numpy.array_equal

numpy. array_equal ( a1, a2, equal_nan=False ) [source]

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

Parameters

a1, a2 array_like

Input arrays.

equal_nan bool

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.

Returns

b bool

Returns True if the arrays are equal.

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