Schema

class polars.Schema(

schema: Mapping[str, SchemaInitDataType] | Iterable[tuple[str, SchemaInitDataType]] | None = None,

*,

check_dtypes: bool = True,

)

Ordered mapping of column names to their data type.

Parameters:

schema

The schema definition given by column names and their associated Polars data type. Accepts a mapping or an iterable of tuples.

Examples

Define a schema by passing instantiated data types.

>>> schema = pl.Schema(
...     {
...         "foo": pl.String(),
...         "bar": pl.Duration("us"),
...         "baz": pl.Array(pl.Int8, 4),
...     }
... )
>>> schema
Schema({'foo': String, 'bar': Duration(time_unit='us'), 'baz': Array(Int8, shape=(4,))})

Access the data type associated with a specific column name.

>>> schema["baz"]
Array(Int8, shape=(4,))

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

>>> schema.names()
['foo', 'bar', 'baz']
>>> schema.dtypes()
[String, Duration(time_unit='us'), Array(Int8, shape=(4,))]
>>> schema.len()
3

Methods:

dtypes	Get the data types of the schema.
len	Get the number of schema entries.
names	Get the column names of the schema.
to_python	Return a dictionary of column names and Python types.

dtypes() → list[DataType]

Get the data types of the schema.

Examples

>>> s = pl.Schema({"x": pl.UInt8(), "y": pl.List(pl.UInt8)})
>>> s.dtypes()
[UInt8, List(UInt8)]

len() → int

Get the number of schema entries.

Examples

>>> s = pl.Schema({"x": pl.Int32(), "y": pl.List(pl.String)})
>>> s.len()
2
>>> len(s)
2

names() → list[str]

Get the column names of the schema.

Examples

>>> s = pl.Schema({"x": pl.Float64(), "y": pl.Datetime(time_zone="UTC")})
>>> s.names()
['x', 'y']

to_python() → dict[str, type]

Return a dictionary of column names and Python types.

Examples

>>> s = pl.Schema(
...     {
...         "x": pl.Int8(),
...         "y": pl.String(),
...         "z": pl.Duration("us"),
...     }
... )
>>> s.to_python()
{'x': <class 'int'>, 'y':  <class 'str'>, 'z': <class 'datetime.timedelta'>}