How to reshape a dataframe to a pivot table, including per-row totals and keeping two-level headers

Question

I want to reshape my dataframe so I can pivot the 'kind' field, but I also want to include per-row aggregations.

df = pd.DataFrame([
    {
        'date': '2022-04-20',
        'kind': 'alpha',
        'scalar_a': 2,
        'scalar_b': 5
    },
    {
        'date': '2022-04-20',
        'kind': 'bravo',
        'scalar_a': 3,
        'scalar_b': 7
    },
    {
        'date': '2022-04-21',
        'kind': 'charlie',
        'scalar_a': 4,
        'scalar_b': 3
    },
    {
        'date': '2022-04-22',
        'kind': 'bravo',
        'scalar_a': 5,
        'scalar_b': 1
    },
])

This produces this table.

I want to reshape to this:

I want to:

1. Aggregate my data by date, and have it reshaped so I can see each kind side-by-side.
2. I want to compare Scalar_A and Scalar_B from each kinds on the same row, including a per-kind aggregation.
3. I want to also have a totals column (per-row/ per-date)

My attempt was to create two dataframes (one for calculating the per-date totals, and another one to perform the pivot transformation), and then concatenate them across the horizontal axis.

totals_df = df.groupby('date').agg(
     total_a=('scalar_a', 'sum'),
     total_b=('scalar_b', 'sum'),
)
# I also need a column calculated by the aggregated fields.
totals_df["Total a*b"] = totals_df["total_a"] * totals_df["total_b"]
# Then I sort by descending date.
totals_df = totals_df.sort_values('date',ascending=False)

# And then to build my pivoted dataframe
pivot_df = df.pivot_table(
    index=['date'],
    columns=['kind'],
    fill_value=0
)

# I attempt to invert the two top-level headers ([scalar_a, scalar_b] with [])
pivot_fundos = pivot_fundos.swaplevel(1,0, axis=1).sort_index(axis=1)

How would I proceed to include a per-date/per-kind aggregation? I want to include a column with scalar_a + scalar_b for each kind sub-column.

Then I concatenate both dataframes.

concatenated_dataframes = pd.concat([pivot_df, totals_df], axis=1)

The resulting dataframe doesn't have the "two-levels" headers I expected when calling the to_table() method. How do I fix that?

Answer 1

I would approach it this way. First pivot_table , then use groupby on columns to compute the sums, finally concat everything (with inner concat to add the missing levels):

df2 = df.pivot_table(index='date', columns='kind',
                     values=['scalar_a', 'scalar_b'], fill_value=0)
df3 = (df2
       .groupby(level=0, axis=1).sum().add_prefix('total_')
       .assign(**{'Total a*b': lambda d: d.prod(1)})
       )

out = pd.concat(
          [df2.swaplevel(axis=1),
           pd.concat({'sum': df2.groupby(level=1, axis=1).sum()}, axis=1)
             .swaplevel(axis=1),
           pd.concat({'Totals': df3}, axis=1),
          ], axis=1).sort_index(axis=1)

output:

kind          Totals                                  alpha                 bravo               charlie             
           Total a*b total_scalar_a total_scalar_b scalar_a scalar_b sum scalar_a scalar_b sum scalar_a scalar_b sum
date                                                                                                                
2022-04-20        60              5             12        2        5   7        3        7  10        0        0   0
2022-04-21        12              4              3        0        0   0        0        0   0        4        3   7
2022-04-22         5              5              1        0        0   0        5        1   6        0        0   0

sorted columns

df2 = df.pivot_table(index='date', columns='kind',
                     values=['scalar_a', 'scalar_b'], fill_value=0)
df3 = (df2
       .groupby(level=0, axis=1).sum().add_prefix('total_')
       .assign(**{'Total a*b': lambda d: d.prod(1)})
       )
df3 = pd.concat({'Totals': df3}, axis=1)

idx = pd.MultiIndex.from_product([df['kind'].unique(),list(df.filter(like='scalar'))+['sum']])

out = pd.concat(
          [df2.swaplevel(axis=1),
           pd.concat({'sum': df2.groupby(level=1, axis=1).sum()}, axis=1)
             .swaplevel(axis=1),
           df3,
          ], axis=1)[list(idx)+list(df3)]

Output:

kind          alpha                 bravo               charlie                      Totals                         
           scalar_a scalar_b sum scalar_a scalar_b sum scalar_a scalar_b sum total_scalar_a total_scalar_b Total a*b
date                                                                                                                
2022-04-20        2        5   7        3        7  10        0        0   0              5             12        60
2022-04-21        0        0   0        0        0   0        4        3   7              4              3        12
2022-04-22        0        0   0        5        1   6        0        0   0              5              1         5

Answer 2

Use:

df2 = df.pivot(index='date', columns='kind', values=['scalar_a', 'scalar_b']).swaplevel(1,0, axis=1).sort_index(axis=1)
cols = [(  x, 'sum') for x in df['kind'].unique()]
df2[cols]=df2.sum(axis=1, level=0).sort_index(axis = 1)

Output: