Opposite of melt in python pandas

Question

I cannot figure out how to do \"reverse melt\" using Pandas in python. This is my starting data

import pandas as pd

from StringIO import StringIO

origin =          


        

Answer 1

DataFrame.set_index + DataFrame.unstack

df.set_index(['label','type'])['value'].unstack()

type   a  b  c
label         
x      1  2  3
y      4  5  6
z      7  8  9

simplifying the passing of pivot arguments

df.pivot(*df)

type   a  b  c
label         
x      1  2  3
y      4  5  6
z      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  c
0     x  1  2  3
1     y  4  5  6
2     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  value
0     x    a      1
1     x    b      2
2     x    c      3
3     y    a      4
4     y    b      5
5     y    c      6
6     z    a      7
7     z    b      8
8     z    c      9
0     x    a      1
1     x    b      2
2     x    c      3
3     y    a      4
4     y    b      5
5     y    c      6
6     z    a      7
7     z    b      8
8     z    c      9

---

df.pivot_table(index = ['label',
                        df.groupby(['label','type']).cumcount()],
               columns = 'type',
               values = 'value')


type     a  b  c
label           
x     0  1  2  3
      1  1  2  3
y     0  4  5  6
      1  4  5  6
z     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'))

Answer 2

there are a few ways;
using .pivot:

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

[3 rows x 3 columns]

using pivot_table:

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

[3 rows x 3 columns]

or .groupby followed by .unstack:

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

[3 rows x 3 columns]