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Why the output of numpy.where(condition) is not an array, but a tuple of arrays?

Learn, why the output of numpy.where(condition) is not an array, but a tuple of arrays? By Pranit Sharma Last updated : December 22, 2023

NumPy is an abbreviated form of Numerical Python. It is used for different types of scientific operations in python. Numpy is a vast library in python which is used for almost every kind of scientific or mathematical operation. It is itself an array which is a collection of various methods and functions for processing the arrays.

Output of numpy.where(condition)

Basically, numpy.where do have 2 'operational modes', first one returns the indices, where the condition is True and if optional parameters x and y are present (same shape as condition, or broadcastable to such shape!), it will return values from x when the condition is True otherwise from y. So, this makes where more versatile and enables it to be used more often.

It returns a tuple of length equal to the dimension of the numpy ndarray on which it is called (in other words ndim) and each item of the tuple is a numpy ndarray of indices of all those values in the initial ndarray for which the condition is True.

When it returns a tuple, each element of the tuple refers to a dimension. We can understand with the help of the following example,

Python code to demonstrate why the output of numpy.where(condition) is not an array, but a tuple of arrays

# Import numpy
import numpy as np

# Creating a numpy array
arr = np.array([
    [1, 2, 3, 4, 5, 6],
    [-2, 1, 2, 3, 4, 5]])

# Display original array
print("Original array:\n",arr,"\n")

# using where
res = np.where(arr > 3)

# Display result
print("Result:\n",res)

Output

As we can see, the first element of the tuple refers to the first dimension of relevant elements; the second element refers to the second dimension.

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