Project description

Covid19India

A Python3 Library to get India's Covid-19 Patient Count.

Installation

pip3 install Covid19India

Requirements

requests
BeautifulSoup

Usage

To get India's total count

In [1]: from Covid19India import CovidIndia                                                                                                                                                                 

In [2]: obj = CovidIndia()                                                                                                                                                                                  

In [3]: stats = obj.getstats()                                                                                                                                                                              

In [4]: stats['total']                                                                                                                                                                                      
Out[4]: {'active': 44029, 'recovered': 20917, 'deaths': 2206, 'confirmed': 67152}

To get State and UT wise data

In [5]: stats['states']                                                                                                                                                                                     
Out[5]: 
{'Andaman and Nicobar Islands': {'active': 0,
  'recovered': 33,
  'confirmed': 33,
  'deaths': 0},
 'Andhra Pradesh': {'active': 1010,
  'recovered': 925,
  'confirmed': 1980,
  'deaths': 45},
 'Arunachal Pradesh': {'active': 0,
  'recovered': 1,
  'confirmed': 1,
  'deaths': 0},
 'Assam': {'active': 27, 'recovered': 34, 'confirmed': 63, 'deaths': 2},
 'Bihar': {'active': 325, 'recovered': 365, 'confirmed': 696, 'deaths': 6},
 'Chandigarh': {'active': 143, 'recovered': 24, 'confirmed': 169, 'deaths': 2},
 'Chhattisgarh': {'active': 10, 'recovered': 49, 'confirmed': 59, 'deaths': 0},
 'Dadar Nagar Haveli': {'active': 1,
  'recovered': 0,
  'confirmed': 1,
  'deaths': 0},
 'Delhi': {'active': 4781, 'recovered': 2069, 'confirmed': 6923, 'deaths': 73},
 'Goa': {'active': 0, 'recovered': 7, 'confirmed': 7, 'deaths': 0},
 'Gujarat': {'active': 5156,
  'recovered': 2545,
  'confirmed': 8194,
  'deaths': 493},
 'Haryana': {'active': 393, 'recovered': 300, 'confirmed': 703, 'deaths': 10},
 'Himachal Pradesh': {'active': 14,
  'recovered': 39,
  'confirmed': 55,
  'deaths': 2},
 'Jammu and Kashmir': {'active': 469,
  'recovered': 383,
  'confirmed': 861,
  'deaths': 9},
 'Jharkhand': {'active': 76, 'recovered': 78, 'confirmed': 157, 'deaths': 3},
 'Karnataka': {'active': 393,
  'recovered': 424,
  'confirmed': 848,
  'deaths': 31},
 'Kerala': {'active': 19, 'recovered': 489, 'confirmed': 512, 'deaths': 4},
 'Ladakh': {'active': 21, 'recovered': 21, 'confirmed': 42, 'deaths': 0},
 'Madhya Pradesh': {'active': 1723,
  'recovered': 1676,
  'confirmed': 3614,
  'deaths': 215},
 'Maharashtra': {'active': 17140,
  'recovered': 4199,
  'confirmed': 22171,
  'deaths': 832},
 'Manipur': {'active': 0, 'recovered': 2, 'confirmed': 2, 'deaths': 0},
 'Meghalaya': {'active': 2, 'recovered': 10, 'confirmed': 13, 'deaths': 1},
 'Mizoram': {'active': 0, 'recovered': 1, 'confirmed': 1, 'deaths': 0},
 'Odisha': {'active': 306, 'recovered': 68, 'confirmed': 377, 'deaths': 3},
 'Puducherry': {'active': 3, 'recovered': 6, 'confirmed': 9, 'deaths': 0},
 'Punjab': {'active': 1626, 'recovered': 166, 'confirmed': 1823, 'deaths': 31},
 'Rajasthan': {'active': 1531,
  'recovered': 2176,
  'confirmed': 3814,
  'deaths': 107},
 'Tamil Nadu': {'active': 5198,
  'recovered': 1959,
  'confirmed': 7204,
  'deaths': 47},
 'Telengana': {'active': 416,
  'recovered': 750,
  'confirmed': 1196,
  'deaths': 30},
 'Tripura': {'active': 148, 'recovered': 2, 'confirmed': 150, 'deaths': 0},
 'Uttarakhand': {'active': 21, 'recovered': 46, 'confirmed': 68, 'deaths': 1},
 'Uttar Pradesh': {'active': 1740,
  'recovered': 1653,
  'confirmed': 3467,
  'deaths': 74},
 'West Bengal': {'active': 1337,
  'recovered': 417,
  'confirmed': 1939,
  'deaths': 185}}

