How to Use re.sub in a Python Dataframe

Python is a popular programming language that is widely used for data analysis tasks. One of the most commonly used libraries for such tasks is Pandas. Pandas is a powerful and versatile library that allows users to perform a wide range of data manipulation tasks with ease. One of the most useful Pandas functions is re.sub(). In this article, we will explore how to use re.sub() in a Python dataframe.

Table of Contents

What is re.sub()?

re.sub() is a Python function that is used to replace text or characters in a string. The re stands for regular expressions, which are a sequence of characters that define a search pattern. The sub stands for substitute, which means to replace one or more occurrences of a pattern in a string with a new string.

Why use re.sub() in a Python dataframe?

Data cleaning is an important part of data analysis. Often, data is not in the format that is required for analysis, and it needs to be cleaned before it can be used. One common data cleaning task is replacing certain values in a dataset. This is where re.sub() comes in handy. By using re.sub() in a Python dataframe, you can easily replace certain values with new ones.

How to use re.sub() in a Python dataframe

To use re.sub() in a Python dataframe, we first need to import the Pandas library. We can then create a dataframe using the read_csv() function. Let’s start with an example dataframe:

import pandas as pd
df = pd.read_csv('example.csv')
print(df)

This will create a dataframe and print its contents:

   id       name         date
0   1  John Doe  2021-01-01
1   2   Jane Doe  2021-02-01
2   3   Jim Smith  2021-03-01

Now, let’s say we want to replace all instances of ‘Doe’ in the name column with ‘Smith’. We can do this using re.sub() as follows:

import re
df['name'] = df['name'].apply(lambda x: re.sub('Doe', 'Smith', x))
print(df)

This will replace all instances of ‘Doe’ in the name column with ‘Smith’ and print the updated dataframe:

   id         name         date
0   1   John Smith  2021-01-01
1   2   Jane Smith  2021-02-01
2   3   Jim Smith  2021-03-01

Using regular expressions with re.sub()

As mentioned earlier, re.sub() uses regular expressions to define a search pattern. This allows for more complex replacements to be made. For example, let’s say we want to replace all instances of ‘Doe’ or ‘Smith’ in the name column with ‘Johnson’. We can do this using the following code:

df['name'] = df['name'].apply(lambda x: re.sub('(Doe|Smith)', 'Johnson', x))
print(df)

This will replace all instances of ‘Doe’ or ‘Smith’ in the name column with ‘Johnson’ and print the updated dataframe:

   id           name         date
0   1  John Johnson  2021-01-01
1   2   Jane Johnson  2021-02-01
2   3   Jim Johnson  2021-03-01

Using re.sub() with conditions

Sometimes, we only want to replace certain values in a dataframe that meet certain conditions. We can use re.sub() in combination with conditional statements to achieve this. For example, let’s say we want to replace all instances of ‘Doe’ in the name column with ‘Johnson’, but only for rows where the id is greater than 1. We can do this using the following code:

df.loc[df['id'] > 1, 'name'] = df.loc[df['id'] > 1, 'name'].apply(lambda x: re.sub('Doe', 'Johnson', x))
print(df)

This will replace all instances of ‘Doe’ in the name column with ‘Johnson’ but only for rows where the id is greater than 1, and print the updated dataframe:

   id           name         date
0   1      John Doe  2021-01-01
1   2  Jane Johnson  2021-02-01
2   3     Jim Smith  2021-03-01

Conclusion

In conclusion, re.sub() is a powerful function in Python that can be used to replace text or characters in a string. By using re.sub() in a Python dataframe, we can easily replace certain values with new ones. This is particularly useful for data cleaning tasks, which are a necessary part of data analysis. By following the examples in this article, you should now have a good understanding of how to use re.sub() in a Python dataframe, and be able to apply it to your own data analysis tasks.

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