polars.col#

polars.col( name: str | PolarsDataType | Iterable[str] | Iterable[PolarsDataType], *more_names: str | PolarsDataType, ) → Expr[source]#

Return an expression representing column(s) in a dataframe.

Parameters:

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

Examples

Pass a single column name to represent that column.

>>> df = pl.DataFrame(
...     {
...         "ham": [1, 2, 3],
...         "hamburger": [11, 22, 33],
...         "foo": [3, 2, 1],
...         "bar": ["a", "b", "c"],
...     }
... )
>>> df.select(pl.col("foo"))
shape: (3, 1)
┌─────┐
│ foo │
│ --- │
│ i64 │
╞═════╡
│ 3   │
│ 2   │
│ 1   │
└─────┘

Use the wildcard * to represent all columns.

>>> df.select(pl.col("*"))
shape: (3, 4)
┌─────┬───────────┬─────┬─────┐
│ ham ┆ hamburger ┆ foo ┆ bar │
│ --- ┆ ---       ┆ --- ┆ --- │
│ i64 ┆ i64       ┆ i64 ┆ str │
╞═════╪═══════════╪═════╪═════╡
│ 1   ┆ 11        ┆ 3   ┆ a   │
│ 2   ┆ 22        ┆ 2   ┆ b   │
│ 3   ┆ 33        ┆ 1   ┆ c   │
└─────┴───────────┴─────┴─────┘
>>> df.select(pl.col("*").exclude("ham"))
shape: (3, 3)
┌───────────┬─────┬─────┐
│ hamburger ┆ foo ┆ bar │
│ ---       ┆ --- ┆ --- │
│ i64       ┆ i64 ┆ str │
╞═══════════╪═════╪═════╡
│ 11        ┆ 3   ┆ a   │
│ 22        ┆ 2   ┆ b   │
│ 33        ┆ 1   ┆ c   │
└───────────┴─────┴─────┘

Regular expression input is supported.

>>> df.select(pl.col("^ham.*$"))
shape: (3, 2)
┌─────┬───────────┐
│ ham ┆ hamburger │
│ --- ┆ ---       │
│ i64 ┆ i64       │
╞═════╪═══════════╡
│ 1   ┆ 11        │
│ 2   ┆ 22        │
│ 3   ┆ 33        │
└─────┴───────────┘

Multiple columns can be represented by passing a list of names.

>>> df.select(pl.col(["hamburger", "foo"]))
shape: (3, 2)
┌───────────┬─────┐
│ hamburger ┆ foo │
│ ---       ┆ --- │
│ i64       ┆ i64 │
╞═══════════╪═════╡
│ 11        ┆ 3   │
│ 22        ┆ 2   │
│ 33        ┆ 1   │
└───────────┴─────┘

Or use positional arguments to represent multiple columns in the same way.

>>> df.select(pl.col("hamburger", "foo"))
shape: (3, 2)
┌───────────┬─────┐
│ hamburger ┆ foo │
│ ---       ┆ --- │
│ i64       ┆ i64 │
╞═══════════╪═════╡
│ 11        ┆ 3   │
│ 22        ┆ 2   │
│ 33        ┆ 1   │
└───────────┴─────┘

Easily select all columns that match a certain data type by passing that datatype.

>>> df.select(pl.col(pl.Utf8))
shape: (3, 1)
┌─────┐
│ bar │
│ --- │
│ str │
╞═════╡
│ a   │
│ b   │
│ c   │
└─────┘
>>> df.select(pl.col(pl.Int64, pl.Float64))
shape: (3, 3)
┌─────┬───────────┬─────┐
│ ham ┆ hamburger ┆ foo │
│ --- ┆ ---       ┆ --- │
│ i64 ┆ i64       ┆ i64 │
╞═════╪═══════════╪═════╡
│ 1   ┆ 11        ┆ 3   │
│ 2   ┆ 22        ┆ 2   │
│ 3   ┆ 33        ┆ 1   │
└─────┴───────────┴─────┘