406 lines
13 KiB
Python
406 lines
13 KiB
Python
"""
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For compatibility with numpy libraries, pandas functions or
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methods have to accept '*args' and '**kwargs' parameters to
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accommodate numpy arguments that are not actually used or
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respected in the pandas implementation.
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To ensure that users do not abuse these parameters, validation
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is performed in 'validators.py' to make sure that any extra
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parameters passed correspond ONLY to those in the numpy signature.
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Part of that validation includes whether or not the user attempted
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to pass in non-default values for these extraneous parameters. As we
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want to discourage users from relying on these parameters when calling
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the pandas implementation, we want them only to pass in the default values
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for these parameters.
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This module provides a set of commonly used default arguments for functions
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and 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 collections import OrderedDict
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from distutils.version import LooseVersion
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from typing import Any, Dict, Optional, Union
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from numpy import __version__ as _np_version, ndarray
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from pandas._libs.lib import is_bool, is_integer
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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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class CompatValidator:
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def __init__(self, defaults, fname=None, method=None, max_fname_arg_count=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__(self, args, kwargs, fname=None, max_fname_arg_count=None, method=None):
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if args or kwargs:
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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 = dict(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, args):
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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, args, kwargs):
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"""
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If 'Series.argmin' is called via the 'numpy' library,
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the third parameter in its signature is 'out', which
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takes either an ndarray or 'None', so check if the
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'skipna' parameter is either an instance of ndarray or
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is 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, args, kwargs):
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"""
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If 'Series.argmax' is called via the 'numpy' library,
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the third parameter in its signature is 'out', which
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takes either an ndarray or 'None', so check if the
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'skipna' parameter is either an instance of ndarray or
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is 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: "OrderedDict[str, Optional[Union[int, str]]]" = OrderedDict()
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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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if LooseVersion(_np_version) >= LooseVersion("1.17.0"):
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# GH-26361. NumPy added radix sort and changed default to None.
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ARGSORT_DEFAULTS["kind"] = 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
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# for when the `kind` param is supported
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ARGSORT_DEFAULTS_KIND: "OrderedDict[str, Optional[int]]" = OrderedDict()
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ARGSORT_DEFAULTS_KIND["axis"] = -1
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ARGSORT_DEFAULTS_KIND["order"] = 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, args, kwargs):
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"""
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If 'Categorical.argsort' is called via the 'numpy' library, the
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first parameter in its signature is 'axis', which takes either
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an integer or 'None', so check if the 'ascending' parameter has
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either integer type or is None, since 'ascending' itself should
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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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return ascending
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CLIP_DEFAULTS = dict(out=None) # type Dict[str, Any]
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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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def validate_clip_with_axis(axis, args, kwargs):
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"""
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If 'NDFrame.clip' is called via the numpy library, the third
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parameter in its signature is 'out', which can takes an ndarray,
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so check if the 'axis' parameter is an instance of ndarray, since
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'axis' itself should either be 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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axis = None
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validate_clip(args, kwargs)
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return axis
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CUM_FUNC_DEFAULTS: "OrderedDict[str, Any]" = OrderedDict()
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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, args, kwargs, name):
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"""
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If this function is called via the 'numpy' library, the third
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parameter in its signature is 'dtype', which takes either a
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'numpy' dtype or 'None', so check if the 'skipna' parameter is
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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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validate_cum_func(args, kwargs, fname=name)
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return skipna
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ALLANY_DEFAULTS: "OrderedDict[str, Optional[bool]]" = OrderedDict()
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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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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 = dict(out=None, keepdims=False)
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validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method="kwargs")
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MINMAX_DEFAULTS = dict(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] = dict(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] = dict(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] = dict(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: "OrderedDict[str, Optional[Union[int, str]]]" = OrderedDict()
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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: "OrderedDict[str, Optional[Any]]" = OrderedDict()
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STAT_FUNC_DEFAULTS["dtype"] = None
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STAT_FUNC_DEFAULTS["out"] = None
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PROD_DEFAULTS = SUM_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
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SUM_DEFAULTS["keepdims"] = False
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SUM_DEFAULTS["initial"] = None
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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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STAT_FUNC_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: "OrderedDict[str, Optional[bool]]" = OrderedDict()
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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: "OrderedDict[str, Optional[str]]" = OrderedDict()
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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, args, kwargs):
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"""
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If this function is called via the 'numpy' library, the third
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parameter in its signature is 'axis', which takes either an
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ndarray or 'None', so check if the 'convert' parameter is either
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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 = dict(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_window_func(name, args, kwargs):
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numpy_args = ("axis", "dtype", "out")
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msg = (
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f"numpy operations are not valid with window objects. "
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f"Use .{name}() directly instead "
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)
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if len(args) > 0:
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raise UnsupportedFunctionCall(msg)
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for arg in numpy_args:
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if arg in kwargs:
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raise UnsupportedFunctionCall(msg)
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def validate_rolling_func(name, args, kwargs):
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numpy_args = ("axis", "dtype", "out")
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msg = (
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f"numpy operations are not valid with window objects. "
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f"Use .rolling(...).{name}() instead "
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)
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if len(args) > 0:
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raise UnsupportedFunctionCall(msg)
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for arg in numpy_args:
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if arg in kwargs:
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raise UnsupportedFunctionCall(msg)
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def validate_expanding_func(name, args, kwargs):
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numpy_args = ("axis", "dtype", "out")
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msg = (
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f"numpy operations are not valid with window objects. "
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f"Use .expanding(...).{name}() instead "
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)
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if len(args) > 0:
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raise UnsupportedFunctionCall(msg)
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for arg in numpy_args:
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if arg in kwargs:
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raise UnsupportedFunctionCall(msg)
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def validate_groupby_func(name, args, kwargs, allowed=None):
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"""
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'args' and 'kwargs' should be empty, except for allowed
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kwargs because all of
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their necessary parameters are explicitly listed in
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the function 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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f"numpy operations are not valid with "
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f"groupby. Use .groupby(...).{name}() "
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f"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, args, kwargs):
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"""
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'args' and 'kwargs' should be empty because all of
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their necessary parameters are explicitly listed in
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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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f"numpy operations are not "
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f"valid with resample. Use "
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f".resample(...).{method}() instead"
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)
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else:
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raise TypeError("too many arguments passed in")
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def validate_minmax_axis(axis):
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"""
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Ensure that the axis argument passed to min, max, argmin, or argmax is
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zero 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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Raises
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------
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ValueError
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"""
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ndim = 1 # hard-coded for Index
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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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