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narwhals.dtypes

DType

__eq__ 

__eq__(other: DType | type[DType]) -> bool

Check if this DType is equivalent to another DType.

Examples:

>>> import narwhals as nw
>>> nw.String() == nw.String()
True
>>> nw.String() == nw.String
True
>>> nw.Int16() == nw.Int32
False
>>> nw.Boolean() == nw.Int8
False
>>> nw.Date() == nw.Datetime
False

base_type classmethod 

base_type() -> type[Self]

Return this DType's fundamental/root type class.

Examples:

>>> import narwhals as nw
>>> nw.Datetime("us").base_type()
<class 'narwhals.dtypes.Datetime'>

>>> nw.String.base_type()
<class 'narwhals.dtypes.String'>

>>> nw.List(nw.Int64).base_type()
<class 'narwhals.dtypes.List'>

is_boolean classmethod 

is_boolean() -> bool

Check whether the data type is a boolean type.

is_decimal classmethod 

is_decimal() -> bool

Check whether the data type is a decimal type.

is_float classmethod 

is_float() -> bool

Check whether the data type is a floating point type.

is_integer classmethod 

is_integer() -> bool

Check whether the data type is an integer type.

is_nested classmethod 

is_nested() -> bool

Check whether the data type is a nested type.

is_numeric classmethod 

is_numeric() -> bool

Check whether the data type is a numeric type.

is_signed_integer classmethod 

is_signed_integer() -> bool

Check whether the data type is a signed integer type.

is_temporal classmethod 

is_temporal() -> bool

Check whether the data type is a temporal type.

is_unsigned_integer classmethod 

is_unsigned_integer() -> bool

Check whether the data type is an unsigned integer type.

NumericType

Bases: DType

Base class for numeric data types.

Decimal

Bases: NumericType

Decimal type.

Examples:

>>> import polars as pl
>>> import narwhals as nw
>>> s = pl.Series(["1.5"], dtype=pl.Decimal)
>>> nw.from_native(s, series_only=True).dtype
Decimal

FloatType

Bases: NumericType

Base class for float data types.

Float32

Bases: FloatType

32-bit floating point type.

Examples:

>>> import polars as pl
>>> import narwhals as nw
>>> s_native = pl.Series([0.001, 0.1, 0.01, 0.1])
>>> s = nw.from_native(s_native, series_only=True)
>>> s.cast(nw.Float32).dtype
Float32

Float64

Bases: FloatType

64-bit floating point type.

Examples:

>>> import pyarrow as pa
>>> import narwhals as nw
>>> s_native = pa.chunked_array([[0.001, 0.1, 0.01, 0.1]])
>>> s = nw.from_native(s_native, series_only=True)
>>> s.cast(nw.Float64).dtype
Float64

IntegerType

Bases: NumericType

Base class for integer data types.

SignedIntegerType

Bases: IntegerType

Base class for signed integer data types.

Int8

Bases: SignedIntegerType

8-bit signed integer type.

Examples:

>>> import pandas as pd
>>> import narwhals as nw
>>> s_native = pd.Series([2, 1, 3, 7])
>>> s = nw.from_native(s_native, series_only=True)
>>> s.cast(nw.Int8).dtype
Int8

Int16

Bases: SignedIntegerType

16-bit signed integer type.

Examples:

>>> import polars as pl
>>> import narwhals as nw
>>> s_native = pl.Series([2, 1, 3, 7])
>>> s = nw.from_native(s_native, series_only=True)
>>> s.cast(nw.Int16).dtype
Int16

Int32

Bases: SignedIntegerType

32-bit signed integer type.

Examples:

>>> import pyarrow as pa
>>> import narwhals as nw
>>> s_native = pa.chunked_array([[2, 1, 3, 7]])
>>> s = nw.from_native(s_native, series_only=True)
>>> s.cast(nw.Int32).dtype
Int32

Int64

Bases: SignedIntegerType

64-bit signed integer type.

Examples:

>>> import polars as pl
>>> import narwhals as nw
>>> s_native = pl.Series([2, 1, 3, 7])
>>> s = nw.from_native(s_native, series_only=True)
>>> s.cast(nw.Int64).dtype
Int64

Int128

Bases: SignedIntegerType

128-bit signed integer type.

Examples:

>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> import duckdb
>>> s_native = pl.Series([2, 1, 3, 7])
>>> s = nw.from_native(s_native, series_only=True)
>>> df_native = pa.table({"a": [2, 1, 3, 7]})
>>> rel = duckdb.sql(" SELECT CAST (a AS INT128) AS a FROM df_native ")

>>> s.cast(nw.Int128).dtype
Int128
>>> nw.from_native(rel).collect_schema()["a"]
Int128

UnsignedIntegerType

Bases: IntegerType

Base class for unsigned integer data types.

