array_equivalent(left, right, strict_nan: 'bool' = False, dtype_equal: 'bool' = False) -> 'bool'
If True, consider NaN and None to be different.
Whether :None:None:`left` and :None:None:`right` are known to have the same dtype according to :None:None:`is_dtype_equal`. Some methods like :None:None:`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 True if the arrays are equivalent.
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.
>>> array_equivalent(This example is valid syntax, but we were not able to check execution
... np.array([1, 2, np.nan]),
... np.array([1, 2, np.nan])) True
>>> array_equivalent(See :
... np.array([1, np.nan, 2]),
... np.array([1, 2, np.nan])) False
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pandas.core.dtypes.missing.array_equivalent
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