NumPy: Remove the trailing whitespaces of all the elements of a given array

Last update on April 29 2025 12:55:23 (UTC/GMT +8 hours)

9. Remove Trailing Whitespaces

Write a NumPy program to remove the trailing whitespaces of all the elements of a given array.

Sample Solution:

Python Code:

# Importing necessary library
import numpy as np

# Creating a NumPy array containing strings with leading and trailing whitespaces
x = np.array([' python exercises ', ' PHP  ', ' java  ', '  C++'], dtype=np.str)

# Displaying the original array
print("Original Array:")
print(x)

# Removing the trailing whitespaces from the strings
rstripped_char = np.char.rstrip(x)

# Displaying the array after removing trailing whitespaces
print("\nRemove the trailing whitespaces : ", rstripped_char)

Sample Input:

([' python exercises ', ' PHP  ', ' java  ', '  C++'], dtype=np.str)

Sample Output:

Original Array:
[' python exercises ' ' PHP  ' ' java  ' '  C++']

Remove the trailing whitespaces :  [' python exercises' ' PHP' ' java' '  C++']

Explanation:

In the above exercise –

x = np.array([' python exercises ', ' PHP ', ' java ', ' C++'], dtype=np.str): This line creates a one dimensional NumPy array x with 4 elements of string data type.

rstripped_char = np.char.rstrip(x): Returns a new NumPy array rstripped_char where the trailing whitespace from each string in x has been removed. The resulting array contains the following values: [' python exercises', ' PHP', ' java', ' C++'].

Pictorial Presentation:

NumPy String: Remove the trailing whitespaces of all the elements of a given array

For more Practice: Solve these Related Problems:

Create a function that removes only trailing whitespaces from a string array using np.char.rstrip.
Test the function on an array with varying trailing whitespace lengths to ensure consistency.
Implement a solution that counts the trailing spaces removed from each element and returns that as an auxiliary output.
Chain trailing whitespace removal with a conversion to uppercase for further string standardization.

Go to:

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Python-Numpy Code Editor:

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