Calculator Input
Paste CSV-style rows, choose a column, and measure distinct values with flexible cleaning rules.
Example Data Table
This sample matches the default pasted data and helps verify the calculator quickly.
| category | score | region |
|---|---|---|
| Alpha | 91 | North |
| Beta | 85 | South |
| Alpha | 91 | North |
| Gamma | 77 | East |
| beta | 88 | South |
| (blank) | 92 | West |
| Delta | 85 | North |
| Alpha | 91 | West |
Formula Used
Unique Count = | set(cleaned column values) |
This is the cardinality of the filtered value set.
Duplicate Entries = Counted Values − Unique Count
This shows how many repeated observations exist after cleaning.
Uniqueness Ratio = (Unique Count ÷ Counted Values) × 100
This measures how diverse the selected column appears.
In pandas terms, the core behavior mirrors Series.nunique(). Trimming, case handling, and blank exclusion change the effective set before cardinality is measured.
How to Use This Calculator
- Paste CSV-style rows into the dataset box.
- Keep the header option enabled if row one has labels.
- Enter the target column name or a column number.
- Select the delimiter that matches your pasted data.
- Choose whether spaces, case, and blanks should affect grouping.
- Set how many top values should appear on the graph.
- Click Count Unique Values to show results above the form.
- Download the summary as CSV or PDF if needed.
Frequently Asked Questions
1. What does this calculator actually count?
It counts distinct values in one selected column after optional cleaning. You can trim spaces, preserve or ignore case, and exclude blank or null-like entries before counting.
2. Does the tool behave like pandas nunique?
Yes, the main idea matches nunique(). The calculator also adds duplicate counts, missing counts, a uniqueness ratio, a frequency table, and a graph for quick inspection.
3. Can I choose a column by name?
Yes. Keep the header option checked and enter the column label. You can also enter a column number. Numbers start at 1 for easier manual selection.
4. What happens to blank cells?
Blank cells are always measured as missing entries. If the exclude blanks option is enabled, they are skipped. If disabled, blanks become a counted category.
5. Why does case sensitivity change the result?
Case-sensitive mode treats values like Alpha and alpha as different entries. Case-insensitive mode normalizes them, which often lowers the unique count and raises frequencies.
6. Why is trimming spaces useful?
Leading or trailing spaces can create false categories. Trimming turns values like “Beta” and “Beta ” into the same cleaned value, improving count accuracy.
7. What does the uniqueness ratio show?
It shows the percentage of distinct values among counted entries. Higher percentages suggest stronger diversity. Lower percentages suggest repeated labels or categorical concentration.
8. Can I export the results?
Yes. After calculation, use the CSV button for spreadsheet-friendly output or the PDF button for a clean portable summary with metrics and frequency rows.