Python Script to Merge Multiple CSV Files into One Excel Workbook

To merge multiple CSV files into a single Excel workbook using Python, the most efficient and robust approach is using the Pandas library alongside openpyxl. This guide provides two production-ready scripts: one to merge CSVs into separate sheets, and another to combine them into a single consolidated sheet.

Prerequisites
You need to install pandas and openpyxl (the engine used to write Excel files). Run the following command in your terminal:
pip install pandas openpyxl

Option 1: Merge CSVs into Separate Sheets (One Workbook)
Use this script if you want each CSV file to represent a distinct tab (sheet) inside a single Excel file. The sheet names will automatically match the CSV filenames.

import os
import pandas as pd

Define the directory containing your CSV files and the output path

csv_directory = "./csv_folder"
output_excel = "merged_output_sheets.xlsx"

Initialize the Excel writer

with pd.ExcelWriter(output_excel, engine='openpyxl') as writer:
# Loop through all files in the directory
for file_name in os.listdir(csv_directory):
if file_name.endswith('.csv'):
file_path = os.path.join(csv_directory, file_name)

        # Read CSV (adjust encoding if you encounter UnicodeDecodeError)
        df = pd.read_csv(file_path, encoding='utf-8')

        # Use the filename (minus .csv) as the sheet name
        # Truncated to 31 characters to comply with Excel sheet name limits
        sheet_name = os.path.splitext(file_name)[0][:31]

        # Write DataFrame to a specific sheet
        df.to_excel(writer, sheet_name=sheet_name, index=False)

print(f"Successfully merged CSVs into separate sheets in: {output_excel}")

Option 2: Concatenate CSVs into a Single Sheet
Use this script if all your CSV files share the same column structure (schema) and you want to stack them vertically into one continuous dataset on a single Excel sheet.

import os
import pandas as pd

csv_directory = "./csv_folder"
output_excel = "merged_output_single_sheet.xlsx"

List to hold individual DataFrames

dataframes = []

Loop through and read all CSV files

for file_name in os.listdir(csv_directory):
if file_name.endswith('.csv'):
file_path = os.path.join(csv_directory, file_name)
df = pd.read_csv(file_path, encoding='utf-8')
dataframes.append(df)

Concatenate all DataFrames vertically

if dataframes:
combined_df = pd.concat(dataframes, ignore_index=True)

# Write the combined DataFrame to Excel
combined_df.to_excel(output_excel, index=False, engine='openpyxl')
print(f"Successfully concatenated CSVs into a single sheet in: {output_excel}")

else:
print("No CSV files found in the specified directory.")

Technical Considerations & Troubleshooting

Excel Row Limits: Excel sheets have a strict limit of 1,048,576 rows. If your concatenated CSVs exceed this limit, the write operation in Option 2 will fail.
Character Encoding: If your CSVs contain special characters, emojis, or non-English text, you might need to change encoding='utf-8' to encoding='utf-8-sig' or encoding='latin1' inside pd.read_csv().
Memory Constraints: Pandas loads datasets entirely into RAM. If you are merging gigabytes of CSV data, ensure your system has sufficient memory, or process the files in chunks.

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