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from __future__ import annotations
import datetime as dt
from decimal import Decimal
from functools import wraps
from typing import TYPE_CHECKING, Any, Callable, Literal, TypeVar, overload
from narwhals._constants import EPOCH, MS_PER_SECOND
from narwhals._namespace import (
is_native_arrow,
is_native_pandas_like,
is_native_polars,
is_native_spark_like,
)
from narwhals._utils import Implementation, Version, has_native_namespace
from narwhals.dependencies import (
get_dask_expr,
get_numpy,
get_pandas,
is_cupy_scalar,
is_dask_dataframe,
is_duckdb_relation,
is_ibis_table,
is_numpy_scalar,
is_pandas_like_dataframe,
is_polars_lazyframe,
is_polars_series,
is_pyarrow_scalar,
is_pyarrow_table,
)
if TYPE_CHECKING:
from narwhals.dataframe import DataFrame, LazyFrame
from narwhals.series import Series
from narwhals.typing import (
DataFrameT,
Frame,
IntoDataFrameT,
IntoFrame,
IntoLazyFrameT,
IntoSeries,
IntoSeriesT,
LazyFrameT,
SeriesT,
)
T = TypeVar("T")
NON_TEMPORAL_SCALAR_TYPES = (bool, bytes, str, int, float, complex, Decimal)
TEMPORAL_SCALAR_TYPES = (dt.date, dt.timedelta, dt.time)
@overload
def to_native(
narwhals_object: DataFrame[IntoDataFrameT], *, pass_through: Literal[False] = ...
) -> IntoDataFrameT: ...
@overload
def to_native(
narwhals_object: LazyFrame[IntoLazyFrameT], *, pass_through: Literal[False] = ...
) -> IntoLazyFrameT: ...
@overload
def to_native(
narwhals_object: Series[IntoSeriesT], *, pass_through: Literal[False] = ...
) -> IntoSeriesT: ...
@overload
def to_native(narwhals_object: Any, *, pass_through: bool) -> Any: ...
def to_native(
narwhals_object: DataFrame[IntoDataFrameT]
| LazyFrame[IntoLazyFrameT]
| Series[IntoSeriesT],
*,
pass_through: bool = False,
) -> IntoDataFrameT | IntoLazyFrameT | IntoSeriesT | Any:
"""Convert Narwhals object to native one.
Arguments:
narwhals_object: Narwhals object.
pass_through: Determine what happens if `narwhals_object` isn't a Narwhals class
- `False` (default): raise an error
- `True`: pass object through as-is
Returns:
Object of class that user started with.
"""
from narwhals.dataframe import BaseFrame
from narwhals.series import Series
if isinstance(narwhals_object, BaseFrame):
return narwhals_object._compliant_frame._native_frame
if isinstance(narwhals_object, Series):
return narwhals_object._compliant_series.native
if not pass_through:
msg = f"Expected Narwhals object, got {type(narwhals_object)}."
raise TypeError(msg)
return narwhals_object
@overload
def from_native(native_object: SeriesT, **kwds: Any) -> SeriesT: ...
@overload
def from_native(native_object: DataFrameT, **kwds: Any) -> DataFrameT: ...
@overload
def from_native(native_object: LazyFrameT, **kwds: Any) -> LazyFrameT: ...
@overload
def from_native(
native_object: IntoDataFrameT | IntoSeriesT,
*,
pass_through: Literal[True],
eager_only: Literal[True],
series_only: Literal[False] = ...,
allow_series: Literal[True],
) -> DataFrame[IntoDataFrameT] | Series[IntoSeriesT]: ...
@overload
def from_native(
native_object: IntoDataFrameT,
*,
pass_through: Literal[True],
eager_only: Literal[False] = ...,
series_only: Literal[False] = ...,
allow_series: None = ...,
) -> DataFrame[IntoDataFrameT]: ...
@overload
def from_native(
native_object: T,
*,
pass_through: Literal[True],
eager_only: Literal[False] = ...,
series_only: Literal[False] = ...,
allow_series: None = ...,
) -> T: ...
@overload
def from_native(
native_object: IntoDataFrameT,
*,
pass_through: Literal[True],
eager_only: Literal[True],
series_only: Literal[False] = ...,
allow_series: None = ...,
) -> DataFrame[IntoDataFrameT]: ...
