Marcus Gallegos :
I have a dataframe with columns that are an aggregation of corona virus cases over time.
I need the data in the date columns to be the new number of cases for that day instead of the aggregation. So for example, I am trying to get the first row to be like
Anhui, Mainland China, 1, 8, 6
I think there might be a quick pandas way to do this but can't find it by google searching. A brute force method would be okay too. Thanks!
Yaniel Cabrera :
You can take take the finite difference along constant rows in the dataframe. If df is a copy of the numerical part of the dataframe then the following will do it:
df.diff(axis=1)
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