CSV Statistics

What this tool does

This tool reads a CSV file and produces a statistical summary for every column at once. Drop a file in or paste the rows, and it works out which columns hold numbers and which hold text, then analyses each one appropriately. Numeric columns get a full descriptive summary — count, mean, median, mode, minimum, maximum, range, standard deviation and the quartiles Q1 and Q3 — plus a count of any blank or non-numeric cells. Text columns get the total cell count, the non-empty count, how many distinct values there are, the most common value and how often it occurs, and the shortest and longest value lengths.

Each column is shown as its own card, labelled with the column header and a “numeric” or “text” badge, laid out in a grid. A summary line at the top tells you how many columns and rows the file has and how the columns split between numeric and text. The page loads with a sample product dataset so you can see the output straight away.

Why and when you would use it

A CSV export is often the first thing you get and the last thing you understand. Before you build a pivot table, import data into another system or hand a file to a colleague, it helps to know what is actually in it. This tool gives you that profile in seconds: the average and spread of every numeric column, the variety in every text column, and a count of missing values that flags data-quality problems early.

Marketers use it to sanity-check a campaign export — is the average click-through rate what you expected, how many distinct audiences are in the file. Operations and finance teams use it to profile an orders or inventory export before a report. Students and researchers use it to understand a dataset before analysis. It is the quick “what am I looking at” step that saves time later. If you then need to narrow the data down, the CSV Filter tool removes the rows you do not want, and the CSV Column Extractor pulls out just the columns you need. To dig into a single list of numbers in depth, the Statistics Calculator gives a fuller breakdown.

How to use it

1. Drop a CSV file onto the upload area, or paste the rows into the text box. Files are read on your device — nothing is uploaded.
2. If the columns look wrong, set the Delimiter. Auto-detect handles most files, but European exports often use semicolons and some use tabs or pipes.
3. Use the First row is a header toggle so your column names are read as names, not as data.
4. Read the summary line, then scan the column cards. Each one is badged numeric or text so you know which analysis applies.
5. Use Copy report to copy a plain-text summary of every column, or Download .txt to save it as column-stats.txt for your records.

Common pitfalls and tips

The most frequent surprise is a numeric column showing up as text. That happens when even one cell contains something that is not a number — a “N/A”, a currency symbol, a thousands separator like “1,200”, or a header row that was not skipped. The tool reports the blank or non-numeric count for each numeric column, so use that as a clue and clean the source cell if you want the full summary.

Watch out for Excel reformatting your data. Excel turns leading zeros in ZIP codes and SKUs into plain numbers, and may convert long ID numbers into scientific notation. If a column of identifiers looks numeric when it should be text, that is usually Excel’s doing — store IDs as text in the source file. Mixed delimiters and smart quotes pasted from a word processor can also confuse parsing; if a file looks scrambled, check the delimiter first.

Privacy

This tool runs entirely inside your browser. The CSV you drop or paste is read and analysed by JavaScript on your own device — nothing is uploaded, stored or logged, and nothing leaves your computer. When you clear the box or close the tab, the data is gone. Because CSV exports often carry customer and financial data, that means you can profile a confidential file here safely.