Merge CSV Files Online: Combine Multiple CSVs Into One

How to merge CSV files

1. 1Add your files. Drag two or more CSV, TSV or TXT files onto the tool, or click “Choose files”. Put them in the order their rows should appear. Each file shows its delimiter, number of rows and columns.
2. 2Choose how to combine them. Pick Strict for files with the same columns, Union to keep every column from every file, or Column mapping to match columns whose names differ. Then choose output options such as a source_file column or duplicate removal.
3. 3Merge and download. Click “Merge & download”. Your browser builds the merged file on your device and saves it as merged.csv. A summary shows the rows, columns, duplicates removed and the time taken.

Union, Strict or Column mapping: which mode to use

The tool suggests a mode from the headers of your files. Here is what each one does with these two small files:

january.csv            february.csv
id,name,amount         id,amount,region
1,Alice,10             3,30,North
2,Bob,20

Union: keep every column

The merged file gets every column from every file, in the order they first appear. A value that a file does not have stays empty. Union is the safe choice for exports whose columns changed over time, and it adds a source_file column by default so you can tell the rows apart:

id,name,amount,region,source_file
1,Alice,10,,january.csv
2,Bob,20,,january.csv
3,,30,North,february.csv

Strict: the same columns in every file

Strict mode checks that every file has exactly the same columns and stops with a clear list of what is missing or extra if not. The column order may differ between files: the tool lines the columns up by name and tells you that it did. Use Strict for monthly exports from the same system, where a different header usually means a mistake. The two files above would be refused, because name and region are not in both.

Column mapping: match columns with different names

Column mapping matches columns by name while ignoring capitals, extra spaces, underscores and hyphens, so Order ID, order_id and ORDER-ID end up in one column. You see the matches in a table and can change any of them: rename an output column, pick a different source column for a file, or skip a column you do not need.

Output options

• source_file column: adds the name of the file each row came from as the last column. On by default in Union and Column mapping.
• Remove duplicate rows: leaves out rows that exactly repeat an earlier row, useful when exports overlap. The source_file column is not part of the comparison.
• Delimiter, line endings and quoting: write commas, semicolons or tabs, with LF or Windows (CRLF) line endings. Quotes are added only where CSV needs them, or around every value if you prefer.
• UTF-8 with BOM: an invisible marker at the start of the file that helps Excel on Windows read accents and symbols correctly.
• Normalise numbers: each file is read with its own delimiter, decimal separator and encoding, and values are copied as written. If you combine a German export (1.234,5) with an English one (1234.5), this option rewrites decimal commas with a point so every number reads the same way.

Metadata lines above the header, such as the operator and sample rate in a test-rig export, are recognised and skipped, and a units row under the header ([s];[N]) is kept once. The output is a CSV or TSV file; Excel (.xlsx) output is coming soon.

Merging very large CSV files

You can merge files far bigger than Excel could open. When the files share the same header, delimiter and line endings, they are appended unchanged with the header kept once, which is the quickest case: in our tests in Chrome, a 143 MB file with 2 million rows appended to itself took well under a second. Merges that change rows, such as Union with a source_file column, take a little longer.

In Chrome and Edge, tick Save straight to disk to choose the destination first: the merged file is then written to disk as it is produced, which keeps memory use low for multi-gigabyte results. In other browsers, the result is assembled as a download. Want to check the merged file afterwards? The CSV row counter counts its lines in seconds.

Private by design: nothing is uploaded

Many online CSV mergers upload your files to a server. MegaRows does not: the merge runs entirely in your browser, on your device, and the files and their names are never sent anywhere. That makes it safe for customer lists, financial exports and test data that must not leave your computer.

Other ways to merge CSV files

macOS and Linux: head and tail

head -n 1 january.csv > merged.csv
tail -n +2 -q january.csv february.csv >> merged.csv

Fast and built in, but only for files with identical headers. A file whose last line has no line break gets glued to the next file’s first row, and nothing checks that the columns match.

Python: pandas

import pandas as pd
files = ["january.csv", "february.csv"]
pd.concat([pd.read_csv(f) for f in files]).to_csv("merged.csv", index=False)

pd.concat lines columns up by name like Union mode. It loads everything into memory and may reformat values: leading zeros disappear unless you read every column as text with dtype=str.

Excel: Power Query

Put the files in one folder, then use Data → Get Data → From File → From Folder → Combine. It works well for a few files with matching columns, as long as the combined result stays under Excel’s limit of 1,048,576 rows.