dask.dataframe.DataFrame.melt

Contents

dask.dataframe.DataFrame.melt#

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

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

This docstring was copied from pandas.DataFrame.melt.

Some inconsistencies with the Dask version may exist.

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 (Not supported in Dask)

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
Contents