476 lines
18 KiB
Python
476 lines
18 KiB
Python
from __future__ import annotations
|
|
|
|
from functools import lru_cache
|
|
from typing import TYPE_CHECKING
|
|
from typing import Any
|
|
from typing import Iterable
|
|
from typing import Iterator
|
|
from typing import Mapping
|
|
from typing import Sequence
|
|
from typing import cast
|
|
|
|
import pyarrow as pa
|
|
import pyarrow.compute as pc
|
|
|
|
from narwhals._compliant.series import _SeriesNamespace
|
|
from narwhals.exceptions import ShapeError
|
|
from narwhals.utils import isinstance_or_issubclass
|
|
|
|
if TYPE_CHECKING:
|
|
from typing_extensions import TypeAlias
|
|
from typing_extensions import TypeIs
|
|
|
|
from narwhals._arrow.series import ArrowSeries
|
|
from narwhals._arrow.typing import ArrayAny
|
|
from narwhals._arrow.typing import ArrayOrScalar
|
|
from narwhals._arrow.typing import ArrayOrScalarT1
|
|
from narwhals._arrow.typing import ArrayOrScalarT2
|
|
from narwhals._arrow.typing import ChunkedArrayAny
|
|
from narwhals._arrow.typing import NativeIntervalUnit
|
|
from narwhals._arrow.typing import ScalarAny
|
|
from narwhals._duration import IntervalUnit
|
|
from narwhals.dtypes import DType
|
|
from narwhals.typing import PythonLiteral
|
|
from narwhals.utils import Version
|
|
|
|
# NOTE: stubs don't allow for `ChunkedArray[StructArray]`
|
|
# Intended to represent the `.chunks` property storing `list[pa.StructArray]`
|
|
ChunkedArrayStructArray: TypeAlias = ChunkedArrayAny
|
|
|
|
def is_timestamp(t: Any) -> TypeIs[pa.TimestampType[Any, Any]]: ...
|
|
def is_duration(t: Any) -> TypeIs[pa.DurationType[Any]]: ...
|
|
def is_list(t: Any) -> TypeIs[pa.ListType[Any]]: ...
|
|
def is_large_list(t: Any) -> TypeIs[pa.LargeListType[Any]]: ...
|
|
def is_fixed_size_list(t: Any) -> TypeIs[pa.FixedSizeListType[Any, Any]]: ...
|
|
def is_dictionary(
|
|
t: Any,
|
|
) -> TypeIs[pa.DictionaryType[Any, Any, Any]]: ...
|
|
def extract_regex(
|
|
strings: ChunkedArrayAny,
|
|
/,
|
|
pattern: str,
|
|
*,
|
|
options: Any = None,
|
|
memory_pool: Any = None,
|
|
) -> ChunkedArrayStructArray: ...
|
|
else:
|
|
from pyarrow.compute import extract_regex
|
|
from pyarrow.types import is_dictionary # noqa: F401
|
|
from pyarrow.types import is_duration
|
|
from pyarrow.types import is_fixed_size_list
|
|
from pyarrow.types import is_large_list
|
|
from pyarrow.types import is_list
|
|
from pyarrow.types import is_timestamp
|
|
|
|
UNITS_DICT: Mapping[IntervalUnit, NativeIntervalUnit] = {
|
|
"y": "year",
|
|
"q": "quarter",
|
|
"mo": "month",
|
|
"d": "day",
|
|
"h": "hour",
|
|
"m": "minute",
|
|
"s": "second",
|
|
"ms": "millisecond",
|
|
"us": "microsecond",
|
|
"ns": "nanosecond",
|
|
}
|
|
|
|
lit = pa.scalar
|
|
"""Alias for `pyarrow.scalar`."""
|
|
|
|
|
|
def extract_py_scalar(value: Any, /) -> Any:
|
|
from narwhals._arrow.series import maybe_extract_py_scalar
|
|
|
|
return maybe_extract_py_scalar(value, return_py_scalar=True)
|
|
|
|
|
|
def chunked_array(
|
|
arr: ArrayOrScalar | list[Iterable[Any]], dtype: pa.DataType | None = None, /
|
|
) -> ChunkedArrayAny:
|
|
if isinstance(arr, pa.ChunkedArray):
|
|
return arr
|
|
if isinstance(arr, list):
|
|
return pa.chunked_array(arr, dtype)
|
|
else:
|
|
return pa.chunked_array([arr], arr.type)
|
|
|
|
|
|
def nulls_like(n: int, series: ArrowSeries) -> ArrayAny:
|
|
"""Create a strongly-typed Array instance with all elements null.
|
|
|
|
Uses the type of `series`, without upseting `mypy`.
