# encoding: utf-8 # module pandas._libs.testing # from C:\Users\xukai\PycharmProjects\pythonProject1\venv\Lib\site-packages\pandas\_libs\testing.cp311-win_amd64.pyd # by generator 1.147 # no doc # imports import builtins as __builtins__ # import cmath as cmath # import math as math # import numpy as np # C:\Users\xukai\PycharmProjects\pythonProject1\venv\Lib\site-packages\numpy\__init__.py # functions def array_equivalent(left, right, strict_nan=False, dtype_equal=False): # reliably restored by inspect """ True if two arrays, left and right, have equal non-NaN elements, and NaNs in corresponding locations. False otherwise. It is assumed that left and right are NumPy arrays of the same dtype. The behavior of this function (particularly with respect to NaNs) is not defined if the dtypes are different. Parameters ---------- left, right : ndarrays strict_nan : bool, default False If True, consider NaN and None to be different. dtype_equal : bool, default False Whether `left` and `right` are known to have the same dtype according to `is_dtype_equal`. Some methods like `BlockManager.equals`. require that the dtypes match. Setting this to ``True`` can improve performance, but will give different results for arrays that are equal but different dtypes. Returns ------- b : bool Returns True if the arrays are equivalent. Examples -------- >>> array_equivalent( ... np.array([1, 2, np.nan]), ... np.array([1, 2, np.nan])) True >>> array_equivalent( ... np.array([1, np.nan, 2]), ... np.array([1, 2, np.nan])) False """ pass def assert_almost_equal(*args, **kwargs): # real signature unknown """ Check that left and right objects are almost equal. Parameters ---------- a : object b : object rtol : float, default 1e-5 Relative tolerance. .. versionadded:: 1.1.0 atol : float, default 1e-8 Absolute tolerance. .. versionadded:: 1.1.0 check_dtype: bool, default True check dtype if both a and b are np.ndarray. obj : str, default None Specify object name being compared, internally used to show appropriate assertion message. lobj : str, default None Specify left object name being compared, internally used to show appropriate assertion message. robj : str, default None Specify right object name being compared, internally used to show appropriate assertion message. index_values : ndarray, default None Specify shared index values of objects being compared, internally used to show appropriate assertion message. .. versionadded:: 1.1.0 """ pass def assert_dict_equal(*args, **kwargs): # real signature unknown pass def isna(obj): # reliably restored by inspect """ Detect missing values for an array-like object. This function takes a scalar or array-like object and indicates whether values are missing (``NaN`` in numeric arrays, ``None`` or ``NaN`` in object arrays, ``NaT`` in datetimelike). Parameters ---------- obj : scalar or array-like Object to check for null or missing values. Returns ------- bool or array-like of bool For scalar input, returns a scalar boolean. For array input, returns an array of boolean indicating whether each corresponding element is missing. See Also -------- notna : Boolean inverse of pandas.isna. Series.isna : Detect missing values in a Series. DataFrame.isna : Detect missing values in a DataFrame. Index.isna : Detect missing values in an Index. Examples -------- Scalar arguments (including strings) result in a scalar boolean. >>> pd.isna('dog') False >>> pd.isna(pd.NA) True >>> pd.isna(np.nan) True ndarrays result in an ndarray of booleans. >>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]]) >>> array array([[ 1., nan, 3.], [ 4., 5., nan]]) >>> pd.isna(array) array([[False, True, False], [False, False, True]]) For indexes, an ndarray of booleans is returned. >>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None, ... "2017-07-08"]) >>> index DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'], dtype='datetime64[ns]', freq=None) >>> pd.isna(index) array([False, False, True, False]) For Series and DataFrame, the same type is returned, containing booleans. >>> df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']]) >>> df 0 1 2 0 ant bee cat 1 dog None fly >>> pd.isna(df) 0 1 2 0 False False False 1 False True False >>> pd.isna(df[1]) 0 False 1 True Name: 1, dtype: bool """ pass def is_dtype_equal(source, target): # reliably restored by inspect """ Check if two dtypes are equal. Parameters ---------- source : The first dtype to compare target : The second dtype to compare Returns ------- boolean Whether or not the two dtypes are equal. Examples -------- >>> is_dtype_equal(int, float) False >>> is_dtype_equal("int", int) True >>> is_dtype_equal(object, "category") False >>> is_dtype_equal(CategoricalDtype(), "category") True >>> is_dtype_equal(DatetimeTZDtype(tz="UTC"), "datetime64") False """ pass # no classes # variables with complex values __loader__ = None # (!) real value is '<_frozen_importlib_external.ExtensionFileLoader object at 0x000002D136DBDA50>' __spec__ = None # (!) real value is "ModuleSpec(name='pandas._libs.testing', loader=<_frozen_importlib_external.ExtensionFileLoader object at 0x000002D136DBDA50>, origin='C:\\\\Users\\\\xukai\\\\PycharmProjects\\\\pythonProject1\\\\venv\\\\Lib\\\\site-packages\\\\pandas\\\\_libs\\\\testing.cp311-win_amd64.pyd')" __test__ = {}