bigframes.pandas.DataFrame.melt#

DataFrame.melt(id_vars: Iterable[Hashable] | None = None, value_vars: Iterable[Hashable] | None = None, var_name: Hashable | Sequence[Hashable] = None, value_name: Hashable = 'value')[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.

Examples:

>>> df = bpd.DataFrame({"A": [1, None, 3, 4, 5],
...                     "B": [1, 2, 3, 4, 5],
...                     "C": [None, 3.5, None, 4.5, 5.0]})
>>> df
        A       B      C
0     1.0       1   <NA>
1    <NA>       2    3.5
2     3.0       3   <NA>
3     4.0       4    4.5
4     5.0       5    5.0

[5 rows x 3 columns]

Using melt without optional arguments:

>>> df.melt()
  variable  value
0        A    1.0
1        A   <NA>
2        A    3.0
3        A    4.0
4        A    5.0
5        B    1.0
6        B    2.0
7        B    3.0
8        B    4.0
9        B    5.0
...

[15 rows x 2 columns]

Using melt with id_vars and value_vars:

>>> df.melt(id_vars='A', value_vars=['B', 'C'])
      A variable  value
0   1.0        B    1.0
1  <NA>        B    2.0
2   3.0        B    3.0
3   4.0        B    4.0
4   5.0        B    5.0
5   1.0        C   <NA>
6  <NA>        C    3.5
7   3.0        C   <NA>
8   4.0        C    4.5
9   5.0        C    5.0

[10 rows x 3 columns]

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.

Returns:

Unpivoted DataFrame.

Return type:

bigframes.pandas.DataFrame