polars.when#

polars.when(expr: polars.internals.expr.expr.Expr | bool) → When[source]#

Start a “when, then, otherwise” expression.

See also

then: Values to return in case of the predicate being True.
otherwise: Values to return in case of the predicate being False.

Examples

Below we add a column with the value 1, where column “foo” > 2 and the value -1 where it isn’t.

>>> df = pl.DataFrame({"foo": [1, 3, 4], "bar": [3, 4, 0]})
>>> df.with_column(pl.when(pl.col("foo") > 2).then(pl.lit(1)).otherwise(pl.lit(-1)))
shape: (3, 3)
┌─────┬─────┬─────────┐
│ foo ┆ bar ┆ literal │
│ --- ┆ --- ┆ ---     │
│ i64 ┆ i64 ┆ i32     │
╞═════╪═════╪═════════╡
│ 1   ┆ 3   ┆ -1      │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 3   ┆ 4   ┆ 1       │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 4   ┆ 0   ┆ 1       │
└─────┴─────┴─────────┘

Or with multiple when, thens chained:

>>> df.with_column(
...     pl.when(pl.col("foo") > 2)
...     .then(1)
...     .when(pl.col("bar") > 2)
...     .then(4)
...     .otherwise(-1)
... )
shape: (3, 3)
┌─────┬─────┬─────────┐
│ foo ┆ bar ┆ literal │
│ --- ┆ --- ┆ ---     │
│ i64 ┆ i64 ┆ i32     │
╞═════╪═════╪═════════╡
│ 1   ┆ 3   ┆ 4       │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 3   ┆ 4   ┆ 1       │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 4   ┆ 0   ┆ 1       │
└─────┴─────┴─────────┘