|
|
"""
|
|
return pa.nulls(n, series.native.type)
|
|
|
|
|
|
@lru_cache(maxsize=16)
|
|
def native_to_narwhals_dtype(dtype: pa.DataType, version: Version) -> DType: # noqa: C901, PLR0912
|
|
dtypes = version.dtypes
|
|
if pa.types.is_int64(dtype):
|
|
return dtypes.Int64()
|
|
if pa.types.is_int32(dtype):
|
|
return dtypes.Int32()
|
|
if pa.types.is_int16(dtype):
|
|
return dtypes.Int16()
|
|
if pa.types.is_int8(dtype):
|
|
return dtypes.Int8()
|
|
if pa.types.is_uint64(dtype):
|
|
return dtypes.UInt64()
|
|
if pa.types.is_uint32(dtype):
|
|
return dtypes.UInt32()
|
|
if pa.types.is_uint16(dtype):
|
|
return dtypes.UInt16()
|
|
if pa.types.is_uint8(dtype):
|
|
return dtypes.UInt8()
|
|
if pa.types.is_boolean(dtype):
|
|
return dtypes.Boolean()
|
|
if pa.types.is_float64(dtype):
|
|
return dtypes.Float64()
|
|
if pa.types.is_float32(dtype):
|
|
return dtypes.Float32()
|
|
# bug in coverage? it shows `31->exit` (where `31` is currently the line number of
|
|
# the next line), even though both when the if condition is true and false are covered
|
|
if ( # pragma: no cover
|
|
pa.types.is_string(dtype)
|
|
or pa.types.is_large_string(dtype)
|
|
or getattr(pa.types, "is_string_view", lambda _: False)(dtype)
|
|
):
|
|
return dtypes.String()
|
|
if pa.types.is_date32(dtype):
|
|
return dtypes.Date()
|
|
if is_timestamp(dtype):
|
|
return dtypes.Datetime(time_unit=dtype.unit, time_zone=dtype.tz)
|
|
if is_duration(dtype):
|
|
return dtypes.Duration(time_unit=dtype.unit)
|
|
if pa.types.is_dictionary(dtype):
|
|
return dtypes.Categorical()
|
|
if pa.types.is_struct(dtype):
|
|
return dtypes.Struct(
|
|
[
|
|
dtypes.Field(
|
|
dtype.field(i).name,
|
|
native_to_narwhals_dtype(dtype.field(i).type, version),
|
|
)
|
|
for i in range(dtype.num_fields)
|
|
]
|
|
)
|
|
if is_list(dtype) or is_large_list(dtype):
|
|
return dtypes.List(native_to_narwhals_dtype(dtype.value_type, version))
|
|
if is_fixed_size_list(dtype):
|
|
return dtypes.Array(
|
|
native_to_narwhals_dtype(dtype.value_type, version), dtype.list_size
|
|
)
|
|
if pa.types.is_decimal(dtype):
|
|
return dtypes.Decimal()
|
|
if pa.types.is_time32(dtype) or pa.types.is_time64(dtype):
|
|
return dtypes.Time()
|
|
if pa.types.is_binary(dtype):
|
|
return dtypes.Binary()
|
|
return dtypes.Unknown() # pragma: no cover
|
|
|
|
|
|
def narwhals_to_native_dtype(dtype: DType | type[DType], version: Version) -> pa.DataType: # noqa: C901, PLR0912
|
|
dtypes = version.dtypes
|
|
if isinstance_or_issubclass(dtype, dtypes.Decimal):
|
|
msg = "Casting to Decimal is not supported yet."
|
|
raise NotImplementedError(msg)
|
|
if isinstance_or_issubclass(dtype, dtypes.Float64):
|
|
return pa.float64()
|
|
if isinstance_or_issubclass(dtype, dtypes.Float32):
|
|
return pa.float32()
|
|
if isinstance_or_issubclass(dtype, dtypes.Int64):
|
|
return pa.int64()
|
|
if isinstance_or_issubclass(dtype, dtypes.Int32):
|
|
return pa.int32()
|
|
if isinstance_or_issubclass(dtype, dtypes.Int16):
|
|
return pa.int16()
|
|
if isinstance_or_issubclass(dtype, dtypes.Int8):
|
|
return pa.int8()
|
|
if isinstance_or_issubclass(dtype, dtypes.UInt64):
|
|
return pa.uint64()
|
|
if isinstance_or_issubclass(dtype, dtypes.UInt32):
|
|
return pa.uint32()
|
|
if isinstance_or_issubclass(dtype, dtypes.UInt16):
|
|
return pa.uint16()
|
|
if isinstance_or_issubclass(dtype, dtypes.UInt8):
|
|
return pa.uint8()
|
|
if isinstance_or_issubclass(dtype, dtypes.String):
|
|
return pa.string()
|
|
if isinstance_or_issubclass(dtype, dtypes.Boolean):
|
|
return pa.bool_()
|
|
if isinstance_or_issubclass(dtype, dtypes.Categorical):
|
|
return pa.dictionary(pa.uint32(), pa.string())
|
|
if isinstance_or_issubclass(dtype, dtypes.Datetime):
|
|
unit = dtype.time_unit
|
|
return pa.timestamp(unit, tz) if (tz := dtype.time_zone) else pa.timestamp(unit)
|
|
if isinstance_or_issubclass(dtype, dtypes.Duration):
|
|
return pa.duration(dtype.time_unit)
|
|
if isinstance_or_issubclass(dtype, dtypes.Date):
|
|
return pa.date32()
|
|
if isinstance_or_issubclass(dtype, dtypes.List):
|
|
return pa.list_(value_type=narwhals_to_native_dtype(dtype.inner, version=version))
|
|
if isinstance_or_issubclass(dtype, dtypes.Struct):
|
|
return pa.struct(
|
|
[
|
|
(field.name, narwhals_to_native_dtype(field.dtype, version=version))
|
|
for field in dtype.fields
|
|
]
|
|
)
|
|
if isinstance_or_issubclass(dtype, dtypes.Array): # pragma: no cover
|
|
inner = narwhals_to_native_dtype(dtype.inner, version=version)
|
|
list_size = dtype.size
|
|
return pa.list_(inner, list_size=list_size)
|
|
if isinstance_or_issubclass(dtype, dtypes.Time):
|
|
return pa.time64("ns")
|
|
if isinstance_or_issubclass(dtype, dtypes.Binary):
|
|
return pa.binary()
|
|
|
|
msg = f"Unknown dtype: {dtype}" # pragma: no cover
|
|
raise AssertionError(msg)
|
|
|
|
|
|
def extract_native(
|
|
lhs: ArrowSeries, rhs: ArrowSeries | PythonLiteral | ScalarAny
|
|
) -> tuple[ChunkedArrayAny | ScalarAny, ChunkedArrayAny | ScalarAny]:
|
|
"""Extract native objects in binary operation.
|
|
|
|
If the comparison isn't supported, return `NotImplemented` so that the
|
|
"right-hand-side" operation (e.g. `__radd__`) can be tried.
|
|
|
|
If one of the two sides has a `_broadcast` flag, then extract the scalar
|
|
underneath it so that PyArrow can do its own broadcasting.
|
|
"""
|
|
from narwhals._arrow.dataframe import ArrowDataFrame
|
|
from narwhals._arrow.series import ArrowSeries
|
|
|
|
if rhs is None: # pragma: no cover
|
|
return lhs.native, lit(None, type=lhs._type)
|
|
|
|
if isinstance(rhs, ArrowDataFrame):
|
|
return NotImplemented
|
|
|
|
if isinstance(rhs, ArrowSeries):
|
|
if lhs._broadcast and not rhs._broadcast:
|
|
return lhs.native[0], rhs.native
|
|
if rhs._broadcast:
|
|
return lhs.native, rhs.native[0]
|
|
return lhs.native, rhs.native
|
|
|
|
if isinstance(rhs, list):
|
|
msg = "Expected Series or scalar, got list."
|
|
raise TypeError(msg)
|
|
|
|
return lhs.native, rhs if isinstance(rhs, pa.Scalar) else lit(rhs)
|
|
|
|
|
|
def align_series_full_broadcast(*series: ArrowSeries) -> Sequence[ArrowSeries]:
|
|
# Ensure all of `series` are of the same length.
|
|
lengths = [len(s) for s in series]
|
|
max_length = max(lengths)
|
|
fast_path = all(_len == max_length for _len in lengths)
|
|
|
|
if fast_path:
|
|
return series
|
|
|
|
reshaped = []
|
|
for s in series:
|
|
if s._broadcast:
|
|
value = s.native[0]
|
|
if s._backend_version < (13,) and hasattr(value, "as_py"):
|
|
value = value.as_py()
|
|
reshaped.append(s._with_native(pa.array([value] * max_length, type=s._type)))
|
|
else:
|
|
if (actual_len := len(s)) != max_length:
|
|
msg = f"Expected object of length {max_length}, got {actual_len}."
