How to Merge CSV Files in Python

Have you ever found yourself in a situation where you have multiple CSV files that you need to combine into one? If you’re a Python programmer, you’re in luck because merging CSV files in Python is a simple process. In this article, we will explore various methods of merging CSV files in Python.

Table of Contents

What is CSV?

CSV stands for "Comma Separated Values." It is a simple file format used to store data in a tabular form. Each line in the CSV file represents a row in the table, and each value is separated by a comma. CSV files are commonly used for storing data in spreadsheets or databases, as they are easy to read and can be opened in a variety of applications.

Why Merge CSV Files in Python?

Merging CSV files in Python can be helpful in various situations. For example, if you have multiple CSV files that contain data for the same set of entities, you can merge them into one file to simplify data management. Additionally, if you have multiple CSV files that contain data for different time periods, you can merge them into one file to analyze trends over time.

Method 1: Using the CSV Module

The easiest way to merge CSV files in Python is by using the built-in CSV module. Here’s an example of how to do it:

import csv

# Open the output file in write mode
with open('merged.csv', 'w', newline='') as outfile:

    # Create a writer object
    writer = csv.writer(outfile)

    # Loop through each input file
    for filename in ['file1.csv', 'file2.csv', 'file3.csv']:

        # Open the input file in read mode
        with open(filename, 'r') as infile:

            # Create a reader object
            reader = csv.reader(infile)

            # Loop through each row in the input file
            for row in reader:

                # Write the row to the output file
                writer.writerow(row)

In this example, we first open the output file in write mode using the open() function. We then create a csv.writer object and loop through each input file. For each input file, we open it in read mode using the open() function and create a csv.reader object. We then loop through each row in the input file and write it to the output file using the writer.writerow() method.

Method 2: Using Pandas

Another popular method of merging CSV files in Python is by using the Pandas library. Pandas is a powerful data analysis library that provides various tools for working with tabular data. Here’s an example of how to merge CSV files using Pandas:

import pandas as pd

# Create a list of input files
files = ['file1.csv', 'file2.csv', 'file3.csv']

# Loop through each file and read it into a pandas DataFrame
dfs = []
for file in files:
    df = pd.read_csv(file)
    dfs.append(df)

# Concatenate the DataFrames
merged = pd.concat(dfs, ignore_index=True)

# Write the merged DataFrame to a CSV file
merged.to_csv('merged.csv', index=False)

In this example, we first create a list of input files. We then loop through each file and read it into a pandas DataFrame using the pd.read_csv() function. We append each DataFrame to a list called ‘dfs’. We then concatenate the DataFrames using the pd.concat() function and write the merged DataFrame to a CSV file using the to_csv() method.

Method 3: Using Dask

If you’re dealing with large CSV files, you may want to consider using Dask for merging CSV files in Python. Dask is a parallel computing library that is designed to handle large datasets. Here’s an example of how to merge CSV files using Dask:

import dask.dataframe as dd

# Create a Dask DataFrame for each input file
df1 = dd.read_csv('file1.csv')
df2 = dd.read_csv('file2.csv')
df3 = dd.read_csv('file3.csv')

# Merge the DataFrames
merged = dd.concat([df1, df2, df3])

# Write the merged DataFrame to a CSV file
merged.to_csv('merged.csv', index=False)

In this example, we first create a Dask DataFrame for each input file using the dd.read_csv() function. We then merge the DataFrames using the dd.concat() function and write the merged DataFrame to a CSV file using the to_csv() method.

Common Issues to Watch Out For

Merging CSV files in Python can be a straightforward process, but there are a few common issues that you should watch out for. Here are some of them:

Header Rows

If your CSV files have header rows, you may end up with duplicate headers when you merge them. To avoid this issue, you can skip the headers in all but the first file using the skiprows parameter in pd.read_csv() function.

Column Mismatches

If your CSV files have different numbers of columns or the columns are in a different order, you may end up with column mismatches when you merge them. To avoid this issue, you can use the usecols parameter in pd.read_csv() function to specify which columns to read, and then rearrange the columns using the DataFrame.reindex() method.

Encoding Issues

If your CSV files use a different encoding than the default encoding used by Pandas or Python, you may encounter encoding issues when you merge them. To avoid this issue, you can specify the encoding used by your CSV files using the encoding parameter in pd.read_csv() function.

Conclusion

Merging CSV files in Python can be a useful skill to have if you work with data. In this article, we explored various methods of merging CSV files in Python, including using the built-in CSV module, Pandas, and Dask. We also discussed common issues that you may encounter when merging CSV files and how to avoid them. With these techniques, you should be able to merge CSV files in Python quickly and easily.

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