552 | 88.0 | 600 | -1 | 270811.0 | 921.0 | 150753 | 1757376 | 85783 | Europe |
176 | Gibraltar | 15573 | 47.0 | 101 | -1.0 | 14973 | 47.0 | 499 | 10 | 462464.0 | 2999.0 | 517168 | 15358080 | 33674 | Europe |
178 | San Marino | 14369 | -1.0 | 112 | -1.0 | 13994 | -1.0 | 263 | 4 | 421997.0 | 3289.0 | 136485 | 4008370 | 34050 | Europe |
182 | Liechtenstein | 12319 | 178.0 | 78 | -1.0 | 11770 | 92.0 | 471 | 7 | 321561.0 | 2036.0 | 80413 | 2099008 | 38310 | Europe |
191 | Monaco | 9500 | 27.0 | 51 | -1.0 | 9264 | 23.0 | 185 | 4 | 239265.0 | 1284.0 | 54960 | 1384209 | 39705 | Europe |
155 | Faeroe Islands | 34237 | -1.0 | 28 | -1.0 | 7693 | -1.0 | 26516 | 5 | 696270.0 | 569.0 | 778000 | 15822013 | 49172 | Europe |
218 | Vatican City | 29 | -1.0 | -1 | -1.0 | 28 | -1.0 | 1 | -1 | 36070.0 | -1.0 | -1 | -1 | 804 | Europe |
# Drowing visualization graph
plt.bar(europe["Countries"].head(10),europe["Total_recovered"].head(10))
plt.xticks(rotation = '45')
plt.title("Europe's Total Recovered cases")
plt.xlabel("Countries")
plt.ylabel("Total Recovered Cases(in ten million)")
Text(0, 0.5, 'Total Recovered Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of critical condition cases
europe.sort_values("Serious/Critical",ascending = False,inplace = True)
europe.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | Germany | 15375508 | 202338.0 | 124265 | 289.0 | 11696600 | 226300.0 | 3554643 | 2494 | 182541.0 | 1475.0 | 104701826 | 1243042 | 84230316 | Europe |
3 | France | 22900531 | 60225.0 | 138942 | 180.0 | 21364892 | 131190.0 | 1396697 | 2484 | 349551.0 | 2121.0 | 246629975 | 3764529 | 65514171 | Europe |
5 | Russia | 16685850 | 93026.0 | 354011 | 781.0 | 14102684 | 170689.0 | 2229155 | 2300 | 114256.0 | 2424.0 | 273400000 | 1872106 | 146038728 | Europe |
9 | Spain | 11078028 | 23140.0 | 100239 | 202.0 | 9800509 | 60554.0 | 1177280 | 890 | 236786.0 | 2143.0 | 471036328 | 10068116 | 46784953 | Europe |
35 | Romania | 2754730 | 5953.0 | 63782 | 105.0 | 2536050 | 11938.0 | 154898 | 848 | 144804.0 | 3353.0 | 21202396 | 1114516 | 19023859 | Europe |
# Drowing visualization graph
plt.bar(europe["Countries"].head(10),europe["Serious/Critical"].head(10))
plt.xticks(rotation = '45')
plt.title("Europe's Total Critical Condition cases")
plt.xlabel("Countries")
plt.ylabel("Total Critical condition Cases(in ten million)")
Text(0, 0.5, 'Total Critical condition Cases(in ten million)')
From the above outputs, we can clearly see that France has the highest number of cases in Europe. From the same outputs we can also see that Vatican City has the lowest number of confirmed covid 19 cases in Europe.
France is leading in the number of covid 19 total number of confirmed cases mainly because of the following reasons:
Vatican City has confirmed low number of covid 19 cases mainly because of the following reasons:
We can also see that Russia is also leading in the total people who have died from covid 19,with Vatican City recording the lowest number of the same hence doing well in the control measures. From this,just like in Noth America and Asia, we can see that population is also a determining factor with the countries having the highest number of population finding it abit hard to control the spread of the disease as compared to those having lower population density.From these, we can ague that in as much as France having higher population density, it's doing well in the control measures.
