narwhals.Schema

Ordered mapping of column names to their data type.

Parameters:

Name	Type	Description	Default
schema	Mapping[str, DType] | Iterable[tuple[str, DType]] | None	The schema definition given by column names and their associated instantiated Narwhals data type. Accepts a mapping or an iterable of tuples.	None

Examples:

Define a schema by passing instantiated data types.

>>> import narwhals as nw
>>> schema = nw.Schema({"foo": nw.Int8(), "bar": nw.String()})
>>> schema
Schema({'foo': Int8, 'bar': String})

Access the data type associated with a specific column name.

>>> schema["foo"]
Int8

Access various schema properties using the names, dtypes, and len methods.

>>> schema.names()
['foo', 'bar']
>>> schema.dtypes()
[Int8, String]
>>> schema.len()
2

names

names() -> list[str]

Get the column names of the schema.

Returns:

Type	Description
list[str]	Column names.

dtypes

dtypes() -> list[DType]

Get the data types of the schema.

Returns:

Type	Description
list[DType]	Data types of schema.

len

len() -> int

Get the number of columns in the schema.

Returns:

Type	Description
int	Number of columns.

to_arrow

to_arrow() -> pa.Schema

Convert Schema to a pyarrow Schema.

Returns:

Type	Description
Schema	A pyarrow Schema.

Examples:

>>> import narwhals as nw
>>> schema = nw.Schema({"a": nw.Int64(), "b": nw.Datetime("ns")})
>>> schema.to_arrow()
a: int64
b: timestamp[ns]

to_pandas

to_pandas(
    dtype_backend: (
        DTypeBackend | Iterable[DTypeBackend]
    ) = None,
) -> dict[str, Any]

Convert Schema to an ordered mapping of column names to their pandas data type.

Parameters:

Name	Type	Description	Default
dtype_backend	DTypeBackend | Iterable[DTypeBackend]	Backend(s) used for the native types. When providing more than one, the length of the iterable must be equal to the length of the schema.	None

Returns:

Type	Description
dict[str, Any]	An ordered mapping of column names to their pandas data type.

Examples:

>>> import narwhals as nw
>>> schema = nw.Schema({"a": nw.Int64(), "b": nw.Datetime("ns")})
>>> schema.to_pandas()
{'a': 'int64', 'b': 'datetime64[ns]'}

>>> schema.to_pandas("pyarrow")
{'a': 'Int64[pyarrow]', 'b': 'timestamp[ns][pyarrow]'}

to_polars

to_polars() -> pl.Schema

Convert Schema to a polars Schema.

Returns:

Type	Description
Schema	A polars Schema or plain dict (prior to polars 1.0).

Examples:

>>> import narwhals as nw
>>> schema = nw.Schema({"a": nw.Int64(), "b": nw.Datetime("ns")})
>>> schema.to_polars()
Schema({'a': Int64, 'b': Datetime(time_unit='ns', time_zone=None)})