├── README.md ├── mohfw.py ├── LICENSE ├── data.csv ├── covid_data.csv ├── scrapy.ipynb └── data.json /README.md: -------------------------------------------------------------------------------- 1 | # Covid-19 2 | This Is A Web Scraping Projects With Covid-19 Data From 2 Very Popular & Authentic Websites 3 | -------------------------------------------------------------------------------- /mohfw.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import json 3 | import pandas as pd 4 | 5 | url = 'https://www.mohfw.gov.in/data/datanew.json' 6 | res = requests.get(url) 7 | 8 | # Load the JSON data 9 | data = res.json() 10 | 11 | # Save the JSON data to a file 12 | with open("data.json", "w") as f: 13 | json.dump(data, f) 14 | 15 | # Convert JSON to DataFrame 16 | df = pd.DataFrame(data) 17 | 18 | # Save DataFrame to CSV 19 | df.to_csv("data.csv", index=False) -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Pabitra Banerjee 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /data.csv: -------------------------------------------------------------------------------- 1 | sno,state_name,active,positive,cured,death,new_active,new_positive,new_cured,new_death,death_reconsille,total,state_code,actualdeath24hrs 2 | 1,Andaman and Nicobar Islands,0,10766,10637,129,0,10766,10637,129,,,35,0 3 | 2,Andhra Pradesh,0,2340672,2325939,14733,0,2340672,2325939,14733,,,28,0 4 | 3,Arunachal Pradesh,2,67046,66748,296,2,67046,66748,296,,,12,0 5 | 4,Assam,7,746150,738108,8035,7,746150,738108,8035,,,18,0 6 | 5,Bihar,4,855260,842942,12314,4,855260,842942,12314,,,10,0 7 | 6,Chandigarh,1,100690,99504,1185,1,100690,99504,1185,,,04,0 8 | 7,Chhattisgarh,5,1187684,1173489,14190,6,1187685,1173489,14190,,,22,0 9 | 8,Dadra and Nagar Haveli and Daman and Diu,0,11592,11588,4,0,11592,11588,4,,,26,0 10 | 9,Delhi,3,2040775,2014107,26665,4,2040776,2014107,26665,,,07,0 11 | 10,Goa*,5,263306,259287,4014,5,263307,259288,4014,,,30,0 12 | 11,Gujarat,8,1291353,1280266,11079,8,1291353,1280266,11079,,,24,0 13 | 12,Haryana,27,1078875,1068094,10754,28,1078877,1068095,10754,,,06,0 14 | 13,Himachal Pradesh,13,322829,318576,4240,13,322829,318576,4240,,,02,0 15 | 14,Jammu and Kashmir,6,482017,477219,4792,0,482017,477225,4792,,,01,0 16 | 15,Jharkhand,0,443824,438490,5334,0,443824,438490,5334,,,20,0 17 | 16,Karnataka,20,4088635,4048258,40357,22,4088638,4048259,40357,,,29,0 18 | 17,Kerala***,1047,6907000,6834010,71943,1043,6907003,6834017,71943,,,32,0 19 | 18,Ladakh,0,29587,29356,231,0,29587,29356,231,,,37,0 20 | 19,Lakshadweep,0,11415,11363,52,0,11415,11363,52,,,31,0 21 | 20,Madhya Pradesh,3,1056351,1045562,10786,3,1056351,1045562,10786,,,23,0 22 | 21,Maharashtra,78,8169851,8021219,148554,84,8169860,8021222,148554,,,27,0 23 | 22,Manipur,18,139973,137806,2149,18,139973,137806,2149,,,14,0 24 | 23,Meghalaya,2,96954,95324,1628,1,96954,95325,1628,,,17,0 25 | 24,Mizoram,7,239545,238805,733,7,239545,238805,733,,,15,0 26 | 25,Nagaland,0,36029,35247,782,0,36029,35247,782,,,13,0 27 | 26,Odisha,7,1348273,1338998,9215,6,1348273,1338999,9215,,,21,0 28 | 27,Puducherry,3,177543,175559,1981,3,177543,175559,1981,,,34,0 29 | 28,Punjab**,4,793626,773056,19338,2,793626,773058,19338,,,03,0 30 | 29,Rajasthan,1,1326457,1316720,9736,1,1326457,1316720,9736,,,08,0 31 | 30,Sikkim,4,44863,44359,500,4,44863,44359,500,,,11,0 32 | 31,Tamil Nadu,8,3610601,3572513,38080,9,3610602,3572513,38080,,,33,0 33 | 32,Telangana,7,844359,840241,4111,7,844361,840243,4111,,,36,0 34 | 33,Tripura,7,108325,107377,941,8,108326,107377,941,,,16,0 35 | 34,Uttarakhand,5,452544,444771,7768,4,452545,444773,7768,,,05,0 