@overload
def from_native(
native_object: T,
*,
pass_through: Literal[True],
eager_only: Literal[True],
series_only: Literal[False] = ...,
allow_series: None = ...,
) -> T: ...
@overload
def from_native(
native_object: IntoDataFrameT | IntoLazyFrameT | IntoSeriesT,
*,
pass_through: Literal[True],
eager_only: Literal[False] = ...,
series_only: Literal[False] = ...,
allow_series: Literal[True],
) -> DataFrame[IntoDataFrameT] | LazyFrame[IntoLazyFrameT] | Series[IntoSeriesT]: ...
@overload
def from_native(
native_object: IntoSeriesT,
*,
pass_through: Literal[True],
eager_only: Literal[False] = ...,
series_only: Literal[True],
allow_series: None = ...,
) -> Series[IntoSeriesT]: ...
@overload
def from_native(
native_object: IntoLazyFrameT,
*,
pass_through: Literal[False] = ...,
eager_only: Literal[False] = ...,
series_only: Literal[False] = ...,
allow_series: None = ...,
) -> LazyFrame[IntoLazyFrameT]: ...
@overload
def from_native(
native_object: IntoDataFrameT,
*,
pass_through: Literal[False] = ...,
eager_only: Literal[False] = ...,
series_only: Literal[False] = ...,
allow_series: None = ...,
) -> DataFrame[IntoDataFrameT]: ...
@overload
def from_native(
native_object: IntoDataFrameT,
*,
pass_through: Literal[False] = ...,
eager_only: Literal[True],
series_only: Literal[False] = ...,
allow_series: None = ...,
) -> DataFrame[IntoDataFrameT]: ...
@overload
def from_native(
native_object: IntoFrame | IntoSeries,
*,
pass_through: Literal[False] = ...,
eager_only: Literal[False] = ...,
series_only: Literal[False] = ...,
allow_series: Literal[True],
) -> DataFrame[Any] | LazyFrame[Any] | Series[Any]: ...
@overload
def from_native(
native_object: IntoSeriesT,
*,
pass_through: Literal[False] = ...,
eager_only: Literal[False] = ...,
series_only: Literal[True],
allow_series: None = ...,
) -> Series[IntoSeriesT]: ...
# All params passed in as variables
@overload
def from_native(
native_object: Any,
*,
pass_through: bool,
eager_only: bool,
series_only: bool,
allow_series: bool | None,
) -> Any: ...
def from_native( # noqa: D417
native_object: IntoLazyFrameT
| IntoDataFrameT
| IntoSeriesT
| IntoFrame
| IntoSeries
| T,
*,
pass_through: bool = False,
eager_only: bool = False,
series_only: bool = False,
allow_series: bool | None = None,
**kwds: Any,
) -> LazyFrame[IntoLazyFrameT] | DataFrame[IntoDataFrameT] | Series[IntoSeriesT] | T:
"""Convert `native_object` to Narwhals Dataframe, Lazyframe, or Series.
Arguments:
native_object: Raw object from user.
Depending on the other arguments, input object can be
- a Dataframe / Lazyframe / Series supported by Narwhals (pandas, Polars, PyArrow, ...)
- an object which implements `__narwhals_dataframe__`, `__narwhals_lazyframe__`,
or `__narwhals_series__`
pass_through: Determine what happens if the object can't be converted to Narwhals
- `False` (default): raise an error
- `True`: pass object through as-is
eager_only: Whether to only allow eager objects
- `False` (default): don't require `native_object` to be eager
- `True`: only convert to Narwhals if `native_object` is eager
series_only: Whether to only allow Series
- `False` (default): don't require `native_object` to be a Series
- `True`: only convert to Narwhals if `native_object` is a Series
allow_series: Whether to allow Series (default is only Dataframe / Lazyframe)
- `False` or `None` (default): don't convert to Narwhals if `native_object` is a Series
- `True`: allow `native_object` to be a Series
Returns:
DataFrame, LazyFrame, Series, or original object, depending
on which combination of parameters was passed.
