Transpose dataframe in pandas

Question

Given this pandas dataframe where for each date and field there's a value:

df = pd.DataFrame({
    'date' : [datetime(2021,3,11), datetime(2021,3,11), 
              datetime(2021,3,12), datetime(2021,3,12), 
              datetime(2021,3,13), datetime(2021,3,13)],
    'field': ['A', 'B', 'A', 'B', 'A', 'B'],
    'value': [150, 140, 130, 280, 260, 120]
})


    date      field value
0   2021-03-11  A   150
1   2021-03-11  B   140
2   2021-03-12  A   130
3   2021-03-12  B   280
4   2021-03-13  A   260
5   2021-03-13  B   120

I need to transpose it where each date is a row, and each field is a column:

df2 = pd.DataFrame({
    'date' : [datetime(2021,3,11), 
              datetime(2021,3,12), 
              datetime(2021,3,13)],
    'A': [150, 130, 260],
    'B': [140, 280, 120]
})

      date       A   B
0   2021-03-11  150 140
1   2021-03-12  130 280
2   2021-03-13  260 120

Note that:

• Each date is a row
• The number of fields is variable (in the example the number of fields is two, A and B)
• The content of column value is converted to the content of each new generated column.

Is this possible to do without iterrows()?

Answer 1

Possible solution;

df.set_index(['date','field']).stack().unstack('field').droplevel(level=1).reset_index()

or

df.pivot(index='date', columns='field', values='value')