pandas.melt#

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_varsscalar, tuple, list, or ndarray, optional: Column(s) to use as identifier variables.
value_varsscalar, tuple, list, or ndarray, optional: Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
var_namescalar, default None: Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.
value_namescalar, default ‘value’: Name to use for the ‘value’ column, can’t be an existing column label.
col_levelscalar, optional: If columns are a MultiIndex then use this level to melt.
ignore_indexbool, 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.

See also

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}})
>>> df
 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')]
>>> df
 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