polars.Expr.exclude#

Expr.exclude( columns: str | PolarsDataType | Collection[str] | Collection[PolarsDataType], *more_columns: str | PolarsDataType, ) → Self[source]#

Exclude columns from a multi-column expression.

Only works after a wildcard or regex column selection, and you cannot provide both string column names and dtypes (you may prefer to use selectors instead).

Parameters:

columns: The name or datatype of the column(s) to exclude. Accepts regular expression input. Regular expressions should start with ^ and end with $.
*more_columns: Additional names or datatypes of columns to exclude, specified as positional arguments.

Examples

>>> df = pl.DataFrame(
...     {
...         "aa": [1, 2, 3],
...         "ba": ["a", "b", None],
...         "cc": [None, 2.5, 1.5],
...     }
... )
>>> df
shape: (3, 3)
┌─────┬──────┬──────┐
│ aa  ┆ ba   ┆ cc   │
│ --- ┆ ---  ┆ ---  │
│ i64 ┆ str  ┆ f64  │
╞═════╪══════╪══════╡
│ 1   ┆ a    ┆ null │
│ 2   ┆ b    ┆ 2.5  │
│ 3   ┆ null ┆ 1.5  │
└─────┴──────┴──────┘

Exclude by column name(s):

>>> df.select(pl.all().exclude("ba"))
shape: (3, 2)
┌─────┬──────┐
│ aa  ┆ cc   │
│ --- ┆ ---  │
│ i64 ┆ f64  │
╞═════╪══════╡
│ 1   ┆ null │
│ 2   ┆ 2.5  │
│ 3   ┆ 1.5  │
└─────┴──────┘

Exclude by regex, e.g. removing all columns whose names end with the letter “a”:

>>> df.select(pl.all().exclude("^.*a$"))
shape: (3, 1)
┌──────┐
│ cc   │
│ ---  │
│ f64  │
╞══════╡
│ null │
│ 2.5  │
│ 1.5  │
└──────┘

Exclude by dtype(s), e.g. removing all columns of type Int64 or Float64:

>>> df.select(pl.all().exclude([pl.Int64, pl.Float64]))
shape: (3, 1)
┌──────┐
│ ba   │
│ ---  │
│ str  │
╞══════╡
│ a    │
│ b    │
│ null │
└──────┘