|
|
raise ShapeError(msg)
|
|
reshaped.append(s)
|
|
|
|
return reshaped
|
|
|
|
|
|
def floordiv_compat(left: ArrayOrScalar, right: ArrayOrScalar, /) -> Any:
|
|
# The following lines are adapted from pandas' pyarrow implementation.
|
|
# Ref: https://github.com/pandas-dev/pandas/blob/262fcfbffcee5c3116e86a951d8b693f90411e68/pandas/core/arrays/arrow/array.py#L124-L154
|
|
|
|
if pa.types.is_integer(left.type) and pa.types.is_integer(right.type):
|
|
divided = pc.divide_checked(left, right)
|
|
# TODO @dangotbanned: Use a `TypeVar` in guards
|
|
# Narrowing to a `Union` isn't interacting well with the rest of the stubs
|
|
# https://github.com/zen-xu/pyarrow-stubs/pull/215
|
|
if pa.types.is_signed_integer(divided.type):
|
|
div_type = cast("pa._lib.Int64Type", divided.type)
|
|
has_remainder = pc.not_equal(pc.multiply(divided, right), left)
|
|
has_one_negative_operand = pc.less(
|
|
pc.bit_wise_xor(left, right), lit(0, div_type)
|
|
)
|
|
result = pc.if_else(
|
|
pc.and_(has_remainder, has_one_negative_operand),
|
|
pc.subtract(divided, lit(1, div_type)),
|
|
divided,
|
|
)
|
|
else:
|
|
result = divided # pragma: no cover
|
|
result = result.cast(left.type)
|
|
else:
|
|
divided = pc.divide(left, right)
|
|
result = pc.floor(divided)
|
|
return result
|
|
|
|
|
|
def cast_for_truediv(
|
|
arrow_array: ArrayOrScalarT1, pa_object: ArrayOrScalarT2
|
|
) -> tuple[ArrayOrScalarT1, ArrayOrScalarT2]:
|
|
# Lifted from:
|
|
# https://github.com/pandas-dev/pandas/blob/262fcfbffcee5c3116e86a951d8b693f90411e68/pandas/core/arrays/arrow/array.py#L108-L122
|
|
# Ensure int / int -> float mirroring Python/Numpy behavior
|
|
# as pc.divide_checked(int, int) -> int
|
|
if pa.types.is_integer(arrow_array.type) and pa.types.is_integer(pa_object.type):
|
|
# GH: 56645. # noqa: ERA001
|
|
# https://github.com/apache/arrow/issues/35563
|
|
# NOTE: `pyarrow==11.*` doesn't allow keywords in `Array.cast`
|
|
return pc.cast(arrow_array, pa.float64(), safe=False), pc.cast(
|
|
pa_object, pa.float64(), safe=False
|
|
)
|
|
|
|
return arrow_array, pa_object
|
|
|
|
|
|
# Regex for date, time, separator and timezone components
|
|
DATE_RE = r"(?P<date>\d{1,4}[-/.]\d{1,2}[-/.]\d{1,4}|\d{8})"
|
|
SEP_RE = r"(?P<sep>\s|T)"
|
|
TIME_RE = r"(?P<time>\d{2}:\d{2}(?::\d{2})?|\d{6}?)" # \s*(?P<period>[AP]M)?)?
|
|
HMS_RE = r"^(?P<hms>\d{2}:\d{2}:\d{2})$"
|
|
HM_RE = r"^(?P<hm>\d{2}:\d{2})$"
|
|
HMS_RE_NO_SEP = r"^(?P<hms_no_sep>\d{6})$"
|
|
TZ_RE = r"(?P<tz>Z|[+-]\d{2}:?\d{2})" # Matches 'Z', '+02:00', '+0200', '+02', etc.
|
|
FULL_RE = rf"{DATE_RE}{SEP_RE}?{TIME_RE}?{TZ_RE}?$"
|
|
|
|
# Separate regexes for different date formats
|
|
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])$"
|
|
DMY_RE = r"^(?P<day>0[1-9]|[12][0-9]|3[01])(?P<sep1>[-/.])(?P<month>0[1-9]|1[0-2])(?P<sep2>[-/.])(?P<year>(?:[12][0-9])?[0-9]{2})$"
|
|
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})$"
|
|
YMD_RE_NO_SEP = r"^(?P<year>(?:[12][0-9])?[0-9]{2})(?P<month>0[1-9]|1[0-2])(?P<day>0[1-9]|[12][0-9]|3[01])$"
|
|
|
|
DATE_FORMATS = (
|
|
(YMD_RE_NO_SEP, "%Y%m%d"),
|
|
(YMD_RE, "%Y-%m-%d"),
|
|
(DMY_RE, "%d-%m-%Y"),
|
|
(MDY_RE, "%m-%d-%Y"),
|
|
)
|
|
TIME_FORMATS = ((HMS_RE, "%H:%M:%S"), (HM_RE, "%H:%M"), (HMS_RE_NO_SEP, "%H%M%S"))
|
|
|
|
|
|
def _extract_regex_concat_arrays(
|
|
strings: ChunkedArrayAny,
|
|
/,
|
|
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: ChunkedArrayAny) -> 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: ChunkedArrayAny, separator: str
|
|
) -> tuple[Iterator[ChunkedArrayAny], ScalarAny]:
|
|
# 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", "ChunkedArrayAny"]):
|
|
def __init__(self, series: ArrowSeries, /) -> None:
|
|
self._compliant_series = series
|