1496 行
49 KiB
Python
1496 行
49 KiB
Python
from __future__ import annotations
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from typing import (
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Literal,
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cast,
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)
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import warnings
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import numpy as np
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from pandas._libs.lib import (
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NoDefault,
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no_default,
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)
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from pandas._libs.missing import is_matching_na
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from pandas._libs.sparse import SparseIndex
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import pandas._libs.testing as _testing
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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_categorical_dtype,
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is_extension_array_dtype,
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is_interval_dtype,
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is_number,
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is_numeric_dtype,
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needs_i8_conversion,
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)
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from pandas.core.dtypes.dtypes import (
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CategoricalDtype,
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PandasDtype,
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)
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from pandas.core.dtypes.missing import array_equivalent
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import pandas as pd
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from pandas import (
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Categorical,
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DataFrame,
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DatetimeIndex,
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Index,
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IntervalIndex,
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MultiIndex,
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PeriodIndex,
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RangeIndex,
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Series,
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TimedeltaIndex,
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)
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from pandas.core.algorithms import take_nd
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from pandas.core.arrays import (
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DatetimeArray,
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ExtensionArray,
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IntervalArray,
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PeriodArray,
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TimedeltaArray,
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)
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from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
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from pandas.core.arrays.string_ import StringDtype
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from pandas.core.indexes.api import safe_sort_index
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from pandas.io.formats.printing import pprint_thing
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def assert_almost_equal(
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left,
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right,
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check_dtype: bool | Literal["equiv"] = "equiv",
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check_less_precise: bool | int | NoDefault = no_default,
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rtol: float = 1.0e-5,
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atol: float = 1.0e-8,
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**kwargs,
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) -> None:
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"""
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Check that the left and right objects are approximately equal.
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By approximately equal, we refer to objects that are numbers or that
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contain numbers which may be equivalent to specific levels of precision.
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Parameters
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----------
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left : object
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right : object
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check_dtype : bool or {'equiv'}, default 'equiv'
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Check dtype if both a and b are the same type. If 'equiv' is passed in,
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then `RangeIndex` and `Int64Index` are also considered equivalent
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when doing type checking.
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check_less_precise : bool or int, default False
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Specify comparison precision. 5 digits (False) or 3 digits (True)
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after decimal points are compared. If int, then specify the number
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of digits to compare.
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When comparing two numbers, if the first number has magnitude less
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than 1e-5, we compare the two numbers directly and check whether
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they are equivalent within the specified precision. Otherwise, we
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compare the **ratio** of the second number to the first number and
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check whether it is equivalent to 1 within the specified precision.
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.. deprecated:: 1.1.0
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Use `rtol` and `atol` instead to define relative/absolute
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tolerance, respectively. Similar to :func:`math.isclose`.
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rtol : float, default 1e-5
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Relative tolerance.
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.. versionadded:: 1.1.0
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atol : float, default 1e-8
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Absolute tolerance.
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.. versionadded:: 1.1.0
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"""
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if check_less_precise is not no_default:
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warnings.warn(
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"The 'check_less_precise' keyword in testing.assert_*_equal "
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"is deprecated and will be removed in a future version. "
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"You can stop passing 'check_less_precise' to silence this warning.",
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FutureWarning,
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stacklevel=find_stack_level(),
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)
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rtol = atol = _get_tol_from_less_precise(check_less_precise)
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if isinstance(left, Index):
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assert_index_equal(
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left,
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right,
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check_exact=False,
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exact=check_dtype,
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rtol=rtol,
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atol=atol,
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**kwargs,
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)
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elif isinstance(left, Series):
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assert_series_equal(
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left,
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right,
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check_exact=False,
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check_dtype=check_dtype,
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rtol=rtol,
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atol=atol,
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**kwargs,
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)
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elif isinstance(left, DataFrame):
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assert_frame_equal(
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left,
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right,
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check_exact=False,
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check_dtype=check_dtype,
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rtol=rtol,
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atol=atol,
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**kwargs,
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)
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else:
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# Other sequences.
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if check_dtype:
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if is_number(left) and is_number(right):
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# Do not compare numeric classes, like np.float64 and float.
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pass
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elif is_bool(left) and is_bool(right):
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# Do not compare bool classes, like np.bool_ and bool.
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pass
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else:
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if isinstance(left, np.ndarray) or isinstance(right, np.ndarray):
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obj = "numpy array"
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else:
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obj = "Input"
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assert_class_equal(left, right, obj=obj)
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# if we have "equiv", this becomes True
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_testing.assert_almost_equal(
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left, right, check_dtype=bool(check_dtype), rtol=rtol, atol=atol, **kwargs
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)
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def _get_tol_from_less_precise(check_less_precise: bool | int) -> float:
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"""
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Return the tolerance equivalent to the deprecated `check_less_precise`
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parameter.
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Parameters
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----------
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check_less_precise : bool or int
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Returns
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-------
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float
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Tolerance to be used as relative/absolute tolerance.
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Examples
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--------
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>>> # Using check_less_precise as a bool:
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>>> _get_tol_from_less_precise(False)
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5e-06
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>>> _get_tol_from_less_precise(True)
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0.0005
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>>> # Using check_less_precise as an int representing the decimal
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>>> # tolerance intended:
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>>> _get_tol_from_less_precise(2)
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0.005
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>>> _get_tol_from_less_precise(8)
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5e-09
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"""
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if isinstance(check_less_precise, bool):
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if check_less_precise:
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# 3-digit tolerance
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return 0.5e-3
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else:
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# 5-digit tolerance
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return 0.5e-5
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else:
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# Equivalent to setting checking_less_precise=<decimals>
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return 0.5 * 10**-check_less_precise
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def _check_isinstance(left, right, cls):
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"""
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Helper method for our assert_* methods that ensures that
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the two objects being compared have the right type before
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proceeding with the comparison.
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Parameters
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----------
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left : The first object being compared.
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right : The second object being compared.
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cls : The class type to check against.
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Raises
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------
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AssertionError : Either `left` or `right` is not an instance of `cls`.
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"""
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cls_name = cls.__name__
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if not isinstance(left, cls):
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raise AssertionError(
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f"{cls_name} Expected type {cls}, found {type(left)} instead"
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)
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if not isinstance(right, cls):
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raise AssertionError(
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f"{cls_name} Expected type {cls}, found {type(right)} instead"
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)
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def assert_dict_equal(left, right, compare_keys: bool = True) -> None:
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_check_isinstance(left, right, dict)
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_testing.assert_dict_equal(left, right, compare_keys=compare_keys)
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def assert_index_equal(
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left: Index,
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right: Index,
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exact: bool | str = "equiv",
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check_names: bool = True,
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check_less_precise: bool | int | NoDefault = no_default,
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check_exact: bool = True,
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check_categorical: bool = True,
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check_order: bool = True,
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rtol: float = 1.0e-5,
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atol: float = 1.0e-8,
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obj: str = "Index",
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) -> None:
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"""
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Check that left and right Index are equal.
