CSV Data Profiler
Drop a CSV and get an instant data-quality report — inferred types, null rates, distinct counts, statistics, duplicate detection, and an overall quality score out of 100. Delimiter and headers are auto-detected.
Your CSV never leaves your browserDrag & drop a .csv file here, or
Data Quality Score
| Column | Type | Nulls | Distinct | Min / Max | Mean | Length | Top values | Flags |
|---|
About this CSV data profiler
Before you load a CSV into a warehouse, hand it to a stakeholder, or trust it in a report, you want to know its shape: which columns have missing values, whether that "amount" column is really numeric, how many duplicate rows are hiding in it, and whether the whole thing is clean enough to ship. This free online CSV data profiler answers those questions in seconds — entirely in your browser using the fast, streaming PapaParse parser, with no upload and no signup. It auto-detects the delimiter (comma, semicolon, or tab) and whether the first row is a header, then builds a per-column report with inferred types, null counts and percentages, distinct counts, min/max/mean for numbers, min/max length for text, the five most frequent values, and data-quality flags such as mixed types and stray whitespace.
It's built for data engineers, analysts, and QA engineers doing quick data-quality
checks and CSV validation — the kind of profiling you'd otherwise script with pandas
df.describe() or ydata-profiling, but instant and private.
The headline is a single 0–100 data quality score combining
completeness, type consistency, and duplicate rate, so you can eyeball a file's health
(and screenshot it) at a glance.
FAQ
Is my CSV uploaded anywhere?
No. Parsing happens in your browser with JavaScript. The file's contents are never sent to a server, which makes this safe for sensitive or internal data.
How is the data quality score calculated?
It's a weighted blend of three signals: overall completeness (how few cells are null), type consistency (how cleanly each column fits a single inferred type), and the duplicate-row rate. Each contributes to a 0–100 score, so a file that's complete, consistently typed, and free of duplicate rows scores near 100.
Which delimiters and encodings are supported?
Comma, semicolon, and tab delimiters are auto-detected, and header detection is automatic. Standard UTF-8 text files work best; the parser handles quoted fields and embedded newlines.
Can it profile large CSV files?
Yes — PapaParse streams the file in chunks so it doesn't load everything into memory at once. Very large files (hundreds of MB) will take longer and depend on your device's memory, but small and medium files profile almost instantly.
Independent community tool. Powered by the open-source PapaParse library — runs entirely on your device.