polars.DataFrame.select

DataFrame.select(

*exprs: IntoExpr | Iterable[IntoExpr],

**named_exprs: IntoExpr,

) → DataFrame

Select columns from this DataFrame.

Parameters:

*exprs

Column(s) to select, specified as positional arguments. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.

**named_exprs

Additional columns to select, specified as keyword arguments. The columns will be renamed to the keyword used.

Examples

Pass the name of a column to select that column.

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.select("foo")
shape: (3, 1)
┌─────┐
│ foo │
│ --- │
│ i64 │
╞═════╡
│ 1   │
│ 2   │
│ 3   │
└─────┘

Multiple columns can be selected by passing a list of column names.

>>> df.select(["foo", "bar"])
shape: (3, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 6   │
│ 2   ┆ 7   │
│ 3   ┆ 8   │
└─────┴─────┘

Multiple columns can also be selected using positional arguments instead of a list. Expressions are also accepted.

>>> df.select(pl.col("foo"), pl.col("bar") + 1)
shape: (3, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 7   │
│ 2   ┆ 8   │
│ 3   ┆ 9   │
└─────┴─────┘

Use keyword arguments to easily name your expression inputs.

>>> df.select(threshold=pl.when(pl.col("foo") > 2).then(10).otherwise(0))
shape: (3, 1)
┌───────────┐
│ threshold │
│ ---       │
│ i32       │
╞═══════════╡
│ 0         │
│ 0         │
│ 10        │
└───────────┘

Expressions with multiple outputs can be automatically instantiated as Structs by enabling the setting Config.set_auto_structify(True):

>>> with pl.Config(auto_structify=True):
...     df.select(
...         is_odd=(pl.col(pl.INTEGER_DTYPES) % 2).name.suffix("_is_odd"),
...     )
shape: (3, 1)
┌───────────┐
│ is_odd    │
│ ---       │
│ struct[2] │
╞═══════════╡
│ {1,0}     │
│ {0,1}     │
│ {1,0}     │
└───────────┘