Developer

CSV Combiner & Analyzer

Drop multiple CSV files, confirm they share the same headers, merge every row, analyze the columns, and download one clean CSV.

Header configuration

My files have a header rowThe first row of each file is validated as headers before rows are combined.Custom output headers (optional)

Leave blank to use the first file's headers. If filled, the count must match the file columns.

Drop CSV files here

Files must include a header row. The tool checks that every CSV has the same header set before combining rows.

Everything runs in your browser. CSV contents are not uploaded.

What is a CSV combiner?

A CSV combiner merges rows from multiple comma-separated value files into one clean table. The risky part is header drift: one file may have different column names, missing fields, or reordered headers. This tool validates the header row in every CSV before combining anything. If all files share the same normalized header set, rows are aligned into the first file's column order, optional source columns are added, and the merged CSV can be downloaded. The analysis panel then profiles each column so you can quickly spot numeric fields, date ranges, missing values, and frequent categories before opening the result in a spreadsheet.

How to combine CSV files

1
Drop or choose your CSV files
Select two or more CSV files with a header row in the first line.
2
Let the header check run
The tool trims and normalizes header names, rejects empty or duplicate headers, and checks every file against the first CSV.
3
Review the analysis
Check file counts, row totals, numeric summaries, date ranges, missing cells, distinct values, grouped counts, and the row preview.
4
Download the combined CSV
Export one merged file in first-file column order. Keep source columns enabled when you need traceability.

Header validation rules

For each file:
  headers = trim(first_row)
  normalized = lowercase(collapse_spaces(headers))
  reject empty headers
  reject duplicate normalized headers
 
For files 2..n:
  normalized_headers must equal file_1 normalized_headers as a set
  rows are written in file_1 header order

Matching by normalized header set allows files with the same columns in a different order while still preventing accidental merges between incompatible exports. Output columns follow the first file so the combined CSV remains predictable.

Reference: RFC 4180 - Common Format and MIME Type for CSV Files

Examples

Input files	Result
Three bank statement CSVs with Date, Description, Debit, Credit, Balance	One combined transaction file with source_file and source_row for traceability. The analysis flags numeric money columns and the date range.
Monthly sales exports with the same headers in different order	Rows are aligned into the first file's column order before download. Column order can differ as long as header names match after normalization.
One file has Amount and another has Total	The tool stops and reports missing and extra headers. This prevents silent bad merges.

Common use cases

Combine monthly bank or credit-card statement CSVs before categorizing expenses.
Merge exports from a CRM, store, ad account, or analytics tool.
Validate that recurring exports kept the same schema over time.
Spot columns with missing values before importing data into Excel, Sheets, or a database.
Summarize numeric columns and grouped counts before deeper analysis.

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Frequently asked questions

Are my CSV files uploaded?

No. Files are read by your browser and processed locally. The combined CSV is created on your device.

Do the columns need to be in the same order?

No. The same header names can appear in a different order. The export follows the first file's header order.

What happens if a file has different headers?

The combine step stops and shows which headers are missing or extra. This avoids merging incompatible files silently.

Can I use this for bank statements?

Yes, if the statements are CSV files with a header row. Enable source columns so every transaction can be traced back to the original statement.

Does the tool understand semicolon-delimited files?

This version expects comma-separated CSV. If your export uses semicolons, save or export it as comma-separated CSV first.

Does the tool understand semicolon-delimited files?

This version expects comma-separated CSV. If your export uses semicolons, save or export it as comma-separated CSV first.

Last updated 2026-05-25

What is a CSV combiner?

How to combine CSV files

Drop or choose your CSV files

Select two or more CSV files with a header row in the first line.

Let the header check run

The tool trims and normalizes header names, rejects empty or duplicate headers, and checks every file against the first CSV.

Review the analysis

Check file counts, row totals, numeric summaries, date ranges, missing cells, distinct values, grouped counts, and the row preview.

Download the combined CSV

Export one merged file in first-file column order. Keep source columns enabled when you need traceability.

Header validation rules

For each file:
  headers = trim(first_row)
  normalized = lowercase(collapse_spaces(headers))
  reject empty headers
  reject duplicate normalized headers
 
For files 2..n:
  normalized_headers must equal file_1 normalized_headers as a set
  rows are written in file_1 header order

Examples

Input files	Result
Three bank statement CSVs with Date, Description, Debit, Credit, Balance	One combined transaction file with source_file and source_row for traceability. The analysis flags numeric money columns and the date range.
Monthly sales exports with the same headers in different order	Rows are aligned into the first file's column order before download. Column order can differ as long as header names match after normalization.
One file has Amount and another has Total	The tool stops and reports missing and extra headers. This prevents silent bad merges.

Common use cases

Combine monthly bank or credit-card statement CSVs before categorizing expenses.

Merge exports from a CRM, store, ad account, or analytics tool.

Validate that recurring exports kept the same schema over time.

Spot columns with missing values before importing data into Excel, Sheets, or a database.

Summarize numeric columns and grouped counts before deeper analysis.

Frequently asked questions

Are my CSV files uploaded?

No. Files are read by your browser and processed locally. The combined CSV is created on your device.

Do the columns need to be in the same order?

No. The same header names can appear in a different order. The export follows the first file's header order.

What happens if a file has different headers?

The combine step stops and shows which headers are missing or extra. This avoids merging incompatible files silently.

Can I use this for bank statements?

Yes, if the statements are CSV files with a header row. Enable source columns so every transaction can be traced back to the original statement.

Does the tool understand semicolon-delimited files?

This version expects comma-separated CSV. If your export uses semicolons, save or export it as comma-separated CSV first.

Does the tool understand semicolon-delimited files?

This version expects comma-separated CSV. If your export uses semicolons, save or export it as comma-separated CSV first.