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Parameters
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----------
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left : Index
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right : Index
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exact : bool or {'equiv'}, default 'equiv'
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Whether to check the Index class, dtype and inferred_type
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are identical. If 'equiv', then RangeIndex can be substituted for
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Int64Index as well.
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check_names : bool, default True
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Whether to check the names attribute.
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check_less_precise : bool or int, default False
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Specify comparison precision. Only used when check_exact is False.
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5 digits (False) or 3 digits (True) after decimal points are compared.
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If int, then specify the digits to compare.
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.. deprecated:: 1.1.0
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Use `rtol` and `atol` instead to define relative/absolute
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tolerance, respectively. Similar to :func:`math.isclose`.
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check_exact : bool, default True
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Whether to compare number exactly.
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check_categorical : bool, default True
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Whether to compare internal Categorical exactly.
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check_order : bool, default True
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Whether to compare the order of index entries as well as their values.
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If True, both indexes must contain the same elements, in the same order.
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If False, both indexes must contain the same elements, but in any order.
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.. versionadded:: 1.2.0
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rtol : float, default 1e-5
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Relative tolerance. Only used when check_exact is False.
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.. versionadded:: 1.1.0
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atol : float, default 1e-8
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Absolute tolerance. Only used when check_exact is False.
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.. versionadded:: 1.1.0
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obj : str, default 'Index'
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Specify object name being compared, internally used to show appropriate
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assertion message.
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Examples
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--------
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>>> from pandas import testing as tm
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>>> a = pd.Index([1, 2, 3])
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>>> b = pd.Index([1, 2, 3])
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>>> tm.assert_index_equal(a, b)
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"""
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__tracebackhide__ = True
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def _check_types(left, right, obj="Index") -> None:
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if not exact:
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return
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assert_class_equal(left, right, exact=exact, obj=obj)
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assert_attr_equal("inferred_type", left, right, obj=obj)
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# Skip exact dtype checking when `check_categorical` is False
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if is_categorical_dtype(left.dtype) and is_categorical_dtype(right.dtype):
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if check_categorical:
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assert_attr_equal("dtype", left, right, obj=obj)
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assert_index_equal(left.categories, right.categories, exact=exact)
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return
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assert_attr_equal("dtype", left, right, obj=obj)
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def _get_ilevel_values(index, level):
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# accept level number only
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unique = index.levels[level]
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level_codes = index.codes[level]
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filled = take_nd(unique._values, level_codes, fill_value=unique._na_value)
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return unique._shallow_copy(filled, name=index.names[level])
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if check_less_precise is not no_default:
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warnings.warn(
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"The 'check_less_precise' keyword in testing.assert_*_equal "
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"is deprecated and will be removed in a future version. "
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"You can stop passing 'check_less_precise' to silence this warning.",
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FutureWarning,
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stacklevel=find_stack_level(),
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)
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rtol = atol = _get_tol_from_less_precise(check_less_precise)
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# instance validation
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_check_isinstance(left, right, Index)
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# class / dtype comparison
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_check_types(left, right, obj=obj)
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# level comparison
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if left.nlevels != right.nlevels:
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msg1 = f"{obj} levels are different"
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msg2 = f"{left.nlevels}, {left}"
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msg3 = f"{right.nlevels}, {right}"
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raise_assert_detail(obj, msg1, msg2, msg3)
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# length comparison
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if len(left) != len(right):
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msg1 = f"{obj} length are different"
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msg2 = f"{len(left)}, {left}"
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msg3 = f"{len(right)}, {right}"
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raise_assert_detail(obj, msg1, msg2, msg3)
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# If order doesn't matter then sort the index entries
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if not check_order:
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left = safe_sort_index(left)
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right = safe_sort_index(right)
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# MultiIndex special comparison for little-friendly error messages
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if left.nlevels > 1:
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left = cast(MultiIndex, left)
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right = cast(MultiIndex, right)
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for level in range(left.nlevels):
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# cannot use get_level_values here because it can change dtype
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llevel = _get_ilevel_values(left, level)
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rlevel = _get_ilevel_values(right, level)
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lobj = f"MultiIndex level [{level}]"
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assert_index_equal(
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llevel,
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rlevel,
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exact=exact,
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check_names=check_names,
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check_exact=check_exact,
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rtol=rtol,
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atol=atol,
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obj=lobj,
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)
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# get_level_values may change dtype
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_check_types(left.levels[level], right.levels[level], obj=obj)
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# skip exact index checking when `check_categorical` is False
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if check_exact and check_categorical:
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if not left.equals(right):
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mismatch = left._values != right._values
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if is_extension_array_dtype(mismatch):
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mismatch = cast("ExtensionArray", mismatch).fillna(True)
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diff = np.sum(mismatch.astype(int)) * 100.0 / len(left)
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msg = f"{obj} values are different ({np.round(diff, 5)} %)"
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raise_assert_detail(obj, msg, left, right)
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else:
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# if we have "equiv", this becomes True
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exact_bool = bool(exact)
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_testing.assert_almost_equal(
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left.values,
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right.values,
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rtol=rtol,
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atol=atol,
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check_dtype=exact_bool,
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obj=obj,
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lobj=left,
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robj=right,
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)
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# metadata comparison
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if check_names:
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assert_attr_equal("names", left, right, obj=obj)
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if isinstance(left, PeriodIndex) or isinstance(right, PeriodIndex):
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assert_attr_equal("freq", left, right, obj=obj)
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if isinstance(left, IntervalIndex) or isinstance(right, IntervalIndex):
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assert_interval_array_equal(left._values, right._values)
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if check_categorical:
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if is_categorical_dtype(left.dtype) or is_categorical_dtype(right.dtype):
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assert_categorical_equal(left._values, right._values, obj=f"{obj} category")
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def assert_class_equal(left, right, exact: bool | str = True, obj="Input") -> None:
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"""
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Checks classes are equal.
