polars.DataFrame.with_columns#

DataFrame.with_columns(

*exprs: IntoExpr | Iterable[IntoExpr],

**named_exprs: IntoExpr,

) → DataFrame[source]#

Add columns to this DataFrame.

Added columns will replace existing columns with the same name.

Parameters:

*exprs: Column(s) to add, 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 add, specified as keyword arguments. The columns will be renamed to the keyword used.

Returns:

DataFrame: A new DataFrame with the columns added.

Notes

Creating a new DataFrame using this method does not create a new copy of existing data.

Examples

Pass an expression to add it as a new column.

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": [0.5, 4, 10, 13],
...         "c": [True, True, False, True],
...     }
... )
>>> df.with_columns((pl.col("a") ** 2).alias("a^2"))
shape: (4, 4)
┌─────┬──────┬───────┬─────┐
│ a   ┆ b    ┆ c     ┆ a^2 │
│ --- ┆ ---  ┆ ---   ┆ --- │
│ i64 ┆ f64  ┆ bool  ┆ i64 │
╞═════╪══════╪═══════╪═════╡
│ 1   ┆ 0.5  ┆ true  ┆ 1   │
│ 2   ┆ 4.0  ┆ true  ┆ 4   │
│ 3   ┆ 10.0 ┆ false ┆ 9   │
│ 4   ┆ 13.0 ┆ true  ┆ 16  │
└─────┴──────┴───────┴─────┘

Added columns will replace existing columns with the same name.

>>> df.with_columns(pl.col("a").cast(pl.Float64))
shape: (4, 3)
┌─────┬──────┬───────┐
│ a   ┆ b    ┆ c     │
│ --- ┆ ---  ┆ ---   │
│ f64 ┆ f64  ┆ bool  │
╞═════╪══════╪═══════╡
│ 1.0 ┆ 0.5  ┆ true  │
│ 2.0 ┆ 4.0  ┆ true  │
│ 3.0 ┆ 10.0 ┆ false │
│ 4.0 ┆ 13.0 ┆ true  │
└─────┴──────┴───────┘

Multiple columns can be added using positional arguments.

>>> df.with_columns(
...     (pl.col("a") ** 2).alias("a^2"),
...     (pl.col("b") / 2).alias("b/2"),
...     (pl.col("c").not_()).alias("not c"),
... )
shape: (4, 6)
┌─────┬──────┬───────┬─────┬──────┬───────┐
│ a   ┆ b    ┆ c     ┆ a^2 ┆ b/2  ┆ not c │
│ --- ┆ ---  ┆ ---   ┆ --- ┆ ---  ┆ ---   │
│ i64 ┆ f64  ┆ bool  ┆ i64 ┆ f64  ┆ bool  │
╞═════╪══════╪═══════╪═════╪══════╪═══════╡
│ 1   ┆ 0.5  ┆ true  ┆ 1   ┆ 0.25 ┆ false │
│ 2   ┆ 4.0  ┆ true  ┆ 4   ┆ 2.0  ┆ false │
│ 3   ┆ 10.0 ┆ false ┆ 9   ┆ 5.0  ┆ true  │
│ 4   ┆ 13.0 ┆ true  ┆ 16  ┆ 6.5  ┆ false │
└─────┴──────┴───────┴─────┴──────┴───────┘

Multiple columns can also be added by passing a list of expressions.

>>> df.with_columns(
...     [
...         (pl.col("a") ** 2).alias("a^2"),
...         (pl.col("b") / 2).alias("b/2"),
...         (pl.col("c").not_()).alias("not c"),
...     ]
... )
shape: (4, 6)
┌─────┬──────┬───────┬─────┬──────┬───────┐
│ a   ┆ b    ┆ c     ┆ a^2 ┆ b/2  ┆ not c │
│ --- ┆ ---  ┆ ---   ┆ --- ┆ ---  ┆ ---   │
│ i64 ┆ f64  ┆ bool  ┆ i64 ┆ f64  ┆ bool  │
╞═════╪══════╪═══════╪═════╪══════╪═══════╡
│ 1   ┆ 0.5  ┆ true  ┆ 1   ┆ 0.25 ┆ false │
│ 2   ┆ 4.0  ┆ true  ┆ 4   ┆ 2.0  ┆ false │
│ 3   ┆ 10.0 ┆ false ┆ 9   ┆ 5.0  ┆ true  │
│ 4   ┆ 13.0 ┆ true  ┆ 16  ┆ 6.5  ┆ false │
└─────┴──────┴───────┴─────┴──────┴───────┘

Use keyword arguments to easily name your expression inputs.

>>> df.with_columns(
...     ab=pl.col("a") * pl.col("b"),
...     not_c=pl.col("c").not_(),
... )
shape: (4, 5)
┌─────┬──────┬───────┬──────┬───────┐
│ a   ┆ b    ┆ c     ┆ ab   ┆ not_c │
│ --- ┆ ---  ┆ ---   ┆ ---  ┆ ---   │
│ i64 ┆ f64  ┆ bool  ┆ f64  ┆ bool  │
╞═════╪══════╪═══════╪══════╪═══════╡
│ 1   ┆ 0.5  ┆ true  ┆ 0.5  ┆ false │
│ 2   ┆ 4.0  ┆ true  ┆ 8.0  ┆ false │
│ 3   ┆ 10.0 ┆ false ┆ 30.0 ┆ true  │
│ 4   ┆ 13.0 ┆ true  ┆ 52.0 ┆ false │
└─────┴──────┴───────┴──────┴───────┘

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.drop("c").with_columns(
...         diffs=pl.col(["a", "b"]).diff().name.suffix("_diff"),
...     )
shape: (4, 3)
┌─────┬──────┬─────────────┐
│ a   ┆ b    ┆ diffs       │
│ --- ┆ ---  ┆ ---         │
│ i64 ┆ f64  ┆ struct[2]   │
╞═════╪══════╪═════════════╡
│ 1   ┆ 0.5  ┆ {null,null} │
│ 2   ┆ 4.0  ┆ {1,3.5}     │
│ 3   ┆ 10.0 ┆ {1,6.0}     │
│ 4   ┆ 13.0 ┆ {1,3.0}     │
└─────┴──────┴─────────────┘