Merging Two CSV Files Based on a Common Column with Different Names

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As a technical blogger, I’ve encountered various challenges while working with data. One such challenge is merging two CSV files based on a common column with different names. In this article, we’ll explore how to achieve this using the pandas library in Python.

Introduction

In today’s data-driven world, it’s not uncommon to work with multiple datasets that need to be merged or combined for further analysis. When dealing with CSV files, one of the common challenges is handling columns with different names. In this article, we’ll focus on creating a custom function that merges two CSV files based on a common column with different names.

Background

Before diving into the solution, let’s take a closer look at how pandas works and its role in data manipulation. Pandas is a powerful library in Python that provides data structures and functions to efficiently handle structured data, including tabular data such as CSV files.

DataFrames and Series

A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. It’s similar to an Excel spreadsheet or SQL table. On the other hand, a Series is a one-dimensional labeled array of values.

When working with DataFrames, you can perform various operations such as filtering, sorting, grouping, and merging. The merge() function in pandas is used to combine two DataFrames based on a common column.

Reading CSV Files

To read CSV files into DataFrames, we use the read_csv() function from pandas. This function takes several parameters, including the file path and the column names.

Creating the Custom Function

Now that we’ve covered the basics of pandas and data manipulation, let’s create our custom function to merge two CSV files based on a common column with different names.

import pandas as pd

def create_result(file1_path, file2_path, x_column_name, y_column_name):
    """
    Merges two CSV files into one DataFrame based on a common column with different names.
    
    Parameters:
    - file1_path (str): Path to the first CSV file.
    - file2_path (str): Path to the second CSV file.
    - x_column_name (str): Name of the common column in the first CSV file.
    - y_column_name (str): Name of the common column in the second CSV file.
    
    Returns:
    - None
    """
    # Read the CSV files into DataFrames
    df1 = pd.read_csv(file1_path)
    df2 = pd.read_csv(file2_path)
    
    # Rename the columns to match the common column names
    df2.rename(columns={y_column_name: x_column_name}, inplace=True)
    
    # Merge the two DataFrames based on the common column
    merged_df = pd.merge(df1, df2, on=x_column_name)
    
    # Write the merged DataFrame to a new CSV file
    merged_df.to_csv("result.csv", index=False)

# Example usage:
create_result("C:/Users/Venkata sai/Desktop/SQL_VENKATASAI_ASSIGNMENT/test/X.csv",
              "C:/Users/Venkata sai/Desktop/SQL_VENKATASAI_ASSIGNMENT/test/Y.csv",
              'a',  # x_column_name
              'd'   )  # y_column_name

How the Function Works

Here’s a step-by-step explanation of how the function works:

1. Read CSV Files: The function takes four parameters: file1_path and file2_path, which are the paths to the two CSV files, and x_column_name and y_column_name, which are the names of the common columns in each file.
2. Read the CSV files into DataFrames: The function uses the read_csv() function from pandas to read the two CSV files into DataFrames, df1 and df2.
3. Rename Columns: The function renames the column in df2 with the name of the common column in df1. This ensures that both DataFrames have the same column names.
4. Merge the Two DataFrames: The function merges the two DataFrames based on the common column using the merge() function from pandas.
5. Write the Merged DataFrame to a New CSV File: Finally, the function writes the merged DataFrame to a new CSV file named “result.csv”.

Example Use Case

To demonstrate how this function works, let’s create two sample CSV files:

X.csv

a,b,c
5,Ugh,wq
2,Kj,asd
3,Yu,Dx
4,Po,Cv

d,e,f
3,8i
4,Y6
2,X09
5,m3

Y.csv

a,d,e
5,a1,b2
2,a3,b4
3,a5,b6
4,a7,b8

g,h,i
3,g1,h2
4,g4,h5
2,g9,h10

We can now use our custom function to merge these two CSV files based on the common column “a” with different names “d” and “e”.

import pandas as pd

def create_result(file1_path, file2_path, x_column_name, y_column_name):
    # ... (function implementation remains the same)

# Example usage:
create_result("X.csv",
              "Y.csv",
              'a',  # x_column_name
              'd')   # y_column_name

The resulting merged CSV file will have columns from both files and values mapped according to the key between the two files, specified as x.a and y.d.

Conclusion

In this article, we’ve explored how to create a custom function using pandas that merges two CSV files based on a common column with different names. We’ve covered the basics of pandas and data manipulation, including DataFrames and Series, reading CSV files, and merging DataFrames. The example usage demonstrates how this function works by merging two sample CSV files.

Last modified on 2024-08-22