552 lines
20 KiB
Python
552 lines
20 KiB
Python
from __future__ import annotations
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from functools import lru_cache
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from itertools import chain
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from typing import TYPE_CHECKING
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from typing import Any
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from typing import Iterable
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from typing import Iterator
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from typing import Sequence
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from typing import cast
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from typing import overload
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import pyarrow as pa
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import pyarrow.compute as pc
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from narwhals._compliant.series import _SeriesNamespace
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from narwhals.exceptions import ShapeError
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from narwhals.utils import import_dtypes_module
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from narwhals.utils import isinstance_or_issubclass
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if TYPE_CHECKING:
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from typing import TypeVar
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from typing_extensions import Self
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from typing_extensions import TypeAlias
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from typing_extensions import TypeIs
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from narwhals._arrow.series import ArrowSeries
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from narwhals._arrow.typing import ArrowArray
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from narwhals._arrow.typing import ArrowChunkedArray
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from narwhals.dtypes import DType
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from narwhals.typing import _AnyDArray
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from narwhals.utils import Version
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# NOTE: stubs don't allow for `ChunkedArray[StructArray]`
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# Intended to represent the `.chunks` property storing `list[pa.StructArray]`
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ChunkedArrayStructArray: TypeAlias = ArrowChunkedArray
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ArrayAny: TypeAlias = "ArrowArray | ArrowChunkedArray"
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_T = TypeVar("_T")
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def is_timestamp(t: Any) -> TypeIs[pa.TimestampType[Any, Any]]: ...
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def is_duration(t: Any) -> TypeIs[pa.DurationType[Any]]: ...
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def is_list(t: Any) -> TypeIs[pa.ListType[Any]]: ...
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def is_large_list(t: Any) -> TypeIs[pa.LargeListType[Any]]: ...
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def is_fixed_size_list(t: Any) -> TypeIs[pa.FixedSizeListType[Any, Any]]: ...
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def is_dictionary(
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t: Any,
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) -> TypeIs[pa.DictionaryType[Any, Any, Any]]: ...
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def extract_regex(
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strings: ArrowChunkedArray,
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/,
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pattern: str,
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*,
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options: Any = None,
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memory_pool: Any = None,
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) -> ChunkedArrayStructArray: ...
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else:
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from pyarrow.compute import extract_regex
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from pyarrow.types import is_dictionary # noqa: F401
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from pyarrow.types import is_duration
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from pyarrow.types import is_fixed_size_list
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from pyarrow.types import is_large_list
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from pyarrow.types import is_list
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from pyarrow.types import is_timestamp
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lit = pa.scalar
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"""Alias for `pyarrow.scalar`."""
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def extract_py_scalar(value: Any, /) -> Any:
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from narwhals._arrow.series import maybe_extract_py_scalar
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return maybe_extract_py_scalar(value, return_py_scalar=True)
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def chunked_array(
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arr: ArrayAny | list[Iterable[Any]], dtype: pa.DataType | None = None, /
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) -> ArrowChunkedArray:
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if isinstance(arr, pa.ChunkedArray):
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return arr
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if isinstance(arr, list):
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return pa.chunked_array(arr, dtype)
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else:
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return pa.chunked_array([arr], arr.type)
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def nulls_like(n: int, series: ArrowSeries) -> ArrowArray:
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"""Create a strongly-typed Array instance with all elements null.
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Uses the type of `series`, without upseting `mypy`.
