polars.Expr.reshape#

Expr.reshape( dimensions: tuple[int, ...], nested_type: type[Array] | type[List] = List, ) → Self[source]#

Reshape this Expr to a flat Series or a Series of Lists.

Parameters:

dimensions: Tuple of the dimension sizes. If a -1 is used in any of the dimensions, that dimension is inferred.
nested_type: The nested data type to create. List only supports 2 dimension, whereas Array supports an arbitrary number of dimensions.

Returns:

Expr: If a single dimension is given, results in an expression of the original data type. If a multiple dimensions are given, results in an expression of data type List with shape (rows, cols) or Array with shape dimensions.

See also

Expr.list.explode: Explode a list column.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5, 6, 7, 8, 9]})
>>> df.select(pl.col("foo").reshape((3, 3)))
shape: (3, 1)
┌───────────┐
│ foo       │
│ ---       │
│ list[i64] │
╞═══════════╡
│ [1, 2, 3] │
│ [4, 5, 6] │
│ [7, 8, 9] │
└───────────┘