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"""
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from pandas.core.indexes.numeric import NumericIndex
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__tracebackhide__ = True
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def repr_class(x):
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if isinstance(x, Index):
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# return Index as it is to include values in the error message
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return x
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return type(x).__name__
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if type(left) == type(right):
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return
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if exact == "equiv":
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# accept equivalence of NumericIndex (sub-)classes
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if isinstance(left, NumericIndex) and isinstance(right, NumericIndex):
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return
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msg = f"{obj} classes are different"
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raise_assert_detail(obj, msg, repr_class(left), repr_class(right))
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def assert_attr_equal(attr: str, left, right, obj: str = "Attributes") -> None:
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"""
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Check attributes are equal. Both objects must have attribute.
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Parameters
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----------
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attr : str
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Attribute name being compared.
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left : object
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right : object
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obj : str, default 'Attributes'
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Specify object name being compared, internally used to show appropriate
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assertion message
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"""
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__tracebackhide__ = True
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left_attr = getattr(left, attr)
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right_attr = getattr(right, attr)
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if left_attr is right_attr or is_matching_na(left_attr, right_attr):
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# e.g. both np.nan, both NaT, both pd.NA, ...
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return None
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try:
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result = left_attr == right_attr
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except TypeError:
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# datetimetz on rhs may raise TypeError
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result = False
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if (left_attr is pd.NA) ^ (right_attr is pd.NA):
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result = False
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elif not isinstance(result, bool):
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result = result.all()
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if not result:
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msg = f'Attribute "{attr}" are different'
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raise_assert_detail(obj, msg, left_attr, right_attr)
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return None
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|
|
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def assert_is_valid_plot_return_object(objs) -> None:
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import matplotlib.pyplot as plt
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if isinstance(objs, (Series, np.ndarray)):
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for el in objs.ravel():
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msg = (
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"one of 'objs' is not a matplotlib Axes instance, "
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f"type encountered {repr(type(el).__name__)}"
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)
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assert isinstance(el, (plt.Axes, dict)), msg
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else:
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msg = (
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"objs is neither an ndarray of Artist instances nor a single "
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"ArtistArtist instance, tuple, or dict, 'objs' is a "
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f"{repr(type(objs).__name__)}"
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)
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assert isinstance(objs, (plt.Artist, tuple, dict)), msg
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def assert_is_sorted(seq) -> None:
|
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"""Assert that the sequence is sorted."""
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if isinstance(seq, (Index, Series)):
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seq = seq.values
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# sorting does not change precisions
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assert_numpy_array_equal(seq, np.sort(np.array(seq)))
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|
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def assert_categorical_equal(
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left, right, check_dtype=True, check_category_order=True, obj="Categorical"
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) -> None:
|
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"""
|
|
Test that Categoricals are equivalent.
|
|
|
|
Parameters
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|
----------
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left : Categorical
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right : Categorical
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check_dtype : bool, default True
|
|
Check that integer dtype of the codes are the same.
|
|
check_category_order : bool, default True
|
|
Whether the order of the categories should be compared, which
|
|
implies identical integer codes. If False, only the resulting
|
|
values are compared. The ordered attribute is
|
|
checked regardless.
|
|
obj : str, default 'Categorical'
|
|
Specify object name being compared, internally used to show appropriate
|
|
assertion message.
|
|
"""
|
|
_check_isinstance(left, right, Categorical)
|
|
|
|
exact: bool | str
|
|
if isinstance(left.categories, RangeIndex) or isinstance(
|
|
right.categories, RangeIndex
|
|
):
|
|
exact = "equiv"
|
|
else:
|
|
# We still want to require exact matches for NumericIndex
|
|
exact = True
|
|
|
|
if check_category_order:
|
|
assert_index_equal(
|
|
left.categories, right.categories, obj=f"{obj}.categories", exact=exact
|
|
)
|
|
assert_numpy_array_equal(
|
|
left.codes, right.codes, check_dtype=check_dtype, obj=f"{obj}.codes"
|
|
)
|
|
else:
|
|
try:
|
|
lc = left.categories.sort_values()
|
|
rc = right.categories.sort_values()
|
|
except TypeError:
|
|
# e.g. '<' not supported between instances of 'int' and 'str'
|
|
lc, rc = left.categories, right.categories
|
|
assert_index_equal(lc, rc, obj=f"{obj}.categories", exact=exact)
|
|
assert_index_equal(
|
|
left.categories.take(left.codes),
|
|
right.categories.take(right.codes),
|
|
obj=f"{obj}.values",
|
|
exact=exact,
|
|
)
|
|
|
|
assert_attr_equal("ordered", left, right, obj=obj)
|
|
|
|
|
|
def assert_interval_array_equal(
|
|
left, right, exact="equiv", obj="IntervalArray"
|
|
) -> None:
|
|
"""
|
|
Test that two IntervalArrays are equivalent.
|
|
|
|
Parameters
|
|
----------
|
|
left, right : IntervalArray
|
|
The IntervalArrays to compare.
|
|
exact : bool or {'equiv'}, default 'equiv'
|
|
Whether to check the Index class, dtype and inferred_type
|
|
are identical. If 'equiv', then RangeIndex can be substituted for
|
|
Int64Index as well.