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"""
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return pa.nulls(n, series.native.type)
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@lru_cache(maxsize=16)
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def native_to_narwhals_dtype(dtype: pa.DataType, version: Version) -> DType:
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dtypes = import_dtypes_module(version)
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if pa.types.is_int64(dtype):
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return dtypes.Int64()
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if pa.types.is_int32(dtype):
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return dtypes.Int32()
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if pa.types.is_int16(dtype):
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return dtypes.Int16()
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if pa.types.is_int8(dtype):
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return dtypes.Int8()
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if pa.types.is_uint64(dtype):
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return dtypes.UInt64()
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if pa.types.is_uint32(dtype):
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return dtypes.UInt32()
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if pa.types.is_uint16(dtype):
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return dtypes.UInt16()
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if pa.types.is_uint8(dtype):
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return dtypes.UInt8()
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if pa.types.is_boolean(dtype):
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return dtypes.Boolean()
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if pa.types.is_float64(dtype):
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return dtypes.Float64()
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if pa.types.is_float32(dtype):
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return dtypes.Float32()
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# bug in coverage? it shows `31->exit` (where `31` is currently the line number of
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# the next line), even though both when the if condition is true and false are covered
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if ( # pragma: no cover
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pa.types.is_string(dtype)
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or pa.types.is_large_string(dtype)
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or getattr(pa.types, "is_string_view", lambda _: False)(dtype)
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):
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return dtypes.String()
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if pa.types.is_date32(dtype):
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return dtypes.Date()
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if is_timestamp(dtype):
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return dtypes.Datetime(time_unit=dtype.unit, time_zone=dtype.tz)
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if is_duration(dtype):
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return dtypes.Duration(time_unit=dtype.unit)
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if pa.types.is_dictionary(dtype):
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return dtypes.Categorical()
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if pa.types.is_struct(dtype):
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return dtypes.Struct(
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[
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dtypes.Field(
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dtype.field(i).name,
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native_to_narwhals_dtype(dtype.field(i).type, version),
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)
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for i in range(dtype.num_fields)
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]
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)
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if is_list(dtype) or is_large_list(dtype):
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return dtypes.List(native_to_narwhals_dtype(dtype.value_type, version))
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if is_fixed_size_list(dtype):
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return dtypes.Array(
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native_to_narwhals_dtype(dtype.value_type, version), dtype.list_size
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)
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if pa.types.is_decimal(dtype):
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return dtypes.Decimal()
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if pa.types.is_time32(dtype) or pa.types.is_time64(dtype):
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return dtypes.Time()
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if pa.types.is_binary(dtype):
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return dtypes.Binary()
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return dtypes.Unknown() # pragma: no cover
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def narwhals_to_native_dtype(dtype: DType | type[DType], version: Version) -> pa.DataType:
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dtypes = import_dtypes_module(version)
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if isinstance_or_issubclass(dtype, dtypes.Decimal):
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msg = "Casting to Decimal is not supported yet."
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raise NotImplementedError(msg)
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if isinstance_or_issubclass(dtype, dtypes.Float64):
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return pa.float64()
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if isinstance_or_issubclass(dtype, dtypes.Float32):
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return pa.float32()
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if isinstance_or_issubclass(dtype, dtypes.Int64):
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return pa.int64()
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if isinstance_or_issubclass(dtype, dtypes.Int32):
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return pa.int32()
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if isinstance_or_issubclass(dtype, dtypes.Int16):
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return pa.int16()
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if isinstance_or_issubclass(dtype, dtypes.Int8):
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return pa.int8()
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if isinstance_or_issubclass(dtype, dtypes.UInt64):
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return pa.uint64()
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if isinstance_or_issubclass(dtype, dtypes.UInt32):
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return pa.uint32()
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if isinstance_or_issubclass(dtype, dtypes.UInt16):
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return pa.uint16()
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if isinstance_or_issubclass(dtype, dtypes.UInt8):
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return pa.uint8()
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if isinstance_or_issubclass(dtype, dtypes.String):
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return pa.string()
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if isinstance_or_issubclass(dtype, dtypes.Boolean):