UInt8

Bases: UnsignedIntegerType

8-bit unsigned integer type.

Examples:

>>> import polars as pl
>>> import narwhals as nw
>>> s_native = pl.Series([2, 1, 3, 7])
>>> s = nw.from_native(s_native, series_only=True)
>>> s.cast(nw.UInt8).dtype
UInt8

UInt16

Bases: UnsignedIntegerType

16-bit unsigned integer type.

Examples:

>>> import polars as pl
>>> import narwhals as nw
>>> s_native = pl.Series([2, 1, 3, 7])
>>> s = nw.from_native(s_native, series_only=True)
>>> s.cast(nw.UInt16).dtype
UInt16

UInt32

Bases: UnsignedIntegerType

32-bit unsigned integer type.

Examples:

>>> import polars as pl
>>> import narwhals as nw
>>> s_native = pl.Series([2, 1, 3, 7])
>>> s = nw.from_native(s_native, series_only=True)
>>> s.cast(nw.UInt32).dtype
UInt32

UInt64

Bases: UnsignedIntegerType

64-bit unsigned integer type.

Examples:

>>> import pandas as pd
>>> import narwhals as nw
>>> s_native = pd.Series([2, 1, 3, 7])
>>> s = nw.from_native(s_native, series_only=True)
>>> s.cast(nw.UInt64).dtype
UInt64

UInt128

Bases: UnsignedIntegerType

128-bit unsigned integer type.

Examples:

>>> import pandas as pd
>>> import narwhals as nw
>>> import duckdb
>>> df_native = pd.DataFrame({"a": [2, 1, 3, 7]})
>>> rel = duckdb.sql(" SELECT CAST (a AS UINT128) AS a FROM df_native ")
>>> nw.from_native(rel).collect_schema()["a"]
UInt128

TemporalType

Bases: DType

Base class for temporal data types.

Date

Bases: TemporalType

Data type representing a calendar date.

Examples:

>>> from datetime import date, timedelta
>>> import pyarrow as pa
>>> import narwhals as nw
>>> s_native = pa.chunked_array(
...     [[date(2024, 12, 1) + timedelta(days=d) for d in range(4)]]
... )
>>> nw.from_native(s_native, series_only=True).dtype
Date

Datetime

Bases: TemporalType

Data type representing a calendar date and time of day.

Parameters:

Name Type Description Default
time_unit TimeUnit

Unit of time. Defaults to 'us' (microseconds).

 'us'
time_zone str | timezone | None

Time zone string, as defined in zoneinfo (to see valid strings run import zoneinfo; zoneinfo.available_timezones() for a full list).

 None
Notes

Adapted from Polars implementation

Examples:

>>> from datetime import datetime, timedelta
>>> import polars as pl
>>> import narwhals as nw
>>> s_native = (
...     pl.Series([datetime(2024, 12, 9) + timedelta(days=n) for n in range(5)])
...     .cast(pl.Datetime("ms"))
...     .dt.replace_time_zone("Africa/Accra")
... )
>>> nw.from_native(s_native, series_only=True).dtype
Datetime(time_unit='ms', time_zone='Africa/Accra')

time_unit instance-attribute 

time_unit: TimeUnit = time_unit

Unit of time.

time_zone instance-attribute 

time_zone: str | None = time_zone

Time zone string, as defined in zoneinfo.

Notes

To see valid strings run import zoneinfo; zoneinfo.available_timezones() for a full list.

__eq__ 

__eq__(other: DType | type[DType]) -> bool

Check if this Datetime is equivalent to another DType.

Examples:

>>> import narwhals as nw
>>> nw.Datetime("s") == nw.Datetime("s")
True
>>> nw.Datetime() == nw.Datetime("us")
True
>>> nw.Datetime("us") == nw.Datetime("ns")
False
>>> nw.Datetime("us", "UTC") == nw.Datetime(time_unit="us", time_zone="UTC")
True
>>> nw.Datetime(time_zone="UTC") == nw.Datetime(time_zone="EST")
False
>>> nw.Datetime() == nw.Duration()
False
>>> nw.Datetime("ms") == nw.Datetime
True

Duration

Bases: TemporalType

Data type representing a time duration.

Parameters:

Name Type Description Default
time_unit TimeUnit

Unit of time. Defaults to 'us' (microseconds).

 'us'
Notes

Adapted from Polars implementation

Examples:

>>> from datetime import timedelta
>>> import pyarrow as pa
>>> import narwhals as nw
>>> s_native = pa.chunked_array(
...     [[timedelta(seconds=d) for d in range(1, 4)]], type=pa.duration("ms")
... )
>>> nw.from_native(s_native, series_only=True).dtype
Duration(time_unit='ms')

time_unit instance-attribute 

time_unit: TimeUnit = time_unit

Unit of time.