It's also evident that the death cases in Germany may increase since there are many Active cases in the country than any other Europe country
australia = data[data["Continent"] == "Australia/Oceania"].copy()
australia.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
29 | Australia | 3297247 | 35897.0 | 5319 | 47.0 | 3052327 | -1.0 | 239601 | 118 | 126853.0 | 205.0 | 63779116 | 2453730 | 25992716 | Australia/Oceania |
109 | New Zealand | 166098 | 23180.0 | 56 | -1.0 | 19263 | 515.0 | 146779 | -1 | 33206.0 | 11.0 | 6768479 | 1353127 | 5002100 | Australia/Oceania |
134 | French Polynesia | 68425 | 774.0 | 642 | 1.0 | 0 | 0.0 | 0 | 7 | 241262.0 | 2264.0 | -1 | -1 | 283613 | Australia/Oceania |
135 | Fiji | 63999 | -1.0 | 834 | -1.0 | 62008 | -1.0 | 1157 | -1 | 70540.0 | 919.0 | 497559 | 548415 | 907267 | Australia/Oceania |
141 | New Caledonia | 56188 | 686.0 | 301 | -1.0 | 48247 | 278.0 | 7640 | 47 | 193705.0 | 1038.0 | 98964 | 341173 | 290070 | Australia/Oceania |
# sorting dataframe to help identify the country with the most number of confirmed cases
australia.sort_values("Total_cases",ascending = False,inplace = True)
australia
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
29 | Australia | 3297247 | 35897.0 | 5319 | 47.0 | 3052327 | -1.0 | 239601 | 118 | 126853.0 | 205.0 | 63779116 | 2453730 | 25992716 | Australia/Oceania |
109 | New Zealand | 166098 | 23180.0 | 56 | -1.0 | 19263 | 515.0 | 146779 | -1 | 33206.0 | 11.0 | 6768479 | 1353127 | 5002100 | Australia/Oceania |
134 | French Polynesia | 68425 | 774.0 | 642 | 1.0 | 0 | 0.0 | 0 | 7 | 241262.0 | 2264.0 | -1 | -1 | 283613 | Australia/Oceania |
135 | Fiji | 63999 | -1.0 | 834 | -1.0 | 62008 | -1.0 | 1157 | -1 | 70540.0 | 919.0 | 497559 | 548415 | 907267 | Australia/Oceania |
141 | New Caledonia | 56188 | 686.0 | 301 | -1.0 | 48247 | 278.0 | 7640 | 47 | 193705.0 | 1038.0 | 98964 | 341173 | 290070 | Australia/Oceania |
147 | Papua New Guinea | 41421 | 86.0 | 638 | -1.0 | 39905 | 191.0 | 878 | 7 | 4488.0 | 69.0 | 249149 | 26996 | 9229213 | Australia/Oceania |
199 | Solomon Islands | 7258 | -1.0 | 111 | 5.0 | 1783 | 22.0 | 5364 | 2 | 10152.0 | 155.0 | 5117 | 7157 | 714920 | Australia/Oceania |
206 | Palau | 3823 | -1.0 | 6 | -1.0 | 3313 | -1.0 | 504 | 5 | 209617.0 | 329.0 | 37269 | 2043481 | 18238 | Australia/Oceania |
208 | Kiribati | 2964 | 11.0 | 11 | -1.0 | 2261 | 20.0 | 692 | 4 | 24196.0 | 90.0 | -1 | -1 | 122501 | Australia/Oceania |
211 | Tonga | 645 | 290.0 | -1 | -1.0 | 303 | 126.0 | 342 | -1 | 5989.0 | -1.0 | -1 | -1 | 107690 | Australia/Oceania |
212 | Wallis and Futuna | 454 | -1.0 | 7 | -1.0 | 438 | -1.0 | 9 | -1 | 41609.0 | 642.0 | 20508 | 1879571 | 10911 | Australia/Oceania |
215 | Cook Islands | 84 | 13.0 | -1 | -1.0 | 7 | 3.0 | 77 | -1 | 4775.0 | -1.0 | 2311 | 131374 | 17591 | Australia/Oceania |
217 | Samoa | 33 | -1.0 | -1 | -1.0 | 28 | -1.0 | 5 | -1 | 164.0 | -1.0 | -1 | -1 | 200611 | Australia/Oceania |
219 | Vanuatu | 18 | -1.0 | 1 | -1.0 | 6 | -1.0 | 11 | -1 | 56.0 | 3.0 | 23000 | 72080 | 319092 | Australia/Oceania |
# Drowing visualization graph
plt.bar(australia["Countries"].head(10),australia["Total_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Australia's Total cases")
plt.xlabel("Countries")
