polars.DataFrame.select#
- DataFrame.select(
- *exprs: IntoExpr | Iterable[IntoExpr],
- **named_exprs: IntoExpr,
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.Int64) % 2 == 1).name.suffix("_is_odd"), ... ) shape: (3, 1) ┌──────────────┐ │ is_odd │ │ --- │ │ struct[2] │ ╞══════════════╡ │ {true,false} │ │ {false,true} │ │ {true,false} │ └──────────────┘