To get time at which data has been updated

In [6]: stats['time']                                                                                                                                                                                       
Out[6]: '11 May 2020, 08:00 IST (GMT+5:30)'

To get India's Historical data

In [7]: hist = obj.gethistorical()                                                                                                                                                                          

In [8]: hist                                                                                                                                                                                                
Out[8]: 
{'cases': {'1/22/20': 0,
  '1/23/20': 0,
  '1/24/20': 0,
  '1/25/20': 0,
  '1/26/20': 0,
  '1/27/20': 0,
  '1/28/20': 0,
  '1/29/20': 0,
  '1/30/20': 1,
  '1/31/20': 1,
  '2/1/20': 1,
  '2/2/20': 2,
  '2/3/20': 3,
  '2/4/20': 3,
  '2/5/20': 3,
  '2/6/20': 3,
  '2/7/20': 3,
  '2/8/20': 3,
  '2/9/20': 3,
  '2/10/20': 3,
  '2/11/20': 3,
  '2/12/20': 3,
  '2/13/20': 3,
  '2/14/20': 3,
  '2/15/20': 3,
  '2/16/20': 3,
  '2/17/20': 3,
  '2/18/20': 3,
  '2/19/20': 3,
  '2/20/20': 3,
  '2/21/20': 3,
  '2/22/20': 3,
  '2/23/20': 3,
  '2/24/20': 3,
  '2/25/20': 3,
  '2/26/20': 3,
  '2/27/20': 3,
  '2/28/20': 3,
  '2/29/20': 3,
  '3/1/20': 3,
  '3/2/20': 5,
  '3/3/20': 5,
  '3/4/20': 28,
  '3/5/20': 30,
  '3/6/20': 31,
  '3/7/20': 34,
  '3/8/20': 39,
  '3/9/20': 43,
  '3/10/20': 56,
  '3/11/20': 62,
  '3/12/20': 73,
  '3/13/20': 82,
  '3/14/20': 102,
  '3/15/20': 113,
  '3/16/20': 119,
  '3/17/20': 142,
  '3/18/20': 156,
  '3/19/20': 194,
  '3/20/20': 244,
  '3/21/20': 330,
  '3/22/20': 396,
  '3/23/20': 499,
  '3/24/20': 536,
  '3/25/20': 657,
  '3/26/20': 727,
  '3/27/20': 887,
  '3/28/20': 987,
  '3/29/20': 1024,
  '3/30/20': 1251,
  '3/31/20': 1397,
  '4/1/20': 1998,
  '4/2/20': 2543,
  '4/3/20': 2567,
  '4/4/20': 3082,
  '4/5/20': 3588,
  '4/6/20': 4778,
  '4/7/20': 5311,
  '4/8/20': 5916,
  '4/9/20': 6725,
  '4/10/20': 7598,
  '4/11/20': 8446,
  '4/12/20': 9205,
  '4/13/20': 10453,
  '4/14/20': 11487,
  '4/15/20': 12322,
  '4/16/20': 13430,
  '4/17/20': 14352,
  '4/18/20': 15722,
  '4/19/20': 17615,
  '4/20/20': 18539,
  '4/21/20': 20080,
  '4/22/20': 21370,
  '4/23/20': 23077,
  '4/24/20': 24530,
  '4/25/20': 26283,
  '4/26/20': 27890,
  '4/27/20': 29451,