plt.ylabel("Total Cases(in ten million)")
Text(0, 0.5, 'Total Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of death cases
australia.sort_values("Total_deaths",ascending = False,inplace = True)
australia
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
29 | Australia | 3297247 | 35897.0 | 5319 | 47.0 | 3052327 | -1.0 | 239601 | 118 | 126853.0 | 205.0 | 63779116 | 2453730 | 25992716 | Australia/Oceania |
135 | Fiji | 63999 | -1.0 | 834 | -1.0 | 62008 | -1.0 | 1157 | -1 | 70540.0 | 919.0 | 497559 | 548415 | 907267 | Australia/Oceania |
134 | French Polynesia | 68425 | 774.0 | 642 | 1.0 | 0 | 0.0 | 0 | 7 | 241262.0 | 2264.0 | -1 | -1 | 283613 | Australia/Oceania |
147 | Papua New Guinea | 41421 | 86.0 | 638 | -1.0 | 39905 | 191.0 | 878 | 7 | 4488.0 | 69.0 | 249149 | 26996 | 9229213 | Australia/Oceania |
141 | New Caledonia | 56188 | 686.0 | 301 | -1.0 | 48247 | 278.0 | 7640 | 47 | 193705.0 | 1038.0 | 98964 | 341173 | 290070 | Australia/Oceania |
199 | Solomon Islands | 7258 | -1.0 | 111 | 5.0 | 1783 | 22.0 | 5364 | 2 | 10152.0 | 155.0 | 5117 | 7157 | 714920 | Australia/Oceania |
109 | New Zealand | 166098 | 23180.0 | 56 | -1.0 | 19263 | 515.0 | 146779 | -1 | 33206.0 | 11.0 | 6768479 | 1353127 | 5002100 | Australia/Oceania |
208 | Kiribati | 2964 | 11.0 | 11 | -1.0 | 2261 | 20.0 | 692 | 4 | 24196.0 | 90.0 | -1 | -1 | 122501 | Australia/Oceania |
212 | Wallis and Futuna | 454 | -1.0 | 7 | -1.0 | 438 | -1.0 | 9 | -1 | 41609.0 | 642.0 | 20508 | 1879571 | 10911 | Australia/Oceania |
206 | Palau | 3823 | -1.0 | 6 | -1.0 | 3313 | -1.0 | 504 | 5 | 209617.0 | 329.0 | 37269 | 2043481 | 18238 | Australia/Oceania |
219 | Vanuatu | 18 | -1.0 | 1 | -1.0 | 6 | -1.0 | 11 | -1 | 56.0 | 3.0 | 23000 | 72080 | 319092 | Australia/Oceania |
211 | Tonga | 645 | 290.0 | -1 | -1.0 | 303 | 126.0 | 342 | -1 | 5989.0 | -1.0 | -1 | -1 | 107690 | Australia/Oceania |
215 | Cook Islands | 84 | 13.0 | -1 | -1.0 | 7 | 3.0 | 77 | -1 | 4775.0 | -1.0 | 2311 | 131374 | 17591 | Australia/Oceania |
217 | Samoa | 33 | -1.0 | -1 | -1.0 | 28 | -1.0 | 5 | -1 | 164.0 | -1.0 | -1 | -1 | 200611 | Australia/Oceania |
# Drowing visualization graph
plt.bar(australia["Countries"].head(10),australia["Total_deaths"].head(10))
plt.xticks(rotation = '45')
plt.title("Australia's Total Death cases")
plt.xlabel("Countries")
plt.ylabel("Total Death Cases(in ten million)")
Text(0, 0.5, 'Total Death Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of active cases
australia.sort_values("Active_cases",ascending = False,inplace = True)
australia.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
29 | Australia | 3297247 | 35897.0 | 5319 | 47.0 | 3052327 | -1.0 | 239601 | 118 | 126853.0 | 205.0 | 63779116 | 2453730 | 25992716 | Australia/Oceania |
109 | New Zealand | 166098 | 23180.0 | 56 | -1.0 | 19263 | 515.0 | 146779 | -1 | 33206.0 | 11.0 | 6768479 | 1353127 | 5002100 | Australia/Oceania |
141 | New Caledonia | 56188 | 686.0 | 301 | -1.0 | 48247 | 278.0 | 7640 | 47 | 193705.0 | 1038.0 | 98964 | 341173 | 290070 | Australia/Oceania |
199 | Solomon Islands | 7258 | -1.0 | 111 | 5.0 | 1783 | 22.0 | 5364 | 2 | 10152.0 | 155.0 | 5117 | 7157 | 714920 | Australia/Oceania |
135 | Fiji | 63999 | -1.0 | 834 | -1.0 | 62008 | -1.0 | 1157 | -1 | 70540.0 | 919.0 | 497559 | 548415 | 907267 | Australia/Oceania |
