Covid – 19 Data Processing is a Program which can load, merge, clean and aggregate the COVID-19 time series data. This program can give graphs and stats about any country in the world. It is designed by using Python Language, Matplotlib, Seaborn, Alter, Numpy and Pandas.
It takes the updated data from the web and show the current stats of Covid – 19 such as Confirmed, Recovered, Death and Active cases around each country for which the user requires. Further this program also compares data of newly confirmed cases between multiple countries.
METHODS AND MATERIAL USED 3.1 MATERIAL USED Jupyter Notebook/ Google Colaboratory 3.2 METHODS 3.2.1 DESIGNING THE PROJECT Confirmed, deaths and recovered are kept in different CSV files. That makes difficult for plotting them in the same data visualization. This COVID-19 Data processing runs the following steps:
- Download raw CSV dataset from JHU CSSE public Github page.
- Load raw CSV dataset and extract the common date list.
- Merges the raw confirmed, deaths, and recovered CSV data into one DataFrame.
- Performs data cleanings due to missing values, wrong datatypes and cases from cruise ships.
- Data Aggregation: Add an active case column Active, which is calculated by active_case = confirmed — deaths — recovered. Aggregate data into Country/Region wise and group them by Date and Country/Region. After that, add day wise New cases, New deaths and New recovered by deducting the corresponding cumulative data on the previous day.