polars.DataFrame.remove#

DataFrame.remove(
*predicates: IntoExprColumn | Iterable[IntoExprColumn] | bool | list[bool] | np.ndarray[Any, Any],
**constraints: Any,
) → DataFrame[source]#

Remove rows, dropping those that match the given predicate expression(s).

The original order of the remaining rows is preserved.

Rows where the filter predicate does not evaluate to True are retained (this includes rows where the predicate evaluates as null).

Parameters:
predicates

Expression that evaluates to a boolean Series.

constraints

Column filters; use name = value to filter columns using the supplied value. Each constraint behaves the same as pl.col(name).eq(value), and is implicitly joined with the other filter conditions using &.

See also

filter

Notes

If you are transitioning from Pandas, and performing filter operations based on the comparison of two or more columns, please note that in Polars any comparison involving null values will result in a null result, not boolean True or False. As a result, these rows will not be removed. Ensure that null values are handled appropriately to avoid unexpected behaviour (see examples below).

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [2, 3, None, 4, 0],
...         "bar": [5, 6, None, None, 0],
...         "ham": ["a", "b", None, "c", "d"],
...     }
... )

Remove rows matching a condition:

>>> df.remove(pl.col("bar") >= 5)
shape: (3, 3)
┌──────┬──────┬──────┐
│ foo  ┆ bar  ┆ ham  │
│ ---  ┆ ---  ┆ ---  │
│ i64  ┆ i64  ┆ str  │
╞══════╪══════╪══════╡
│ null ┆ null ┆ null │
│ 4    ┆ null ┆ c    │
│ 0    ┆ 0    ┆ d    │
└──────┴──────┴──────┘

Discard rows based on multiple conditions, combined with and/or operators:

>>> df.remove(
...     (pl.col("foo") >= 0) & (pl.col("bar") >= 0),
... )
shape: (2, 3)
┌──────┬──────┬──────┐
│ foo  ┆ bar  ┆ ham  │
│ ---  ┆ ---  ┆ ---  │
│ i64  ┆ i64  ┆ str  │
╞══════╪══════╪══════╡
│ null ┆ null ┆ null │
│ 4    ┆ null ┆ c    │
└──────┴──────┴──────┘

>>> df.remove(
...     (pl.col("foo") >= 0) | (pl.col("bar") >= 0),
... )
shape: (1, 3)
┌──────┬──────┬──────┐
│ foo  ┆ bar  ┆ ham  │
│ ---  ┆ ---  ┆ ---  │
│ i64  ┆ i64  ┆ str  │
╞══════╪══════╪══════╡
│ null ┆ null ┆ null │
└──────┴──────┴──────┘

Provide multiple constraints using *args syntax:

>>> df.remove(
...     pl.col("ham").is_not_null(),
...     pl.col("bar") >= 0,
... )
shape: (2, 3)
┌──────┬──────┬──────┐
│ foo  ┆ bar  ┆ ham  │
│ ---  ┆ ---  ┆ ---  │
│ i64  ┆ i64  ┆ str  │
╞══════╪══════╪══════╡
│ null ┆ null ┆ null │
│ 4    ┆ null ┆ c    │
└──────┴──────┴──────┘

Provide constraints(s) using **kwargs syntax:

>>> df.remove(foo=0, bar=0)
shape: (4, 3)
┌──────┬──────┬──────┐
│ foo  ┆ bar  ┆ ham  │
│ ---  ┆ ---  ┆ ---  │
│ i64  ┆ i64  ┆ str  │
╞══════╪══════╪══════╡
│ 2    ┆ 5    ┆ a    │
│ 3    ┆ 6    ┆ b    │
│ null ┆ null ┆ null │
│ 4    ┆ null ┆ c    │
└──────┴──────┴──────┘

Remove rows by comparing two columns against each other:

>>> df.remove(
...     pl.col("foo").ne_missing(pl.col("bar")),
... )
shape: (2, 3)
┌──────┬──────┬──────┐
│ foo  ┆ bar  ┆ ham  │
│ ---  ┆ ---  ┆ ---  │
│ i64  ┆ i64  ┆ str  │
╞══════╪══════╪══════╡
│ null ┆ null ┆ null │
│ 0    ┆ 0    ┆ d    │
└──────┴──────┴──────┘
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