Numpy where() Function

---

---

The Numpy where() function is used to return the indices of elements in an input array that satisfy a specified condition. This function can also be used to replace elements of an array based on a condition. It is particularly useful for conditional logic operations within arrays.

We can broadcast the values of an array based on whether a condition is satisfied or not. If the condition is satisfied, the specified value is returned; otherwise, an alternative value is returned. If these values are not specified, the indices of the elements that satisfy the condition are returned.

Syntax

Following is the syntax of the Numpy where() function −

numpy.where(condition, [x, y])

Parameters

Following are the parameters of the Numpy where() function −

• condition: The condition which tests each element of the array. It should be a boolean expression.
• x (optional): Values from array x will be returned where the condition is True. If this parameter is not provided, the function will return the indices of the True elements in the condition.
• y (optional): Values from array y will be returned where the condition is False. If this parameter is not provided, the function will return the indices of the True elements in the condition.

Return Type

This function returns the indices of elements that satisfy the condition if only the condition parameter is provided. If x and y are provided, it returns an array with elements selected from x or y based on the condition.

Example

Following is a basic example to find the indices of elements which satisfy's given condition using Numpy where() function in 1D array −

import numpy as np
my_array = np.array([10, 20, 30, 40, 50])
print("Array:",my_array)
result = np.where(my_array < 30)
print("Indices where elements are less than 30:", result)

Output

Following is the output of the above code −

Array: [10 20 30 40 50]
Indices where elements are less than 30: (array([0, 1]),)

Example: Replacing Values Based on Condition

We can use the where() function to replace elements in an array that satisfy a condition.

In the following example, we have replaced the elements that are greater than 3 with 0, while the elements that do not satisfy the condition remain unchanged −

import numpy as np
my_array = np.array([1, 2, 3, 4, 5])
result = np.where(my_array > 3, 0, my_array)
print("Array after replacement:", result)

Output

Following is the output of the above code −

Array after replacement: [1 2 3 0 0]

Example: Using Multiple Conditions

We can also use multiple conditions with where() function. Here, we have replaced elements that are divisible by 2 with True, and the elements that are not divisible by 2 with False −

import numpy as np
my_array = np.array([56, 79, 83, 96, 12])
result = np.where(my_array%2==0, 'True', 'False')
print("Array after multiple conditions:", result)

Output

Following is the output of the above code −

Array after multiple conditions: ['True' 'False' 'False' 'True' 'True']

Example: Multi-dimensional Array

The where() function can also be used with multi-dimensional arrays. In the following example, we have replaced elements in a 2D array based on a condition −

import numpy as np
my_array = np.array([[1, 2], [3, 4], [5, 6]])
result = np.where(my_array > 3, 0, my_array)
print("2D Array after replacement:\n", result)

Output

Following is the output of the above code −

2D Array after replacement:
 [[1 2]
  [3 0]
  [0 0]]