NumPy: Access an array by column

Last update on April 26 2025 12:38:48 (UTC/GMT +8 hours)

Access Array by Column

Write a NumPy program to access an array by column.

Pictorial Presentation:

Sample Solution:

Python Code:

# Importing the NumPy library and aliasing it as 'np'
import numpy as np

# Creating a 1-dimensional array 'x' with values from 0 to 8 and reshaping it into a 3x3 array
x = np.arange(9).reshape(3, 3)

# Printing a message indicating the original array elements will be shown
print("Original array elements:")

# Printing the original array 'x' with its elements
print(x)

# Printing a message indicating that an array will be accessed by columns
print("Access an array by column:")

# Printing a message indicating the display of the first column of the array
print("First column:")

# Printing the first column of the array 'x' using slicing with ":" for rows and "0" for the first column index
print(x[:, 0])

# Printing a message indicating the display of the second column of the array
print("Second column:")

# Printing the second column of the array 'x' using slicing with ":" for rows and "1" for the second column index
print(x[:, 1])

# Printing a message indicating the display of the third column of the array
print("Third column:")

# Printing the third column of the array 'x' using slicing with ":" for rows and "2" for the third column index
print(x[:, 2]) 

Sample Output:

Original array elements:
[[0 1 2]
 [3 4 5]
 [6 7 8]]
Access an array by column:
First column:
[0 3 6]
Second column:
[1 4 7]
Third column:
[2 5 8]

Explanation:

In the above example -

x = np.arange(9).reshape(3,3): Create a NumPy array x using np.arange(9), which generates an array with values from 0 to 8. Then, reshape the array into a 3x3 matrix using the reshape method.

print(x[:,0]) - Print the first column of the array x. The colon : in the row position indicates selecting all rows, and the 0 in the column position indicates selecting the first column.

print(x[:,1]) - Print the second column of the array x. The colon : in the row position indicates selecting all rows, and the 1 in the column position indicates selecting the second column.

print(x[:,2]) - Print the third column of the array x. The colon : in the row position indicates selecting all rows, and the 2 in the column position indicates selecting the third column.

For more Practice: Solve these Related Problems:

• Write a NumPy program to extract a specific column from a 2D array using slicing and indexing.
• Create a function that returns any column of an array given its index and verifies the output data.
• Use advanced indexing to access multiple non-consecutive columns from an array and check the resulting shape.
• Implement a method that swaps two columns in an array and then accesses each column individually to verify changes.

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