__eq__ 

__eq__(other: DType | type[DType]) -> bool

Check if this Duration is equivalent to another DType.

Examples:

>>> import narwhals as nw
>>> nw.Duration("us") == nw.Duration("us")
True
>>> nw.Duration() == nw.Duration("us")
True
>>> nw.Duration("us") == nw.Duration("ns")
False
>>> nw.Duration() == nw.Datetime()
False
>>> nw.Duration("ms") == nw.Duration
True

Time

Bases: TemporalType

Data type representing the time of day.

Examples:

>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> import duckdb
>>> from datetime import time
>>> data = [time(9, 0), time(9, 1, 10), time(9, 2)]
>>> ser_pl = pl.Series(data)
>>> ser_pa = pa.chunked_array([pa.array(data, type=pa.time64("ns"))])
>>> rel = duckdb.sql(
...     " SELECT * FROM (VALUES (TIME '12:00:00'), (TIME '14:30:15')) df(t)"
... )

>>> nw.from_native(ser_pl, series_only=True).dtype
Time
>>> nw.from_native(ser_pa, series_only=True).dtype
Time
>>> nw.from_native(rel).collect_schema()["t"]
Time

NestedType

Bases: DType

Base class for nested data types.

Array

Bases: NestedType

Fixed length list type.

Parameters:

Name Type Description Default
inner IntoDType

The datatype of the values within each array.

 required
shape int | tuple[int, ...]

The shape of the arrays.

 required

Examples:

>>> import polars as pl
>>> import narwhals as nw
>>> s_native = pl.Series([[1, 2], [3, 4], [5, 6]], dtype=pl.Array(pl.Int32, 2))
>>> nw.from_native(s_native, series_only=True).dtype
Array(Int32, shape=(2,))

inner instance-attribute 

inner: IntoDType

The DType of the values within each array.

shape instance-attribute 

shape: tuple[int, ...]

The shape of the arrays.

size instance-attribute 

size: int

The size of the Array.

__eq__ 

__eq__(other: DType | type[DType]) -> bool

Check if this Array is equivalent to another DType.

Examples:

>>> import narwhals as nw
>>> nw.Array(nw.Int64, 2) == nw.Array(nw.Int64, 2)
True
>>> nw.Array(nw.Int64, 2) == nw.Array(nw.String, 2)
False
>>> nw.Array(nw.Int64, 2) == nw.Array(nw.Int64, 4)
False

If a parent type is not specific about its inner type, we infer it as equal

>>> nw.Array(nw.Int64, 2) == nw.Array
True

List

Bases: NestedType

Variable length list type.

Examples:

>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> s_native = pd.Series(
...     [["narwhal", "orca"], ["beluga", "vaquita"]],
...     dtype=pd.ArrowDtype(pa.large_list(pa.large_string())),
... )
>>> nw.from_native(s_native, series_only=True).dtype
List(String)

inner instance-attribute 

inner: IntoDType = inner

The DType of the values within each list.

__eq__ 

__eq__(other: DType | type[DType]) -> bool

Check if this List is equivalent to another DType.

Examples:

>>> import narwhals as nw
>>> nw.List(nw.Int64) == nw.List(nw.Int64)
True
>>> nw.List(nw.Int64) == nw.List(nw.Float32)
False

If a parent type is not specific about its inner type, we infer it as equal

>>> nw.List(nw.Int64) == nw.List
True

Field

Definition of a single field within a Struct DType.

Parameters:

Name Type Description Default
name str

The name of the field within its parent Struct.

 required
dtype IntoDType

The DType of the field's values.

 required

Examples:

>>> import pyarrow as pa
>>> import narwhals as nw
>>> data = [{"a": 1, "b": ["narwhal", "beluga"]}, {"a": 2, "b": ["orca"]}]
>>> ser_pa = pa.chunked_array([data])
>>> nw.from_native(ser_pa, series_only=True).dtype.fields
[Field('a', Int64), Field('b', List(String))]

dtype instance-attribute 

dtype: IntoDType = dtype

The DType of the field's values.

name instance-attribute 

name: str = name

The name of the field within its parent Struct.

__eq__ 

__eq__(other: Field) -> bool

Check if this Field is equivalent to another Field.

Two fields are equivalent if they have the same name and the same dtype.

Examples:

>>> import narwhals as nw
>>> nw.Field("a", nw.String) == nw.Field("a", nw.String())
True
>>> nw.Field("a", nw.String) == nw.Field("a", nw.String)
True
>>> nw.Field("a", nw.String) == nw.Field("a", nw.Datetime)
False
>>> nw.Field("a", nw.String) == nw.Field("b", nw.String)
False
>>> nw.Field("a", nw.String) == nw.String
False

Struct

Bases: NestedType

Struct composite type.

