polars.DataFrame.map_columns#

DataFrame.map_columns(

column_names: str | Sequence[str] | Selector,

function: Callable[[Series], Series],

*args: P.args,

**kwargs: P.kwargs,

) → DataFrame[source]#

Apply eager functions to columns of a DataFrame.

Users should always prefer with_columns() unless they are using expressions that are only possible on Series and not on Expr. This is almost never the case, except for a very select few functions that cannot know the output datatype without looking at the data.

Parameters:

column_names: The columns to apply the UDF to.
function: Callable; will receive a column series as the first parameter, followed by any given args/kwargs.
*args: Arguments to pass to the UDF.
**kwargs: Keyword arguments to pass to the UDF.

See also

with_columns

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3, 4], "b": ["10", "20", "30", "40"]})
>>> df.map_columns("a", lambda s: s.shrink_dtype())
shape: (4, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i8  ┆ str │
╞═════╪═════╡
│ 1   ┆ 10  │
│ 2   ┆ 20  │
│ 3   ┆ 30  │
│ 4   ┆ 40  │
└─────┴─────┘

>>> df = pl.DataFrame(
...     {
...         "a": ['{"x":"a"}', None, '{"x":"b"}', None],
...         "b": ['{"a":1, "b": true}', None, '{"a":2, "b": false}', None],
...     }
... )
>>> df.map_columns(["a", "b"], lambda s: s.str.json_decode())
shape: (4, 2)
┌───────────┬───────────┐
│ a         ┆ b         │
│ ---       ┆ ---       │
│ struct[1] ┆ struct[2] │
╞═══════════╪═══════════╡
│ {"a"}     ┆ {1,true}  │
│ null      ┆ null      │
│ {"b"}     ┆ {2,false} │
│ null      ┆ null      │
└───────────┴───────────┘
>>> import polars.selectors as cs
>>> df.map_columns(cs.all(), lambda s: s.str.json_decode())
shape: (4, 2)
┌───────────┬───────────┐
│ a         ┆ b         │
│ ---       ┆ ---       │
│ struct[1] ┆ struct[2] │
╞═══════════╪═══════════╡
│ {"a"}     ┆ {1,true}  │
│ null      ┆ null      │
│ {"b"}     ┆ {2,false} │
│ null      ┆ null      │
└───────────┴───────────┘