Transpose repeated values into multiple columns

Question

I have this dataframe:

index	Col1	Col2	Col3
0	A	Day1	10
1	A	Day2	9
2	A	Day3	11
3	B	Day1	12
4	B	Day2	7
5	B	Day3	5
6	C	Day1	9
7	C	Day2	10
8	C	Day3	6

I want to transpose the columns so that it looks like this

Date	A	B	C
Day1	10	12	9
Day2	9	7	10
Day3	11	5	6

Is there a way I can do that with Python? Thank you!

Answer 1

Yes, it's called pivoting.

df = pd.DataFrame({'Col1': ['A'] * 3 + ['B'] * 3 + ['C'] * 3, 'Col2': np.tile(['Day1', 'Day2', 'Day3'], 3), 'Col3': [10, 9, 11, 12, 7, 5, 9, 10, 6]})
df.pivot('Col2', 'Col1', 'Col3')

Answer 2

You can do it like @Alex shows you also there is another way that provides you more customization which is pivot_table which enables you to get average or any other aggregation on your data the default is mean

df.pivot_table(index="Col2",columns="Col1",values='Col3',aggfunc=sum)

df.pivot_table(index="Col2",columns="Col1",values='Col3',aggfunc='count')