Opposite of melt in python pandas

there are a few ways;
using .pivot:

>>> origin.pivot(index='label', columns='type')['value']type   a  b  clabel         x      1  2  3y      4  5  6z      7  8  9[3 rows x 3 columns]

using pivot_table:

>>> origin.pivot_table(values='value', index='label', columns='type')       value      type       a  b  clabel             x          1  2  3y          4  5  6z          7  8  9[3 rows x 3 columns]

or .groupby followed by .unstack:

>>> origin.groupby(['label', 'type'])['value'].aggregate('mean').unstack()type   a  b  clabel         x      1  2  3y      4  5  6z      7  8  9[3 rows x 3 columns]

DataFrame.set_index + DataFrame.unstack

df.set_index(['label','type'])['value'].unstack()type   a  b  clabel         x      1  2  3y      4  5  6z      7  8  9

simplifying the passing of pivot arguments

df.pivot(*df)type   a  b  clabel         x      1  2  3y      4  5  6z      7  8  9

[*df]#['label', 'type', 'value']

For expected output we need DataFrame.reset_index and DataFrame.rename_axis

df.pivot(*df).rename_axis(columns = None).reset_index()  label  a  b  c0     x  1  2  31     y  4  5  62     z  7  8  9

if there are duplicates in a,b columns we could lose information so we need GroupBy.cumcount

print(df)  label type  value0     x    a      11     x    b      22     x    c      33     y    a      44     y    b      55     y    c      66     z    a      77     z    b      88     z    c      90     x    a      11     x    b      22     x    c      33     y    a      44     y    b      55     y    c      66     z    a      77     z    b      88     z    c      9

df.pivot_table(index = ['label',                        df.groupby(['label','type']).cumcount()],               columns = 'type',               values = 'value')type     a  b  clabel           x     0  1  2  3      1  1  2  3y     0  4  5  6      1  4  5  6z     0  7  8  9      1  7  8  9

Or:

(df.assign(type_2 = df.groupby(['label','type']).cumcount())   .set_index(['label','type','type_2'])['value']   .unstack('type'))