pandas.melt — pandas 2.2.3 documentation

pandas. melt ( frame , id_vars = None , value_vars = None , var_name = None , value_name = 'value' , col_level = None , ignore_index = True ) [source] #

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 scalar, tuple, list, or ndarray, optional

Column(s) to use as identifier variables.

value_vars scalar, 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, default None

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, can’t be an existing column label.

col_level scalar, optional

If columns are a MultiIndex then use this level to melt.

ignore_index bool, default True

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

Returns :

DataFrame

Unpivoted DataFrame.


     
      DataFrame.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}})
   A  B  C
0  a  1  2
1  b  3  4
2  c  5  6
>>> pd.melt(df, id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
>>> pd.melt(df, 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:
>>> pd.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
Original index values can be kept around:
>>> pd.melt(df, 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')]
   A  B  C
   D  E  F
0  a  1  2
1  b  3  4
2  c  5  6
>>> pd.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
>>> pd.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