PYTHON

numpy compare arrays

bool isEqual = numpy.array_equal(Array1, Array2)

numpy arrays equality

np.array_equal(A,B)  # test if same shape, same elements values
np.array_equiv(A,B)  # test if broadcastable shape, same elements values
np.allclose(A,B,...) # test if same shape, elements have close enough values

numpy compare

import numpy as np

arr = np.array([1, 2, 1, 2,  3, 4, 5, 4, 6, 7])
# create a set array with no duplicates
arr = np.unique(arr)
print(arr)
# [1 2 3 4 5 6 7]

arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([3, 4, 5, 6])

# create a 1d set array without from both arrays removing duplicates
arr = np.union1d(arr1, arr2)
print(arr)
# output [1 2 3 4 5 6]

arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([3, 4, 5, 6])

# create a 1d set array where both numbers are found in both arrays
arr = np.intersect1d(arr1, arr2, assume_unique=True)
print(arr)
# output [3 4]

arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([3, 4, 5, 6])

# create a 1d set array that contained only numbers found in the first array but not the second
arr = np.setdiff1d(arr1, arr2, assume_unique=True)
print(arr)
# output [1 2]

arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([3, 4, 5, 6])

# create a 1d set array where numbers from both arrays are not in each other
arr = np.setxor1d(arr1, arr2, assume_unique=True)

print(arr)
# output [1 2 5 6]