Count distinct values in every CSV column
Count distinct values per CSV column and generate a value-count frequency table for each.
🛡️ Your rows never leave your browser.
Why Onfyt: Shows both the unique count summary and a detailed frequency table for selected columns in one operation.
Interactive Sample Data
| id | region | status |
|---|---|---|
| 1 | Nairobi | active |
| 2 | Mombasa | active |
| 3 | Nairobi | inactive |
How this tool works
Knowing the number of distinct values in each column quickly identifies: high-cardinality columns that might not be appropriate for GROUP BY (like free-text fields), categorical columns with a small number of valid states (like status), and potential data quality issues (like a 'country' column with 50 unique values where 'USA', 'US', 'United States' should be the same).
DataFix returns two outputs: a summary table with unique count per column, and an optional detailed value-frequency table for selected columns.
Measuring cardinality across categorical columns identifies dominant categories, unique customer identifiers, and low-variance flags essential for feature engineering and indexing.
Measuring distinct value counts across columns reveals data cardinality, identifying key dimensions for database indexing, categorical feature encoding, and data hygiene audits. Our cardinality counter computes total distinct values, unique ratios, and top frequencies directly in your browser tab.
High cardinality in categorical columns can exhaust memory in indexing systems and cause high-entropy issues in tree-based machine learning models. Our frequency analysis computes distinct ratios and entropy flags to guide database schema optimization.
Measuring column cardinality identifies distinct categorical attributes, low-variance flags, and primary key candidates. Understanding unique value ratios guides optimal database indexing and machine learning feature encoding. Distinct count reports highlight percentage distributions, revealing whether a column behaves as a unique identifier, categorical grouping, or static constant.
Things that usually go wrong
- Trailing spaces create duplicate unique values — 'Nairobi' and 'Nairobi ' appear as two distinct values.
- Case differences also split values — normalise case first.
Edge cases we tested
🧪 Case sensitivity ('Nairobi' vs 'nairobi')
Counted as different by default; case-insensitive mode available.
🧪 Column with all unique values (e.g., UUID column)
Unique count equals total row count; flagged as high cardinality.
Frequently Asked Questions
How is this different from Group By and Count?
Count Unique Values works on a single column and produces a full frequency table. Group By and Count aggregates multiple columns simultaneously.
Can I see only the top N values?
Yes — set 'Top N' to limit the output to the most frequent values.
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