452 lines
14 KiB
Python
452 lines
14 KiB
Python
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"""
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Module that contains many useful utilities
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for validating data or function arguments
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"""
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from __future__ import annotations
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from typing import (
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Iterable,
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Sequence,
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TypeVar,
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overload,
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)
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import numpy as np
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from pandas._libs import lib
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from pandas.core.dtypes.common import (
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is_bool,
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is_integer,
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)
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BoolishT = TypeVar("BoolishT", bool, int)
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BoolishNoneT = TypeVar("BoolishNoneT", bool, int, None)
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def _check_arg_length(fname, args, max_fname_arg_count, compat_args):
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"""
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Checks whether 'args' has length of at most 'compat_args'. Raises
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a TypeError if that is not the case, similar to in Python when a
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function is called with too many arguments.
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"""
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if max_fname_arg_count < 0:
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raise ValueError("'max_fname_arg_count' must be non-negative")
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if len(args) > len(compat_args):
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max_arg_count = len(compat_args) + max_fname_arg_count
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actual_arg_count = len(args) + max_fname_arg_count
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argument = "argument" if max_arg_count == 1 else "arguments"
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raise TypeError(
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f"{fname}() takes at most {max_arg_count} {argument} "
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f"({actual_arg_count} given)"
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)
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def _check_for_default_values(fname, arg_val_dict, compat_args):
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"""
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Check that the keys in `arg_val_dict` are mapped to their
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default values as specified in `compat_args`.
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Note that this function is to be called only when it has been
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checked that arg_val_dict.keys() is a subset of compat_args
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"""
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for key in arg_val_dict:
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# try checking equality directly with '=' operator,
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# as comparison may have been overridden for the left
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# hand object
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try:
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v1 = arg_val_dict[key]
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v2 = compat_args[key]
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# check for None-ness otherwise we could end up
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# comparing a numpy array vs None
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if (v1 is not None and v2 is None) or (v1 is None and v2 is not None):
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match = False
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else:
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match = v1 == v2
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if not is_bool(match):
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raise ValueError("'match' is not a boolean")
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# could not compare them directly, so try comparison
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# using the 'is' operator
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except ValueError:
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match = arg_val_dict[key] is compat_args[key]
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if not match:
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raise ValueError(
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f"the '{key}' parameter is not supported in "
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f"the pandas implementation of {fname}()"
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)
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def validate_args(fname, args, max_fname_arg_count, compat_args) -> None:
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"""
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Checks whether the length of the `*args` argument passed into a function
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has at most `len(compat_args)` arguments and whether or not all of these
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elements in `args` are set to their default values.
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Parameters
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----------
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fname : str
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The name of the function being passed the `*args` parameter
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args : tuple
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The `*args` parameter passed into a function
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max_fname_arg_count : int
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The maximum number of arguments that the function `fname`
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can accept, excluding those in `args`. Used for displaying
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appropriate error messages. Must be non-negative.
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compat_args : dict
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A dictionary of keys and their associated default values.
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In order to accommodate buggy behaviour in some versions of `numpy`,
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where a signature displayed keyword arguments but then passed those
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arguments **positionally** internally when calling downstream
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implementations, a dict ensures that the original
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order of the keyword arguments is enforced.
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Raises
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------
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TypeError
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If `args` contains more values than there are `compat_args`
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ValueError
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If `args` contains values that do not correspond to those
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of the default values specified in `compat_args`
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"""
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_check_arg_length(fname, args, max_fname_arg_count, compat_args)
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# We do this so that we can provide a more informative
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# error message about the parameters that we are not
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# supporting in the pandas implementation of 'fname'
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kwargs = dict(zip(compat_args, args))
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_check_for_default_values(fname, kwargs, compat_args)
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def _check_for_invalid_keys(fname, kwargs, compat_args):
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"""
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Checks whether 'kwargs' contains any keys that are not
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in 'compat_args' and raises a TypeError if there is one.
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"""
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# set(dict) --> set of the dictionary's keys
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diff = set(kwargs) - set(compat_args)
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if diff:
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bad_arg = list(diff)[0]
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raise TypeError(f"{fname}() got an unexpected keyword argument '{bad_arg}'")
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def validate_kwargs(fname, kwargs, compat_args) -> None:
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"""
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Checks whether parameters passed to the **kwargs argument in a
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function `fname` are valid parameters as specified in `*compat_args`
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and whether or not they are set to their default values.