# Drowing visualization graph
plt.bar(australia["Countries"].head(10),australia["Active_cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Australia's Total Active cases")
plt.xlabel("Countries")
plt.ylabel("Total Active Cases(in ten million)")
Text(0, 0.5, 'Total Active Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of new cases
australia.sort_values("New cases",ascending = False,inplace = True)
australia.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
29 | Australia | 3297247 | 35897.0 | 5319 | 47.0 | 3052327 | -1.0 | 239601 | 118 | 126853.0 | 205.0 | 63779116 | 2453730 | 25992716 | Australia/Oceania |
109 | New Zealand | 166098 | 23180.0 | 56 | -1.0 | 19263 | 515.0 | 146779 | -1 | 33206.0 | 11.0 | 6768479 | 1353127 | 5002100 | Australia/Oceania |
134 | French Polynesia | 68425 | 774.0 | 642 | 1.0 | 0 | 0.0 | 0 | 7 | 241262.0 | 2264.0 | -1 | -1 | 283613 | Australia/Oceania |
141 | New Caledonia | 56188 | 686.0 | 301 | -1.0 | 48247 | 278.0 | 7640 | 47 | 193705.0 | 1038.0 | 98964 | 341173 | 290070 | Australia/Oceania |
211 | Tonga | 645 | 290.0 | -1 | -1.0 | 303 | 126.0 | 342 | -1 | 5989.0 | -1.0 | -1 | -1 | 107690 | Australia/Oceania |
# Drowing visualization graph
plt.bar(australia["Countries"].head(10),australia["New cases"].head(10))
plt.xticks(rotation = '45')
plt.title("Australia's Total New cases")
plt.xlabel("Countries")
plt.ylabel("Total New Cases(in ten million)")
Text(0, 0.5, 'Total New Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of new death cases
australia.sort_values("New_death",ascending = False,inplace = True)
australia.head()
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
29 | Australia | 3297247 | 35897.0 | 5319 | 47.0 | 3052327 | -1.0 | 239601 | 118 | 126853.0 | 205.0 | 63779116 | 2453730 | 25992716 | Australia/Oceania |
199 | Solomon Islands | 7258 | -1.0 | 111 | 5.0 | 1783 | 22.0 | 5364 | 2 | 10152.0 | 155.0 | 5117 | 7157 | 714920 | Australia/Oceania |
134 | French Polynesia | 68425 | 774.0 | 642 | 1.0 | 0 | 0.0 | 0 | 7 | 241262.0 | 2264.0 | -1 | -1 | 283613 | Australia/Oceania |
109 | New Zealand | 166098 | 23180.0 | 56 | -1.0 | 19263 | 515.0 | 146779 | -1 | 33206.0 | 11.0 | 6768479 | 1353127 | 5002100 | Australia/Oceania |
141 | New Caledonia | 56188 | 686.0 | 301 | -1.0 | 48247 | 278.0 | 7640 | 47 | 193705.0 | 1038.0 | 98964 | 341173 | 290070 | Australia/Oceania |
# Drowing visualization graph
plt.bar(australia["Countries"].head(10),australia["New_death"].head(10))
plt.xticks(rotation = '45')
plt.title("Australia's New Death cases")
plt.xlabel("Countries")
plt.ylabel("New Death Cases(in ten million)")
Text(0, 0.5, 'New Death Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of recovered cases
australia.sort_values("Total_recovered",ascending = False,inplace = True)
australia
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
29 | Australia | 3297247 | 35897.0 | 5319 | 47.0 | 3052327 | -1.0 | 239601 | 118 | 126853.0 | 205.0 | 63779116 | 2453730 | 25992716 | Australia/Oceania |
135 | Fiji | 63999 | -1.0 | 834 | -1.0 | 62008 | -1.0 | 1157 | -1 | 70540.0 | 919.0 | 497559 | 548415 | 907267 | Australia/Oceania |
141 | New Caledonia | 56188 | 686.0 | 301 | -1.0 | 48247 | 278.0 | 7640 | 47 | 193705.0 | 1038.0 | 98964 | 341173 | 290070 | Australia/Oceania |