"""
if kwds:
msg = f"from_native() got an unexpected keyword argument {next(iter(kwds))!r}"
raise TypeError(msg)
return _from_native_impl( # type: ignore[no-any-return]
native_object,
pass_through=pass_through,
eager_only=eager_only,
eager_or_interchange_only=False,
series_only=series_only,
allow_series=allow_series,
version=Version.MAIN,
)
def _from_native_impl( # noqa: C901, PLR0911, PLR0912, PLR0915
native_object: Any,
*,
pass_through: bool = False,
eager_only: bool = False,
# Interchange-level was removed after v1
eager_or_interchange_only: bool = False,
series_only: bool = False,
allow_series: bool | None = None,
version: Version,
) -> Any:
from narwhals._interchange.dataframe import supports_dataframe_interchange
from narwhals._utils import (
is_compliant_dataframe,
is_compliant_lazyframe,
is_compliant_series,
)
from narwhals.dataframe import DataFrame, LazyFrame
from narwhals.series import Series
# Early returns
if isinstance(native_object, (DataFrame, LazyFrame)) and not series_only:
return native_object
if isinstance(native_object, Series) and (series_only or allow_series):
return native_object
if series_only:
if allow_series is False:
msg = "Invalid parameter combination: `series_only=True` and `allow_series=False`"
raise ValueError(msg)
allow_series = True
if eager_only and eager_or_interchange_only:
msg = "Invalid parameter combination: `eager_only=True` and `eager_or_interchange_only=True`"
raise ValueError(msg)
# Extensions
if is_compliant_dataframe(native_object):
if series_only:
if not pass_through:
msg = "Cannot only use `series_only` with dataframe"
raise TypeError(msg)
return native_object
return version.dataframe(
native_object.__narwhals_dataframe__()._with_version(version), level="full"
)
if is_compliant_lazyframe(native_object):
if series_only:
if not pass_through:
msg = "Cannot only use `series_only` with lazyframe"
raise TypeError(msg)
return native_object
if eager_only or eager_or_interchange_only:
if not pass_through:
msg = "Cannot only use `eager_only` or `eager_or_interchange_only` with lazyframe"
raise TypeError(msg)
return native_object
return version.lazyframe(
native_object.__narwhals_lazyframe__()._with_version(version), level="full"
)
if is_compliant_series(native_object):
if not allow_series:
if not pass_through:
msg = "Please set `allow_series=True` or `series_only=True`"
raise TypeError(msg)
return native_object
return version.series(
native_object.__narwhals_series__()._with_version(version), level="full"
)
# Polars
if is_native_polars(native_object):
if series_only and not is_polars_series(native_object):
if not pass_through:
msg = f"Cannot only use `series_only` with {type(native_object).__qualname__}"
raise TypeError(msg)
return native_object
if (eager_only or eager_or_interchange_only) and is_polars_lazyframe(
native_object
):
if not pass_through:
msg = "Cannot only use `eager_only` or `eager_or_interchange_only` with polars.LazyFrame"
raise TypeError(msg)
return native_object
if (not allow_series) and is_polars_series(native_object):
if not pass_through:
msg = "Please set `allow_series=True` or `series_only=True`"
raise TypeError(msg)
return native_object
return (
version.namespace.from_native_object(native_object)
.compliant.from_native(native_object)
.to_narwhals()
)
# PandasLike
if is_native_pandas_like(native_object):
if is_pandas_like_dataframe(native_object):
if series_only:
if not pass_through:
msg = f"Cannot only use `series_only` with {type(native_object).__qualname__}"
raise TypeError(msg)
return native_object
elif not allow_series:
if not pass_through:
msg = "Please set `allow_series=True` or `series_only=True`"
raise TypeError(msg)
return native_object
return (
version.namespace.from_native_object(native_object)
.compliant.from_native(native_object)
.to_narwhals()
)
# PyArrow
if is_native_arrow(native_object):
if is_pyarrow_table(native_object):
if series_only:
if not pass_through:
msg = f"Cannot only use `series_only` with {type(native_object).__qualname__}"
raise TypeError(msg)
return native_object
elif not allow_series:
if not pass_through:
msg = "Please set `allow_series=True` or `series_only=True`"
raise TypeError(msg)
return native_object
return (
version.namespace.from_native_object(native_object)
.compliant.from_native(native_object)
.to_narwhals()
)
# Dask
if is_dask_dataframe(native_object):
if series_only:
if not pass_through:
msg = "Cannot only use `series_only` with dask DataFrame"
raise TypeError(msg)
return native_object
if eager_only or eager_or_interchange_only:
