Metadata-Version: 2.1
Name: Covid19India
Version: 0.0.5
Summary: A Python3 Library to get India's Covid-19 Patient Count.
Home-page: https://github.com/suraj-deshmukh/Covid19India
Author: Suraj Deshmukh
Author-email: surajdeshmukh96@gmail.com
License: MIT LICENSE
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3
Description-Content-Type: text/markdown
Requires-Dist: requests
Requires-Dist: beautifulsoup4


# 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](https://www.mohfw.gov.in/): For latest counts
* [NovelCovid](https://github.com/NovelCOVID/API): For historical data