36 | 35,Uttar Pradesh,74,2145405,2121624,23707,74,2145405,2121624,23707,,,09,0 37 | 36,West Bengal,99,2125431,2103780,21552,94,2125432,2103786,21552,,,19,0 38 | 37,,1475,44995606,44460942,531908,1468,44995632,44460975,531908,,,00,0 39 | -------------------------------------------------------------------------------- /covid_data.csv: -------------------------------------------------------------------------------- 1 | State,Confirmed,Recovered,Deaths,Active,Last_Updated_Time,Migrated_Other,State_code,Delta_Confirmed,Delta_Recovered,Delta_Deaths,State_Notes 2 | Total,34285612,33661339,458470,152606,13/08/2021 23:27:22,13197,TT,0,0,0, 3 | Andaman and Nicobar Islands,7651,7518,129,4,13/08/2021 23:27:22,0,AN,0,0,0, 4 | Andhra Pradesh,2066450,2047722,14373,4355,13/08/2021 23:27:22,0,AP,0,0,0, 5 | Arunachal Pradesh,55155,54774,280,101,13/08/2021 23:27:22,0,AR,0,0,0,[July 25]: All numbers corresponding to Papum Pare and Captial Complex are tracked under Papum Pare district. 6 | Assam,610645,600974,5997,2327,13/08/2021 23:27:22,1347,AS,0,0,0,[Jan 1]: 1347 cases i.e Covid +'tive patients dead for other reasons have been deducted fom active count. 7 | Bihar,726098,716390,9661,46,13/08/2021 23:27:22,1,BR,0,0,0,[June 9] : 3951 deceased cases have been reported in the bulletin after reconciliation of records by the authorities 8 | Chandigarh,65351,64495,820,36,13/08/2021 23:27:22,0,CH,0,0,0, 9 | Chhattisgarh,1006052,992159,13577,316,13/08/2021 23:27:22,0,CT,0,0,0, 10 | Dadra and Nagar Haveli and Daman and Diu,10681,10644,4,2,13/08/2021 23:27:22,31,DN,0,0,0, 11 | Delhi,1439870,1414431,25091,348,13/08/2021 23:27:22,0,DL,0,0,0,"[July 14]: Value for the total tests conducted has been reduced by 97008 in the state bulletin. Reason given : ""Reconciled with ICMR figures"". We have made the same change." 12 | Goa,178108,174392,3364,352,13/08/2021 23:27:22,0,GA,0,0,0, 13 | Gujarat,826577,816283,10089,205,13/08/2021 23:27:22,0,GJ,0,0,0, 14 | Haryana,771252,761068,10049,135,13/08/2021 23:27:22,0,HR,0,0,0, 15 | Himachal Pradesh,224106,218410,3738,1942,13/08/2021 23:27:22,16,HP,0,0,0, 16 | Jammu and Kashmir,332249,326915,4432,902,13/08/2021 23:27:22,0,JK,0,0,0, 17 | Jharkhand,348764,343518,5138,108,13/08/2021 23:27:22,0,JH,0,0,0, 18 | Karnataka,2988333,2941578,38082,8644,13/08/2021 23:27:22,29,KA,0,0,0, 19 | Kerala,4968657,4857181,31681,79266,13/08/2021 23:27:22,529,KL,0,0,0, 20 | Ladakh,20962,20687,208,67,13/08/2021 23:27:22,0,LA,0,0,0, 21 | Lakshadweep,10365,10270,51,0,13/08/2021 23:27:22,44,LD,0,0,0, 22 | Madhya Pradesh,792854,782215,10524,115,13/08/2021 23:27:22,0,MP,0,0,0,"[14 Oct'20]: 4469 confirmed cases and 4469 recoveries added in MP bulletin as reconciliation with private labs and hospitals 23 | [12 July'21] :1478 deceased cases have been added after reconciliation of deceased cases records from Home Isolation(208), Private Hospitals(762) and other districts data (508)" 24 | Maharashtra,6611078,6450585,140216,16658,13/08/2021 23:27:22,3619,MH,0,0,0,"[Dec 16]:10,218 duplicate cases & other state cases removed from total cases.791 recovered cases also removed from total recovered cases while reconciling 25 | [Sep 9] :239 cases have been removed from the hospitalized figures owing to the removal of duplicates and change of addresses as per the original residence 26 | [Aug 15] : MH bulletin has reduced 819 confirmed cases in Mumbai and 72 confirmed cases from 'Other States' from the tally 27 | [Jun 16] : 1328 deceased cases have been retroactively added to MH bulletin. 28 | [Jun 20] : 69 deceased cases have been reduced based on state bulletin. 