147 | Papua New Guinea | 41421 | 86.0 | 638 | -1.0 | 39905 | 191.0 | 878 | 7 | 4488.0 | 69.0 | 249149 | 26996 | 9229213 | Australia/Oceania |
109 | New Zealand | 166098 | 23180.0 | 56 | -1.0 | 19263 | 515.0 | 146779 | -1 | 33206.0 | 11.0 | 6768479 | 1353127 | 5002100 | Australia/Oceania |
206 | Palau | 3823 | -1.0 | 6 | -1.0 | 3313 | -1.0 | 504 | 5 | 209617.0 | 329.0 | 37269 | 2043481 | 18238 | Australia/Oceania |
208 | Kiribati | 2964 | 11.0 | 11 | -1.0 | 2261 | 20.0 | 692 | 4 | 24196.0 | 90.0 | -1 | -1 | 122501 | Australia/Oceania |
199 | Solomon Islands | 7258 | -1.0 | 111 | 5.0 | 1783 | 22.0 | 5364 | 2 | 10152.0 | 155.0 | 5117 | 7157 | 714920 | Australia/Oceania |
212 | Wallis and Futuna | 454 | -1.0 | 7 | -1.0 | 438 | -1.0 | 9 | -1 | 41609.0 | 642.0 | 20508 | 1879571 | 10911 | Australia/Oceania |
211 | Tonga | 645 | 290.0 | -1 | -1.0 | 303 | 126.0 | 342 | -1 | 5989.0 | -1.0 | -1 | -1 | 107690 | Australia/Oceania |
217 | Samoa | 33 | -1.0 | -1 | -1.0 | 28 | -1.0 | 5 | -1 | 164.0 | -1.0 | -1 | -1 | 200611 | Australia/Oceania |
215 | Cook Islands | 84 | 13.0 | -1 | -1.0 | 7 | 3.0 | 77 | -1 | 4775.0 | -1.0 | 2311 | 131374 | 17591 | Australia/Oceania |
219 | Vanuatu | 18 | -1.0 | 1 | -1.0 | 6 | -1.0 | 11 | -1 | 56.0 | 3.0 | 23000 | 72080 | 319092 | Australia/Oceania |
134 | French Polynesia | 68425 | 774.0 | 642 | 1.0 | 0 | 0.0 | 0 | 7 | 241262.0 | 2264.0 | -1 | -1 | 283613 | Australia/Oceania |
# Drowing visualization graph
plt.bar(australia["Countries"].head(10),australia["Total_recovered"].head(10))
plt.xticks(rotation = '45')
plt.title("Australia's Total Recovered cases")
plt.xlabel("Countries")
plt.ylabel("Total Recovered Cases(in ten million)")
Text(0, 0.5, 'Total Recovered Cases(in ten million)')
# sorting dataframe to help identify the country with the most number of critical cases
australia.sort_values("Serious/Critical",ascending = False,inplace = True)
australia
Countries | Total_cases | New cases | Total_deaths | New_death | Total_recovered | New_recovered | Active_cases | Serious/Critical | Total_cases/1M | Total_deaths/1M | Total_tests | Test/1M | Popultion | Continent | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
29 | Australia | 3297247 | 35897.0 | 5319 | 47.0 | 3052327 | -1.0 | 239601 | 118 | 126853.0 | 205.0 | 63779116 | 2453730 | 25992716 | Australia/Oceania |
141 | New Caledonia | 56188 | 686.0 | 301 | -1.0 | 48247 | 278.0 | 7640 | 47 | 193705.0 | 1038.0 | 98964 | 341173 | 290070 | Australia/Oceania |
147 | Papua New Guinea | 41421 | 86.0 | 638 | -1.0 | 39905 | 191.0 | 878 | 7 | 4488.0 | 69.0 | 249149 | 26996 | 9229213 | Australia/Oceania |
134 | French Polynesia | 68425 | 774.0 | 642 | 1.0 | 0 | 0.0 | 0 | 7 | 241262.0 | 2264.0 | -1 | -1 | 283613 | Australia/Oceania |
206 | Palau | 3823 | -1.0 | 6 | -1.0 | 3313 | -1.0 | 504 | 5 | 209617.0 | 329.0 | 37269 | 2043481 | 18238 | Australia/Oceania |
208 | Kiribati | 2964 | 11.0 | 11 | -1.0 | 2261 | 20.0 | 692 | 4 | 24196.0 | 90.0 | -1 | -1 | 122501 | Australia/Oceania |
199 | Solomon Islands | 7258 | -1.0 | 111 | 5.0 | 1783 | 22.0 | 5364 | 2 | 10152.0 | 155.0 | 5117 | 7157 | 714920 | Australia/Oceania |
