xorbits.pandas.DataFrame.melt#

DataFrame.melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)#

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

Parameters

  • id_vars (tuple, list, or ndarray, optional) – Column(s) to use as identifier variables.

  • value_vars (tuple, list, or ndarray, optional) – Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

  • var_name (scalar) – Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.

  • value_name (scalar, default 'value') – Name to use for the ‘value’ column.

  • col_level (int or str, optional) – If columns are a MultiIndex then use this level to melt.

  • ignore_index (bool, default True (Not supported yet)) –

    If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.

    New in version 1.1.0.

Returns

Unpivoted DataFrame.

Return type

DataFrame

See also

melt

Identical method.

pivot_table

Create a spreadsheet-style pivot table as a DataFrame.

DataFrame.pivot

Return reshaped DataFrame organized by given index / column values.

DataFrame.explode

Explode a DataFrame from list-like columns to long format.

Notes

Reference the user guide for more examples.

Examples

>>> 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

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

>>> df.melt(id_vars=['A'], value_vars=['B', 'C'])  
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
3  a        C      2
4  b        C      4
5  c        C      6

The names of ‘variable’ and ‘value’ columns can be customized:

>>> df.melt(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

Original index values can be kept around:

>>> df.melt(id_vars=['A'], value_vars=['B', 'C'], ignore_index=False)  
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
0  a        C      2
1  b        C      4
2  c        C      6

If you have multi-index columns:

>>> df.columns = [list('ABC'), list('DEF')]  
>>> df  
   A  B  C
   D  E  F
0  a  1  2
1  b  3  4
2  c  5  6

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

>>> df.melt(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

This docstring was copied from pandas.core.frame.DataFrame.