merge-json-files.com

Merge & Combine CSV Files Online

Merging CSV files by hand often leads to messy columns and lost headers. This tool takes the headache out of combining spreadsheets. Just upload your CSVs, and we’ll join them into one clean dataset. You can keep your headers aligned and even track which file each row came from.

Practice Merging with Sample CSV Files

Download Sample CSVs

CSV Merge Preview

Two sales files merged into one output with source tracking

sales_q1.csv
NameAmountRegion
Alice1200North
Bob800South
Carol1500East
sales_q2.csv
NameAmountRegion
Dave950West
Eve1100North
Frank700South
merged_output.csv6 rows merged
NameAmountRegionSource
Alice1200Northsales_q1.csv
Bob800Southsales_q1.csv
Carol1500Eastsales_q1.csv
Dave950Westsales_q2.csv
Eve1100Northsales_q2.csv
Frank700Southsales_q2.csv

Key Features

Built specifically for tabular CSV data

Intelligent Header Management

Three header modes: keep first, keep all, or skip all. No more duplicate header rows in your merged output.

Multi-Delimiter Support

Auto-detects comma, semicolon, tab, and pipe delimiters per file. Handles quoted fields and embedded delimiters correctly.

Source File Tracking

Add a source column to trace every row back to its original file. Essential for auditing merged reports from multiple departments.

Three Ways to Handle Headers

Choose the mode that fits your workflow

RecommendedKeep First Header Only
CSVmerged_output.csv
Name,Amount,Region
Alice,1200,North
Bob,800,South
Carol,950,East
Dave,1100,North
Keep All HeadersHeaders from every file preserved as rows
CSVmerged_output.csv
Name,Amount,Region
Alice,1200,North
Bob,800,South
Name,Amount,Region
Carol,950,East
Dave,1100,North
Skip All HeadersPure data rows only, no headers
CSVmerged_output.csv
Alice,1200,North
Bob,800,South
Carol,950,East
Dave,1100,North

FAQ

Common questions about our CSV merger

Programmatic Merge

Python pandas & Bash

How to Merge CSV Files in Python

Merging CSV files in Python is best done with the pandas library using pd.concat(). Read each CSV with pd.read_csv(), combine them into a list, and concatenate with ignore_index=True to reset row numbers. Pandas automatically aligns columns by header name, handles missing columns gracefully, and preserves data types. This is ideal for combining exported data, merging reports from multiple sources, or consolidating database dumps.

Pythonmerge_csv.py
import pandas as pd
import glob

# Read all CSV files in the current directory
csv_files = glob.glob("sales_*.csv")
dataframes = [pd.read_csv(f) for f in csv_files]

# Concatenate and add a source column
for df, path in zip(dataframes, csv_files):
    df["Source"] = path

merged = pd.concat(dataframes, ignore_index=True)
merged.to_csv("merged_output.csv", index=False)

How to Merge CSV Files in Bash

For simple CSV merging in bash, use cat combined with tail to skip duplicate headers. The command cat file1.csv > merged.csv; tail -n +2 file2.csv >> merged.csv keeps the first header and appends data rows from subsequent files. For multiple files, use a loop with tail -n +2 to strip headers. This works well for quick merges in shell scripts, cron jobs, or data pipelines where you don't need complex column alignment.

Bashterminal
# Merge CSVs: keep header from first file, skip headers in the rest
head -1 sales_q1.csv > merged_output.csv
tail -n +2 -q sales_q1.csv sales_q2.csv sales_q3.csv >> merged_output.csv

# Verify row count (excluding header)
echo "Total data rows: $(tail -n +2 merged_output.csv | wc -l)"

Complete Guide

In-depth walkthrough

What happens when your CSV files have different columns

If file A has columns name, email and file B has columns name, phone, the tool fills missing values with empty cells so every row has all columns. The output will have columns: name, email, phone.

Rows from file A will have an empty phone cell. Rows from file B will have an empty email cell.

This is the expected behavior for most merging use cases. No data gets lost, you just get blank cells where a file didn't have that column.

If you need all files to have exactly the same columns, add the missing columns to your source files before merging. Or filter the merged output afterward to remove rows with too many blanks.

Merging CSVs with different encodings or delimiters

The tool expects standard UTF-8 comma-separated CSV files. If your file uses semicolons (common in European Excel exports) or tabs instead of commas, convert the delimiter first.

In Excel: File > Save As > CSV UTF-8 (Comma delimited). In Google Sheets: File > Download > Comma Separated Values (.csv).

If you see garbled characters like é or ’ in the output, the file is likely Latin-1 or Windows-1252 encoded. Open it in a text editor, re-save as UTF-8, then try merging again.

Most modern systems export UTF-8 by default, but older Windows software and some European Excel versions use different encodings that need conversion.

When to use the tool vs pandas or csvkit

Use the tool for quick one-off merges when you have 2-5 files and just need them combined. It's faster than writing code and works in any browser.

Use pandas (pd.concat) when you need to merge 10+ files, filter rows based on conditions, or automate the merge as part of a data pipeline. Pandas gives you full control over the merge logic.

Use csvkit (csvstack command) when you're already in a terminal and need a one-liner. It's perfect for shell scripts and command-line workflows where you don't want to open a browser or write Python.

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