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return pa.bool_()
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if isinstance_or_issubclass(dtype, dtypes.Categorical):
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return pa.dictionary(pa.uint32(), pa.string())
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if isinstance_or_issubclass(dtype, dtypes.Datetime):
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return pa.timestamp(dtype.time_unit, tz=dtype.time_zone) # pyright: ignore[reportArgumentType]
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if isinstance_or_issubclass(dtype, dtypes.Duration):
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return pa.duration(dtype.time_unit)
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if isinstance_or_issubclass(dtype, dtypes.Date):
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return pa.date32()
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if isinstance_or_issubclass(dtype, dtypes.List):
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return pa.list_(value_type=narwhals_to_native_dtype(dtype.inner, version=version))
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if isinstance_or_issubclass(dtype, dtypes.Struct):
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return pa.struct(
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[
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(field.name, narwhals_to_native_dtype(field.dtype, version=version))
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for field in dtype.fields
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]
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)
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if isinstance_or_issubclass(dtype, dtypes.Array): # pragma: no cover
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inner = narwhals_to_native_dtype(dtype.inner, version=version)
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list_size = dtype.size
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return pa.list_(inner, list_size=list_size)
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if isinstance_or_issubclass(dtype, dtypes.Time):
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return pa.time64("ns")
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if isinstance_or_issubclass(dtype, dtypes.Binary):
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return pa.binary()
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msg = f"Unknown dtype: {dtype}" # pragma: no cover
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raise AssertionError(msg)
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def extract_native(
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lhs: ArrowSeries, rhs: ArrowSeries | object
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) -> tuple[
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ArrowChunkedArray | pa.Scalar[Any], ArrowChunkedArray | pa.Scalar[Any] | object
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]:
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"""Extract native objects in binary operation.
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If the comparison isn't supported, return `NotImplemented` so that the
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"right-hand-side" operation (e.g. `__radd__`) can be tried.
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If one of the two sides has a `_broadcast` flag, then extract the scalar
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underneath it so that PyArrow can do its own broadcasting.
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"""
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from narwhals._arrow.dataframe import ArrowDataFrame
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from narwhals._arrow.series import ArrowSeries
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if rhs is None: # pragma: no cover
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return lhs.native, lit(None, type=lhs._type)
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if isinstance(rhs, ArrowDataFrame):
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return NotImplemented
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if isinstance(rhs, ArrowSeries):
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if lhs._broadcast and not rhs._broadcast:
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return lhs.native[0], rhs.native
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if rhs._broadcast:
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return lhs.native, rhs.native[0]
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return lhs.native, rhs.native
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if isinstance(rhs, list):
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msg = "Expected Series or scalar, got list."
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raise TypeError(msg)
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return lhs.native, rhs
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def align_series_full_broadcast(*series: ArrowSeries) -> Sequence[ArrowSeries]:
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# Ensure all of `series` are of the same length.
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lengths = [len(s) for s in series]
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max_length = max(lengths)
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fast_path = all(_len == max_length for _len in lengths)
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if fast_path:
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return series
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reshaped = []
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for s in series:
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if s._broadcast:
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value = s.native[0]
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if s._backend_version < (13,) and hasattr(value, "as_py"):
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value = value.as_py()
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reshaped.append(s._with_native(pa.array([value] * max_length, type=s._type)))
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else:
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if (actual_len := len(s)) != max_length:
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msg = f"Expected object of length {max_length}, got {actual_len}."
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raise ShapeError(msg)
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reshaped.append(s)
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return reshaped
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def horizontal_concat(dfs: list[pa.Table]) -> pa.Table:
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"""Concatenate (native) DataFrames horizontally.
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Should be in namespace.
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"""
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names = [name for df in dfs for name in df.column_names]
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if len(set(names)) < len(names): # pragma: no cover
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msg = "Expected unique column names"
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raise ValueError(msg)
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arrays = list(chain.from_iterable(df.itercolumns() for df in dfs))
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return pa.Table.from_arrays(arrays, names=names)
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def vertical_concat(dfs: list[pa.Table]) -> pa.Table:
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"""Concatenate (native) DataFrames vertically.
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Should be in namespace.