|
|
obj : str, default 'IntervalArray'
|
|
Specify object name being compared, internally used to show appropriate
|
|
assertion message
|
|
"""
|
|
_check_isinstance(left, right, IntervalArray)
|
|
|
|
kwargs = {}
|
|
if left._left.dtype.kind in ["m", "M"]:
|
|
# We have a DatetimeArray or TimedeltaArray
|
|
kwargs["check_freq"] = False
|
|
|
|
assert_equal(left._left, right._left, obj=f"{obj}.left", **kwargs)
|
|
assert_equal(left._right, right._right, obj=f"{obj}.left", **kwargs)
|
|
|
|
assert_attr_equal("closed", left, right, obj=obj)
|
|
|
|
|
|
def assert_period_array_equal(left, right, obj="PeriodArray") -> None:
|
|
_check_isinstance(left, right, PeriodArray)
|
|
|
|
assert_numpy_array_equal(left._data, right._data, obj=f"{obj}._data")
|
|
assert_attr_equal("freq", left, right, obj=obj)
|
|
|
|
|
|
def assert_datetime_array_equal(
|
|
left, right, obj="DatetimeArray", check_freq=True
|
|
) -> None:
|
|
__tracebackhide__ = True
|
|
_check_isinstance(left, right, DatetimeArray)
|
|
|
|
assert_numpy_array_equal(left._data, right._data, obj=f"{obj}._data")
|
|
if check_freq:
|
|
assert_attr_equal("freq", left, right, obj=obj)
|
|
assert_attr_equal("tz", left, right, obj=obj)
|
|
|
|
|
|
def assert_timedelta_array_equal(
|
|
left, right, obj="TimedeltaArray", check_freq=True
|
|
) -> None:
|
|
__tracebackhide__ = True
|
|
_check_isinstance(left, right, TimedeltaArray)
|
|
assert_numpy_array_equal(left._data, right._data, obj=f"{obj}._data")
|
|
if check_freq:
|
|
assert_attr_equal("freq", left, right, obj=obj)
|
|
|
|
|
|
def raise_assert_detail(obj, message, left, right, diff=None, index_values=None):
|
|
__tracebackhide__ = True
|
|
|
|
msg = f"""{obj} are different
|
|
|
|
{message}"""
|
|
|
|
if isinstance(index_values, np.ndarray):
|
|
msg += f"\n[index]: {pprint_thing(index_values)}"
|
|
|
|
if isinstance(left, np.ndarray):
|
|
left = pprint_thing(left)
|
|
elif (
|
|
isinstance(left, CategoricalDtype)
|
|
or isinstance(left, PandasDtype)
|
|
or isinstance(left, StringDtype)
|
|
):
|
|
left = repr(left)
|
|
|
|
if isinstance(right, np.ndarray):
|
|
right = pprint_thing(right)
|
|
elif (
|
|
isinstance(right, CategoricalDtype)
|
|
or isinstance(right, PandasDtype)
|
|
or isinstance(right, StringDtype)
|
|
):
|
|
right = repr(right)
|
|
|
|
msg += f"""
|
|
[left]: {left}
|
|
[right]: {right}"""
|
|
|
|
if diff is not None:
|
|
msg += f"\n[diff]: {diff}"
|
|
|
|
raise AssertionError(msg)
|
|
|
|
|
|
def assert_numpy_array_equal(
|
|
left,
|
|
right,
|
|
strict_nan=False,
|
|
check_dtype: bool | Literal["equiv"] = True,
|
|
err_msg=None,
|
|
check_same=None,
|
|
obj="numpy array",
|
|
index_values=None,
|
|
) -> None:
|
|
"""
|
|
Check that 'np.ndarray' is equivalent.
|
|
|
|
Parameters
|
|
----------
|
|
left, right : numpy.ndarray or iterable
|
|
The two arrays to be compared.
|
|
strict_nan : bool, default False
|
|
If True, consider NaN and None to be different.
|
|
check_dtype : bool, default True
|
|
Check dtype if both a and b are np.ndarray.
|
|
err_msg : str, default None
|
|
If provided, used as assertion message.
|
|
check_same : None|'copy'|'same', default None
|
|
Ensure left and right refer/do not refer to the same memory area.
|
|
obj : str, default 'numpy array'
|
|
Specify object name being compared, internally used to show appropriate
|
|
assertion message.
|
|
index_values : numpy.ndarray, default None
|
|
optional index (shared by both left and right), used in output.
|
|
"""
|
|
__tracebackhide__ = True
|
|
|
|
# instance validation
|
|
# Show a detailed error message when classes are different
|
|
assert_class_equal(left, right, obj=obj)
|
|
# both classes must be an np.ndarray
|
|
_check_isinstance(left, right, np.ndarray)
|
|
|
|
def _get_base(obj):
|
|
return obj.base if getattr(obj, "base", None) is not None else obj
|
|
|
|
left_base = _get_base(left)
|
|
right_base = _get_base(right)
|
|
|
|
if check_same == "same":
|
|
if left_base is not right_base:
|
|
raise AssertionError(f"{repr(left_base)} is not {repr(right_base)}")
|
|
elif check_same == "copy":
|
|
if left_base is right_base:
|
|
raise AssertionError(f"{repr(left_base)} is {repr(right_base)}")
|
|
|
|
def _raise(left, right, err_msg):
|
|
if err_msg is None:
|
|
if left.shape != right.shape:
|
|
raise_assert_detail(
|
|
obj, f"{obj} shapes are different", left.shape, right.shape
|
|
)
|
|
|
|
diff = 0
|
|
for left_arr, right_arr in zip(left, right):
|
|
# count up differences
|
|
if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
|
|
diff += 1
|
|
|
|
diff = diff * 100.0 / left.size
|
|
msg = f"{obj} values are different ({np.round(diff, 5)} %)"
|
|
raise_assert_detail(obj, msg, left, right, index_values=index_values)
|
|
|
|
raise AssertionError(err_msg)
|
|
|
|
# compare shape and values
|
|
if not array_equivalent(left, right, strict_nan=strict_nan):
|
|
_raise(left, right, err_msg)
|
|
|
|
if check_dtype:
|
|
if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
|
|
assert_attr_equal("dtype", left, right, obj=obj)
|
|
|
|
|
|
def assert_extension_array_equal(
|
|
left,
|
|
right,
|
|
check_dtype: bool | Literal["equiv"] = True,
|
|
index_values=None,
|
|
check_less_precise=no_default,
|
|
check_exact=False,
|
|
rtol: float = 1.0e-5,
|
|
atol: float = 1.0e-8,
|
|
) -> None:
|
|
"""
|
|
Check that left and right ExtensionArrays are equal.
|
|
|
|
Parameters
|
|
----------
|
|
left, right : ExtensionArray
|
|
The two arrays to compare.
|
|
check_dtype : bool, default True
|
|
Whether to check if the ExtensionArray dtypes are identical.
|
|
index_values : numpy.ndarray, default None
|
|
Optional index (shared by both left and right), used in output.
|
|
check_less_precise : bool or int, default False
|
|
Specify comparison precision. Only used when check_exact is False.
|
|
5 digits (False) or 3 digits (True) after decimal points are compared.
|
|
If int, then specify the digits to compare.
|
|
|
|
.. deprecated:: 1.1.0
|
|
Use `rtol` and `atol` instead to define relative/absolute
|
|
tolerance, respectively. Similar to :func:`math.isclose`.
|
|
check_exact : bool, default False
|
|
Whether to compare number exactly.
|
|
rtol : float, default 1e-5
|
|
Relative tolerance. Only used when check_exact is False.
|
|
|
|
.. versionadded:: 1.1.0
|
|
atol : float, default 1e-8
|
|
Absolute tolerance. Only used when check_exact is False.
|
|
|
|
.. versionadded:: 1.1.0
|
|
|
|
Notes
|
|
-----
|
|
Missing values are checked separately from valid values.
|
|
A mask of missing values is computed for each and checked to match.
|
|
The remaining all-valid values are cast to object dtype and checked.
