CSV Explorer (YC F1) - Explore CSVs with Millions of Rows
Search, Aggregate, & Plot CSVs with millions of rows
CSV Explorer (YC F1) - Explore CSVs with Millions of Rows launched on April 5, 2017 with 60 votes, #632 of 3,007 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 9 years on.
In plain words
CSV Explorer is a tool for opening and analyzing large CSV files containing millions of rows. Users can search, filter, and manipulate data to find specific information or see broader patterns. The tool allows users to plot data, save graphs for presentations, and export results to Excel. It is designed for anyone working with large spreadsheets who needs quick analysis without extensive technical setup.
written from the facts on this page · September 2026
From the sources
A few rows or a few million rows, CSV Explorer makes opening and analyzing big CSV files quick and easy. CSV Explorer is simple to use. In a few clicks, filter, search, and manipulate data to find the needle in the haystack or to get the big picture. Plot data, save graphs for presentations, or export the results to Excel for furtherfrom csvexplorer.com
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CSV GB+ by Data.olllo – Open and Process CSVs Locally2025 · apps.microsoft.com · ▲51I built CSV GB+ by Data.olllo, a local data tool that lets you open, clean, and export gigabyte-sized CSVs (even billions of rows) without writing code. Most spreadsheet apps choke on big files. Coding in pandas or Polars works—but not everyone wants to write scripts just to filter or merge CSVs. CSV GB+ gives you a fast, point-and-click interface built on dual backends (memory-optimized or disk-backed) so you can process huge datasets offline. Key Features: Handles massive CSVs with ease — merge, split, dedup, filter, batch export Smart engine switch: disk-based "V Core" or RAM-based "P…
GS-Calc – A modern spreadsheet with Python integration2025 · citadel5.com · ▲112Process large (e.g. 4GB+) data sets in a spreadsheet. Load GB/32 million-row files in seconds and use them without any crashes using up to about 500GB RAM. Load/edit in-place/split/merge/clean CSV/text files with up to 32 million rows and 1 million columns. Use your Python functions as UDF formulas that can return to GS-Calc images and entire CSV files. Use a set of statistical pivot data functions. Solver functions virtually without limits for the number of variables. Create and display all popular chart types with millions of data points instantly. Suggestions…

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