CSV Data Profiler
Understand a dataset before you use it. The profiler reads CSV or JSON and, for every column, infers the type, measures how complete it is, counts distinct values, shows ranges and means for numbers and dates, lists the most common values and flags data quality problems: missing values, mixed types, leading or trailing spaces, outliers and duplicate rows. Numeric columns get a histogram.
- Runs in your browser
- No sign-up
- Free to use
Processed entirely in your browser – your data is not uploaded. Files up to 50 MB.
How to use CSV Data Profiler
- Paste data or open a file.
- Click Profile data.
- Read the column table and findings.
- Fix issues with the cleaning tools.
CSV Data Profiler features
Type inference
Integer, decimal, date, email, URL, boolean, text.
Completeness
Filled share per column.
Quality findings
Mixed types, spaces, outliers, duplicates.
Histograms
For numeric columns.
CSV and JSON
Same report for both.
Private
Runs in your browser; nothing is uploaded.
When to use CSV Data Profiler
- Checking exports before analysis.
- Data migration projects.
- Reviewing third-party data.
- Teaching data quality.
CSV Data Profiler FAQ
How are outliers found?
Values outside 1.5 × the interquartile range (Tukey’s rule).
What counts as missing?
Empty cells and placeholders like NULL, N/A or “-”.
How big can the file be?
Up to 50 MB, depending on your device.
Profile first, analyse second
Most analysis errors come from data that is not what you assumed. A two-minute profile shows gaps, wrong types and duplicates before they reach a report.
Private by design: your data is read and processed entirely in your browser with JavaScript. Nothing is uploaded to our server, so you can work with customer lists, exports and internal reports safely. Files up to 50 MB are supported; very large files depend on your device’s memory.
Input formats: paste or open CSV (comma, semicolon, tab or pipe – detected automatically), TSV or JSON (an array of objects, an object with a data, items or results array, or JSON Lines). The first CSV row is used as the header; JSON keys become columns, and nested values are kept as JSON text.
Missing values are recognised consistently across the data tools: empty cells and common placeholders such as NULL, N/A, NA, NaN, none, “-” and undefined. Types are inferred from the values – integer, decimal, boolean, date, email, URL, JSON or text – and columns with mixed types are flagged.
Related tools on this site cover the rest of a data workflow: the CSV cleaner, viewer, splitter and merger, converters between CSV, JSON, Excel and SQL, the JSON formatter and validator, and the other profiling, validation and statistics tools in this group.
Who it is for: analysts preparing data for a report, developers checking an API export or a database dump, marketers cleaning contact lists before an import, and students learning data quality. No account, no installation and no spreadsheet formulas are needed.