NumPy Replace Function

Introduction

Numpy is one of the most powerful scientific computing libraries in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.

The numpy.char.replace() function of the Numpy library is used to replace the occurrences of a substring in an array of strings or a string with a new substring. In this lab, we will learn how to use the replace() function to replace the content of an array of strings.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Skills Graph

Import Numpy

To use the numpy library, we have to first import it. We can do so using the import statement as follows:

import numpy as np

Define a String

For the purpose of this lab, we will define a string called string1 and initialize it with some value.

string1 = "The quick brown fox jumps over the lazy dog"
print("The original string is:\n", string1)

Replace Substring

To replace a substring within the string, we can use the numpy.char.replace() function. The function takes in four parameters:

numpy.char.replace(a, old, new, count=None)

where:

• a: is an array of strings or a string.
• old: is the old substring that is to be replaced.
• new: is the new substring that will take the place of the old substring.
• count: is an optional parameter that specifies the number of occurrences of the old substring to be converted.

Let's replace the substring 'brown' in string1 with the new substring of 'red':

string2 = np.char.replace(string1, 'brown', 'red')
print("The string with replaced substring is:\n", string2)

Replace Multiple Occurrences

To replace multiple occurrences of a substring, we can pass the optional parameter count to the function. For example, let's replace both the occurrences of the substring 'the' with 'an':

string3 = np.char.replace(string1, 'the', 'an', count=2)
print("The string with replaced substrings is:\n", string3)

Replacing Elements in an Array

We can also apply the replace() function to an array of strings. Let's start by creating an array of strings:

string_array = np.array(['hello world', 'goodbye world', 'world peace', 'world health'])
print("The original string array is:\n", string_array)

Now let's replace the substring 'world' in all the elements of the array with 'universe':

new_string_array = np.char.replace(string_array, 'world', 'universe')
print("The new string array is:\n", new_string_array)

Replacing Repeated Substrings

We can also use the count parameter to replace repeated substrings. Let's replace the first appearance of the substring 'universe' in all the elements of the array with 'space':

new_string_array2 = np.char.replace(string_array, 'universe', 'space', count=1)
print("The new string array is:\n", new_string_array2)

Summary

In this lab, we have learned how to use the numpy.char.replace() function to replace substrings within a string or an array of strings. We learned how to replace a single occurrence of a substring or multiple occurrences of a substring using the count parameter. Finally, we learned how to apply the replace() function to an array of strings. These skills can be used in NLP and text processing applications, as well as data preprocessing tasks.