if not pass_through:
msg = "Cannot only use `eager_only` or `eager_or_interchange_only` with dask DataFrame"
raise TypeError(msg)
return native_object
if (
Implementation.DASK._backend_version() <= (2024, 12, 1)
and get_dask_expr() is None
): # pragma: no cover
msg = "Please install dask-expr"
raise ImportError(msg)
return (
version.namespace.from_backend(Implementation.DASK)
.compliant.from_native(native_object)
.to_narwhals()
)
# DuckDB
if is_duckdb_relation(native_object):
if eager_only or series_only: # pragma: no cover
if not pass_through:
msg = "Cannot only use `series_only=True` or `eager_only=False` with DuckDBPyRelation"
raise TypeError(msg)
return native_object
return (
version.namespace.from_native_object(native_object)
.compliant.from_native(native_object)
.to_narwhals()
)
# Ibis
if is_ibis_table(native_object):
if eager_only or series_only: # pragma: no cover
if not pass_through:
msg = "Cannot only use `series_only=True` or `eager_only=False` with ibis.Table"
raise TypeError(msg)
return native_object
return (
version.namespace.from_native_object(native_object)
.compliant.from_native(native_object)
.to_narwhals()
)
# PySpark
if is_native_spark_like(native_object): # pragma: no cover
ns_spark = version.namespace.from_native_object(native_object)
if series_only or eager_only or eager_or_interchange_only:
if not pass_through:
msg = (
"Cannot only use `series_only`, `eager_only` or `eager_or_interchange_only` "
f"with {ns_spark.implementation} DataFrame"
)
raise TypeError(msg)
return native_object
return ns_spark.compliant.from_native(native_object).to_narwhals()
# Interchange protocol
if supports_dataframe_interchange(native_object):
from narwhals._interchange.dataframe import InterchangeFrame
if eager_only or series_only:
if not pass_through:
msg = (
"Cannot only use `series_only=True` or `eager_only=False` "
"with object which only implements __dataframe__"
)
raise TypeError(msg)
return native_object
if version is not Version.V1:
if pass_through:
return native_object
msg = (
"The Dataframe Interchange Protocol is no longer supported in the main `narwhals` namespace.\n\n"
"You may want to:\n"
" - Use `narwhals.stable.v1`, where it is still supported.\n"
" - See https://narwhals-dev.github.io/narwhals/backcompat\n"
" - Use `pass_through=True` to pass the object through without raising."
)
raise TypeError(msg)
return Version.V1.dataframe(InterchangeFrame(native_object), level="interchange")
if not pass_through:
msg = f"Expected pandas-like dataframe, Polars dataframe, or Polars lazyframe, got: {type(native_object)}"
raise TypeError(msg)
return native_object
def get_native_namespace(*obj: Frame | Series[Any] | IntoFrame | IntoSeries) -> Any:
"""Get native namespace from object.
Arguments:
obj: Dataframe, Lazyframe, or Series. Multiple objects can be
passed positionally, in which case they must all have the
same native namespace (else an error is raised).
Returns:
Native module.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import narwhals as nw
>>> df = nw.from_native(pd.DataFrame({"a": [1, 2, 3]}))
>>> nw.get_native_namespace(df)
<module 'pandas'...>
>>> df = nw.from_native(pl.DataFrame({"a": [1, 2, 3]}))
>>> nw.get_native_namespace(df)
<module 'polars'...>
"""
if not obj:
msg = "At least one object must be passed to `get_native_namespace`."
raise ValueError(msg)
result = {_get_native_namespace_single_obj(x) for x in obj}
if len(result) != 1:
msg = f"Found objects with different native namespaces: {result}."
raise ValueError(msg)
return result.pop()
def _get_native_namespace_single_obj(
obj: Frame | Series[Any] | IntoFrame | IntoSeries,
) -> Any:
if has_native_namespace(obj):
return obj.__native_namespace__()
return Version.MAIN.namespace.from_native_object(
obj
).implementation.to_native_namespace()
def narwhalify(
func: Callable[..., Any] | None = None,
*,
pass_through: bool = True,
eager_only: bool = False,
series_only: bool = False,
allow_series: bool | None = True,
) -> Callable[..., Any]:
"""Decorate function so it becomes dataframe-agnostic.
This will try to convert any dataframe/series-like object into the Narwhals
respective DataFrame/Series, while leaving the other parameters as they are.
Similarly, if the output of the function is a Narwhals DataFrame or Series, it will be
converted back to the original dataframe/series type, while if the output is another
type it will be left as is.
By setting `pass_through=False`, then every input and every output will be required to be a
dataframe/series-like object.
