My goal is to convert below - Current Data Output
By adding a new column called Cases which is non-cumulative for further analysis.
I have tried this solution but I am not sure what is my mistake -
Data["Cases"] = Data.groupby(level=0).diff().fillna(Data).reset_index()
Please advise
A simple approach will be to take the lag of values in cumulative column and then calculating the difference between cumulative and lag.
import pandas as pd
date = ['2020-03-01','2020-03-02','2020-03-03','2020-03-04','2020-03-05']
cases = [1, 2, 4, 7, 10]
data = list(zip(date, cases))
df = pd.DataFrame(data, columns=['Date','Cases'])
df['Lag'] = df.Cases.shift(1).fillna(0)
df['Daily Cases'] = df.Cases - df.Lag
print(df)
Date Cases Lag Daily Cases
0 2020-03-01 1 0.0 1.0
1 2020-03-02 2 1.0 1.0
2 2020-03-03 4 2.0 2.0
3 2020-03-04 7 4.0 3.0
4 2020-03-05 10 7.0 3.0
Just remember that order is important here. So sort your cumulative column in ascending order.
Don't forget to mark this as accepted answer if it helps you