How to make values into rows instead of columns when using pivot table in pandas

Say I have this data frame:

import pandas as pd

x = pd.DataFrame([[1, 'step', 'id', 22, 33], 
                  [2, 'step', 'id', 55, 66]], 
                  columns=['time', 'head_1', 'head_2', 'value_1', 'value_2'])

print(x)

   time head_1 head_2  value_1  value_2
0     1   step     id       22       33
1     2   step     id       55       66

Then I use pivot table like below

print(x.pivot_table(values=['value_1', 'value_2'], columns='time', index=['head_1', 'head_2']))

              value_1     value_2    
time                1   2       1   2
head_1 head_2                        
step   id          22  55      33  66

However, I really want to have value_1 and value_2 in rows instead of columns like below (a new header as head_3). That is, put value_1 and value_2 in rows and only time as column. How do I do that?

time                       1   2
head_1 head_2 head_3                       
step   id     value_1     22  55
step   id     value_2     33  66

One straightforward way is to melt your dataframe so value_1 and value_2 become labels in single column and then pivot on that. Like:

import pandas as pd

x = pd.DataFrame(
    [
        [1, 'step', 'id', 22, 33],
        [2, 'step', 'id', 55, 66]
    ],
    columns=['time','head_1', 'head_2', 'value_1', 'value_2']
)

melted = x.melt(
    id_vars=['time','head_1', 'head_2'],
    value_vars=['value_1','value_2'],
    var_name='head_3',
    value_name='val'
)

result = melted.pivot_table(
    index=['head_1','head_2','head_3'],
    columns='time',
    values='val',
    aggfunc='first'
)

You’ll get:

time                 1   2
head_1 head_2 head_3        
step   id     value_1  22  55
               value_2 33  66

This places value_1 and `value_2 in rows as you wanted with only time going across columns