419 lines
13 KiB
Python
419 lines
13 KiB
Python
"""
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For compatibility with numpy libraries, pandas functions or methods have to
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accept '*args' and '**kwargs' parameters to accommodate numpy arguments that
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are not actually used or respected in the pandas implementation.
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To ensure that users do not abuse these parameters, validation is performed in
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'validators.py' to make sure that any extra parameters passed correspond ONLY
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to those in the numpy signature. Part of that validation includes whether or
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not the user attempted to pass in non-default values for these extraneous
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parameters. As we want to discourage users from relying on these parameters
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when calling the pandas implementation, we want them only to pass in the
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default values for these parameters.
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This module provides a set of commonly used default arguments for functions and
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methods that are spread throughout the codebase. This module will make it
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easier to adjust to future upstream changes in the analogous numpy signatures.
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"""
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from __future__ import annotations
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from typing import (
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TYPE_CHECKING,
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Any,
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TypeVar,
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cast,
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overload,
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)
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import numpy as np
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from numpy import ndarray
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from pandas._libs.lib import (
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is_bool,
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is_integer,
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)
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from pandas.errors import UnsupportedFunctionCall
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from pandas.util._validators import (
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validate_args,
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validate_args_and_kwargs,
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validate_kwargs,
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)
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if TYPE_CHECKING:
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from pandas._typing import (
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Axis,
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AxisInt,
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)
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AxisNoneT = TypeVar("AxisNoneT", Axis, None)
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class CompatValidator:
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def __init__(
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self,
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defaults,
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fname=None,
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method: str | None = None,
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max_fname_arg_count=None,
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) -> None:
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self.fname = fname
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self.method = method
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self.defaults = defaults
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self.max_fname_arg_count = max_fname_arg_count
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def __call__(
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self,
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args,
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kwargs,
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fname=None,
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max_fname_arg_count=None,
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method: str | None = None,
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) -> None:
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if not args and not kwargs:
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return None
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fname = self.fname if fname is None else fname
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max_fname_arg_count = (
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self.max_fname_arg_count
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if max_fname_arg_count is None
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else max_fname_arg_count
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)
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method = self.method if method is None else method
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if method == "args":
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validate_args(fname, args, max_fname_arg_count, self.defaults)
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elif method == "kwargs":
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validate_kwargs(fname, kwargs, self.defaults)
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elif method == "both":
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validate_args_and_kwargs(
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fname, args, kwargs, max_fname_arg_count, self.defaults
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)
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else:
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raise ValueError(f"invalid validation method '{method}'")
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ARGMINMAX_DEFAULTS = {"out": None}
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validate_argmin = CompatValidator(
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ARGMINMAX_DEFAULTS, fname="argmin", method="both", max_fname_arg_count=1
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)
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validate_argmax = CompatValidator(
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ARGMINMAX_DEFAULTS, fname="argmax", method="both", max_fname_arg_count=1
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)
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def process_skipna(skipna: bool | ndarray | None, args) -> tuple[bool, Any]:
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if isinstance(skipna, ndarray) or skipna is None:
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args = (skipna,) + args
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skipna = True
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return skipna, args
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def validate_argmin_with_skipna(skipna: bool | ndarray | None, args, kwargs) -> bool:
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"""
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If 'Series.argmin' is called via the 'numpy' library, the third parameter
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in its signature is 'out', which takes either an ndarray or 'None', so
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check if the 'skipna' parameter is either an instance of ndarray or is
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None, since 'skipna' itself should be a boolean
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"""
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skipna, args = process_skipna(skipna, args)
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validate_argmin(args, kwargs)
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return skipna
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def validate_argmax_with_skipna(skipna: bool | ndarray | None, args, kwargs) -> bool:
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"""
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If 'Series.argmax' is called via the 'numpy' library, the third parameter
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in its signature is 'out', which takes either an ndarray or 'None', so
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check if the 'skipna' parameter is either an instance of ndarray or is
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None, since 'skipna' itself should be a boolean
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"""
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skipna, args = process_skipna(skipna, args)
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validate_argmax(args, kwargs)
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return skipna
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ARGSORT_DEFAULTS: dict[str, int | str | None] = {}
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ARGSORT_DEFAULTS["axis"] = -1
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ARGSORT_DEFAULTS["kind"] = "quicksort"
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ARGSORT_DEFAULTS["order"] = None
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ARGSORT_DEFAULTS["kind"] = None
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ARGSORT_DEFAULTS["stable"] = None
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validate_argsort = CompatValidator(
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ARGSORT_DEFAULTS, fname="argsort", max_fname_arg_count=0, method="both"
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)
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# two different signatures of argsort, this second validation for when the
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# `kind` param is supported
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ARGSORT_DEFAULTS_KIND: dict[str, int | None] = {}
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ARGSORT_DEFAULTS_KIND["axis"] = -1
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ARGSORT_DEFAULTS_KIND["order"] = None
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ARGSORT_DEFAULTS_KIND["stable"] = None
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validate_argsort_kind = CompatValidator(
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ARGSORT_DEFAULTS_KIND, fname="argsort", max_fname_arg_count=0, method="both"
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)
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def validate_argsort_with_ascending(ascending: bool | int | None, args, kwargs) -> bool:
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"""
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If 'Categorical.argsort' is called via the 'numpy' library, the first
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parameter in its signature is 'axis', which takes either an integer or
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'None', so check if the 'ascending' parameter has either integer type or is
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None, since 'ascending' itself should be a boolean
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"""
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if is_integer(ascending) or ascending is None:
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args = (ascending,) + args
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ascending = True
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validate_argsort_kind(args, kwargs, max_fname_arg_count=3)
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ascending = cast(bool, ascending)
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return ascending
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CLIP_DEFAULTS: dict[str, Any] = {"out": None}
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validate_clip = CompatValidator(
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CLIP_DEFAULTS, fname="clip", method="both", max_fname_arg_count=3
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)
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@overload
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def validate_clip_with_axis(axis: ndarray, args, kwargs) -> None:
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...
