Numpy where

We sometimes want to know where a value is in an array.

import numpy as np

By “where” we mean, the position of the element that contains a particular value.

Here is an array.

arr = np.array([2, 99, -1, 4, 99])
arr

array([ 2, 99, -1,  4, 99])

As you know, we can use indexing to get elements using their index (position) in the array. In Python, array indices start at zero.

Here’s the value at index (position) 0:

arr[0]

2

We might also be interested to find which positions hold particular values.

In our array above, by reading, and counting positions, we can see that the values of 99 are in positions 1 and 4. We can ask for these elements by passing a list or an array between the square brackets, to index the array:

positions_with_99 = np.array([1, 4])
arr[positions_with_99]

array([99, 99])

Of course, we are already used to finding and then selecting elements according to various conditions, using Boolean vectors.

Here we identify the elements that contain 99. There is a True at the position where the array contains 99, and False otherwise.

contains_99 = arr == 99
contains_99

array([False,  True, False, False,  True])

We can then get the 99 values with:

arr[contains_99]

array([99, 99])

Enter “where”

Sometimes we really do need to know the index of the values that meet a certain condition.

In that case, you can use the Numpy where function. where finds the index positions of the True values in Boolean vectors.

indices = np.where(arr == 99)
indices

(array([1, 4]),)

We can use the returned indices to index into the array, using square brackets.

arr[indices]

array([99, 99])

Where summary

Numpy where returns the indices of True values in a Boolean array.

You can use these indices to index into an array, and get the matching elements.