polars.DataFrame.filter#
- DataFrame.filter(
- *predicates: IntoExprColumn | Iterable[IntoExprColumn] | bool | list[bool] | np.ndarray[Any, Any],
- **constraints: Any,
Filter the rows in the DataFrame based on one or more predicate expressions.
The original order of the remaining rows is preserved.
Rows where the filter does not evaluate to True are discarded, including nulls.
- Parameters:
- predicates
Expression(s) that evaluates to a boolean Series.
- constraints
Column filters; use
name = value
to filter columns by the supplied value. Each constraint will behave the same aspl.col(name).eq(value)
, and will be implicitly joined with the other filter conditions using&
.
Examples
>>> df = pl.DataFrame( ... { ... "foo": [1, 2, 3], ... "bar": [6, 7, 8], ... "ham": ["a", "b", "c"], ... } ... )
Filter on one condition:
>>> df.filter(pl.col("foo") > 1) shape: (2, 3) ┌─────┬─────┬─────┐ │ foo ┆ bar ┆ ham │ │ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ str │ ╞═════╪═════╪═════╡ │ 2 ┆ 7 ┆ b │ │ 3 ┆ 8 ┆ c │ └─────┴─────┴─────┘
Filter on multiple conditions, combined with and/or operators:
>>> df.filter((pl.col("foo") < 3) & (pl.col("ham") == "a")) shape: (1, 3) ┌─────┬─────┬─────┐ │ foo ┆ bar ┆ ham │ │ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ str │ ╞═════╪═════╪═════╡ │ 1 ┆ 6 ┆ a │ └─────┴─────┴─────┘
>>> df.filter((pl.col("foo") == 1) | (pl.col("ham") == "c")) shape: (2, 3) ┌─────┬─────┬─────┐ │ foo ┆ bar ┆ ham │ │ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ str │ ╞═════╪═════╪═════╡ │ 1 ┆ 6 ┆ a │ │ 3 ┆ 8 ┆ c │ └─────┴─────┴─────┘
Provide multiple filters using
*args
syntax:>>> df.filter( ... pl.col("foo") <= 2, ... ~pl.col("ham").is_in(["b", "c"]), ... ) shape: (1, 3) ┌─────┬─────┬─────┐ │ foo ┆ bar ┆ ham │ │ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ str │ ╞═════╪═════╪═════╡ │ 1 ┆ 6 ┆ a │ └─────┴─────┴─────┘
Provide multiple filters using
**kwargs
syntax:>>> df.filter(foo=2, ham="b") shape: (1, 3) ┌─────┬─────┬─────┐ │ foo ┆ bar ┆ ham │ │ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ str │ ╞═════╪═════╪═════╡ │ 2 ┆ 7 ┆ b │ └─────┴─────┴─────┘