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Parameters
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----------
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fname : str
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The name of the function being passed the `**kwargs` parameter
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kwargs : dict
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The `**kwargs` parameter passed into `fname`
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compat_args: dict
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A dictionary of keys that `kwargs` is allowed to have and their
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associated default values
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Raises
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------
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TypeError if `kwargs` contains keys not in `compat_args`
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ValueError if `kwargs` contains keys in `compat_args` that do not
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map to the default values specified in `compat_args`
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"""
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kwds = kwargs.copy()
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_check_for_invalid_keys(fname, kwargs, compat_args)
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_check_for_default_values(fname, kwds, compat_args)
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def validate_args_and_kwargs(
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fname, args, kwargs, max_fname_arg_count, compat_args
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) -> None:
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"""
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Checks whether parameters passed to the *args and **kwargs argument in a
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function `fname` are valid parameters as specified in `*compat_args`
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and whether or not they are set to their default values.
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Parameters
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----------
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fname: str
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The name of the function being passed the `**kwargs` parameter
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args: tuple
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The `*args` parameter passed into a function
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kwargs: dict
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The `**kwargs` parameter passed into `fname`
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max_fname_arg_count: int
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The minimum number of arguments that the function `fname`
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requires, excluding those in `args`. Used for displaying
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appropriate error messages. Must be non-negative.
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compat_args: dict
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A dictionary of keys that `kwargs` is allowed to
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have and their associated default values.
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Raises
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------
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TypeError if `args` contains more values than there are
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`compat_args` OR `kwargs` contains keys not in `compat_args`
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ValueError if `args` contains values not at the default value (`None`)
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`kwargs` contains keys in `compat_args` that do not map to the default
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value as specified in `compat_args`
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See Also
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--------
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validate_args : Purely args validation.
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validate_kwargs : Purely kwargs validation.
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"""
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# Check that the total number of arguments passed in (i.e.
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# args and kwargs) does not exceed the length of compat_args
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_check_arg_length(
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fname, args + tuple(kwargs.values()), max_fname_arg_count, compat_args
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)
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# Check there is no overlap with the positional and keyword
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# arguments, similar to what is done in actual Python functions
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args_dict = dict(zip(compat_args, args))
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for key in args_dict:
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if key in kwargs:
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raise TypeError(
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f"{fname}() got multiple values for keyword argument '{key}'"
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)
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kwargs.update(args_dict)
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validate_kwargs(fname, kwargs, compat_args)
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def validate_bool_kwarg(
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value: BoolishNoneT, arg_name, none_allowed: bool = True, int_allowed: bool = False
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) -> BoolishNoneT:
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"""
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Ensure that argument passed in arg_name can be interpreted as boolean.
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Parameters
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----------
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value : bool
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Value to be validated.
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arg_name : str
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Name of the argument. To be reflected in the error message.
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none_allowed : bool, default True
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Whether to consider None to be a valid boolean.
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int_allowed : bool, default False
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Whether to consider integer value to be a valid boolean.
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Returns
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-------
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value
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The same value as input.
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Raises
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------
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ValueError
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If the value is not a valid boolean.
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"""
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good_value = is_bool(value)
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if none_allowed:
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good_value = good_value or value is None
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if int_allowed:
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good_value = good_value or isinstance(value, int)
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if not good_value:
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raise ValueError(
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f'For argument "{arg_name}" expected type bool, received '
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f"type {type(value).__name__}."
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)
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return value
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def validate_fillna_kwargs(value, method, validate_scalar_dict_value: bool = True):
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"""
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Validate the keyword arguments to 'fillna'.
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This checks that exactly one of 'value' and 'method' is specified.
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If 'method' is specified, this validates that it's a valid method.
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Parameters
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----------
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value, method : object
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The 'value' and 'method' keyword arguments for 'fillna'.
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validate_scalar_dict_value : bool, default True
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Whether to validate that 'value' is a scalar or dict. Specifically,
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validate that it is not a list or tuple.
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Returns
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-------
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value, method : object
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"""
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from pandas.core.missing import clean_fill_method
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if value is None and method is None:
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raise ValueError("Must specify a fill 'value' or 'method'.")
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if value is None and method is not None:
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method = clean_fill_method(method)
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elif value is not None and method is None:
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if validate_scalar_dict_value and isinstance(value, (list, tuple)):
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raise TypeError(
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'"value" parameter must be a scalar or dict, but '
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f'you passed a "{type(value).__name__}"'
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)
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elif value is not None and method is not None:
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raise ValueError("Cannot specify both 'value' and 'method'.")
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return value, method
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def validate_percentile(q: float | Iterable[float]) -> np.ndarray:
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"""
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Validate percentiles (used by describe and quantile).