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas import testing as tm
|
|
>>> a = pd.Series([1, 2, 3, 4])
|
|
>>> b, c = a.array, a.array
|
|
>>> tm.assert_extension_array_equal(b, c)
|
|
"""
|
|
if check_less_precise is not no_default:
|
|
warnings.warn(
|
|
"The 'check_less_precise' keyword in testing.assert_*_equal "
|
|
"is deprecated and will be removed in a future version. "
|
|
"You can stop passing 'check_less_precise' to silence this warning.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
rtol = atol = _get_tol_from_less_precise(check_less_precise)
|
|
|
|
assert isinstance(left, ExtensionArray), "left is not an ExtensionArray"
|
|
assert isinstance(right, ExtensionArray), "right is not an ExtensionArray"
|
|
if check_dtype:
|
|
assert_attr_equal("dtype", left, right, obj="ExtensionArray")
|
|
|
|
if (
|
|
isinstance(left, DatetimeLikeArrayMixin)
|
|
and isinstance(right, DatetimeLikeArrayMixin)
|
|
and type(right) == type(left)
|
|
):
|
|
# Avoid slow object-dtype comparisons
|
|
# np.asarray for case where we have a np.MaskedArray
|
|
assert_numpy_array_equal(
|
|
np.asarray(left.asi8), np.asarray(right.asi8), index_values=index_values
|
|
)
|
|
return
|
|
|
|
left_na = np.asarray(left.isna())
|
|
right_na = np.asarray(right.isna())
|
|
assert_numpy_array_equal(
|
|
left_na, right_na, obj="ExtensionArray NA mask", index_values=index_values
|
|
)
|
|
|
|
left_valid = left[~left_na].to_numpy(dtype=object)
|
|
right_valid = right[~right_na].to_numpy(dtype=object)
|
|
if check_exact:
|
|
assert_numpy_array_equal(
|
|
left_valid, right_valid, obj="ExtensionArray", index_values=index_values
|
|
)
|
|
else:
|
|
_testing.assert_almost_equal(
|
|
left_valid,
|
|
right_valid,
|
|
check_dtype=bool(check_dtype),
|
|
rtol=rtol,
|
|
atol=atol,
|
|
obj="ExtensionArray",
|
|
index_values=index_values,
|
|
)
|
|
|
|
|
|
# This could be refactored to use the NDFrame.equals method
|
|
def assert_series_equal(
|
|
left,
|
|
right,
|
|
check_dtype: bool | Literal["equiv"] = True,
|
|
check_index_type: bool | Literal["equiv"] = "equiv",
|
|
check_series_type=True,
|
|
check_less_precise: bool | int | NoDefault = no_default,
|
|
check_names=True,
|
|
check_exact=False,
|
|
check_datetimelike_compat=False,
|
|
check_categorical=True,
|
|
check_category_order=True,
|
|
check_freq=True,
|
|
check_flags=True,
|
|
rtol=1.0e-5,
|
|
atol=1.0e-8,
|
|
obj="Series",
|
|
*,
|
|
check_index=True,
|
|
check_like=False,
|
|
) -> None:
|
|
"""
|
|
Check that left and right Series are equal.
|
|
|
|
Parameters
|
|
----------
|
|
left : Series
|
|
right : Series
|
|
check_dtype : bool, default True
|
|
Whether to check the Series dtype is identical.
|
|
check_index_type : bool or {'equiv'}, default 'equiv'
|
|
Whether to check the Index class, dtype and inferred_type
|
|
are identical.
|
|
check_series_type : bool, default True
|
|
Whether to check the Series class is identical.
|
|
check_less_precise : bool or int, default False
|
|
Specify comparison precision. Only used when check_exact is False.
|
|
5 digits (False) or 3 digits (True) after decimal points are compared.
|
|
If int, then specify the digits to compare.
|
|
|
|
When comparing two numbers, if the first number has magnitude less
|
|
than 1e-5, we compare the two numbers directly and check whether
|
|
they are equivalent within the specified precision. Otherwise, we
|
|
compare the **ratio** of the second number to the first number and
|
|
check whether it is equivalent to 1 within the specified precision.
|
|
|
|
.. deprecated:: 1.1.0
|
|
Use `rtol` and `atol` instead to define relative/absolute
|
|
tolerance, respectively. Similar to :func:`math.isclose`.
|
|
check_names : bool, default True
|
|
Whether to check the Series and Index names attribute.
|
|
check_exact : bool, default False
|
|
Whether to compare number exactly.
|
|
check_datetimelike_compat : bool, default False
|
|
Compare datetime-like which is comparable ignoring dtype.
|
|
check_categorical : bool, default True
|
|
Whether to compare internal Categorical exactly.
|
|
check_category_order : bool, default True
|
|
Whether to compare category order of internal Categoricals.
|
|
|
|
.. versionadded:: 1.0.2
|
|
check_freq : bool, default True
|
|
Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex.
|
|
|
|
.. versionadded:: 1.1.0
|
|
check_flags : bool, default True
|
|
Whether to check the `flags` attribute.
|
|
|
|
.. versionadded:: 1.2.0
|
|
|
|
rtol : float, default 1e-5
|
|
Relative tolerance. Only used when check_exact is False.
|
|
|
|
.. versionadded:: 1.1.0
|
|
atol : float, default 1e-8
|
|
Absolute tolerance. Only used when check_exact is False.
|
|
|
|
.. versionadded:: 1.1.0
|
|
obj : str, default 'Series'
|
|
Specify object name being compared, internally used to show appropriate
|
|
assertion message.
|
|
check_index : bool, default True
|
|
Whether to check index equivalence. If False, then compare only values.
|
|
|
|
.. versionadded:: 1.3.0
|
|
check_like : bool, default False
|
|
If True, ignore the order of the index. Must be False if check_index is False.
|
|
Note: same labels must be with the same data.
