n_field_strategy: ToStructStrategy = 'first_non_null',
fields: Sequence[str] | Callable[[int], str] | None = None,
upper_bound: int = 0,
) Expr[source]#

Convert the series of type List to a series of type Struct.

n_field_strategy{‘first_non_null’, ‘max_width’}

Strategy to determine the number of fields of the struct.

  • “first_non_null”: set number of fields equal to the length of the first non zero-length sublist.

  • “max_width”: set number of fields as max length of all sublists.


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.


A polars LazyFrame needs to know the schema at all times, so the caller must provide an upper bound of the number of struct fields that will be created; if set incorrectly, subsequent operations may fail. (For example, an all().sum() expression will look in the current schema to determine which columns to select).

When operating on a DataFrame, the schema does not need to be tracked or pre-determined, as the result will be eagerly evaluated, so you can leave this parameter unset.


Convert list to struct with default field name assignment:

>>> df = pl.DataFrame({"n": [[0, 1, 2], [0, 1]]})
>>> df.select(pl.col("n").list.to_struct())
shape: (2, 1)
│ n          │
│ ---        │
│ struct[3]  │
│ {0,1,2}    │
│ {0,1,null} │

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

>>> df.select(pl.col("n").list.to_struct(fields=lambda idx: f"n{idx}")).rows(
...     named=True
... )
[{'n': {'n0': 0, 'n1': 1, 'n2': 2}}, {'n': {'n0': 0, 'n1': 1, 'n2': None}}]

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

>>> df.select(pl.col("n").list.to_struct(fields=["one", "two", "three"])).rows(
...     named=True
... )
[{'n': {'one': 0, 'two': 1, 'three': 2}},
{'n': {'one': 0, 'two': 1, 'three': None}}]