COVID-19-India_Data

Data regarding region-wise spread of COVID-19 in India.

Live Dashboard:

• These datasets have been beautifully visualized by @rahulrajpl on his dashboard.

Introduction:

• This repository stores datasets pertaining to the region-wise spread of COVID-19 in India.
• The data are arranged into two directories: datasets and time-series.
• I thank the MoHFW and @covid19india for providing a reliable source of raw data.
• I also thank each and every on-duty personnel on the frontlines of this battle against COVID-19. Let's help them by staying indoors.

Functional Details:

• The datasets directory stores the daily updates in the CSV format.
• The time-series directory houses the CSV file storing the region-wise trends of the COVID-19 spread in India over time.
• The update_scripts directory contains the python scripts used for fetching data from their respective sources and updating the datasets accordingly.
• All the dates are represented in '%d-%m-%Y' format. Use the dayfirst = True argument of pandas.read_csv() function to correctly read the dates while loading the dataset into a dataframe.
• The COVID19-fetch_India_regional_data.py script scrapes the MoHFW webpage for updated data and generates/updates the corresponding files accordingly. I shall run this script daily to retrieve and record updates on the spread of COVID-19.
• The COVID19-fetch_India_regional_historical_data.py script extracts old historical data from the CovidCrowd repository on GitHub. This script was used once to load the old data that was no longer available on the MoHFW website.

Sources:

• The primary source of these data is the home page of the Ministry of Health & Family Welfare, Govt. of India. Daily updates are retrieved from this source since 28th March, 2020.
• Historical data before 28th March, 2020 have been extracted from the raw data available within the CovidCrowd repository of @covid19india.
• The datasets housed in this repository are a mixture of data from these two sources and have been forged into a uniform format.

Licensing:

This project is entirely from and for the public domain.
Please feel free to utilise and distribute these datasets without any restrictions. (See MIT License)