Null Value Analyzer
See exactly where data is missing. The analyzer counts empty cells and placeholders (NULL, N/A, NaN, “-”, plus your own such as “unknown”) per column, shows how they were written, how many rows are complete and which combinations of missing columns occur together. Export a CSV with all placeholders made empty, without incomplete rows, or without columns that are more than half empty.
- 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 Null Value Analyzer
- Paste data or open a file.
- Add extra placeholders (optional).
- Review columns and patterns.
- Export a cleaned CSV if needed.
Null Value Analyzer features
Per-column counts
With share and bar.
Placeholders
Shows how missing values were written.
Patterns
Which columns are missing together.
Clean exports
Blank, drop rows or drop columns.
JSON keys
Absent keys count as missing.
Private
Runs in your browser; nothing is uploaded.
When to use Null Value Analyzer
- Preparing data for analysis or machine learning.
- Checking CRM exports.
- Survey data cleaning.
- Data migration QA.
Null Value Analyzer FAQ
Should I delete rows with missing values?
Only if few rows are affected; otherwise you may bias the data.
Why do patterns matter?
Values missing together often share a cause, such as an optional form section.
Is “0” missing?
No, unless you add it as a placeholder.
Missing is information
How data is missing tells you about the process that produced it.
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.