polars.Expr.arr.to_struct#

Expr.arr.to_struct(fields: Sequence[str] | Callable[[int], str] | None = None) → Expr[source]#

Convert the Series of type Array to a Series of type Struct.

Parameters:

fields: If the name and number of the desired fields is known in advance a list of field names can be given, which will be assigned by index. Otherwise, to dynamically assign field names, a custom function can be used; if neither are set, fields will be field_0, field_1 .. field_n.

Examples

Convert array to struct with default field name assignment:

>>> df = pl.DataFrame(
...     {"n": [[0, 1, 2], [3, 4, 5]]}, schema={"n": pl.Array(pl.Int8, 3)}
... )
>>> df.with_columns(struct=pl.col("n").arr.to_struct())
shape: (2, 2)
┌──────────────┬───────────┐
│ n            ┆ struct    │
│ ---          ┆ ---       │
│ array[i8, 3] ┆ struct[3] │
╞══════════════╪═══════════╡
│ [0, 1, 2]    ┆ {0,1,2}   │
│ [3, 4, 5]    ┆ {3,4,5}   │
└──────────────┴───────────┘

Convert array to struct with field name assignment by function/index:

>>> df = pl.DataFrame(
...     {"n": [[0, 1, 2], [3, 4, 5]]}, schema={"n": pl.Array(pl.Int8, 3)}
... )
>>> df.select(pl.col("n").arr.to_struct(fields=lambda idx: f"n{idx}")).rows(
...     named=True
... )
[{'n': {'n0': 0, 'n1': 1, 'n2': 2}}, {'n': {'n0': 3, 'n1': 4, 'n2': 5}}]

Convert array to struct with field name assignment by index from a list of names:

>>> df.select(pl.col("n").arr.to_struct(fields=["c1", "c2", "c3"])).rows(
...     named=True
... )
[{'n': {'c1': 0, 'c2': 1, 'c3': 2}}, {'n': {'c1': 3, 'c2': 4, 'c3': 5}}]