CSV Statistics Summary — Calculate Descriptive Analytics and Data Distribution
Paste CSV data to get per-column min/max/mean/null-count and automatic data type inference. Instantly profiles every column as integer, float, date, boolean, or string with fill rate, unique count, and mode. 100% client-side and free.
How to Use CSV Statistics Summary — Calculate Descriptive Analytics and Data Distribution
How to Use the CSV Statistics Summary
Step 1: Paste Your CSV
Paste any CSV data — with or without a header row — into the input panel. Statistics update instantly as you type.
Example input:
id,product,price,in_stock,created_at
1,Laptop,1299.99,true,2024-01-10
2,Monitor,449.99,true,2024-01-11
3,Keyboard,,false,2024-01-12
Step 2: Read the Per-Column Table
Each column gets a row in the statistics table:
| Metric | Description |
|---|---|
| Type | Inferred type: integer, float, boolean, date, or string |
| Fill rate | Percentage of non-empty cells with a progress bar |
| Nulls | Count of empty or missing values |
| Unique | Number of distinct values (cardinality) |
| Min / Max | Value range — numeric range for numbers, date span for dates, length span for strings |
| Mean | Average value (numeric columns only) |
| Mode | Most frequent value with occurrence count |
Type Inference Rules
- integer — all non-null values are whole numbers
- float — all non-null values are numeric with decimals
- boolean — all values are
true/false,yes/no,1/0,t/f, ory/n(case-insensitive) - date — all non-null values match
YYYY-MM-DD,DD/MM/YYYY, orYYYY-MM-DDTHH:MM:SSpatterns - string — fallback for mixed or text data
Tips
- Load the built-in examples to see how Sales Data, User Records, and Mixed Null datasets profile
- Null count is highlighted in amber when a column has missing values — useful for data cleaning
- The fill rate bar turns red when more than 20% of values are missing
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