pandas.melt

pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)

“Unpivots” a DataFrame from wide format to long format, optionally leaving id variables set

Parameters :

frame : DataFrame

id_vars : tuple, list, or ndarray

value_vars : tuple, list, or ndarray

var_name : scalar, if None uses frame.column.name or ‘variable’

value_name : scalar, default ‘value’

col_level : scalar, if columns are a MultiIndex then use this level to melt

Examples

>>> import pandas as pd
>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
...                    'B': {0: 1, 1: 3, 2: 5},
...                    'C': {0: 2, 1: 4, 2: 6}})

>>> df
   A  B  C
0  a  1  2
1  b  3  4
2  c  5  6

>>> melt(df, id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5

>>> melt(df, id_vars=['A'], value_vars=['B'],
... var_name='myVarname', value_name='myValname')
   A myVarname  myValname
0  a         B          1
1  b         B          3
2  c         B          5

>>> df.columns = [list('ABC'), list('DEF')]

>>> melt(df, col_level=0, id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5

>>> melt(df, id_vars=[('A', 'D')], value_vars=[('B', 'E')])
  (A, D) variable_0 variable_1  value
0      a          B          E      1
1      b          B          E      3
2      c          B          E      5