Boolean indexing in Python using Numpy

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               It is the advanced indexing technique which makes you to select the elements of an array. It uses a Boolean mask (has two values True/False).

Let us create this in 3 different ways as follows.

  • Boolean indexing in 1D array
  • Boolean indexing in 2D array
  • Boolean indexing using Multiple conditions.

Each one is explained with sample code as below.

👉Boolean indexing in 1D array

              This program reads a single dimensional array as input. It uses a single condition to find Boolean mask. It finds the value and displays the result.

𜰘Code:

import numpy as np1

s_arr = np1.array([11, 22, 33, 40, 55 ,66, 75])

mask = s_arr > 33

print("The resultant values are:\n",mask)

result = s_arr[mask]

print("The result which satisfies the condition s_arr>33:\n",result)

🖵Output:

The resultant values are:

 [False False False  True  True  True  True]

The result which satisfies the condition s_arr>33:

 [40 55 66 75]

Boolean indexing with 2D arrays:

This program uses 2D array as input. It checks each and every value, whether it meets the condition. Based on that, it displays the output.

𜰘Code:

import numpy as np1

s_arr = np1.array([[1, 2, 3],

                [4, 5, 6],

                [7, 8, 9]])

mask = s_arr > 4

print("The resultant values are:\n",mask)

result = s_arr[mask]

print("The result which satisfies the condition s_arr>5:\n",result)

🖵Output:

The resultant values are:

 [[False False False]

 [False  True  True]

 [ True  True  True]]

The result which satisfies the condition s_arr>5:

 [5 6 7 8 9]

Boolean indexing with multiple conditions:

              This program uses multiple conditions for mask.

𜰘Code:

import numpy as np1

s_arr = np1.array([[1, 2, 3],

                [4, 5, 6],

                [7, 8, 9]])

mask = (s_arr > 4) & (s_arr <8)

print("The resultant values are:\n",mask)

result = s_arr[mask]

print("The result which satisfies the multiple conditions:\n",result)

🖵Output:

The resultant values are:

 [[False False False]

 [False  True  True]

 [ True False False]]

The result which satisfies the multiple conditions:

 [5 6 7]

These are the different ways to find Boolean indexing in python using Numpy. Hope, this code is useful to you. Keep Coding!!!

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