29 | [Sep 9 2021]: 15066 positive cases and 15793 discharged cases have been reduced from state total based on reconciliation (as mentioned in the bulletin)" 30 | Manipur,123731,121102,1921,708,13/08/2021 23:27:22,0,MN,0,0,0,[Dec 14]:824 out of 980 recovered cases are backdated. 31 | Meghalaya,83627,81746,1450,431,13/08/2021 23:27:22,0,ML,0,0,0, 32 | Mizoram,121359,114612,432,6315,13/08/2021 23:27:22,0,MZ,0,0,0, 33 | Nagaland,31842,29904,685,210,13/08/2021 23:27:22,1043,NL,0,0,0, 34 | Odisha,1041457,1029147,8386,3924,13/08/2021 23:27:22,0,OR,0,0,0,[July 12th] :20 non-covid deaths reported in state dashboard are included in the deceased count 35 | Puducherry,128013,125726,1857,430,13/08/2021 23:27:22,0,PY,0,0,0, 36 | Punjab,602401,585591,16559,251,13/08/2021 23:27:22,0,PB,0,0,0, 37 | Rajasthan,954429,945443,8954,32,13/08/2021 23:27:22,0,RJ,0,0,0, 38 | Sikkim,31979,31063,396,195,13/08/2021 23:27:22,325,SK,0,0,0, 39 | State Unassigned,0,0,0,0,13/08/2021 23:27:22,0,UN,0,0,0,MoHFW website reports that these are the 'cases that are being reassigned to states'. 40 | Tamil Nadu,2702623,2655015,36116,11492,13/08/2021 23:27:22,0,TN,0,0,0,"[June 29 2021]: TN has introduced a new district ""Mayiladuthurai"" in the bulletin and has assigned cases from Nagapattinam to the new district. 41 | [July 22]: 444 backdated deceased entries added to Chennai in TN bulletin. 42 | 2 deaths cross notified to other states from Chennai and Coimbatore. 43 | 1 patient died after turning negative for infection in Chengalpattu. 44 | These cases have been added to TN deceased tally" 45 | Telangana,671463,663498,3956,4009,13/08/2021 23:27:22,0,TG,0,0,0,"[July 27] : Telangana bulletin for the previous day is released on the next day. We will add the cases for Telangana against the date of release, as that is the convention that we are following for other states." 46 | Tripura,84468,83466,813,126,13/08/2021 23:27:22,63,TR,0,0,0,"[Aug 4]: Tripura bulletin for the previous day is released on the next day. We will add the cases for Tripura against the date of release, as that is the convention that we are following for other states." 47 | Uttar Pradesh,1710158,1687151,22900,107,13/08/2021 23:27:22,0,UP,0,0,0,"[Jan 1]:As no bulletin was provided for 31'st Dec'20,its count has been combined with 1st Jan'21 48 | [Jan 9]:Due to reconcillation there are 1286 cases." 49 | Uttarakhand,343896,330195,7400,151,13/08/2021 23:27:22,6150,UT,0,0,0,"[Oct 30]: Metric of capturing the testing data has switched to ""Samples Tested"" from ""Samples Collected"" 50 | [Jun 30th'21]:145 confirmed cases and 218 deceased cases added of previous days after reconciliation with district and ICMR portal" 51 | West Bengal,1592908,1565471,19141,8296,13/08/2021 23:27:22,0,WB,0,0,0, -------------------------------------------------------------------------------- /scrapy.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import requests\n", 10 | "import pandas as pd" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 2, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "# URL for the COVID-19 data in India\n", 20 | "url = \"https://api.covid19india.org/csv/latest/state_wise.csv\"" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 3, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "# Send a GET request to the URL\n", 30 | "response = requests.get(url)" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 4, 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [ 39 | "# Read the response as text\n", 40 | "data = response.text" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 