543 lines
17 KiB
Python
543 lines
17 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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Any,
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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 warnings
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import numpy as np
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from pandas.util._exceptions import find_stack_level
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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=True, int_allowed=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_axis_style_args(
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data, args, kwargs, arg_name, method_name
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) -> dict[str, Any]:
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"""
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Argument handler for mixed index, columns / axis functions
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In an attempt to handle both `.method(index, columns)`, and
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`.method(arg, axis=.)`, we have to do some bad things to argument
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parsing. This translates all arguments to `{index=., columns=.}` style.
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Parameters
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----------
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data : DataFrame
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args : tuple
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All positional arguments from the user
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kwargs : dict
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All keyword arguments from the user
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arg_name, method_name : str
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Used for better error messages
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Returns
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-------
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kwargs : dict
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A dictionary of keyword arguments. Doesn't modify ``kwargs``
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inplace, so update them with the return value here.
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Examples
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--------
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>>> df = pd.DataFrame(range(2))
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>>> validate_axis_style_args(df, (str.upper,), {'columns': id},
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... 'mapper', 'rename')
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{'columns': <built-in function id>, 'index': <method 'upper' of 'str' objects>}
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This emits a warning
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>>> validate_axis_style_args(df, (str.upper, id), {},
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... 'mapper', 'rename')
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{'index': <method 'upper' of 'str' objects>, 'columns': <built-in function id>}
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"""
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# TODO: Change to keyword-only args and remove all this
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out = {}
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# Goal: fill 'out' with index/columns-style arguments
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# like out = {'index': foo, 'columns': bar}
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# Start by validating for consistency
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if "axis" in kwargs and any(x in kwargs for x in data._AXIS_TO_AXIS_NUMBER):
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msg = "Cannot specify both 'axis' and any of 'index' or 'columns'."
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raise TypeError(msg)
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# First fill with explicit values provided by the user...
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if arg_name in kwargs:
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if args:
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msg = f"{method_name} got multiple values for argument '{arg_name}'"
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raise TypeError(msg)
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axis = data._get_axis_name(kwargs.get("axis", 0))
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out[axis] = kwargs[arg_name]
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# More user-provided arguments, now from kwargs
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for k, v in kwargs.items():
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try:
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ax = data._get_axis_name(k)
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except ValueError:
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pass
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else:
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out[ax] = v
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# All user-provided kwargs have been handled now.
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# Now we supplement with positional arguments, emitting warnings
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# when there's ambiguity and raising when there's conflicts
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if len(args) == 0:
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pass # It's up to the function to decide if this is valid
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elif len(args) == 1:
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axis = data._get_axis_name(kwargs.get("axis", 0))
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out[axis] = args[0]
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elif len(args) == 2:
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if "axis" in kwargs:
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# Unambiguously wrong
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msg = "Cannot specify both 'axis' and any of 'index' or 'columns'"
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raise TypeError(msg)
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msg = (
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f"Interpreting call\n\t'.{method_name}(a, b)' as "
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f"\n\t'.{method_name}(index=a, columns=b)'.\nUse named "
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"arguments to remove any ambiguity. In the future, using "
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"positional arguments for 'index' or 'columns' will raise "
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"a 'TypeError'."
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)
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warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
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out[data._get_axis_name(0)] = args[0]
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out[data._get_axis_name(1)] = args[1]
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else:
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msg = f"Cannot specify all of '{arg_name}', 'index', 'columns'."
