numpy.array_equal(a1, a2, equal_nan=False)
True if two arrays have the same shape and elements, False otherwise.
Parameters:a1, a2array_likeInput arrays.
equal_nanboolWhether 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.
Returns:bboolReturns True if the arrays are equal.
See also
Returns True if two arrays are element-wise equal within a tolerance.
Returns True if input arrays are shape consistent and all elements equal.
Examples
>>> importnumpyasnp>>> 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)TrueWhen 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