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@overload
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def validate_clip_with_axis(axis: AxisNoneT, args, kwargs) -> AxisNoneT:
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...
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def validate_clip_with_axis(
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axis: ndarray | AxisNoneT, args, kwargs
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) -> AxisNoneT | None:
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"""
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If 'NDFrame.clip' is called via the numpy library, the third parameter in
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its signature is 'out', which can takes an ndarray, so check if the 'axis'
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parameter is an instance of ndarray, since 'axis' itself should either be
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an integer or None
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"""
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if isinstance(axis, ndarray):
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args = (axis,) + args
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# error: Incompatible types in assignment (expression has type "None",
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# variable has type "Union[ndarray[Any, Any], str, int]")
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axis = None # type: ignore[assignment]
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validate_clip(args, kwargs)
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# error: Incompatible return value type (got "Union[ndarray[Any, Any],
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# str, int]", expected "Union[str, int, None]")
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return axis # type: ignore[return-value]
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CUM_FUNC_DEFAULTS: dict[str, Any] = {}
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CUM_FUNC_DEFAULTS["dtype"] = None
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CUM_FUNC_DEFAULTS["out"] = None
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validate_cum_func = CompatValidator(
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CUM_FUNC_DEFAULTS, method="both", max_fname_arg_count=1
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)
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validate_cumsum = CompatValidator(
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CUM_FUNC_DEFAULTS, fname="cumsum", method="both", max_fname_arg_count=1
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)
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def validate_cum_func_with_skipna(skipna: bool, args, kwargs, name) -> bool:
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"""
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If this function is called via the 'numpy' library, the third parameter in
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its signature is 'dtype', which takes either a 'numpy' dtype or 'None', so
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check if the 'skipna' parameter is a boolean or not
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"""
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if not is_bool(skipna):
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args = (skipna,) + args
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skipna = True
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elif isinstance(skipna, np.bool_):
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skipna = bool(skipna)
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validate_cum_func(args, kwargs, fname=name)
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return skipna
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ALLANY_DEFAULTS: dict[str, bool | None] = {}
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ALLANY_DEFAULTS["dtype"] = None
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ALLANY_DEFAULTS["out"] = None
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ALLANY_DEFAULTS["keepdims"] = False
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ALLANY_DEFAULTS["axis"] = None
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validate_all = CompatValidator(
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ALLANY_DEFAULTS, fname="all", method="both", max_fname_arg_count=1
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)
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validate_any = CompatValidator(
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ALLANY_DEFAULTS, fname="any", method="both", max_fname_arg_count=1
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)
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LOGICAL_FUNC_DEFAULTS = {"out": None, "keepdims": False}
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validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method="kwargs")
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MINMAX_DEFAULTS = {"axis": None, "dtype": None, "out": None, "keepdims": False}
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validate_min = CompatValidator(
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MINMAX_DEFAULTS, fname="min", method="both", max_fname_arg_count=1
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)
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validate_max = CompatValidator(
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MINMAX_DEFAULTS, fname="max", method="both", max_fname_arg_count=1
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)
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RESHAPE_DEFAULTS: dict[str, str] = {"order": "C"}
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validate_reshape = CompatValidator(
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RESHAPE_DEFAULTS, fname="reshape", method="both", max_fname_arg_count=1
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)
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REPEAT_DEFAULTS: dict[str, Any] = {"axis": None}
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validate_repeat = CompatValidator(
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REPEAT_DEFAULTS, fname="repeat", method="both", max_fname_arg_count=1
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)
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ROUND_DEFAULTS: dict[str, Any] = {"out": None}
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validate_round = CompatValidator(
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ROUND_DEFAULTS, fname="round", method="both", max_fname_arg_count=1
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)
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SORT_DEFAULTS: dict[str, int | str | None] = {}
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SORT_DEFAULTS["axis"] = -1
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SORT_DEFAULTS["kind"] = "quicksort"
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SORT_DEFAULTS["order"] = None
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validate_sort = CompatValidator(SORT_DEFAULTS, fname="sort", method="kwargs")
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STAT_FUNC_DEFAULTS: dict[str, Any | None] = {}
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STAT_FUNC_DEFAULTS["dtype"] = None
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STAT_FUNC_DEFAULTS["out"] = None
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SUM_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
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SUM_DEFAULTS["axis"] = None
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SUM_DEFAULTS["keepdims"] = False
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SUM_DEFAULTS["initial"] = None
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PROD_DEFAULTS = SUM_DEFAULTS.copy()
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MEAN_DEFAULTS = SUM_DEFAULTS.copy()
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MEDIAN_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
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MEDIAN_DEFAULTS["overwrite_input"] = False
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MEDIAN_DEFAULTS["keepdims"] = False