135 | Fiji | 63999 | -1.0 | 834 | -1.0 | 62008 | -1.0 | 1157 | -1 | 70540.0 | 919.0 | 497559 | 548415 | 907267 | Australia/Oceania |
109 | New Zealand | 166098 | 23180.0 | 56 | -1.0 | 19263 | 515.0 | 146779 | -1 | 33206.0 | 11.0 | 6768479 | 1353127 | 5002100 | Australia/Oceania |
212 | Wallis and Futuna | 454 | -1.0 | 7 | -1.0 | 438 | -1.0 | 9 | -1 | 41609.0 | 642.0 | 20508 | 1879571 | 10911 | Australia/Oceania |
211 | Tonga | 645 | 290.0 | -1 | -1.0 | 303 | 126.0 | 342 | -1 | 5989.0 | -1.0 | -1 | -1 | 107690 | Australia/Oceania |
217 | Samoa | 33 | -1.0 | -1 | -1.0 | 28 | -1.0 | 5 | -1 | 164.0 | -1.0 | -1 | -1 | 200611 | Australia/Oceania |
215 | Cook Islands | 84 | 13.0 | -1 | -1.0 | 7 | 3.0 | 77 | -1 | 4775.0 | -1.0 | 2311 | 131374 | 17591 | Australia/Oceania |
219 | Vanuatu | 18 | -1.0 | 1 | -1.0 | 6 | -1.0 | 11 | -1 | 56.0 | 3.0 | 23000 | 72080 | 319092 | Australia/Oceania |
# Drowing visualization graph
plt.bar(australia["Countries"].head(10),australia["Serious/Critical"].head(10))
plt.xticks(rotation = '45')
plt.title("Australia's Serious/Critical condition cases")
plt.xlabel("Countries")
plt.ylabel("Serious/Critical cases(in ten million)")
Text(0, 0.5, 'Serious/Critical cases(in ten million)')
From the above outputs, we can clearly see that Australia has the highest number of cases in Australia. From the same outputs we can also see that Vanuatu has the lowest number of confirmed covid 19 cases.
Australia is leading in the number of covid 19 total number of confirmed cases mainly because of the following reasons:
Vanuatu has confirmed low number of covid 19 cases mainly because of the following reasons:
We can also see that Australia is also leading in the total people who have died from covid 19,with Samoa recording the lowest number of the same hence doing well in the control measures. From this,just like in other continents, we can see that population is also a determining factor with the countries having the highest number of population finding it abit hard to control the spread of the disease as compared to those having lower population density.From these, we can ague that in as much as Australia having higher population density, it's doing well in the control measures.
It's also evident that the death cases in Australia may increase since there are many Active cases in the country than any other Australia country
As per my analysis and from the outputs of this projects, many countries share the same problems e.g the problems that led to the increase and fast spread of Covid 19 in Africa is more less the same with those of the other continents like Asia and many other continents. Following health guidline and regulations can actually stop the spread of this virus.
In addition, from my analysis, I also noticed that there was an initial luxury when handling the outbreak of the disease which should not be the case.I therefore recommend that alot of seriuosness be considered in future right from the beginning of any outbreak if there will be.
Like from my analysis, I realised that so many continents never took it serious at first and so lead to those continents being afected in a larger percentage. Like for instance taking a particular case with USA was never serious at the beginning and that has contributed to them loosing many lives to covid 19