xorbits.numpy.array_equal#

xorbits.numpy.array_equal(a1, a2)[source]#

True if two arrays have the same shape and elements, False otherwise.

Parameters

  • a1 (array_like) – Input arrays.

  • a2 (array_like) – Input arrays.

  • equal_nan (bool (Not supported yet)) –

    Whether to compare NaN’s as equal. If the dtype of a1 and a2 is complex, values will be considered equal if either the real or the imaginary component of a given value is nan.

    New in version 1.19.0(numpy).

Returns

b – Returns True if the arrays are equal.

Return type

bool

See also

allclose

Returns True if two arrays are element-wise equal within a tolerance.

array_equiv

Returns True if input arrays are shape consistent and all elements equal.

Examples

>>> np.array_equal([1, 2], [1, 2])  
True
>>> np.array_equal(np.array([1, 2]), np.array([1, 2]))  
True
>>> np.array_equal([1, 2], [1, 2, 3])  
False
>>> np.array_equal([1, 2], [1, 4])  
False
>>> a = np.array([1, np.nan])  
>>> np.array_equal(a, a)  
False
>>> np.array_equal(a, a, equal_nan=True)  
True

When equal_nan is True, complex values with nan components are considered equal if either the real or the imaginary components are nan.

>>> a = np.array([1 + 1j])  
>>> b = a.copy()  
>>> a.real = np.nan  
>>> b.imag = np.nan  
>>> np.array_equal(a, b, equal_nan=True)  
True

This docstring was copied from numpy.