In Python, NumPy has a number of library functions to create the array and where is one of them to create an array from the satisfied conditions of another array. The numpy.where() function returns the indices of elements in an input array where the given condition is satisfied.
Syntax:
numpy.where(condition[, x, y])
Parameters:
- condition : When True, yield x, otherwise yield y.
- x, y : Values from which to choose. x, y and condition need to be broadcastable to some shape.
Returns: [ndarray or tuple of ndarrays] If both x and y are specified, the output array contains elements of x where condition is True, and elements from y elsewhere.
If the only condition is given, return the tuple condition.nonzero(), the indices where the condition is True. In the above syntax, we can see the where() function can take two arguments in which one is mandatory and another one is optional. If the value of the condition is true an array will be created based on the indices.
Example 1:
Numpy where() with multiple conditions using logical OR.
Python3
# Import NumPy library import numpy as np # Create an array using the list np_arr1 = np.array([23, 11, 45, 43, 60, 18, 33, 71, 52, 38])print("The values of the input array :\n", np_arr1) # Create another array based on the # multiple conditions and one arraynew_arr1 = np.where((np_arr1)) # Print the new arrayprint("The filtered values of the array :\n", new_arr1) # Create an array using range valuesnp_arr2 = np.arange(40, 50) # Create another array based on the # multiple conditions and two arraysnew_arr2 = np.where((np_arr1), np_arr1, np_arr2) # Print the new arrayprint("The filtered values of the array :\n", new_arr2) |
Output:
Example 2:
Numpy where() with multiple conditions using logical AND.
Python3
# Import NumPy library import numpy as np # Create two arrays of random valuesnp_arr1 = np.random.rand(10)*100np_arr2 = np.random.rand(10)*100 # Print the array valuesprint("\nThe values of the first array :\n", np_arr1)print("\nThe values of the second array :\n", np_arr2) # Create a new array based on the conditionsnew_arr = np.where((np_arr1), np_arr1, np_arr2) # Print the new arrayprint("\nThe filtered values of both arrays :\n", new_arr) |
Output:
Example 3:
Numpy where() with multiple conditions in multiple dimensional arrays.
Python3
# Import NumPy library import numpy as np # Create two multidimensional arrays of # integer valuesnp_arr1 = np.array([[6, 13, 22, 7, 12], [7, 11, 16, 32, 9]])np_arr2 = np.array([[44, 20, 8, 35, 10], [98, 23, 42, 6, 13]]) # Print the array valuesprint("\nThe values of the first array :\n", np_arr1)print("\nThe values of the second array :\n", np_arr2) # Create a new array from two arrays based on# the conditionsnew_arr = np.where(((np_arr1 % 2 == 0) & (np_arr2 % 2 == 1)), np_arr1, np_arr2) # Print the new arrayprint("\nThe filtered values of both arrays :\n", new_arr) |
Output:
Conclusion:
The where() function in NumPy is used for creating a new array from the existing array with multiple numbers of conditions.