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"""
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cols_0 = dfs[0].column_names
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for i, df in enumerate(dfs[1:], start=1):
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cols_current = df.column_names
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if cols_current != cols_0:
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msg = (
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"unable to vstack, column names don't match:\n"
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f" - dataframe 0: {cols_0}\n"
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f" - dataframe {i}: {cols_current}\n"
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)
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raise TypeError(msg)
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return pa.concat_tables(dfs)
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def diagonal_concat(dfs: list[pa.Table], backend_version: tuple[int, ...]) -> pa.Table:
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"""Concatenate (native) DataFrames diagonally.
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Should be in namespace.
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"""
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kwargs: dict[str, Any] = (
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{"promote": True}
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if backend_version < (14, 0, 0)
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else {"promote_options": "default"}
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)
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return pa.concat_tables(dfs, **kwargs)
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def floordiv_compat(left: Any, right: Any) -> Any:
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# The following lines are adapted from pandas' pyarrow implementation.
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# Ref: https://github.com/pandas-dev/pandas/blob/262fcfbffcee5c3116e86a951d8b693f90411e68/pandas/core/arrays/arrow/array.py#L124-L154
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if isinstance(left, (int, float)):
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left = lit(left)
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if isinstance(right, (int, float)):
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right = lit(right)
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if pa.types.is_integer(left.type) and pa.types.is_integer(right.type):
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divided = pc.divide_checked(left, right)
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if pa.types.is_signed_integer(divided.type):
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# GH 56676
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has_remainder = pc.not_equal(pc.multiply(divided, right), left)
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has_one_negative_operand = pc.less(
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pc.bit_wise_xor(left, right), lit(0, type=divided.type)
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)
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result = pc.if_else(
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pc.and_(has_remainder, has_one_negative_operand),
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pc.subtract(divided, lit(1, type=divided.type)),
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divided,
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)
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else:
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result = divided # pragma: no cover
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result = result.cast(left.type)
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else:
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divided = pc.divide(left, right)
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result = pc.floor(divided)
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return result
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def cast_for_truediv(
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arrow_array: ArrowChunkedArray | pa.Scalar[Any],
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pa_object: ArrowChunkedArray | ArrowArray | pa.Scalar[Any],
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) -> tuple[
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ArrowChunkedArray | pa.Scalar[Any],
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ArrowChunkedArray | ArrowArray | pa.Scalar[Any],
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]:
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# Lifted from:
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# https://github.com/pandas-dev/pandas/blob/262fcfbffcee5c3116e86a951d8b693f90411e68/pandas/core/arrays/arrow/array.py#L108-L122
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# Ensure int / int -> float mirroring Python/Numpy behavior
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# as pc.divide_checked(int, int) -> int
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if pa.types.is_integer(arrow_array.type) and pa.types.is_integer(pa_object.type):
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# GH: 56645. # noqa: ERA001
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# https://github.com/apache/arrow/issues/35563
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return pc.cast(arrow_array, pa.float64(), safe=False), pc.cast(
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pa_object, pa.float64(), safe=False
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)
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return arrow_array, pa_object
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@overload
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def convert_slice_to_nparray(num_rows: int, rows_slice: slice) -> _AnyDArray: ...
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@overload
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def convert_slice_to_nparray(num_rows: int, rows_slice: _T) -> _T: ...
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def convert_slice_to_nparray(num_rows: int, rows_slice: slice | _T) -> _AnyDArray | _T:
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if isinstance(rows_slice, slice):
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import numpy as np # ignore-banned-import
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return np.arange(num_rows)[rows_slice]
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else:
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return rows_slice
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def select_rows(
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table: pa.Table, rows: slice | int | Sequence[int] | _AnyDArray
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) -> pa.Table:
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if isinstance(rows, slice) and rows == slice(None):
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selected_rows = table
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elif isinstance(rows, Sequence) and not rows:
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selected_rows = table.slice(0, 0)
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else:
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range_ = convert_slice_to_nparray(num_rows=len(table), rows_slice=rows)
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selected_rows = table.take(cast("list[int]", range_))
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return selected_rows
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def convert_str_slice_to_int_slice(
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str_slice: slice, columns: list[str]
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) -> tuple[int | None, int | None, int | None]:
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start = columns.index(str_slice.start) if str_slice.start is not None else None
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stop = columns.index(str_slice.stop) + 1 if str_slice.stop is not None else None
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step = str_slice.step
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return (start, stop, step)
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# Regex for date, time, separator and timezone components
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DATE_RE = r"(?P<date>\d{1,4}[-/.]\d{1,2}[-/.]\d{1,4}|\d{8})"
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SEP_RE = r"(?P<sep>\s|T)"
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TIME_RE = r"(?P<time>\d{2}:\d{2}(?::\d{2})?|\d{6}?)" # \s*(?P<period>[AP]M)?)?
