In this article, we are going to discuss how to find out the common values between 2 arrays. To find the common values, we can use the numpy.intersect1d(), which will do the intersection operation and return the common values between the 2 arrays in sorted order.
Syntax: numpy.intersect1d(arr1, arr2, assume_unique = False, return_indices = False)
Parameters :
arr1, arr2 : [array_like] Input arrays.
assume_unique : [bool] If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.
return_indices : [bool] If True, the indices which correspond to the intersection of the two arrays are returned. The first instance of a value is used if there are multiple. Default is False.Return : [ndarray] Sorted 1D array of common and unique elements.
Example #1: Finding common values between 1d arrays
Python3
import numpy as np # create 2 arrays a = np.array([2, 4, 7, 1, 4]) b = np.array([7, 2, 9, 0, 5]) # Display the arrays print("Original arrays", a, ' ', b) # use the np.intersect1d method c = np.intersect1d(a, b) # Display result print("Common values", c) |
Output:
Original arrays [2 4 7 1 4] [7 2 9 0 5] Common values [2 7]
Example #2: Finding common values between n-dimensional arrays
Python3
import numpy as np # create 2 arrays a = np.array([2,4,7,1,4,9]).reshape(3,2) b = np.array([7,2,9,0,5,3]).reshape(2,3) # Display the arrays print("Original arrays") print(a) print(b) # use the np.intersect1d method c = np.intersect1d(a,b) # Display result print("Common values",c) |
Output:
Original arrays [[2 4] [7 1] [4 9]] [[7 2 9] [0 5 3]] Common values [2 7 9]
Note: No matter what dimension arrays are passed, the common values will be returned in a 1d flattened manner
