Loading tool…
Loading tool…
Upload a CSV and inspect column types, missing values, uniques and numeric stats.
Upload a CSV and inspect column types, missing values, uniques and numeric stats.
Nextooly’s CSV Column Analyzer helps you understand the structure and quality of your CSV data in seconds. Upload a file or paste raw CSV, and the tool automatically detects column types (number, integer, text, boolean, date, or mixed), counts missing and non-empty values, computes unique counts, and summarizes numeric columns with min, max, and mean statistics.
You can optionally normalize missing markers like NA/NULL/N/A, toggle header detection, and control the maximum number of analyzed rows to keep performance smooth for large datasets. Everything runs fully in your browser using PapaParse and JavaScript, so your data never leaves your device.
Example
Input: The source text, file, or settings you want to work with.
Output: A clean csv column analyzer result ready for the next step.
If CSV Column Analyzer is close but not quite the right fit, these related Nextooly tools cover adjacent data & analysis workflows without sending you to another service.
Best if you need to clean messy CSV files: trim whitespace, normalize headers, remove empty/duplicate rows and reformat safely in your browser.
Best if you need to convert CSV to JSON or JSON to CSV with delimiter, header, flattening, and preview controls in your browser.
Best if you need to analyze CSV columns, stats, and distributions.
Does this CSV analyzer upload my data to any server?
No. All parsing, inspection, and column analysis are done entirely in your browser using PapaParse and JavaScript. Your CSV content never leaves your device, making the tool fully private and secure.
Why does the tool limit analysis to a maximum number of rows?
For performance and stability, the tool analyzes only the first N rows (default 2000). CSV files can be very large, and scanning everything at once might slow down your browser. You can increase the limit up to 10,000 rows if your device can handle it.
How does the tool detect data types like number, integer, date, or boolean?
The analyzer reviews each non-empty value in a column and checks if it looks like a number, integer, boolean (true/false/yes/no), or date. If a majority of values match a type, the column is classified accordingly. Mixed or inconsistent data results in a 'mixed' type.
Why are NA, null, or N/A values sometimes treated as empty?
If the 'Treat NA/NULL/N/A as empty' option is enabled, values like 'NA', 'null', 'none', or 'N/A' are normalized to blank cells. This helps clean inconsistent missing-data markers in messy datasets.
What does the tool show for numeric columns?
For numeric or integer columns, the tool computes minimum, maximum, mean (average), and also counts unique values. All numeric detection is done after trimming whitespace and normalizing the cell values.
What happens if my CSV does not include headers?
If the 'First row contains headers' option is disabled, the tool auto-generates column names as 'Column 1', 'Column 2', etc. This is useful for raw CSV exports or inconsistent data sources.
Why do I see the error 'Failed to parse CSV'?
This usually happens if the CSV contains mismatched columns, broken delimiters, invalid encoding, or if the chosen delimiter is incorrect. Switching to Auto-detect or choosing the correct delimiter often fixes the issue.
Why is only part of my CSV analyzed?
The tool uses PapaParse’s 'preview' option to load only the first chunk of rows (as configured). This keeps the UI responsive, especially for very large files. The scanning cap can be increased from the Advanced Settings panel.
Can I paste CSV instead of uploading a file?
Yes. If you paste CSV content in the text box, the tool analyzes that instead of an uploaded file. This is useful for quickly testing or cleaning small data extracts.
2026-03-10
Related category
Related comparison/alternatives article