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raise TypeError(msg)
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return out
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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
|
||
|
|
|
||
|
|
if value is None and method is None:
|
||
|
|
raise ValueError("Must specify a fill 'value' or 'method'.")
|
||
|
|
elif value is None and method is not None:
|
||
|
|
method = clean_fill_method(method)
|
||
|
|
|
||
|
|
elif value is not None and method is None:
|
||
|
|
if validate_scalar_dict_value and isinstance(value, (list, tuple)):
|
||
|
|
raise TypeError(
|
||
|
|
'"value" parameter must be a scalar or dict, but '
|
||
|
|
f'you passed a "{type(value).__name__}"'
|
||
|
|
)
|
||
|
|
|
||
|
|
elif value is not None and method is not None:
|
||
|
|
raise ValueError("Cannot specify both 'value' and 'method'.")
|
||
|
|
|
||
|
|
return value, method
|
||
|
|
|
||
|
|
|
||
|
|
def validate_percentile(q: float | Iterable[float]) -> np.ndarray:
|
||
|
|
"""
|
||
|
|
Validate percentiles (used by describe and quantile).
|
||
|
|
|
||
|
|
This function checks if the given float or iterable of floats is a valid percentile
|
||
|
|
otherwise raises a ValueError.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
q: float or iterable of floats
|
||
|
|
A single percentile or an iterable of percentiles.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
ndarray
|
||
|
|
An ndarray of the percentiles if valid.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ValueError if percentiles are not in given interval([0, 1]).
|
||
|
|
"""
|
||
|
|
q_arr = np.asarray(q)
|
||
|
|
# Don't change this to an f-string. The string formatting
|
||
|
|
# is too expensive for cases where we don't need it.
|
||
|
|
msg = "percentiles should all be in the interval [0, 1]. Try {} instead."
|
||
|
|
if q_arr.ndim == 0:
|
||
|
|
if not 0 <= q_arr <= 1:
|
||
|
|
raise ValueError(msg.format(q_arr / 100.0))
|
||
|
|
else:
|
||
|
|
if not all(0 <= qs <= 1 for qs in q_arr):
|
||
|
|
raise ValueError(msg.format(q_arr / 100.0))
|
||
|
|
return q_arr
|
||
|
|
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def validate_ascending(ascending: BoolishT) -> BoolishT:
|
||
|
|
...
|
||
|
|
|
||
|
|
|
||
|
|
@overload
|
||
|
|
def validate_ascending(ascending: Sequence[BoolishT]) -> list[BoolishT]:
|
||
|
|
...
|
||
|
|
|
||
|
|
|
||
|
|
def validate_ascending(
|
||
|
|
ascending: bool | int | Sequence[BoolishT],
|
||
|
|
) -> bool | int | list[BoolishT]:
|
||
|
|
"""Validate ``ascending`` kwargs for ``sort_index`` method."""
|
||
|
|
kwargs = {"none_allowed": False, "int_allowed": True}
|
||
|
|
if not isinstance(ascending, Sequence):
|
||
|
|
return validate_bool_kwarg(ascending, "ascending", **kwargs)
|
||
|
|
|
||
|
|
return [validate_bool_kwarg(item, "ascending", **kwargs) for item in ascending]
|
||
|
|
|
||
|
|
|
||
|
|
def validate_endpoints(closed: str | None) -> tuple[bool, bool]:
|
||
|
|
"""
|
||
|
|
Check that the `closed` argument is among [None, "left", "right"]
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
closed : {None, "left", "right"}
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
left_closed : bool
|
||
|
|
right_closed : bool
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ValueError : if argument is not among valid values
|
||
|
|
"""
|
||
|
|
left_closed = False
|
||
|
|
right_closed = False
|
||
|
|
|
||
|
|
if closed is None:
|
||
|
|
left_closed = True
|
||
|
|
right_closed = True
|
||
|
|
elif closed == "left":
|
||
|
|
left_closed = True
|
||
|
|
elif closed == "right":
|
||
|
|
right_closed = True
|
||
|
|
else:
|
||
|
|
raise ValueError("Closed has to be either 'left', 'right' or None")
|
||
|
|
|
||
|
|
return left_closed, right_closed
|
||
|
|
|
||
|
|
|
||
|
|
def validate_inclusive(inclusive: str | None) -> tuple[bool, bool]:
|
||
|
|
"""
|
||
|
|
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
|