polars.Expr.reshape#

Expr.reshape(dimensions: tuple[int, ...]) → Expr[source]#

Reshape this Expr to a flat column or an Array column.

Parameters:

dimensions: Tuple of the dimension sizes. If a -1 is used in any of the dimensions, that dimension is inferred.

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