|
|
|
|
.. versionadded:: 1.5.0
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas import testing as tm
|
|
>>> a = pd.Series([1, 2, 3, 4])
|
|
>>> b = pd.Series([1, 2, 3, 4])
|
|
>>> tm.assert_series_equal(a, b)
|
|
"""
|
|
__tracebackhide__ = True
|
|
|
|
if not check_index and check_like:
|
|
raise ValueError("check_like must be False if check_index is False")
|
|
|
|
if check_less_precise is not no_default:
|
|
warnings.warn(
|
|
"The 'check_less_precise' keyword in testing.assert_*_equal "
|
|
"is deprecated and will be removed in a future version. "
|
|
"You can stop passing 'check_less_precise' to silence this warning.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
rtol = atol = _get_tol_from_less_precise(check_less_precise)
|
|
|
|
# instance validation
|
|
_check_isinstance(left, right, Series)
|
|
|
|
if check_series_type:
|
|
assert_class_equal(left, right, obj=obj)
|
|
|
|
# length comparison
|
|
if len(left) != len(right):
|
|
msg1 = f"{len(left)}, {left.index}"
|
|
msg2 = f"{len(right)}, {right.index}"
|
|
raise_assert_detail(obj, "Series length are different", msg1, msg2)
|
|
|
|
if check_flags:
|
|
assert left.flags == right.flags, f"{repr(left.flags)} != {repr(right.flags)}"
|
|
|
|
if check_index:
|
|
# GH #38183
|
|
assert_index_equal(
|
|
left.index,
|
|
right.index,
|
|
exact=check_index_type,
|
|
check_names=check_names,
|
|
check_exact=check_exact,
|
|
check_categorical=check_categorical,
|
|
check_order=not check_like,
|
|
rtol=rtol,
|
|
atol=atol,
|
|
obj=f"{obj}.index",
|
|
)
|
|
|
|
if check_like:
|
|
left, right = left.reindex_like(right), right
|
|
|
|
if check_freq and isinstance(left.index, (DatetimeIndex, TimedeltaIndex)):
|
|
lidx = left.index
|
|
ridx = right.index
|
|
assert lidx.freq == ridx.freq, (lidx.freq, ridx.freq)
|
|
|
|
if check_dtype:
|
|
# We want to skip exact dtype checking when `check_categorical`
|
|
# is False. We'll still raise if only one is a `Categorical`,
|
|
# regardless of `check_categorical`
|
|
if (
|
|
isinstance(left.dtype, CategoricalDtype)
|
|
and isinstance(right.dtype, CategoricalDtype)
|
|
and not check_categorical
|
|
):
|
|
pass
|
|
else:
|
|
assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}")
|
|
|
|
if check_exact and is_numeric_dtype(left.dtype) and is_numeric_dtype(right.dtype):
|
|
left_values = left._values
|
|
right_values = right._values
|
|
# Only check exact if dtype is numeric
|
|
if isinstance(left_values, ExtensionArray) and isinstance(
|
|
right_values, ExtensionArray
|
|
):
|
|
assert_extension_array_equal(
|
|
left_values,
|
|
right_values,
|
|
check_dtype=check_dtype,
|
|
index_values=np.asarray(left.index),
|
|
)
|
|
else:
|
|
assert_numpy_array_equal(
|
|
left_values,
|
|
right_values,
|
|
check_dtype=check_dtype,
|
|
obj=str(obj),
|
|
index_values=np.asarray(left.index),
|
|
)
|
|
elif check_datetimelike_compat and (
|
|
needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype)
|
|
):
|
|
# we want to check only if we have compat dtypes
|
|
# e.g. integer and M|m are NOT compat, but we can simply check
|
|
# the values in that case
|
|
|
|
# datetimelike may have different objects (e.g. datetime.datetime
|
|
# vs Timestamp) but will compare equal
|
|
if not Index(left._values).equals(Index(right._values)):
|
|
msg = (
|
|
f"[datetimelike_compat=True] {left._values} "
|
|
f"is not equal to {right._values}."
|
|
)
|
|
raise AssertionError(msg)
|
|
elif is_interval_dtype(left.dtype) and is_interval_dtype(right.dtype):
|
|
assert_interval_array_equal(left.array, right.array)
|
|
elif isinstance(left.dtype, CategoricalDtype) or isinstance(
|
|
right.dtype, CategoricalDtype
|
|
):
|
|
_testing.assert_almost_equal(
|
|
left._values,
|
|
right._values,
|
|
rtol=rtol,
|
|
atol=atol,
|
|
check_dtype=bool(check_dtype),
|
|
obj=str(obj),
|
|
index_values=np.asarray(left.index),
|
|
)
|
|
elif is_extension_array_dtype(left.dtype) and is_extension_array_dtype(right.dtype):
|
|
assert_extension_array_equal(
|
|
left._values,
|
|
right._values,
|
|
rtol=rtol,
|
|
atol=atol,
|
|
check_dtype=check_dtype,
|
|
index_values=np.asarray(left.index),
|
|
)
|
|
elif is_extension_array_dtype_and_needs_i8_conversion(
|
|
left.dtype, right.dtype
|
|
) or is_extension_array_dtype_and_needs_i8_conversion(right.dtype, left.dtype):
|
|
assert_extension_array_equal(
|
|
left._values,
|
|
right._values,
|
|
check_dtype=check_dtype,
|
|
index_values=np.asarray(left.index),
|
|
)
|
|
elif needs_i8_conversion(left.dtype) and needs_i8_conversion(right.dtype):
|
|
# DatetimeArray or TimedeltaArray
|
|
assert_extension_array_equal(
|
|
left._values,
|
|
right._values,
|
|
check_dtype=check_dtype,
|
|
index_values=np.asarray(left.index),
|
|
)
|
|
else:
|
|
_testing.assert_almost_equal(
|
|
left._values,
|
|
right._values,
|
|
rtol=rtol,
|
|
atol=atol,
|
|
check_dtype=bool(check_dtype),
|
|
obj=str(obj),
|
|
index_values=np.asarray(left.index),
|
|
)
|
|
|
|
# metadata comparison
|
|
if check_names:
|
|
assert_attr_equal("name", left, right, obj=obj)
|
|
|
|
if check_categorical:
|
|
if isinstance(left.dtype, CategoricalDtype) or isinstance(
|
|
right.dtype, CategoricalDtype
|
|
):
|
|
assert_categorical_equal(
|
|
left._values,
|
|
right._values,
|
|
obj=f"{obj} category",
|
|
check_category_order=check_category_order,
|
|
)
|
|
|
|
|
|
# This could be refactored to use the NDFrame.equals method
|
|
def assert_frame_equal(
|
|
left,
|
|
right,
|
|
check_dtype: bool | Literal["equiv"] = True,
|
|
check_index_type: bool | Literal["equiv"] = "equiv",
|
|
check_column_type="equiv",
|
|
check_frame_type=True,
|
|
check_less_precise=no_default,
|
|
check_names=True,
|
|
by_blocks=False,
|
|
check_exact=False,
|
|
check_datetimelike_compat=False,
|
|
check_categorical=True,
|
|
check_like=False,
|
|
check_freq=True,
|
|
check_flags=True,
|
|
rtol=1.0e-5,
|
|
atol=1.0e-8,
|
|
obj="DataFrame",
|
|
) -> None:
|
|
"""
|
|
Check that left and right DataFrame are equal.
