Drop CSV files here
or click to browse
Merged CSV will appear here
Upload 2+ CSV files then click Merge// combine multiple csv files into one in seconds
Merge multiple CSV files with the same columns into one. Stack rows vertically, deduplicate, and download the combined CSV instantly in your browser.
Drop CSV files here
or click to browse
Merged CSV will appear here
Upload 2+ CSV files then click MergeDrag and drop or click to select 2 or more CSV files with the same column headers.
Choose your delimiter, toggle deduplication, and optionally add a source filename column.
Click Merge, preview the result, then copy or download the combined CSV file.
CSV File Merger stacks multiple CSV files vertically into a single file. It aligns columns by header name, so even if column order differs between files, the output stays correct. All processing happens in your browser — no files are sent to any server.
They should have the same column headers for best results. If columns differ between files, the merger aligns by header name — missing columns in a file will produce empty cells for those rows.
There's no hard limit on the number of files. Performance depends on your browser and the total combined file size. For very large datasets (hundreds of MB), it may take a few seconds to process.
No. All processing is done entirely in your browser using JavaScript's FileReader API. Your CSV data never leaves your device and is not sent to any server.
When enabled, this option compares each row (excluding the header) as a full string. If two rows are completely identical across all columns, only the first occurrence is kept in the output.
Adds an extra column named _source at the end of each row, containing the name of the original file that row came from. Useful for tracking which file each record originated from.
The tool uses a single delimiter setting for all files. If your files use different delimiters, you may need to standardize them first using the CSV Formatter tool.
A CSV File Merger is a tool that combines multiple CSV (Comma-Separated Values) files into a single unified file by stacking their rows vertically. This is one of the most common data preparation tasks in analytics, reporting, and data engineering workflows. Whether you have monthly exports from a CRM, daily logs from a server, or segmented datasets from different departments, a CSV merger saves you from manually copying and pasting data across files.
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The merging process is straightforward: each CSV file is parsed into rows and columns. The tool reads the header row of each file and aligns columns by name. This means even if File A has columns in the order name, email, age and File B has email, name, age, the output will correctly align each column. Rows from all files are then concatenated vertically into one large dataset with a single header row at the top.
Column alignment by name is critical for reliable merges. Without it, a naive row-level concatenation can mix up values from different columns when the order differs between files — a common mistake when merging manually in Excel.
CSV merging is useful in a wide range of scenarios:
When merging CSVs from overlapping time periods or redundant exports, duplicate rows are a common problem. The deduplication option in this tool performs an exact-match comparison across all columns in a row. If two rows are identical in every field, only the first occurrence is retained. This is a simple but effective way to clean the merged output before further processing.
Note that exact deduplication may not catch near-duplicates with minor formatting differences (e.g., different capitalization or trailing whitespace). For more advanced deduplication, consider post-processing the merged file in Python with pandas or in a SQL environment.
While "CSV" stands for Comma-Separated Values, many files use other delimiters. Semicolons are common in European locales where commas are used as decimal separators. Tabs are used in TSV (Tab-Separated Values) files. Pipes (|) appear in legacy data exports. This tool supports all four common delimiter types. Be sure to select the correct delimiter before merging — using the wrong one will result in the entire row being treated as a single column.
The "Add source filename column" option appends a _source column to the merged output. Each row in this column contains the name of the file it originated from. This is invaluable when you need to trace which records came from which file after merging — for auditing, debugging, or reporting purposes. The source filename is taken directly from the uploaded file's name, so naming your files descriptively (e.g., sales_jan_2024.csv, sales_feb_2024.csv) makes this feature most useful.
CSV remains the most universal format for tabular data exchange precisely because of its simplicity. Compared to Excel XLSX files, CSVs are plain text — they can be opened by any text editor, processed by any programming language without a special library, and version-controlled in Git. For merging purposes, CSV's flat structure makes row concatenation trivial. If your source files are in Excel format, export them as CSV first before using this tool.
This tool handles CSV merging entirely in the browser using the FileReader API and JavaScript. For typical use cases — a few files totaling a few megabytes — this is fast and efficient. For very large files (50MB+), browser-based processing may be slower due to memory constraints. In those cases, command-line tools like cat on Linux/macOS or Python's pandas.concat() may be more appropriate.