Select Subset of elements using combined Indexing in NumPy

Last update on April 29 2025 12:55:09 (UTC/GMT +8 hours)

12. 2D Array & Combined Boolean and Integer Indexing

NumPy: Advanced Indexing Exercise-12 with Solution

Combined Indexing:

Write a NumPy program that creates a 2D NumPy array and uses a combination of boolean and integer indexing to select a subset of elements.

Sample Solution:

Python Code:

import numpy as np

# Create a 2D NumPy array of shape (5, 5) with random integers
array_2d = np.random.randint(0, 100, size=(5, 5))

# Define the boolean condition to select elements greater than 50
condition = array_2d > 50

# Define the specific rows to apply the condition
row_indices = np.array([1, 3, 4])

# Use a combination of boolean and integer indexing to select elements
selected_elements = array_2d[row_indices][:, condition[row_indices][0]]

# Print the original array and the selected elements
print('Original 2D array:\n', array_2d)
print('Condition (elements > 50):\n', condition)
print('Row indices:\n', row_indices)
print('Selected elements using combined indexing:\n', selected_elements)

Output:

Original 3D array:
 [[[ 5 97 52 61 57]
  [79 87 75 83 21]
  [52  1 33 54 10]
  [76 58 44  0 72]]

 [[40  7 30 18 61]
  [24  1  9 98 25]
  [77 75  3 82  5]
  [90 63 59 79 52]]

 [[49 69 60 80 28]
  [45 60 63 31 69]
  [18 49 62 25 87]
  [85 94 35  9  8]]]
Depth indices:
 [0 1 2]
Row indices:
 [1 2 3]
Column indices:
 [2 3 4]
Selected elements:
 [[[75 83 21]
  [33 54 10]
  [44  0 72]]

 [[ 9 98 25]
  [ 3 82  5]
  [59 79 52]]

 [[63 31 69]
  [62 25 87]
  [35  9  8]]]

runfile('C:/Users/ME/untitled1.py', wdir='C:/Users/ME')
Original 2D array:
 [[69 78  3  8 84]
 [93 66 89 88  5]
 [55 29 47 79 33]
 [ 4  5 73  6 28]
 [27 18 66 70 61]]
Condition (elements > 50):
 [[ True  True False False  True]
 [ True  True  True  True False]
 [ True False False  True False]
 [False False  True False False]
 [False False  True  True  True]]
Row indices:
 [1 3 4]
Selected elements using combined indexing:
 [[93 66 89 88]
 [ 4  5 73  6]
 [27 18 66 70]]

Explanation:

Import Libraries:

Imported numpy as "np" for array creation and manipulation.

Create 2D NumPy Array:

Create a 2D NumPy array named array_2d with random integers ranging from 0 to 99 and a shape of (5, 5).

Define Boolean Condition:

Define a boolean condition to select elements in the array that are greater than 50.

Define Specific Rows:

Define row_indices to specify the rows to which the condition will be applied.

Combined Indexing:

Used a combination of boolean and integer indexing to select elements from the specified rows that meet the condition.

Print Results:

Print the original 2D array, the boolean condition array, the row indices, and the selected elements to verify the indexing operation.

For more Practice: Solve these Related Problems:

Create a 2D array and use a combination of boolean and integer indexing to select elements from alternating rows.
Write a function that filters a 2D array by selecting specific columns using an integer index array and further filtering those rows with a boolean mask.
Implement a solution that first applies a boolean condition to rows and then uses integer indexing to select specific columns from the filtered rows.
Test the combined indexing approach on a 2D array with mixed types to ensure the proper selection of elements.

Go to:

Previous: Select elements from 3D NumPy array using integer Indexing.
Next: Select elements using Boolean Indexing with logical operators.

Python-Numpy Code Editor:

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