|
|
|
|
This function is intended to compare two DataFrames and output any
|
|
differences. It is mostly intended for use in unit tests.
|
|
Additional parameters allow varying the strictness of the
|
|
equality checks performed.
|
|
|
|
Parameters
|
|
----------
|
|
left : DataFrame
|
|
First DataFrame to compare.
|
|
right : DataFrame
|
|
Second DataFrame to compare.
|
|
check_dtype : bool, default True
|
|
Whether to check the DataFrame dtype is identical.
|
|
check_index_type : bool or {'equiv'}, default 'equiv'
|
|
Whether to check the Index class, dtype and inferred_type
|
|
are identical.
|
|
check_column_type : bool or {'equiv'}, default 'equiv'
|
|
Whether to check the columns class, dtype and inferred_type
|
|
are identical. Is passed as the ``exact`` argument of
|
|
:func:`assert_index_equal`.
|
|
check_frame_type : bool, default True
|
|
Whether to check the DataFrame class is identical.
|
|
check_less_precise : bool or int, default False
|
|
Specify comparison precision. Only used when check_exact is False.
|
|
5 digits (False) or 3 digits (True) after decimal points are compared.
|
|
If int, then specify the digits to compare.
|
|
|
|
When comparing two numbers, if the first number has magnitude less
|
|
than 1e-5, we compare the two numbers directly and check whether
|
|
they are equivalent within the specified precision. Otherwise, we
|
|
compare the **ratio** of the second number to the first number and
|
|
check whether it is equivalent to 1 within the specified precision.
|
|
|
|
.. deprecated:: 1.1.0
|
|
Use `rtol` and `atol` instead to define relative/absolute
|
|
tolerance, respectively. Similar to :func:`math.isclose`.
|
|
check_names : bool, default True
|
|
Whether to check that the `names` attribute for both the `index`
|
|
and `column` attributes of the DataFrame is identical.
|
|
by_blocks : bool, default False
|
|
Specify how to compare internal data. If False, compare by columns.
|
|
If True, compare by blocks.
|
|
check_exact : bool, default False
|
|
Whether to compare number exactly.
|
|
check_datetimelike_compat : bool, default False
|
|
Compare datetime-like which is comparable ignoring dtype.
|
|
check_categorical : bool, default True
|
|
Whether to compare internal Categorical exactly.
|
|
check_like : bool, default False
|
|
If True, ignore the order of index & columns.
|
|
Note: index labels must match their respective rows
|
|
(same as in columns) - same labels must be with the same data.
|
|
check_freq : bool, default True
|
|
Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex.
|
|
|
|
.. versionadded:: 1.1.0
|
|
check_flags : bool, default True
|
|
Whether to check the `flags` attribute.
|
|
rtol : float, default 1e-5
|
|
Relative tolerance. Only used when check_exact is False.
|
|
|
|
.. versionadded:: 1.1.0
|
|
atol : float, default 1e-8
|
|
Absolute tolerance. Only used when check_exact is False.
|
|
|
|
.. versionadded:: 1.1.0
|
|
obj : str, default 'DataFrame'
|
|
Specify object name being compared, internally used to show appropriate
|
|
assertion message.
|
|
|
|
See Also
|
|
--------
|
|
assert_series_equal : Equivalent method for asserting Series equality.
|
|
DataFrame.equals : Check DataFrame equality.
|
|
|
|
Examples
|
|
--------
|
|
This example shows comparing two DataFrames that are equal
|
|
but with columns of differing dtypes.
|
|
|
|
>>> from pandas.testing import assert_frame_equal
|
|
>>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
|
|
>>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})
|
|
|
|
df1 equals itself.
|
|
|
|
>>> assert_frame_equal(df1, df1)
|
|
|
|
df1 differs from df2 as column 'b' is of a different type.
|
|
|
|
>>> assert_frame_equal(df1, df2)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different
|
|
|
|
Attribute "dtype" are different
|
|
[left]: int64
|
|
[right]: float64
|
|
|
|
Ignore differing dtypes in columns with check_dtype.
|
|
|
|
>>> assert_frame_equal(df1, df2, check_dtype=False)
|
|
"""
|
|
__tracebackhide__ = True
|
|
|
|
if check_less_precise is not no_default:
|
|
warnings.warn(
|
|
"The 'check_less_precise' keyword in testing.assert_*_equal "
|
|
"is deprecated and will be removed in a future version. "
|
|
"You can stop passing 'check_less_precise' to silence this warning.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
rtol = atol = _get_tol_from_less_precise(check_less_precise)
|
|
|
|
# instance validation
|
|
_check_isinstance(left, right, DataFrame)
|
|
|
|
if check_frame_type:
|
|
assert isinstance(left, type(right))
|
|
# assert_class_equal(left, right, obj=obj)
|
|
|
|
# shape comparison
|
|
if left.shape != right.shape:
|
|
raise_assert_detail(
|
|
obj, f"{obj} shape mismatch", f"{repr(left.shape)}", f"{repr(right.shape)}"
|
|
)
|
|
|
|
if check_flags:
|
|
assert left.flags == right.flags, f"{repr(left.flags)} != {repr(right.flags)}"
|
|
|
|
# index comparison
|
|
assert_index_equal(
|
|
left.index,
|
|
right.index,
|
|
exact=check_index_type,
|
|
check_names=check_names,
|
|
check_exact=check_exact,
|
|
check_categorical=check_categorical,
|
|
check_order=not check_like,
|
|
rtol=rtol,
|
|
atol=atol,
|
|
obj=f"{obj}.index",
|
|
)
|
|
|
|
# column comparison
|
|
assert_index_equal(
|
|
left.columns,
|
|
right.columns,
|
|
exact=check_column_type,
|
|
check_names=check_names,
|
|
check_exact=check_exact,
|
|
check_categorical=check_categorical,
|
|
check_order=not check_like,
|
|
rtol=rtol,
|
|
atol=atol,
|
|
obj=f"{obj}.columns",
|
|
)
|
|
|
|
if check_like:
|
|
left, right = left.reindex_like(right), right
|
|
|
|
# compare by blocks
|
|
if by_blocks:
|
|
rblocks = right._to_dict_of_blocks()
|
|
lblocks = left._to_dict_of_blocks()
|
|
for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))):
|
|
assert dtype in lblocks
|
|
assert dtype in rblocks
|
|
assert_frame_equal(
|
|
lblocks[dtype], rblocks[dtype], check_dtype=check_dtype, obj=obj
|
|
)
|
|
|
|
# compare by columns
|
|
else:
|
|
for i, col in enumerate(left.columns):
|
|
# We have already checked that columns match, so we can do
|
|
# fast location-based lookups
|
|
lcol = left._ixs(i, axis=1)
|
|
rcol = right._ixs(i, axis=1)