Arguments:
func: Function to wrap in a `from_native`-`to_native` block.
pass_through: Determine what happens if the object can't be converted to Narwhals
- `False`: raise an error
- `True` (default): pass object through as-is
eager_only: Whether to only allow eager objects
- `False` (default): don't require `native_object` to be eager
- `True`: only convert to Narwhals if `native_object` is eager
series_only: Whether to only allow Series
- `False` (default): don't require `native_object` to be a Series
- `True`: only convert to Narwhals if `native_object` is a Series
allow_series: Whether to allow Series (default is only Dataframe / Lazyframe)
- `False` or `None`: don't convert to Narwhals if `native_object` is a Series
- `True` (default): allow `native_object` to be a Series
Returns:
Decorated function.
Examples:
Instead of writing
>>> import narwhals as nw
>>> def agnostic_group_by_sum(df):
... df = nw.from_native(df, pass_through=True)
... df = df.group_by("a").agg(nw.col("b").sum())
... return nw.to_native(df)
you can just write
>>> @nw.narwhalify
... def agnostic_group_by_sum(df):
... return df.group_by("a").agg(nw.col("b").sum())
"""
def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
@wraps(func)
def wrapper(*args: Any, **kwargs: Any) -> Any:
args = [
from_native(
arg,
pass_through=pass_through,
eager_only=eager_only,
series_only=series_only,
allow_series=allow_series,
)
for arg in args
] # type: ignore[assignment]
kwargs = {
name: from_native(
value,
pass_through=pass_through,
eager_only=eager_only,
series_only=series_only,
allow_series=allow_series,
)
for name, value in kwargs.items()
}
backends = {
b()
for v in (*args, *kwargs.values())
if (b := getattr(v, "__native_namespace__", None))
}
if len(backends) > 1:
msg = "Found multiple backends. Make sure that all dataframe/series inputs come from the same backend."
raise ValueError(msg)
result = func(*args, **kwargs)
return to_native(result, pass_through=pass_through)
return wrapper
if func is None:
return decorator
# If func is not None, it means the decorator is used without arguments
return decorator(func)
def to_py_scalar(scalar_like: Any) -> Any:
"""If a scalar is not Python native, converts it to Python native.
Arguments:
scalar_like: Scalar-like value.
Returns:
Python scalar.
Raises:
ValueError: If the object is not convertible to a scalar.
Examples:
>>> import narwhals as nw
>>> import pandas as pd
>>> df = nw.from_native(pd.DataFrame({"a": [1, 2, 3]}))
>>> nw.to_py_scalar(df["a"].item(0))
1
>>> import pyarrow as pa
>>> df = nw.from_native(pa.table({"a": [1, 2, 3]}))
>>> nw.to_py_scalar(df["a"].item(0))
1
>>> nw.to_py_scalar(1)
1
"""
scalar: Any
pd = get_pandas()
if scalar_like is None or isinstance(scalar_like, NON_TEMPORAL_SCALAR_TYPES):
scalar = scalar_like
elif (
(np := get_numpy())
and isinstance(scalar_like, np.datetime64)
and scalar_like.dtype == "datetime64[ns]"
):
ms = scalar_like.item() // MS_PER_SECOND
scalar = EPOCH + dt.timedelta(microseconds=ms)
elif is_numpy_scalar(scalar_like) or is_cupy_scalar(scalar_like):
scalar = scalar_like.item()
elif pd and isinstance(scalar_like, pd.Timestamp):
scalar = scalar_like.to_pydatetime()
elif pd and isinstance(scalar_like, pd.Timedelta):
scalar = scalar_like.to_pytimedelta()
# pd.Timestamp and pd.Timedelta subclass datetime and timedelta,
# so we need to check this separately
elif isinstance(scalar_like, TEMPORAL_SCALAR_TYPES):
scalar = scalar_like
elif _is_pandas_na(scalar_like):
scalar = None
elif is_pyarrow_scalar(scalar_like):
scalar = scalar_like.as_py()
else:
msg = (
f"Expected object convertible to a scalar, found {type(scalar_like)}.\n"
f"{scalar_like!r}"
)
raise ValueError(msg)
return scalar
def _is_pandas_na(obj: Any) -> bool:
return bool((pd := get_pandas()) and pd.api.types.is_scalar(obj) and pd.isna(obj))
__all__ = ["get_native_namespace", "narwhalify", "to_native", "to_py_scalar"]