5, 46 | "metadata": {}, 47 | "outputs": [], 48 | "source": [ 49 | "# Write the response to a CSV file\n", 50 | "with open(\"covid_data.csv\", \"w\") as file:\n", 51 | " file.write(data)\n" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 6, 57 | "metadata": {}, 58 | "outputs": [], 59 | "source": [ 60 | "# Read the CSV file using pandas\n", 61 | "df = pd.read_csv(\"covid_data.csv\")" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 7, 67 | "metadata": {}, 68 | "outputs": [ 69 | { 70 | "name": "stdout", 71 | "output_type": "stream", 72 | "text": [ 73 | " State Confirmed Deaths Recovered \\\n", 74 | "0 Total 34285612 458470 33661339 \n", 75 | "1 Andaman and Nicobar Islands 7651 129 7518 \n", 76 | "2 Andhra Pradesh 2066450 14373 2047722 \n", 77 | "3 Arunachal Pradesh 55155 280 54774 \n", 78 | "4 Assam 610645 5997 600974 \n", 79 | "5 Bihar 726098 9661 716390 \n", 80 | "6 Chandigarh 65351 820 64495 \n", 81 | "7 Chhattisgarh 1006052 13577 992159 \n", 82 | "8 Dadra and Nagar Haveli and Daman and Diu 10681 4 10644 \n", 83 | "9 Delhi 1439870 25091 1414431 \n", 84 | "10 Goa 178108 3364 174392 \n", 85 | "11 Gujarat 826577 10089 816283 \n", 86 | "12 Haryana 771252 10049 761068 \n", 87 | "13 Himachal Pradesh 224106 3738 218410 \n", 88 | "14 Jammu and Kashmir 332249 4432 326915 \n", 89 | "15 Jharkhand 348764 5138 343518 \n", 90 | "16 Karnataka 2988333 38082 2941578 \n", 91 | "17 Kerala 4968657 31681 4857181 \n", 92 | "18 Ladakh 20962 208 20687 \n", 93 | "19 Lakshadweep 10365 51 10270 \n", 94 | "20 Madhya Pradesh 792854 10524 782215 \n", 95 | "21 Maharashtra 6611078 140216 6450585 \n", 96 | "22 Manipur 123731 1921 121102 \n", 97 | "23 Meghalaya 83627 1450 81746 \n", 98 | "24 Mizoram 121359 432 114612 \n", 99 | "25 Nagaland 31842 685 29904 \n", 100 | "26 Odisha 1041457 8386 1029147 \n", 101 | "27 Puducherry 128013 1857 125726 \n", 102 | "28 Punjab 602401 16559 585591 \n", 103 | "29 Rajasthan 954429 8954 945443 \n", 104 | "30 Sikkim 31979 396 31063 \n", 105 | "31 State Unassigned 0 0 0 \n", 106 | "32 Tamil Nadu 2702623 36116 2655015 \n", 107 | "33 Telangana 671463 3956 663498 \n", 108 | "34 Tripura 84468 813 83466 \n", 109 | "35 Uttar Pradesh 1710158 22900 1687151 \n", 110 | "36 Uttarakhand 343896 7400 330195 \n", 111 | "37 West Bengal 1592908 19141 1565471 \n", 112 | "\n", 113 | " Active \n", 114 | "0 152606 \n", 115 | "1 4 \n", 116 | "2 4355 \n", 117 | "3 101 \n", 118 | "4 2327 \n", 119 | "5 46 \n", 120 | "6 36 \n", 121 | "7 316 \n", 122 | "8 2 \n", 123 | "9 348 \n", 124 | "10 352 \n", 125 | "11 205 \n", 126 | "12 135 \n", 127 | "13 1942 \n", 128 | "14 902 \n", 129 | "15 108 \n", 130 | "16 8644 \n", 131 | "17 79266 \n", 132 | "18 67 \n", 133 | "19 0 \n", 134 | "20 115 \n", 135 | "21 16658 \n", 136 | "22 708 \n", 137 | "23 431 \n", 138 | "24 6315 \n", 139 | "25 210 \n", 140 | "26 3924 \n", 141 | "27 430 \n", 142 | "28 251 \n", 143 | "29 32 \n", 144 | "30 195 \n", 145 | "31 0 \n", 146 | "32 11492 \n", 147 | "33 4009 \n", 148 | "34 126 \n", 149 | "35 107 \n", 150 | "36 151 \n", 151 | "37 8296 \n" 152 | ] 153 | } 154 | ], 155 | "source": [ 156 | "# Filter necessary columns\n", 157 | "df = df[[\"State\", \"Confirmed\", \"Deaths\", \"Recovered\", \"Active\"]]\n", 158 | "\n", 159 | "# Print the data\n", 160 | "print(df)" 161 | ] 162 | } 163 | ], 164 | "metadata": { 165 | "kernelspec": { 166 | "display_name": "venv", 167 | "language": "python", 168 | "name": "python3" 169 | }, 170 | "language_info": { 171 | "codemirror_mode": { 172 | "name": "ipython", 173 | "version": 3 174 | }, 175 | "file_extension": ".py", 176 | "mimetype": "text/x-python", 177 | "name": "python", 178 | "nbconvert_exporter": "python", 179 | "pygments_lexer": "ipython3", 180 | "version": "3.11.3" 181 | }, 182 | "orig_nbformat": 4 183 | }, 184 | "nbformat": 4, 185 | "nbformat_minor": 2 186 | } 187 | -------------------------------------------------------------------------------- /data.json: -------------------------------------------------------------------------------- 1 | [{"sno": "1", "state_name": "Andaman and Nicobar Islands", "active": "0", "positive": "10766", "cured": "10637", "death": 129, "new_active": "0", "new_positive": "10766", "new_cured": "10637", "new_death": "129", "death_reconsille": "", "total": "", "state_code": "35", "actualdeath24hrs": "0"}, {"sno": "2", "state_name": "Andhra Pradesh", "active": "0", "positive": "2340672", "cured": "2325939", "death": 14733, "new_active": "0", "new_positive": "2340672", "new_cured": "2325939", "new_death": "14733", "death_reconsille": "", "total": "", "state_code": "28", "actualdeath24hrs": "0"}, {"sno": "3", "state_name": "Arunachal Pradesh", "active": "2", "positive": "67046", "cured": "66748", "death": 296, "new_active": "2", "new_positive": "67046", "new_cured": "66748", "new_death": "296", "death_reconsille": "", "total": "", "state_code": "12", "actualdeath24hrs": "0"}, {"sno": "4", "state_name": "Assam", "active": "7", "positive": "746150", "cured": "738108", "death": 8035, "new_active": "7", "new_positive": "746150", "new_cured": "738108", "new_death": "8035", "death_reconsille": "", "total": "", "state_code": "18", "actualdeath24hrs": "0"}, {"sno": "5", "state_name": "Bihar", "active": "4", "positive": "855260", "cured": "842942", "death": 12314, "new_active": "4", "new_positive": "855260", "new_cured": "842942", "new_death": "12314", "death_reconsille": "", "total": "", "state_code": "10", "actualdeath24hrs": "0"}, {"sno": "6", "state_name": "Chandigarh", "active": "1", "positive": "100690", "cured": "99504", "death": 1185, "new_active": "1", "new_positive": "100690", "new_cured": "99504", "new_death": "1185", "death_reconsille": "", "total": "", "state_code": "04", "actualdeath24hrs": "0"}, {"sno": "7", "state_name": "Chhattisgarh", "active": "5", "positive": "1187684", "cured": "1173489", "death": 14190, "new_active": "6", "new_positive": "1187685", "new_cured": "1173489", "new_death": "14190", "death_reconsille": "", "total": "", "state_code": "22", "actualdeath24hrs": "0"}, {"sno": "8", "state_name": "Dadra and Nagar Haveli and Daman and Diu", "active": "0", "positive": "11592", "cured": "11588", "death": 4, "new_active": "0", "new_positive": "11592", "new_cured": "11588", "new_death": "4", "death_reconsille": "", "total": "", "state_code": "26", "actualdeath24hrs": "0"}, {"sno": "9", "state_name": "Delhi", "active": "3", "positive": "2040775", "cured": "2014107", "death": 26665, "new_active": "4", "new_positive": "2040776", "new_cured": "2014107", "new_death": "26665", "death_reconsille": "", "total": "", "state_code": "07", "actualdeath24hrs": "0"}, {"sno": "10", "state_name": "Goa*", "active": "5", "positive": "263306", "cured": "259287", "death": 4014, "new_active": "5", "new_positive": "263307", "new_cured": "259288", "new_death": "4014", "death_reconsille": "", "total": "", "state_code": "30", "actualdeath24hrs": "0"}, {"sno": "11", "state_name": "Gujarat", "active": "8", "positive": "1291353", "cured": "1280266", "death": 11079, "new_active": "8", "new_positive": "1291353", "new_cured": "1280266", 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