Parameters:

Name Type Description Default
fields Sequence[Field] | Mapping[str, IntoDType]

The fields that make up the struct. Can be either a sequence of Field objects or a mapping of column names to data types.

 required

Examples:

>>> import pyarrow as pa
>>> import narwhals as nw
>>> s_native = pa.chunked_array(
...     [[{"a": 1, "b": ["narwhal", "beluga"]}, {"a": 2, "b": ["orca"]}]]
... )
>>> nw.from_native(s_native, series_only=True).dtype
Struct({'a': Int64, 'b': List(String)})

fields instance-attribute 

fields: list[Field]

The fields that make up the struct.

__eq__ 

__eq__(other: DType | type[DType]) -> bool

Check if this Struct is equivalent to another DType.

Examples:

>>> import narwhals as nw
>>> nw.Struct({"a": nw.Int64}) == nw.Struct({"a": nw.Int64})
True
>>> nw.Struct({"a": nw.Int64}) == nw.Struct({"a": nw.Boolean})
False
>>> nw.Struct({"a": nw.Int64}) == nw.Struct({"b": nw.Int64})
False
>>> nw.Struct({"a": nw.Int64}) == nw.Struct([nw.Field("a", nw.Int64)])
True

If a parent type is not specific about its inner type, we infer it as equal

>>> nw.Struct({"a": nw.Int64}) == nw.Struct
True

to_schema 

to_schema() -> OrderedDict[str, IntoDType]

Return Struct dtype as a schema dict.

String

Bases: DType

UTF-8 encoded string type.

Examples:

>>> import pandas as pd
>>> import narwhals as nw
>>> s_native = pd.Series(["beluga", "narwhal", "orca", "vaquita"])
>>> nw.from_native(s_native, series_only=True).dtype
String

Categorical

Bases: DType

A categorical encoding of a set of strings.

Examples:

>>> import polars as pl
>>> import narwhals as nw
>>> s_native = pl.Series(["beluga", "narwhal", "orca"])
>>> nw.from_native(s_native, series_only=True).cast(nw.Categorical).dtype
Categorical

Enum

Bases: DType

A fixed categorical encoding of a unique set of strings.

Polars has an Enum data type. In pandas, ordered categories get mapped to Enum. PyArrow has no Enum equivalent.

Examples:

>>> import narwhals as nw
>>> nw.Enum(["beluga", "narwhal", "orca"])
Enum(categories=['beluga', 'narwhal', 'orca'])

categories property 

categories: tuple[str, ...]

The categories in the dataset.

__eq__ 

__eq__(other: DType | type[DType]) -> bool

Check if this Enum is equivalent to another DType.

Examples:

>>> import narwhals as nw
>>> nw.Enum(["a", "b", "c"]) == nw.Enum(["a", "b", "c"])
True
>>> import polars as pl
>>> categories = pl.Series(["a", "b", "c"])
>>> nw.Enum(["a", "b", "c"]) == nw.Enum(categories)
True
>>> nw.Enum(["a", "b", "c"]) == nw.Enum(["b", "a", "c"])
False
>>> nw.Enum(["a", "b", "c"]) == nw.Enum(["a"])
False
>>> nw.Enum(["a", "b", "c"]) == nw.Categorical
False
>>> nw.Enum(["a", "b", "c"]) == nw.Enum
True

Binary

Bases: DType

Binary type.

Examples:

>>> import polars as pl
>>> import narwhals as nw
>>> import pyarrow as pa
>>> import duckdb
>>> data = [b"test1", b"test2"]
>>> ser_pl = pl.Series(data, dtype=pl.Binary)
>>> ser_pa = pa.chunked_array([pa.array(data, type=pa.binary())])
>>> rel = duckdb.sql(
...     "SELECT * FROM (VALUES (BLOB 'test1'), (BLOB 'test2')) AS df(t)"
... )

>>> nw.from_native(ser_pl, series_only=True).dtype
Binary
>>> nw.from_native(ser_pa, series_only=True).dtype
Binary
>>> nw.from_native(rel).collect_schema()["t"]
Binary

Boolean

Bases: DType

Boolean type.

Examples:

>>> import pyarrow as pa
>>> import narwhals as nw
>>> s_native = pa.chunked_array([[True, False, False, True]])
>>> nw.from_native(s_native, series_only=True).dtype
Boolean

Object

Bases: DType

Data type for wrapping arbitrary Python objects.

Examples:

>>> import pandas as pd
>>> import narwhals as nw
>>> class Foo: ...
>>> s_native = pd.Series([Foo(), Foo()])
>>> nw.from_native(s_native, series_only=True).dtype
Object

Unknown

Bases: DType

Type representing DataType values that could not be determined statically.

Examples:

>>> import pandas as pd
>>> import narwhals as nw
>>> s_native = pd.Series(pd.period_range("2000-01", periods=4, freq="M"))
>>> nw.from_native(s_native, series_only=True).dtype
Unknown