Where in 2D

Where in 2D#

This follows from where and argmin.

That page covers how np.where and np.argmin work for one-dimensional arrays.

This page covers what happens for np.where with arrays of two dimensions and more.

Arrays can have two dimensions#

import numpy as np

So far we have only seen one-dimensional arrays.

A vector is another term for a one-dimensional array.

Here’s a one-dimensional array (vector):

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

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

Notice the shape of the array:

arr_one_d.shape

(5,)

You can also think of this one-dimensional array as a single row.

In fact you can have arrays with more than one row, like this:

arr_two_d = np.array([[1, 2, 3, -2, -3], [10, 11, 12, -3, -13]])
arr_two_d

array([[  1,   2,   3,  -2,  -3],
       [ 10,  11,  12,  -3, -13]])

Notice the shape. This array has 2 rows, each with 5 columns.

arr_two_d.shape

(2, 5)

Remember, we can ask for a single element from a one-dimensional array, using indexing with an integer between square brackets. The integer gives the position (or offset) of the element we want.

arr_one_d[1]

np.int64(99)

With a two-dimensional array, we need to specify the row and column of the element we want:

arr_two_d[1, 4]

np.int64(-13)

The first value between the brackets is the row position, and the second is the column position.

Revision - using where on one-dimensional arrays#

np.where gets the indices of the True values in a Boolean array.

# A Boolean array.
equal_to_99 = arr_one_d == 99
equal_to_99

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

# Indices of the True values.
indices = np.where(equal_to_99)
indices

(array([1, 4]),)

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

arr_one_d[indices]

array([99, 99])

The two-dimensional case#

This also works in two or more dimensions.

np.where now returns two index arrays, one for the rows, and one for the columns.

indices2d = np.where(arr_two_d == -3)
indices2d

(array([0, 1]), array([4, 3]))

Just as for the one-dimensional case, we can use the returned indices to index into the array, and get the elements.

arr_two_d[indices2d]

array([-3, -3])
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