How to Replace Substring in a DataFrame Column with Values from Another Column in Python

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What will you learn?

In this tutorial, you will learn how to replace substrings in a Pandas DataFrame column with values from another column. Specifically, you will understand how to handle scenarios where the first column contains specific substring matches that need to be replaced with corresponding values from a different column.

Introduction to the Problem and Solution

Imagine we have two Pandas DataFrames: df1 and df2. Our task is to replace substrings in df1[‘Column 1’] with values from df2[‘Column 2’] whenever there are specific substring matches found in df2[‘Column 1’]. The solution involves iterating over each row of df2, searching for occurrences of its values within df1, and performing replacements where necessary.

Code

import pandas as pd

# Sample DataFrames (Replace these with your actual DataFrames)
df1 = pd.DataFrame({'Column 1': ['apple orange', 'banana', 'cherry']})
df2 = pd.DataFrame({'Column 1': ['apple', 'berry'], 'Column 2': ['fruit', 'red fruit']})

for index, row in df2.iterrows():
    df1['Column 1'] = df1['Column 1'].str.replace(row['Column 1'], row['Column 2'])

# Displaying the updated DataFrame
print(df1)

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Explanation

To accomplish this task effectively: – Import the Pandas library for data manipulation. – Create sample DataFrames, df1 and df2. – Iterate over rows of df2 utilizing .iterrows(). – Utilize .str.replace() method to substitute substrings in df1 based on matches identified. – Finally, showcase the updated DataFrame.

    How can we replace multiple substrings efficiently?

    To efficiently replace multiple substrings, use regular expressions along with Pandas’ .replace() method for handling complex substring replacement patterns.

    Can we replace substrings case-insensitively?

    Yes, set the case parameter of .replace() to False for conducting case-insensitive replacements.

    What if I want to replace only exact whole word matches?

    For replacing only exact whole word matches, include regex word boundaries (\b) around the search string during replacement.

    Is there a way to limit the number of replacements made per substring match?

    Certainly, leverage the optional parameter n within .replace() to specify maximum replacements per string match.

    How do I handle missing values during replacement operations?

    You can manage missing values by specifying a default value using the optional parameter regex=True.

    Conclusion

    In this comprehensive tutorial, we have explored how to efficiently replace substrings within a DataFrame column based on matches found in another column. By harnessing Pandas’ functionalities, such operations can be executed seamlessly. For further guidance or additional coding insights, feel free to explore our platform at PythonHelpDesk.com.

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