NumPy: View inputs as arrays with at least two dimensions, three dimensions

Last update on August 29 2025 12:46:40 (UTC/GMT +8 hours)

Inputs as Arrays with 2D/3D Views

Write a NumPy program to view inputs as arrays with at least two dimensions, three dimensions.

Sample Solution:

Python Code:

# Importing the NumPy library with an alias 'np'
import numpy as np

# Defining a scalar value
x = 10

# Printing view inputs as arrays with at least two dimensions for scalar x
print("View inputs as arrays with at least two dimensions:")
print(np.atleast_1d(x))

# Creating a NumPy array with shape (2, 2) using arange and reshape functions
x = np.arange(4.0).reshape(2, 2)

# Viewing x as an array with at least two dimensions
print(np.atleast_1d(x))

# Printing view inputs as arrays with at least three dimensions for scalar x
print("View inputs as arrays with at least three dimensions:")

# Redefining the scalar value
x = 15

# Viewing x as an array with at least three dimensions
print(np.atleast_3d(x))

# Creating a NumPy array using arange function
x = np.arange(3.0)

# Viewing x as an array with at least three dimensions
print(np.atleast_3d(x))

Sample Output:

View inputs as arrays with at least two dimensions:                    
[10]                                                                   
[[ 0.  1.]                                                             
 [ 2.  3.]]                                                            
View inputs as arrays with at least three dimensions:                  
[[[15]]]                                                               
[[[ 0.]                                                                
  [ 1.]                                                                
  [ 2.]]]

Explanation:

In the above code –

‘x = 10’ defines an integer variable x with the value 10.
print(np.atleast_1d(x)): The np.atleast_1d() function takes the input x and converts it into an array with at least one dimension. Since x is a scalar, it is converted into a 1D array with one element. The output is [10].
‘x = np.arange(4.0).reshape(2, 2)’ creates a 2D array x with shape (2, 2) using the np.arange() and reshape() functions.
print(np.atleast_1d(x)): Since x is already a 2D array, the np.atleast_1d() function doesn't change its dimensions.
‘x = 15’ This line defines an integer variable x with the value 15.
print(np.atleast_3d(x)): The np.atleast_3d() function takes the input x and converts it into an array with at least three dimensions. Since x is a scalar, it is converted into a 3D array with shape (1, 1, 1). The output is [[[15]]].
x = np.arange(3.0): This line creates a 1D array x with elements [0., 1., 2.].
print(np.atleast_3d(x)): The np.atleast_3d() function takes the input x and converts it into an array with at least three dimensions. Since x is a 1D array, it is converted into a 3D array with shape (1, 3, 1).

For more Practice: Solve these Related Problems:

Write a NumPy program to convert a 1D input into a 2D array view using np.atleast_2d.
Transform a list of lists into a 3D array view with np.atleast_3d and verify its dimensions.
Create a function that converts various inputs to arrays with at least two or three dimensions based on a parameter.
Apply np.atleast_2d and np.atleast_3d on different inputs and compare the shapes to ensure correctness.

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