I'm having difficulty using transpose with pandas.
I have the following df:
date name quantity
1/1/2018 A 5
1/1/2018 B 6
1/1/2018 C 7
1/2/2018 A 9
1/2/2018 B 8
1/2/2018 C 6
I eventually want to create a pairwise correlation for all the names and their quantities on each date. To to that end, I'm trying to create the following output from this df first:
date A B C
1/1/2018 5 6 7
1/2/2018 9 8 6
The transpose is difficult to me since I can get duplicate column headers, but I also don't want to lose any data by dropping them first. I have a feeling the answer may be with a panda utility that I don't really use and I may be tunneling on transpose...
Since you aren't performing an aggregation, pd.DataFrame.pivot should be preferred to groupby / pivot_table:
res = df.pivot(index='date', columns='name', values='quantity')
print(res)
name A B C
date
1/1/2018 5 6 7
1/2/2018 9 8 6
If you wish you can use reset_index to elevate date to a column.
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