pandas.DataFrame.melt#

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

Unpivot 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, original index is retained. Index labels will be repeated as necessary.

Returns:

DataFrame: Unpivoted 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

pandas.DataFrame.melt#

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