|
|
|
|
# GH #38183
|
|
# use check_index=False, because we do not want to run
|
|
# assert_index_equal for each column,
|
|
# as we already checked it for the whole dataframe before.
|
|
assert_series_equal(
|
|
lcol,
|
|
rcol,
|
|
check_dtype=check_dtype,
|
|
check_index_type=check_index_type,
|
|
check_exact=check_exact,
|
|
check_names=check_names,
|
|
check_datetimelike_compat=check_datetimelike_compat,
|
|
check_categorical=check_categorical,
|
|
check_freq=check_freq,
|
|
obj=f'{obj}.iloc[:, {i}] (column name="{col}")',
|
|
rtol=rtol,
|
|
atol=atol,
|
|
check_index=False,
|
|
check_flags=False,
|
|
)
|
|
|
|
|
|
def assert_equal(left, right, **kwargs) -> None:
|
|
"""
|
|
Wrapper for tm.assert_*_equal to dispatch to the appropriate test function.
|
|
|
|
Parameters
|
|
----------
|
|
left, right : Index, Series, DataFrame, ExtensionArray, or np.ndarray
|
|
The two items to be compared.
|
|
**kwargs
|
|
All keyword arguments are passed through to the underlying assert method.
|
|
"""
|
|
__tracebackhide__ = True
|
|
|
|
if isinstance(left, Index):
|
|
assert_index_equal(left, right, **kwargs)
|
|
if isinstance(left, (DatetimeIndex, TimedeltaIndex)):
|
|
assert left.freq == right.freq, (left.freq, right.freq)
|
|
elif isinstance(left, Series):
|
|
assert_series_equal(left, right, **kwargs)
|
|
elif isinstance(left, DataFrame):
|
|
assert_frame_equal(left, right, **kwargs)
|
|
elif isinstance(left, IntervalArray):
|
|
assert_interval_array_equal(left, right, **kwargs)
|
|
elif isinstance(left, PeriodArray):
|
|
assert_period_array_equal(left, right, **kwargs)
|
|
elif isinstance(left, DatetimeArray):
|
|
assert_datetime_array_equal(left, right, **kwargs)
|
|
elif isinstance(left, TimedeltaArray):
|
|
assert_timedelta_array_equal(left, right, **kwargs)
|
|
elif isinstance(left, ExtensionArray):
|
|
assert_extension_array_equal(left, right, **kwargs)
|
|
elif isinstance(left, np.ndarray):
|
|
assert_numpy_array_equal(left, right, **kwargs)
|
|
elif isinstance(left, str):
|
|
assert kwargs == {}
|
|
assert left == right
|
|
else:
|
|
assert kwargs == {}
|
|
assert_almost_equal(left, right)
|
|
|
|
|
|
def assert_sp_array_equal(left, right) -> None:
|
|
"""
|
|
Check that the left and right SparseArray are equal.
|
|
|
|
Parameters
|
|
----------
|
|
left : SparseArray
|
|
right : SparseArray
|
|
"""
|
|
_check_isinstance(left, right, pd.arrays.SparseArray)
|
|
|
|
assert_numpy_array_equal(left.sp_values, right.sp_values)
|
|
|
|
# SparseIndex comparison
|
|
assert isinstance(left.sp_index, SparseIndex)
|
|
assert isinstance(right.sp_index, SparseIndex)
|
|
|
|
left_index = left.sp_index
|
|
right_index = right.sp_index
|
|
|
|
if not left_index.equals(right_index):
|
|
raise_assert_detail(
|
|
"SparseArray.index", "index are not equal", left_index, right_index
|
|
)
|
|
else:
|
|
# Just ensure a
|
|
pass
|
|
|
|
assert_attr_equal("fill_value", left, right)
|
|
assert_attr_equal("dtype", left, right)
|
|
assert_numpy_array_equal(left.to_dense(), right.to_dense())
|
|
|
|
|
|
def assert_contains_all(iterable, dic) -> None:
|
|
for k in iterable:
|
|
assert k in dic, f"Did not contain item: {repr(k)}"
|
|
|
|
|
|
def assert_copy(iter1, iter2, **eql_kwargs) -> None:
|
|
"""
|
|
iter1, iter2: iterables that produce elements
|
|
comparable with assert_almost_equal
|
|
|
|
Checks that the elements are equal, but not
|
|
the same object. (Does not check that items
|
|
in sequences are also not the same object)
|
|
"""
|
|
for elem1, elem2 in zip(iter1, iter2):
|
|
assert_almost_equal(elem1, elem2, **eql_kwargs)
|
|
msg = (
|
|
f"Expected object {repr(type(elem1))} and object {repr(type(elem2))} to be "
|
|
"different objects, but they were the same object."
|
|
)
|
|
assert elem1 is not elem2, msg
|
|
|
|
|
|
def is_extension_array_dtype_and_needs_i8_conversion(left_dtype, right_dtype) -> bool:
|
|
"""
|
|
Checks that we have the combination of an ExtensionArraydtype and
|
|
a dtype that should be converted to int64
|
|
|
|
Returns
|
|
-------
|
|
bool
|
|
|
|
Related to issue #37609
|
|
"""
|
|
return is_extension_array_dtype(left_dtype) and needs_i8_conversion(right_dtype)
|
|
|
|
|
|
def assert_indexing_slices_equivalent(ser: Series, l_slc: slice, i_slc: slice) -> None:
|
|
"""
|
|
Check that ser.iloc[i_slc] matches ser.loc[l_slc] and, if applicable,
|
|
ser[l_slc].
|
|
"""
|
|
expected = ser.iloc[i_slc]
|
|
|
|
assert_series_equal(ser.loc[l_slc], expected)
|
|
|
|
if not ser.index.is_integer():
|
|
# For integer indices, .loc and plain getitem are position-based.
|
|
assert_series_equal(ser[l_slc], expected)
|
|
|
|
|
|
def assert_metadata_equivalent(left, right) -> None:
|
|
"""
|
|
Check that ._metadata attributes are equivalent.
|
|
"""
|
|
for attr in left._metadata:
|
|
val = getattr(left, attr, None)
|
|
if right is None:
|
|
assert val is None
|
|
else:
|
|
assert val == getattr(right, attr, None)
|