Create New Dataframe Columns Based off of Values in Existing Dataframe

Asked Feb 20 '20 at 20:45

Active Feb 20 '20 at 20:45

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I have a dataframe that looks like this:

Publisher   Week End Date   Cost
A   1/4/2020    302.4242948
A   1/11/2020   527.256899
A   1/18/2020   54.75997765
A   1/25/2020   378.5780062
A   2/1/2020    189.8760722
C   2/8/2020    212.1528952
A   2/15/2020   218.2095771
E   1/4/2020    1
D   1/11/2020   1
B   1/18/2020   1

I want to reverse-flatten it so that for each Publisher, a new Cost column is generated. So the output would look like this:

Week End Date   A Cost  B Cost  C Cost  D Cost  E Cost
1/4/2020    302.4242948             
1/11/2020   527.256899              
1/18/2020   54.75997765             
1/25/2020   378.5780062             
2/1/2020    189.8760722             
2/8/2020            212.1528952     
2/15/2020   218.2095771             
1/4/2020                    1
1/11/2020               1   
1/18/2020       1

How do I do this in Python?

asked Feb 20 '20 at 20:45

dataelephant

Does `df.pivot` not work for you? – Quang Hoang Feb 20 '20 at 20:47
I need an output where each Publisher Cost is its own column – dataelephant Feb 20 '20 at 20:49
I know how to pivot the data - the issue is the new separate columns based off of each Publisher in the Publisher column – dataelephant Feb 20 '20 at 20:51
1

`df.pivot(index='Week End Date', columns='Publisher', values='Cost')`? – Quang Hoang Feb 20 '20 at 20:51
*the new separate columns based off of each Publisher in the Publisher column* is exactly what `pivot` is (for). – Quang Hoang Feb 20 '20 at 20:53

Create New Dataframe Columns Based off of Values in Existing Dataframe

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