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HMS_RE = r"^(?P<hms>\d{2}:\d{2}:\d{2})$"
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HM_RE = r"^(?P<hm>\d{2}:\d{2})$"
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HMS_RE_NO_SEP = r"^(?P<hms_no_sep>\d{6})$"
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TZ_RE = r"(?P<tz>Z|[+-]\d{2}:?\d{2})" # Matches 'Z', '+02:00', '+0200', '+02', etc.
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FULL_RE = rf"{DATE_RE}{SEP_RE}?{TIME_RE}?{TZ_RE}?$"
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# Separate regexes for different date formats
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YMD_RE = r"^(?P<year>(?:[12][0-9])?[0-9]{2})(?P<sep1>[-/.])(?P<month>0[1-9]|1[0-2])(?P<sep2>[-/.])(?P<day>0[1-9]|[12][0-9]|3[01])$"
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DMY_RE = r"^(?P<day>0[1-9]|[12][0-9]|3[01])(?P<sep1>[-/.])(?P<month>0[1-9]|1[0-2])(?P<sep2>[-/.])(?P<year>(?:[12][0-9])?[0-9]{2})$"
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MDY_RE = r"^(?P<month>0[1-9]|1[0-2])(?P<sep1>[-/.])(?P<day>0[1-9]|[12][0-9]|3[01])(?P<sep2>[-/.])(?P<year>(?:[12][0-9])?[0-9]{2})$"
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YMD_RE_NO_SEP = r"^(?P<year>(?:[12][0-9])?[0-9]{2})(?P<month>0[1-9]|1[0-2])(?P<day>0[1-9]|[12][0-9]|3[01])$"
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DATE_FORMATS = (
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(YMD_RE_NO_SEP, "%Y%m%d"),
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(YMD_RE, "%Y-%m-%d"),
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(DMY_RE, "%d-%m-%Y"),
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(MDY_RE, "%m-%d-%Y"),
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)
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TIME_FORMATS = ((HMS_RE, "%H:%M:%S"), (HM_RE, "%H:%M"), (HMS_RE_NO_SEP, "%H%M%S"))
|
|
|
|
|
|
def _extract_regex_concat_arrays(
|
|
strings: ArrowChunkedArray,
|
|
/,
|
|
pattern: str,
|
|
*,
|
|
options: Any = None,
|
|
memory_pool: Any = None,
|
|
) -> pa.StructArray:
|
|
r = pa.concat_arrays(
|
|
extract_regex(strings, pattern, options=options, memory_pool=memory_pool).chunks
|
|
)
|
|
return cast("pa.StructArray", r)
|
|
|
|
|
|
def parse_datetime_format(arr: ArrowChunkedArray) -> str:
|
|
"""Try to infer datetime format from StringArray."""
|
|
matches = _extract_regex_concat_arrays(arr.drop_null().slice(0, 10), pattern=FULL_RE)
|
|
if not pc.all(matches.is_valid()).as_py():
|
|
msg = (
|
|
"Unable to infer datetime format, provided format is not supported. "
|
|
"Please report a bug to https://github.com/narwhals-dev/narwhals/issues"
|
|
)
|
|
raise NotImplementedError(msg)
|
|
|
|
separators = matches.field("sep")
|
|
tz = matches.field("tz")
|
|
|
|
# separators and time zones must be unique
|
|
if pc.count(pc.unique(separators)).as_py() > 1:
|
|
msg = "Found multiple separator values while inferring datetime format."
