polars.struct#

polars.struct(exprs: IntoExpr | Iterable[IntoExpr] = None, eager: Literal[False] = False, schema: SchemaDict | None = None, **named_exprs: IntoExpr) → Expr[source]#

polars.struct(exprs: IntoExpr | Iterable[IntoExpr] = None, eager: Literal[True] = False, schema: SchemaDict | None = None, **named_exprs: IntoExpr) → Series

polars.struct(exprs: IntoExpr | Iterable[IntoExpr] = None, eager: bool = False, schema: SchemaDict | None = None, **named_exprs: IntoExpr) → Expr | Series

Collect columns into a struct column.

Parameters:

exprs: Column(s) to collect into a struct column. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.
eager: Evaluate immediately and return a Series. If set to False (default), return an expression instead.
schema: Optional schema that explicitly defines the struct field dtypes.
**named_exprs: Additional column(s) 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 passing a list of columns.

>>> 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
{'my_struct': Struct([Field('p', Int64), Field('q', Boolean)])}