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STAT_FUNC_DEFAULTS["keepdims"] = False
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validate_stat_func = CompatValidator(STAT_FUNC_DEFAULTS, method="kwargs")
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validate_sum = CompatValidator(
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SUM_DEFAULTS, fname="sum", method="both", max_fname_arg_count=1
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)
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validate_prod = CompatValidator(
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PROD_DEFAULTS, fname="prod", method="both", max_fname_arg_count=1
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)
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validate_mean = CompatValidator(
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MEAN_DEFAULTS, fname="mean", method="both", max_fname_arg_count=1
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)
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validate_median = CompatValidator(
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MEDIAN_DEFAULTS, fname="median", method="both", max_fname_arg_count=1
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)
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STAT_DDOF_FUNC_DEFAULTS: dict[str, bool | None] = {}
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STAT_DDOF_FUNC_DEFAULTS["dtype"] = None
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STAT_DDOF_FUNC_DEFAULTS["out"] = None
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STAT_DDOF_FUNC_DEFAULTS["keepdims"] = False
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validate_stat_ddof_func = CompatValidator(STAT_DDOF_FUNC_DEFAULTS, method="kwargs")
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TAKE_DEFAULTS: dict[str, str | None] = {}
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TAKE_DEFAULTS["out"] = None
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TAKE_DEFAULTS["mode"] = "raise"
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validate_take = CompatValidator(TAKE_DEFAULTS, fname="take", method="kwargs")
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def validate_take_with_convert(convert: ndarray | bool | None, args, kwargs) -> bool:
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"""
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If this function is called via the 'numpy' library, the third parameter in
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its signature is 'axis', which takes either an ndarray or 'None', so check
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if the 'convert' parameter is either an instance of ndarray or is None
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"""
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if isinstance(convert, ndarray) or convert is None:
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args = (convert,) + args
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convert = True
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validate_take(args, kwargs, max_fname_arg_count=3, method="both")
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return convert
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TRANSPOSE_DEFAULTS = {"axes": None}
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validate_transpose = CompatValidator(
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TRANSPOSE_DEFAULTS, fname="transpose", method="both", max_fname_arg_count=0
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)
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def validate_groupby_func(name: str, args, kwargs, allowed=None) -> None:
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"""
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'args' and 'kwargs' should be empty, except for allowed kwargs because all
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of their necessary parameters are explicitly listed in the function
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signature
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"""
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if allowed is None:
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allowed = []
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kwargs = set(kwargs) - set(allowed)
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if len(args) + len(kwargs) > 0:
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raise UnsupportedFunctionCall(
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"numpy operations are not valid with groupby. "
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f"Use .groupby(...).{name}() instead"
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)
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RESAMPLER_NUMPY_OPS = ("min", "max", "sum", "prod", "mean", "std", "var")
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def validate_resampler_func(method: str, args, kwargs) -> None:
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"""
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'args' and 'kwargs' should be empty because all of their necessary
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parameters are explicitly listed in the function signature
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"""
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if len(args) + len(kwargs) > 0:
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if method in RESAMPLER_NUMPY_OPS:
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raise UnsupportedFunctionCall(
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"numpy operations are not valid with resample. "
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f"Use .resample(...).{method}() instead"
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)
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raise TypeError("too many arguments passed in")
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def validate_minmax_axis(axis: AxisInt | None, ndim: int = 1) -> None:
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"""
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Ensure that the axis argument passed to min, max, argmin, or argmax is zero
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or None, as otherwise it will be incorrectly ignored.
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Parameters
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----------
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axis : int or None
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ndim : int, default 1
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Raises
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------
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ValueError
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"""
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if axis is None:
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return
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if axis >= ndim or (axis < 0 and ndim + axis < 0):
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raise ValueError(f"`axis` must be fewer than the number of dimensions ({ndim})")
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_validation_funcs = {
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"median": validate_median,
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"mean": validate_mean,
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"min": validate_min,
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"max": validate_max,
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"sum": validate_sum,
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"prod": validate_prod,
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}
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def validate_func(fname, args, kwargs) -> None:
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if fname not in _validation_funcs:
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return validate_stat_func(args, kwargs, fname=fname)
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validation_func = _validation_funcs[fname]
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return validation_func(args, kwargs)
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