# load libraries
import pandas as pd
import numpy as np
import urllib.request
import seaborn as sb
import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings(action='ignore')

# load data
web_path = urllib.request.urlopen("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
dataset = pd.read_csv(web_path)

df = pd.DataFrame(data=dataset)
df

# Process data
col = df.keys()[4:]
df_india = df[df['Country/Region'] == 'India'].iloc[0][col]
df_us = df[df['Country/Region'] == 'US'][col].iloc[0][col]
df_india, df_us

(1/22/20        0
 1/23/20        0
 1/24/20        0
 1/25/20        0
 1/26/20        0
            ...  
 5/13/20    78055
 5/14/20    81997
 5/15/20    85784
 5/16/20    90648
 5/17/20    95698
 Name: 131, Length: 117, dtype: object, 1/22/20          1
 1/23/20          1
 1/24/20          2
 1/25/20          2
 1/26/20          5
             ...   
 5/13/20    1390406
 5/14/20    1417774
 5/15/20    1442824
 5/16/20    1467820
 5/17/20    1486757
 Name: 225, Length: 117, dtype: int64)

# Plot data
plt.plot(df_india, color = 'g', label = 'India')
plt.plot(df_us, color = 'b', label = 'US')
plt.legend()
plt.show()

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	5/8/20	5/9/20	5/10/20	5/11/20	5/12/20	5/13/20	5/14/20	5/15/20	5/16/20	5/17/20
0	NaN	Afghanistan	33.000000	65.000000	0	0	0	0	0	0	...	3778	4033	4402	4687	4963	5226	5639	6053	6402	6664
1	NaN	Albania	41.153300	20.168300	0	0	0	0	0	0	...	850	856	868	872	876	880	898	916	933	946
2	NaN	Algeria	28.033900	1.659600	0	0	0	0	0	0	...	5369	5558	5723	5891	6067	6253	6442	6629	6821	7019
3	NaN	Andorra	42.506300	1.521800	0	0	0	0	0	0	...	752	754	755	755	758	760	761	761	761	761
4	NaN	Angola	-11.202700	17.873900	0	0	0	0	0	0	...	43	43	45	45	45	45	48	48	48	48
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
261	NaN	Sao Tome and Principe	0.186360	6.613081	0	0	0	0	0	0	...	208	208	208	208	208	220	235	235	235	235
262	NaN	Yemen	15.552727	48.516388	0	0	0	0	0	0	...	34	34	51	56	65	70	85	106	122	128
263	NaN	Comoros	-11.645500	43.333300	0	0	0	0	0	0	...	8	11	11	11	11	11	11	11	11	11
264	NaN	Tajikistan	38.861034	71.276093	0	0	0	0	0	0	...	522	612	612	661	729	801	907	1118	1322	1524
265	NaN	Lesotho	-29.609988	28.233608	0	0	0	0	0	0	...	0	0	0	0	0	1	1	1	1	1

India vs USA COVID-19