polars.Expr.replace#

Expr.replace(
old: IntoExpr | Sequence[Any] | Mapping[Any, Any],
new: IntoExpr | Sequence[Any] | NoDefault = _NoDefault.no_default,
*,
default: IntoExpr | NoDefault = _NoDefault.no_default,
return_dtype: PolarsDataType | None = None,
) Self[source]#

Replace values by different values.

Parameters:
old

Value or sequence of values to replace. Accepts expression input. Sequences are parsed as Series, other non-expression inputs are parsed as literals. Also accepts a mapping of values to their replacement as syntactic sugar for replace(new=Series(mapping.keys()), old=Series(mapping.values())).

new

Value or sequence of values to replace by. Accepts expression input. Sequences are parsed as Series, other non-expression inputs are parsed as literals. Length must match the length of old or have length 1.

default

Set values that were not replaced to this value. Defaults to keeping the original value. Accepts expression input. Non-expression inputs are parsed as literals.

return_dtype

The data type of the resulting expression. If set to None (default), the data type is determined automatically based on the other inputs.

See also

str.replace

Notes

The global string cache must be enabled when replacing categorical values.

Examples

Replace a single value by another value. Values that were not replaced remain unchanged.

>>> df = pl.DataFrame({"a": [1, 2, 2, 3]})
>>> df.with_columns(replaced=pl.col("a").replace(2, 100))
shape: (4, 2)
┌─────┬──────────┐
│ a   ┆ replaced │
│ --- ┆ ---      │
│ i64 ┆ i64      │
╞═════╪══════════╡
│ 1   ┆ 1        │
│ 2   ┆ 100      │
│ 2   ┆ 100      │
│ 3   ┆ 3        │
└─────┴──────────┘

Replace multiple values by passing sequences to the old and new parameters.

>>> df.with_columns(replaced=pl.col("a").replace([2, 3], [100, 200]))
shape: (4, 2)
┌─────┬──────────┐
│ a   ┆ replaced │
│ --- ┆ ---      │
│ i64 ┆ i64      │
╞═════╪══════════╡
│ 1   ┆ 1        │
│ 2   ┆ 100      │
│ 2   ┆ 100      │
│ 3   ┆ 200      │
└─────┴──────────┘

Passing a mapping with replacements is also supported as syntactic sugar. Specify a default to set all values that were not matched.

>>> mapping = {2: 100, 3: 200}
>>> df.with_columns(replaced=pl.col("a").replace(mapping, default=-1))
shape: (4, 2)
┌─────┬──────────┐
│ a   ┆ replaced │
│ --- ┆ ---      │
│ i64 ┆ i64      │
╞═════╪══════════╡
│ 1   ┆ -1       │
│ 2   ┆ 100      │
│ 2   ┆ 100      │
│ 3   ┆ 200      │
└─────┴──────────┘

Replacing by values of a different data type sets the return type based on a combination of the new data type and either the original data type or the default data type if it was set.

>>> df = pl.DataFrame({"a": ["x", "y", "z"]})
>>> mapping = {"x": 1, "y": 2, "z": 3}
>>> df.with_columns(replaced=pl.col("a").replace(mapping))
shape: (3, 2)
┌─────┬──────────┐
│ a   ┆ replaced │
│ --- ┆ ---      │
│ str ┆ str      │
╞═════╪══════════╡
│ x   ┆ 1        │
│ y   ┆ 2        │
│ z   ┆ 3        │
└─────┴──────────┘
>>> df.with_columns(replaced=pl.col("a").replace(mapping, default=None))
shape: (3, 2)
┌─────┬──────────┐
│ a   ┆ replaced │
│ --- ┆ ---      │
│ str ┆ i64      │
╞═════╪══════════╡
│ x   ┆ 1        │
│ y   ┆ 2        │
│ z   ┆ 3        │
└─────┴──────────┘

Set the return_dtype parameter to control the resulting data type directly.

>>> df.with_columns(
...     replaced=pl.col("a").replace(mapping, return_dtype=pl.UInt8)
... )
shape: (3, 2)
┌─────┬──────────┐
│ a   ┆ replaced │
│ --- ┆ ---      │
│ str ┆ u8       │
╞═════╪══════════╡
│ x   ┆ 1        │
│ y   ┆ 2        │
│ z   ┆ 3        │
└─────┴──────────┘

Expression input is supported for all parameters.

>>> df = pl.DataFrame({"a": [1, 2, 2, 3], "b": [1.5, 2.5, 5.0, 1.0]})
>>> df.with_columns(
...     replaced=pl.col("a").replace(
...         old=pl.col("a").max(),
...         new=pl.col("b").sum(),
...         default=pl.col("b"),
...     )
... )
shape: (4, 3)
┌─────┬─────┬──────────┐
│ a   ┆ b   ┆ replaced │
│ --- ┆ --- ┆ ---      │
│ i64 ┆ f64 ┆ f64      │
╞═════╪═════╪══════════╡
│ 1   ┆ 1.5 ┆ 1.5      │
│ 2   ┆ 2.5 ┆ 2.5      │
│ 2   ┆ 5.0 ┆ 5.0      │
│ 3   ┆ 1.0 ┆ 10.0     │
└─────┴─────┴──────────┘