NumPy: Create an array (a) of shape 3, 4, 8

NumPy: Array Object Exercise-162 with Solution

Extract elements from a specific axis using an index array.

Create an array (a) of shape 3, 4, 8 (K=3, J=4, I=8). tidx is an array of the same length as a.shape[1], i.e. contains J = 4 elements where each index denotes which element of K should be chosen.
Write a NumPy program to select from the first axis (K) by the indices tidx to get an array of shape (J=4, I=8) back.

Sample Solution:

Python Code:

# Importing the NumPy library
import numpy as np

# Generating a random 3D array of integers between 0 and 9 with a shape of (3, 4, 8)
a = np.random.randint(0, 10, (3, 4, 8))

# Displaying the original array and its shape
print("Original array and shape:")
print(a)
print(a.shape)

# Printing a separator
print("--------------------------------")

# Generating random indices within a specified range (0 to 2) with a length of 4
tidx = np.random.randint(0, 3, 4)

# Displaying the generated random indices
print("tidex: ", tidx)

# Extracting specific elements using the generated random indices along different axes of the array
# The resulting array contains elements from 'a' based on the generated indices along the first axis, the second axis as the arange, and the third axis
print("Result:")
print(a[tidx, np.arange(len(tidx)), :]) 

Sample Output:

Original array and shape:
[[[3 2 2 7 7 7 0 3]
  [5 8 4 2 9 9 3 9]
  [6 8 2 8 5 7 8 7]
  [5 2 4 0 4 9 2 5]]

 [[4 3 1 8 2 5 2 0]
  [9 1 5 8 8 5 6 5]
  [3 2 2 0 1 5 6 1]
  [5 1 9 4 2 6 9 2]]

 [[4 6 6 3 8 6 8 8]
  [3 9 2 6 3 3 1 0]
  [5 4 0 6 0 2 7 8]
  [6 3 1 8 8 1 5 7]]]
(3, 4, 8)
--------------------------------
tidex:  [0 2 2 2]
Result:
[[3 2 2 7 7 7 0 3]
 [3 9 2 6 3 3 1 0]
 [5 4 0 6 0 2 7 8]
 [6 3 1 8 8 1 5 7]]

Explanation:

In the above code –

• a = np.random.randint(0, 10, (3, 4, 8)): This statement creates a NumPy array a of random integers between 0 (inclusive) and 10 (exclusive) with a shape of (3, 4, 8).
• print(a.shape): This statement prints the shape of the array ‘a’, which is (3, 4, 8).
• tidx = np.random.randint(0, 3, 4): This statement generates a NumPy array ‘tidx ‘ containing 4 random integers between 0 (inclusive) and 3 (exclusive).
• print("tidex: ",tidx): Print the array ‘tidx’.
• print(a[tidx, np.arange(len(tidx)),:]): This statement select rows from the 3D array a using the indices in ‘tidx’. For each index in ‘tidx’, the corresponding row in the same position in np.arange(len(tidx)) is selected. In other words, for each column in the 4x8 matrix, one row is selected from one of the 3 "layers" based on the index in ‘tidx’. The result is a 2D array with the shape (4, 8). Finally print() function prints the resulting array.

Note:

numpy.arange([start, ]stop, [step, ]dtype=None) function: The numpy.arange() function is used to generate an array with evenly spaced values within a specified interval. The function returns a one-dimensional array of type numpy.ndarray.

Python-Numpy Code Editor:

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