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This function checks if the given float or iterable of floats is a valid percentile
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otherwise raises a ValueError.
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Parameters
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----------
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q: float or iterable of floats
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A single percentile or an iterable of percentiles.
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Returns
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-------
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ndarray
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An ndarray of the percentiles if valid.
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Raises
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------
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ValueError if percentiles are not in given interval([0, 1]).
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"""
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q_arr = np.asarray(q)
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# Don't change this to an f-string. The string formatting
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# is too expensive for cases where we don't need it.
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msg = "percentiles should all be in the interval [0, 1]. Try {} instead."
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if q_arr.ndim == 0:
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if not 0 <= q_arr <= 1:
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raise ValueError(msg.format(q_arr / 100.0))
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else:
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if not all(0 <= qs <= 1 for qs in q_arr):
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raise ValueError(msg.format(q_arr / 100.0))
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return q_arr
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@overload
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def validate_ascending(ascending: BoolishT) -> BoolishT:
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...
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@overload
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def validate_ascending(ascending: Sequence[BoolishT]) -> list[BoolishT]:
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...
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def validate_ascending(
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ascending: bool | int | Sequence[BoolishT],
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) -> bool | int | list[BoolishT]:
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"""Validate ``ascending`` kwargs for ``sort_index`` method."""
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kwargs = {"none_allowed": False, "int_allowed": True}
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if not isinstance(ascending, Sequence):
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return validate_bool_kwarg(ascending, "ascending", **kwargs)
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return [validate_bool_kwarg(item, "ascending", **kwargs) for item in ascending]
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def validate_endpoints(closed: str | None) -> tuple[bool, bool]:
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"""
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Check that the `closed` argument is among [None, "left", "right"]
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Parameters
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----------
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closed : {None, "left", "right"}
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Returns
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-------
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left_closed : bool
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right_closed : bool
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Raises
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------
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ValueError : if argument is not among valid values
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"""
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left_closed = False
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right_closed = False
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if closed is None:
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left_closed = True
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right_closed = True
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elif closed == "left":
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left_closed = True
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elif closed == "right":
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right_closed = True
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else:
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raise ValueError("Closed has to be either 'left', 'right' or None")
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return left_closed, right_closed
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def validate_inclusive(inclusive: str | None) -> tuple[bool, bool]:
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"""
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|
|
Check that the `inclusive` argument is among {"both", "neither", "left", "right"}.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
inclusive : {"both", "neither", "left", "right"}
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
left_right_inclusive : tuple[bool, bool]
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ValueError : if argument is not among valid values
|
||
|
|
"""
|
||
|
|
left_right_inclusive: tuple[bool, bool] | None = None
|
||
|
|
|
||
|
|
if isinstance(inclusive, str):
|
||
|
|
left_right_inclusive = {
|
||
|
|
"both": (True, True),
|
||
|
|
"left": (True, False),
|
||
|
|
"right": (False, True),
|
||
|
|
"neither": (False, False),
|
||
|
|
}.get(inclusive)
|
||
|
|
|
||
|
|
if left_right_inclusive is None:
|
||
|
|
raise ValueError(
|
||
|
|
"Inclusive has to be either 'both', 'neither', 'left' or 'right'"
|
||
|
|
)
|
||
|
|
|
||
|
|
return left_right_inclusive
|
||
|
|
|
||
|
|
|
||
|
|
def validate_insert_loc(loc: int, length: int) -> int:
|
||
|
|
"""
|
||
|
|
Check that we have an integer between -length and length, inclusive.
|
||
|
|
|
||
|
|
Standardize negative loc to within [0, length].
|
||
|
|
|
||
|
|
The exceptions we raise on failure match np.insert.
|
||
|
|
"""
|
||
|
|
if not is_integer(loc):
|
||
|
|
raise TypeError(f"loc must be an integer between -{length} and {length}")
|
||
|
|
|
||
|
|
if loc < 0:
|
||
|
|
loc += length
|
||
|
|
if not 0 <= loc <= length:
|
||
|
|
raise IndexError(f"loc must be an integer between -{length} and {length}")
|
||
|
|
return loc
|
||
|
|
|
||
|
|
|
||
|
|
def check_dtype_backend(dtype_backend) -> None:
|
||
|
|
if dtype_backend is not lib.no_default:
|
||
|
|
if dtype_backend not in ["numpy_nullable", "pyarrow"]:
|
||
|
|
raise ValueError(
|
||
|
|
f"dtype_backend {dtype_backend} is invalid, only 'numpy_nullable' and "
|
||
|
|
f"'pyarrow' are allowed.",
|
||
|
|
)
|