Sometimes we need to test whether certain values are present in an array. Using Numpy array, we can easily find whether specific values are present or not. For this purpose, we use the “in” operator. “in” operator is used to check whether certain element and values are present in a given sequence and hence return Boolean values ‘True” and “False“.
Example 1:
Python3
# importing Numpy package import numpy as np # creating a Numpy array n_array = np.array([[ 2 , 3 , 0 ], [ 4 , 1 , 6 ]]) print ( "Given array:" ) print (n_array) # Checking whether specific values # are present in "n_array" or not print ( 2 in n_array) print ( 0 in n_array) print ( 6 in n_array) print ( 50 in n_array) print ( 10 in n_array) |
Output:
Given array: [[2 3 0] [4 1 6]] True True True False False
In the above example, we check whether values 2, 0, 6, 50, 10 are present in Numpy array ‘n_array‘ using the ‘in‘ operator.
Example 2:
Python3
# importing Numpy package import numpy as np # creating a Numpy array n_array = np.array([[ 2.14 , 3 , 0.5 ], [ 4.5 , 1.2 , 6.2 ], [ 20.2 , 5.9 , 8.8 ]]) print ( "Given array:" ) print (n_array) # Checking whether specific values # are present in "n_array" or not print ( 2.14 in n_array) print ( 5.28 in n_array) print ( 6.2 in n_array) print ( 5.9 in n_array) print ( 8.5 in n_array) |
Output:
Given array: [[ 2.14 3. 0.5 ] [ 4.5 1.2 6.2 ] [20.2 5.9 8.8 ]] True False True True False
In the above example, we check whether values 2.14, 5.28, 6.2, 5.9, 8.5 are present in Numpy array ‘n_array‘.
Example 3:
Python3
# importing Numpy package import numpy as np # creating a Numpy array n_array = np.array([[ 4 , 5.5 , 7 , 6.9 , 10 ], [ 7.1 , 5.3 , 40 , 8.8 , 1 ], [ 4.4 , 9.3 , 6 , 2.2 , 11 ], [ 7.1 , 4 , 5 , 9 , 10.5 ]]) print ( "Given array:" ) print (n_array) # Checking whether specific values # are present in "n_array" or not print ( 2.14 in n_array) print ( 5.28 in n_array) print ( 8.5 in n_array) |
Output:
Given array: [[ 4. 5.5 7. 6.9 10. ] [ 7.1 5.3 40. 8.8 1. ] [ 4.4 9.3 6. 2.2 11. ] [ 7.1 4. 5. 9. 10.5]] False False False
In the above example, we check whether values 2.14, 5.28, 8.5 are present in Numpy array ‘n_array‘.