|
|
raise ValueError(msg)
|
|
|
|
if pc.count(pc.unique(tz)).as_py() > 1:
|
|
msg = "Found multiple timezone values while inferring datetime format."
|
|
raise ValueError(msg)
|
|
|
|
date_value = _parse_date_format(cast("pc.StringArray", matches.field("date")))
|
|
time_value = _parse_time_format(cast("pc.StringArray", matches.field("time")))
|
|
|
|
sep_value = separators[0].as_py()
|
|
tz_value = "%z" if tz[0].as_py() else ""
|
|
|
|
return f"{date_value}{sep_value}{time_value}{tz_value}"
|
|
|
|
|
|
def _parse_date_format(arr: pc.StringArray) -> str:
|
|
for date_rgx, date_fmt in DATE_FORMATS:
|
|
matches = pc.extract_regex(arr, pattern=date_rgx)
|
|
if date_fmt == "%Y%m%d" and pc.all(matches.is_valid()).as_py():
|
|
return date_fmt
|
|
elif (
|
|
pc.all(matches.is_valid()).as_py()
|
|
and pc.count(pc.unique(sep1 := matches.field("sep1"))).as_py() == 1
|
|
and pc.count(pc.unique(sep2 := matches.field("sep2"))).as_py() == 1
|
|
and (date_sep_value := sep1[0].as_py()) == sep2[0].as_py()
|
|
):
|
|
return date_fmt.replace("-", date_sep_value)
|
|
|
|
msg = (
|
|
"Unable to infer datetime format. "
|
|
"Please report a bug to https://github.com/narwhals-dev/narwhals/issues"
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
|
|
def _parse_time_format(arr: pc.StringArray) -> str:
|
|
for time_rgx, time_fmt in TIME_FORMATS:
|
|
matches = pc.extract_regex(arr, pattern=time_rgx)
|
|
if pc.all(matches.is_valid()).as_py():
|
|
return time_fmt
|
|
return ""
|
|
|
|
|
|
def pad_series(
|
|
series: ArrowSeries, *, window_size: int, center: bool
|
|
) -> tuple[ArrowSeries, int]:
|
|
"""Pad series with None values on the left and/or right side, depending on the specified parameters.
|
|
|
|
Arguments:
|
|
series: The input ArrowSeries to be padded.
|
|
window_size: The desired size of the window.
|
|
center: Specifies whether to center the padding or not.
|
|
|
|
Returns:
|
|
A tuple containing the padded ArrowSeries and the offset value.
|
|
"""
|
|
if not center:
|
|
return series, 0
|
|
offset_left = window_size // 2
|
|
# subtract one if window_size is even
|
|
offset_right = offset_left - (window_size % 2 == 0)
|
|
pad_left = pa.array([None] * offset_left, type=series._type)
|
|
pad_right = pa.array([None] * offset_right, type=series._type)
|
|
concat = pa.concat_arrays([pad_left, *series.native.chunks, pad_right])
|
|
return series._with_native(concat), offset_left + offset_right
|
|
|
|
|
|
def cast_to_comparable_string_types(
|
|
*chunked_arrays: ArrowChunkedArray,
|
|
separator: str,
|
|
) -> tuple[Iterator[ArrowChunkedArray], pa.Scalar[Any]]:
|
|
# Ensure `chunked_arrays` are either all `string` or all `large_string`.
|
|
dtype = (
|
|
pa.string() # (PyArrow default)
|
|
if not any(pa.types.is_large_string(ca.type) for ca in chunked_arrays)
|
|
else pa.large_string()
|
|
)
|
|
return (ca.cast(dtype) for ca in chunked_arrays), lit(separator, dtype)
|
|
|
|
|
|
class ArrowSeriesNamespace(_SeriesNamespace["ArrowSeries", "ArrowChunkedArray"]):
|
|
def __init__(self: Self, series: ArrowSeries, /) -> None:
|
|
self._compliant_series = series
|