CSV Pivot Table Builder

Excel and Google Sheets pivot tables are powerful but slow to set up and require uploading your data. This tool runs the same logic in your browser: paste a CSV (or drop a file), pick row / column / value columns and an aggregation function, and get a pivot table you can copy or download. No data leaves your machine.

How to use the CSV Pivot Table Builder

Paste a CSV with headers in the first row (or upload a file). Click Parse headers — the column pickers populate. Pick which column to break out by rows, by columns, and which numeric column to aggregate. The pivot table renders below.

For "Count", the value column doesn't need to be numeric; for sum / avg / min / max, non-numeric cells are skipped.

About CSV Pivot Table Builder

A pivot table is a 2-D cross-tabulation of a source dataset, where the rows are unique values of one column, the columns are unique values of another, and the cells are an aggregate (sum, count, etc.) of a third. For example, with a sales CSV (region, product, revenue), pivoting by region × product on sum(revenue) gives you a table showing how much each product made in each region — the kind of summary that takes 30 seconds in Excel and is much harder to eyeball from raw rows.

This implementation uses a streaming CSV parser (handles quoted fields with commas and embedded newlines), then groups data into a Map of Maps keyed by (row, col), applies the aggregation, and renders. For pivots with many unique column values, the table can get wide — the renderer adds horizontal scroll.

For very large CSVs (millions of rows), browser-based pivoting becomes slow — pandas (df.pivot_table) or DuckDB (PIVOT SQL syntax) are better suited. This tool is for the typical case of "I have a CSV with hundreds to tens of thousands of rows and want a quick cross-tab without uploading it anywhere."

Common use cases

  • Sales analysis — sum of revenue by region × product
  • Survey results — count of responses by question × demographic
  • Log analysis — count of errors by service × time bucket
  • Inventory — sum of stock by warehouse × SKU category
  • Anything you'd use a spreadsheet pivot table for — without leaving the browser tab

Frequently asked questions

How big a CSV can it handle?
Up to ~50K rows / 200KB stays snappy. Larger and the browser will get sluggish. For multi-MB CSVs, use a desktop tool.

Does it handle quoted CSV with embedded commas?
Yes — the parser handles RFC 4180 quoted fields including escaped quotes and newlines inside quoted values.

Can I aggregate by multiple columns at once?
Not in this tool — for multi-column aggregation, use a real query engine like DuckDB.