polars.struct#

polars.struct(

*exprs: IntoExpr | Iterable[IntoExpr],

schema: SchemaDict | None = None,

eager: bool = False,

**named_exprs: IntoExpr,

) → Expr | Series[source]#

Collect columns into a struct column.

Parameters:

*exprs: Column(s) to collect into a struct column, specified as positional arguments. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.
schema: Optional schema that explicitly defines the struct field dtypes. If no columns or expressions are provided, schema keys are used to define columns.
eager: Evaluate immediately and return a Series. If set to False (default), return an expression instead.
**named_exprs: Additional columns to collect into the struct column, specified as keyword arguments. The columns will be renamed to the keyword used.

Examples

Collect all columns of a dataframe into a struct by passing pl.all().

>>> df = pl.DataFrame(
...     {
...         "int": [1, 2],
...         "str": ["a", "b"],
...         "bool": [True, None],
...         "list": [[1, 2], [3]],
...     }
... )
>>> df.select(pl.struct(pl.all()).alias("my_struct"))
shape: (2, 1)
┌─────────────────────┐
│ my_struct           │
│ ---                 │
│ struct[4]           │
╞═════════════════════╡
│ {1,"a",true,[1, 2]} │
│ {2,"b",null,[3]}    │
└─────────────────────┘

Collect selected columns into a struct by either passing a list of columns, or by specifying each column as a positional argument.

>>> df.select(pl.struct("int", False).alias("my_struct"))
shape: (2, 1)
┌───────────┐
│ my_struct │
│ ---       │
│ struct[2] │
╞═══════════╡
│ {1,false} │
│ {2,false} │
└───────────┘

Use keyword arguments to easily name each struct field.

>>> df.select(pl.struct(p="int", q="bool").alias("my_struct")).schema
OrderedDict({'my_struct': Struct({'p': Int64, 'q': Boolean})})