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 = pd.read_table(StringIO('''label    type    value x   a   1 x   b   2 x   c   3 y   a   4 y   b   5 y   c   6 z   a   7 z   b   8 z   c   9'''))  origin Out[5]:    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 

This is the output I would like to have:

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

I'm sure there is an easy way to do this, but I don't know how.

Answer 1

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] 

Answer 2

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'))