  '4/28/20': 31324,
  '4/29/20': 33062,
  '4/30/20': 34863,
  '5/1/20': 37257,
  '5/2/20': 39699,
  '5/3/20': 42505,
  '5/4/20': 46437,
  '5/5/20': 49400,
  '5/6/20': 52987,
  '5/7/20': 56351,
  '5/8/20': 59695,
  '5/9/20': 62808,
  '5/10/20': 67161},
 'deaths': {'1/22/20': 0,
  '1/23/20': 0,
  '1/24/20': 0,
  '1/25/20': 0,
  '1/26/20': 0,
  '1/27/20': 0,
  '1/28/20': 0,
  '1/29/20': 0,
  '1/30/20': 0,
  '1/31/20': 0,
  '2/1/20': 0,
  '2/2/20': 0,
  '2/3/20': 0,
  '2/4/20': 0,
  '2/5/20': 0,
  '2/6/20': 0,
  '2/7/20': 0,
  '2/8/20': 0,
  '2/9/20': 0,
  '2/10/20': 0,
  '2/11/20': 0,
  '2/12/20': 0,
  '2/13/20': 0,
  '2/14/20': 0,
  '2/15/20': 0,
  '2/16/20': 0,
  '2/17/20': 0,
  '2/18/20': 0,
  '2/19/20': 0,
  '2/20/20': 0,
  '2/21/20': 0,
  '2/22/20': 0,
  '2/23/20': 0,
  '2/24/20': 0,
  '2/25/20': 0,
  '2/26/20': 0,
  '2/27/20': 0,
  '2/28/20': 0,
  '2/29/20': 0,
  '3/1/20': 0,
  '3/2/20': 0,
  '3/3/20': 0,
  '3/4/20': 0,
  '3/5/20': 0,
  '3/6/20': 0,
  '3/7/20': 0,
  '3/8/20': 0,
  '3/9/20': 0,
  '3/10/20': 0,
  '3/11/20': 1,
  '3/12/20': 1,
  '3/13/20': 2,
  '3/14/20': 2,
  '3/15/20': 2,
  '3/16/20': 2,
  '3/17/20': 3,
  '3/18/20': 3,
  '3/19/20': 4,
  '3/20/20': 5,
  '3/21/20': 4,
  '3/22/20': 7,
  '3/23/20': 10,
  '3/24/20': 10,
  '3/25/20': 12,
  '3/26/20': 20,
  '3/27/20': 20,
  '3/28/20': 24,
  '3/29/20': 27,
  '3/30/20': 32,
  '3/31/20': 35,
  '4/1/20': 58,
  '4/2/20': 72,
  '4/3/20': 72,
  '4/4/20': 86,
  '4/5/20': 99,
  '4/6/20': 136,
  '4/7/20': 150,
  '4/8/20': 178,
  '4/9/20': 226,
  '4/10/20': 246,
  '4/11/20': 288,
  '4/12/20': 331,
  '4/13/20': 358,
  '4/14/20': 393,
  '4/15/20': 405,
  '4/16/20': 448,
  '4/17/20': 486,
  '4/18/20': 521,
  '4/19/20': 559,
  '4/20/20': 592,
  '4/21/20': 645,
  '4/22/20': 681,
  '4/23/20': 721,
  '4/24/20': 780,
  '4/25/20': 825,
  '4/26/20': 881,
  '4/27/20': 939,
  '4/28/20': 1008,
  '4/29/20': 1079,
  '4/30/20': 1154,
  '5/1/20': 1223,
  '5/2/20': 1323,
  '5/3/20': 1391,
  '5/4/20': 1566,
  '5/5/20': 1693,
  '5/6/20': 1785,
  '5/7/20': 1889,
  '5/8/20': 1985,
  '5/9/20': 2101,
  '5/10/20': 2212},
 'recovered': {'1/22/20': 0,
  '1/23/20': 0,
  '1/24/20': 0,
  '1/25/20': 0,
  '1/26/20': 0,
  '1/27/20': 0,
  '1/28/20': 0,
  '1/29/20': 0,
  '1/30/20': 0,
  '1/31/20': 0,
  '2/1/20': 0,
  '2/2/20': 0,
  '2/3/20': 0,
  '2/4/20': 0,
  '2/5/20': 0,
  '2/6/20': 0,
  '2/7/20': 0,
  '2/8/20': 0,
  '2/9/20': 0,
  '2/10/20': 0,
