polars.api.register_lazyframe_namespace#

polars.api.register_lazyframe_namespace(name: str) Callable[[type[NS]], type[NS]][source]#

Decorator for registering custom functionality with a polars LazyFrame.

Parameters:
name

Name under which the functionality will be accessed.

See also

register_expr_namespace

Register functionality on an Expr.

register_dataframe_namespace

Register functionality on a DataFrame.

register_series_namespace

Register functionality on a Series.

Examples

>>> @pl.api.register_lazyframe_namespace("types")
... class DTypeOperations:
...     def __init__(self, ldf: pl.LazyFrame):
...         self._ldf = ldf
...
...     def split_by_column_dtypes(self) -> list[pl.LazyFrame]:
...         return [
...             self._ldf.select(pl.col(tp))
...             for tp in dict.fromkeys(self._ldf.dtypes)
...         ]
...
...     def upcast_integer_types(self) -> pl.LazyFrame:
...         return self._ldf.with_columns(
...             pl.col(tp).cast(pl.Int64) for tp in (pl.Int8, pl.Int16, pl.Int32)
...         )
>>>
>>> ldf = pl.DataFrame(
...     data={"a": [1, 2], "b": [3, 4], "c": [5.6, 6.7]},
...     columns=[("a", pl.Int16), ("b", pl.Int32), ("c", pl.Float32)],
... ).lazy()
>>>
>>> ldf.collect()
shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ i16 ┆ i32 ┆ f32 │
╞═════╪═════╪═════╡
│ 1   ┆ 3   ┆ 5.6 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 2   ┆ 4   ┆ 6.7 │
└─────┴─────┴─────┘
>>> ldf.types.upcast_integer_types().collect()
shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ f32 │
╞═════╪═════╪═════╡
│ 1   ┆ 3   ┆ 5.6 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 2   ┆ 4   ┆ 6.7 │
└─────┴─────┴─────┘
>>>
>>> ldf = pl.DataFrame(
...     data=[["xx", 2, 3, 4], ["xy", 4, 5, 6], ["yy", 5, 6, 7], ["yz", 6, 7, 8]],
...     columns=["a1", "a2", "b1", "b2"],
...     orient="row",
... ).lazy()
>>>
>>> ldf.collect()
shape: (4, 4)
┌─────┬─────┬─────┬─────┐
│ a1  ┆ a2  ┆ b1  ┆ b2  │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╪═════╡
│ xx  ┆ 2   ┆ 3   ┆ 4   │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ xy  ┆ 4   ┆ 5   ┆ 6   │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ yy  ┆ 5   ┆ 6   ┆ 7   │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ yz  ┆ 6   ┆ 7   ┆ 8   │
└─────┴─────┴─────┴─────┘
>>> [ldf.collect() for ldf in ldf.types.split_by_column_dtypes()]
[shape: (4, 1)
┌─────┐
│ a1  │
│ --- │
│ str │
╞═════╡
│ xx  │
├╌╌╌╌╌┤
│ xy  │
├╌╌╌╌╌┤
│ yy  │
├╌╌╌╌╌┤
│ yz  │
└─────┘, shape: (4, 3)
┌─────┬─────┬─────┐
│ a2  ┆ b1  ┆ b2  │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 2   ┆ 3   ┆ 4   │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 4   ┆ 5   ┆ 6   │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 5   ┆ 6   ┆ 7   │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 6   ┆ 7   ┆ 8   │
└─────┴─────┴─────┘]