CSV splitter — break large files into parts
Split a big CSV into smaller files by row count. Headers are preserved in every part, and the file never leaves your browser.
Click to upload or drag and drop
Drop your CSV, Excel, or JSON file
Max 10MB
Runs 100% in your browser — this page makes no network request with your data.
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Drop your CSV file and choose how many rows each part should contain.
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Download the parts you need — every part keeps the header row.
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Or skip the splitting entirely: the workspace analyzes large files directly.
Worked example: splitting a 40,000-row export
An operations manager pulls a 40,000-row customer list out of the company ERP and needs to load it into an email platform that caps each import at 5,000 rows. Rather than copy-paste blocks in a spreadsheet, they drop the CSV onto the splitter and set 5,000 rows per part.
The tool produces eight downloadable files, and every part begins with the original header row, so each one imports cleanly on its own without manually re-adding column names. Because the split runs in the browser, the customer records never leave the manager's machine, which matters for a list full of names and email addresses.
If the real goal is analysis rather than a size-limited upload, the manager can skip splitting altogether: clicking through to the workspace analyzes the full 40,000 rows at once, then turns the result into a shareable report with charts of customers by region or signup month.
Splitting a CSV means breaking one large file into several smaller files that are each valid on their own, most often so they fit an upload limit or open in a program that chokes on the full size. To split a CSV, choose how many rows go in each part; a good splitter copies the header row to the top of every part so each one imports cleanly.
The part people get wrong is the header. A split that leaves parts two, three, and four without column names will fail every import after the first. The tool above puts the header on all of them, in your browser, with nothing uploaded.
When you actually need to split a CSV
You split a CSV to get under a limit, not for its own sake. The limit is usually one of a few familiar walls, and knowing which one you have hit tells you what row count to split on.
- Import row caps: many CRMs, email platforms, and ad tools cap a single import at 1,000, 5,000, or 10,000 rows. Split to that exact number and each part uploads in one pass.
- Excel's ceiling: a worksheet holds 1,048,576 rows, and Excel silently drops anything beyond that. Split a larger export into sub-million-row parts so nothing disappears.
- Attachment and app size limits: email attachments, ticket systems, and some web forms reject files over a few megabytes. Splitting by row count also brings each part under a size limit.
- Distribution by segment: you may need one file per region, per rep, or per month to hand to different owners, which is a split by column value rather than by count.
Ways to split a CSV, compared
There are four common ways to split a CSV by row count, and they trade convenience against control. The browser tool and the command line sit at opposite ends: one is point-and-click with headers handled for you, the other is scriptable but leaves the header work to you.
| Method | How | The catch |
|---|---|---|
| Browser splitter (this page) | Drop the file, set rows per part, download | Header kept on every part automatically; runs client-side |
| Command line (split) | split -l 5000 file.csv part_ | Fast, but the header lands only on the first part; you must re-add it to the rest |
| Python (pandas) | Read in chunks, write each with header=True | Full control, but you write and run a script |
| Excel, by hand | Copy blocks of rows into new sheets, save each as CSV | Tedious, error-prone, and cannot exceed 1,048,576 rows to begin with |
Split by row count versus by column value
These are two different jobs. Splitting by row count makes equal-sized chunks to fit a limit; splitting by column value groups rows that share a key, one file per region or per month, so each recipient gets only their slice.
Row-count splitting is what the tool above does, and it is the right choice for upload caps and file-size problems. Column-value splitting is a grouping operation: it depends on which column defines the groups and how many distinct values it has.
How to split by a column value
A per-value split is really a filter-and-export repeated for each group. In pandas that is a groupby over the key column, writing each group to its own file. Rather than script it, you can send the file to the workspace, ask for it broken out by that column, and get the grouped result plus a summary of how many rows fell into each group, which is usually the real question behind the split.
Gotchas that quietly corrupt a split file
A split can produce files that look fine but break on import. Four issues cause almost all of it, and every one traces back to treating a CSV as plain lines of text rather than structured records.
- Missing headers: any method that just cuts the file into line ranges gives the header to part one and leaves the rest headerless. Each part must start with the column names or it imports as anonymous columns.
- Quoted newlines: a field wrapped in quotes can legally contain a line break, so a naive split on newline characters cuts a record in half, corrupting the last row of one part and the first of the next. A real CSV parser counts records, not lines.
- Encoding drift: if the original has a UTF-8 byte-order mark, splitting by lines puts it only on the first part; the others may be misread. Keep the encoding consistent across every part.
- Delimiter assumptions: a semicolon-delimited export split by a comma-aware tool can miscount fields. Confirm the delimiter before splitting.
After splitting: reading and recombining the parts
Once split, each part is a standalone CSV you can open in the CSV viewer to spot-check that the header and columns line up. If you later need the pieces back as one file, the CSV merger recombines them and takes the union of columns, so a split-then-edit-then-recombine round trip does not lose data even if the parts drifted apart.
Think of splitting as a transport step. The parts exist to clear an upload wall; the moment they are past it, they are meant to become whole data again in whatever tool consumes them.
Frequently Asked Questions
Everything you need to know about using AnalyzeData.
Usually to fit an upload limit — many tools cap imports at 1,000–10,000 rows — or to distribute a large export across teams. Each generated part is a valid standalone CSV with headers.
Yes — every part starts with the original header row, so each file imports cleanly anywhere.
No. Parsing and splitting run in your browser; download links are generated locally from memory.
Set the rows-per-part value to your destination's cap; for example, 2,000 for a tool that limits imports to 2,000 rows, then download the parts. Each part is a complete, valid CSV with the header row included, so it imports cleanly on its own. The whole split happens in your browser, so a large or sensitive export never gets uploaded.
Yes. You do not need a spreadsheet macro, a pandas script, or a terminal command; drop the file in, choose a row count, and download the parts. It runs client-side in the browser, so it handles files too big to comfortably open in Excel, and nothing about your data is transmitted while it splits.
Match the destination's limit exactly. If a tool caps imports at 5,000 rows, split at 5,000 so each part fills one upload without going over. If you are splitting to fit a file-size limit rather than a row cap, start smaller, around 20,000 to 50,000 rows per part, and adjust based on how wide your columns are, since more columns mean fewer rows per megabyte.
It should not. A correct split copies every row exactly once across the parts and adds the header to each, so concatenating the parts reproduces the original. Data is only lost when a splitter cuts on line breaks and slices through a quoted field, or when it forgets to repeat the header. A record-aware splitter avoids both.
You can, but row count is more predictable. Rows vary in length, so a fixed-size split can leave the last record of a chunk cut in the middle unless the tool is careful to break only between records. If your real constraint is a size limit, pick a conservative row count that keeps each part comfortably under it rather than splitting on raw bytes.
Use a tool that streams the file rather than loading it whole. A browser splitter reads the file in chunks on your machine, so a multi-million-row export that Excel refuses to open fully still splits into parts you can then open individually. The command-line split utility works too, but you will have to re-add the header row to every part after.
When a table isn't enough
The workspace runs verified AI analysis on the same file and turns the results into a report you can send.
Analyze this data instead