  '2/11/20': 0,
  '2/12/20': 0,
  '2/13/20': 0,
  '2/14/20': 0,
  '2/15/20': 0,
  '2/16/20': 3,
  '2/17/20': 3,
  '2/18/20': 3,
  '2/19/20': 3,
  '2/20/20': 3,
  '2/21/20': 3,
  '2/22/20': 3,
  '2/23/20': 3,
  '2/24/20': 3,
  '2/25/20': 3,
  '2/26/20': 3,
  '2/27/20': 3,
  '2/28/20': 3,
  '2/29/20': 3,
  '3/1/20': 3,
  '3/2/20': 3,
  '3/3/20': 3,
  '3/4/20': 3,
  '3/5/20': 3,
  '3/6/20': 3,
  '3/7/20': 3,
  '3/8/20': 3,
  '3/9/20': 3,
  '3/10/20': 4,
  '3/11/20': 4,
  '3/12/20': 4,
  '3/13/20': 4,
  '3/14/20': 4,
  '3/15/20': 13,
  '3/16/20': 13,
  '3/17/20': 14,
  '3/18/20': 14,
  '3/19/20': 15,
  '3/20/20': 20,
  '3/21/20': 23,
  '3/22/20': 27,
  '3/23/20': 27,
  '3/24/20': 40,
  '3/25/20': 43,
  '3/26/20': 45,
  '3/27/20': 73,
  '3/28/20': 84,
  '3/29/20': 95,
  '3/30/20': 102,
  '3/31/20': 123,
  '4/1/20': 148,
  '4/2/20': 191,
  '4/3/20': 192,
  '4/4/20': 229,
  '4/5/20': 229,
  '4/6/20': 375,
  '4/7/20': 421,
  '4/8/20': 506,
  '4/9/20': 620,
  '4/10/20': 774,
  '4/20/20': 3273,
  '4/21/20': 3975,
  '4/22/20': 4370,
  '4/23/20': 5012,
  '4/24/20': 5498,
  '4/25/20': 5939,
  '4/26/20': 6523,
  '4/27/20': 7137,
  '4/28/20': 7747,
  '4/29/20': 8437,
  '4/30/20': 9068,
  '5/1/20': 10007,
  '5/2/20': 10819,
  '5/3/20': 11775,
  '5/4/20': 12847,
  '5/5/20': 14142,
  '5/6/20': 15331,
  '5/7/20': 16776,
  '5/8/20': 17887,
  '5/9/20': 19301,
  '5/10/20': 20969}}11/20': 969,
  '4/12/20': 1080,
  '4/13/20': 1181,
  '4/14/20': 1359,
  '4/15/20': 1432,
  '4/16/20': 1768,
  '4/17/20': 2041,
  '4/18/20': 2463,
  '4/19/20': 2854,
  '4/20/20': 3273,
  '4/21/20': 3975,
  '4/22/20': 4370,
  '4/23/20': 5012,
  '4/24/20': 5498,
  '4/25/20': 5939,
  '4/26/20': 6523,
  '4/27/20': 7137,
  '4/28/20': 7747,
  '4/29/20': 8437,
  '4/30/20': 9068,
  '5/1/20': 10007,
  '5/2/20': 10819,
  '5/3/20': 11775,
  '5/4/20': 12847,
  '5/5/20': 14142,
  '5/6/20': 15331,
  '5/7/20': 16776,
  '5/8/20': 17887,
  '5/9/20': 19301,
  '5/10/20': 20969}}

Data Source

Ministry of Health and Family Welfar Govt of India: For latest counts
NovelCovid: For historical data

Project details

These details have not been verified by PyPI

Project links

Homepage

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Release history Release notifications | RSS feed

This version

0.0.5

May 31, 2020

0.0.4

May 21, 2020

0.0.3

May 16, 2020

0.0.2

May 11, 2020

0.0.1

May 10, 2020

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