polars.DataFrame.filter

DataFrame.filter(

*predicates: IntoExprColumn | Iterable[IntoExprColumn] | bool | list[bool] | np.ndarray[Any, Any],

**constraints: Any,

) → DataFrame

Filter the rows in the DataFrame based on a predicate expression.

The original order of the remaining rows is preserved.

Parameters:

predicates

Expression that evaluates to a boolean Series.

constraints

Column filters. Use name=value to filter column name by the supplied value.

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   │
└─────┴─────┴─────┘