Combining Two CSV Files with Same Columns but Additional Entries

by

What will you learn?

In this tutorial, you will learn how to merge two CSV files with identical column structures but different data entries into a single file using Python and Pandas. By the end of this guide, you will be able to efficiently combine datasets with ease.

Introduction to the Problem and Solution

When working with multiple CSV files that share the same column headers but contain varying data entries, merging them can present challenges. However, leveraging Python’s Pandas library simplifies this task by allowing us to read, combine, and write back the merged data effortlessly.

Code

import pandas as pd

# Load both CSV files into DataFrames
df1 = pd.read_csv('file1.csv')
df2 = pd.read_csv('file2.csv')

# Concatenate both DataFrames vertically (appending rows)
merged_df = pd.concat([df1, df2], ignore_index=True)

# Save the merged DataFrame to a new CSV file
merged_df.to_csv('merged_file.csv', index=False)  # pythonhelpdesk.com

# Copyright PHD

Explanation

To merge two CSV files with identical columns but additional entries: – Utilize the pandas library for efficient tabular data manipulation. – Read each CSV file into separate DataFrames using pd.read_csv(). – Combine these DataFrames vertically through pd.concat() for merging all rows. – Save the merged data into a new CSV file using to_csv().

    How can I install pandas in my Python environment?

    You can install pandas by running pip install pandas in your command line or terminal.

    Can I merge more than two CSV files following this approach?

    Yes, you can merge multiple CSV files by sequentially reading and concatenating them using Pandas.

    Does the order of columns need to be exactly similar in both input files?

    For effective dataset merging, ensure that both input files have matching column names arranged in the same order.

    Is there any limit on the number of rows that can be merged using this method?

    There is no specific limit; you can concatenate numerous rows from various sources based on your system’s memory capacity.

    Can I perform additional transformations or filtering while merging these datasets?

    Yes, before or after merging datasets, you can apply operations like filtering duplicates or transforming columns as needed.

    Conclusion:

    Combining datasets with similar structures but different content is a common task across various industries. Python’s Pandas library provides a powerful toolset for efficiently handling such scenarios. By mastering these techniques, professionals can streamline their data processing workflows and extract valuable insights from diverse datasets.

    Leave a Comment