NumPy: Extract any array of shape from a given array of integer values

Last update on March 24 2025 13:38:29 (UTC/GMT +8 hours)

Extract shape (6,6,3) array from a 12x12x4 matrix.

Write a NumPy program to create a 12x12x4 array with random values and extract any array of shape(6,6,3) from the said array.

Sample Solution:

Python Code:

# Importing NumPy library
import numpy as np

# Creating a NumPy array of shape (8, 8, 3) filled with random float values between 0 and 1
nums = np.random.random((8, 8, 3))

# Displaying the original array
print("Original array:")
print(nums)

# Extracting a sub-array of shape (6, 6, 3) from the original array using slicing
new_nums = nums[:6, :6, :]

# Displaying the extracted sub-array
print("\nExtract array of shape (6, 6, 3) from the said array:")
print(new_nums) 

Sample Output:

Original array:
[[[0.67420382 0.5592584  0.27096382]
  [0.2896283  0.68346522 0.13996167]
  [0.74283318 0.06323309 0.0980022 ]
  [0.43203954 0.38888969 0.44801756]
  [0.04391897 0.4851516  0.34044817]
  [0.2202623  0.81434798 0.51900666]
  [0.12442371 0.91247823 0.01874549]
  [0.2287782  0.88089638 0.40583551]]

 [[0.42389039 0.21162537 0.39794135]
  [0.70118645 0.77166133 0.02891853]
  [0.13635083 0.77918942 0.93913105]
  [0.82898906 0.7946305  0.01150909]
  [0.05929642 0.59762989 0.7248887 ]
  [0.15162473 0.55354106 0.92956369]
  [0.21183182 0.92999156 0.57835296]
  [0.41929845 0.19565816 0.18662498]]

 [[0.87032715 0.50662785 0.54217065]
  [0.82355975 0.20349718 0.01538959]
  [0.24959705 0.62056295 0.07042258]
  [0.64422139 0.88416751 0.49174671]
  [0.21436044 0.55439367 0.61435127]
  [0.38492133 0.66637382 0.66080096]
  [0.3957137  0.48913863 0.67103063]
  [0.81766502 0.38401956 0.06543795]]

 [[0.78656194 0.86104692 0.70951371]
  [0.50050861 0.14680231 0.08608443]
  [0.73766323 0.32358634 0.70397593]
  [0.04155761 0.23332385 0.98193348]
  [0.60442283 0.6441864  0.40536901]
  [0.81750344 0.87604709 0.03573838]
  [0.14154893 0.24558416 0.62685956]
  [0.44853079 0.90103491 0.4339039 ]]

 [[0.51162248 0.32017957 0.28754968]
  [0.27526172 0.06626226 0.60503387]
  [0.90903854 0.05226501 0.26241159]
  [0.73163092 0.98252245 0.44887237]
  [0.94349225 0.14615167 0.83662707]
  [0.25880778 0.61251959 0.82794232]
  [0.00672891 0.1271131  0.65880109]
  [0.88851577 0.75109775 0.56399842]]

 [[0.85965509 0.11357479 0.27325381]
  [0.74156642 0.35108524 0.40305073]
  [0.44791592 0.28270286 0.45377936]
  [0.01543443 0.14978493 0.47738367]
  [0.63671823 0.75239388 0.59118693]
  [0.55932007 0.32759274 0.25519358]
  [0.79183605 0.18399144 0.84579649]
  [0.06608463 0.63129404 0.78672545]]

 [[0.23577256 0.24679561 0.46901338]
  [0.43949749 0.93467498 0.9023869 ]
  [0.58850225 0.24534939 0.92965553]
  [0.26322984 0.13130557 0.67981953]
  [0.36389878 0.74552644 0.25606283]
  [0.77564163 0.50464125 0.3598317 ]
  [0.49057984 0.9482408  0.84635511]
  [0.70071485 0.43268376 0.39706312]]

 [[0.28021231 0.47537742 0.72971633]
  [0.87380873 0.83031311 0.56713737]
  [0.23093306 0.22830678 0.54439754]
  [0.88130002 0.37081258 0.78148687]
  [0.00318428 0.62297164 0.58875116]
  [0.68102061 0.31822913 0.04432477]
  [0.70410386 0.56770957 0.42998752]
  [0.5891714  0.25692428 0.19184309]]]

Extract array of shape (6,6,3) from the said array:
[[[0.67420382 0.5592584  0.27096382]
  [0.2896283  0.68346522 0.13996167]
  [0.74283318 0.06323309 0.0980022 ]
  [0.43203954 0.38888969 0.44801756]
  [0.04391897 0.4851516  0.34044817]
  [0.2202623  0.81434798 0.51900666]]

 [[0.42389039 0.21162537 0.39794135]
  [0.70118645 0.77166133 0.02891853]
  [0.13635083 0.77918942 0.93913105]
  [0.82898906 0.7946305  0.01150909]
  [0.05929642 0.59762989 0.7248887 ]
  [0.15162473 0.55354106 0.92956369]]

 [[0.87032715 0.50662785 0.54217065]
  [0.82355975 0.20349718 0.01538959]
  [0.24959705 0.62056295 0.07042258]
  [0.64422139 0.88416751 0.49174671]
  [0.21436044 0.55439367 0.61435127]
  [0.38492133 0.66637382 0.66080096]]

 [[0.78656194 0.86104692 0.70951371]
  [0.50050861 0.14680231 0.08608443]
  [0.73766323 0.32358634 0.70397593]
  [0.04155761 0.23332385 0.98193348]
  [0.60442283 0.6441864  0.40536901]
  [0.81750344 0.87604709 0.03573838]]

 [[0.51162248 0.32017957 0.28754968]
  [0.27526172 0.06626226 0.60503387]
  [0.90903854 0.05226501 0.26241159]
  [0.73163092 0.98252245 0.44887237]
  [0.94349225 0.14615167 0.83662707]
  [0.25880778 0.61251959 0.82794232]]

 [[0.85965509 0.11357479 0.27325381]
  [0.74156642 0.35108524 0.40305073]
  [0.44791592 0.28270286 0.45377936]
  [0.01543443 0.14978493 0.47738367]
  [0.63671823 0.75239388 0.59118693]
  [0.55932007 0.32759274 0.25519358]]]

Explanation:

In the above exercise -

nums = np.random.random((8, 8, 3)): This code first creates a 3-dimensional array nums with shape (8, 8, 3) filled with random values between 0 and 1.

new_nums = nums[:6, :6, :]: This code slices the “nums” array to create a new array new_nums. The slicing operation is performed on the first two dimensions (axis 0 and axis 1) using the slice :6, which means selecting elements from the beginning up to (but not including) index 6. The third dimension (axis 2) is kept entirely using the slice :.

For more Practice: Solve these Related Problems:

• Write a NumPy program to extract a sub-array of shape (6,6,3) from a randomly generated 12x12x4 matrix using slicing.
• Create a function that selects a contiguous block from a 3D array based on provided start indices and block shape.
• Implement a solution that uses np.copy to ensure the extracted sub-array is independent of the original array.
• Test the extraction function with different starting indices to confirm flexible sub-array selection.

Python-Numpy Code Editor: