Generate a full data profile report for every column in your CSV
Automatically profile every CSV column: type inference, null rate, unique count, min/max, mean, and top-5 values.
🛡️ Your rows never leave your browser.
Why Onfyt: Produces pandas-profiling-style output entirely in-browser—no Python environment or server upload required.
Interactive Sample Data
| id | name | revenue | region |
|---|---|---|---|
| 1 | Grace Kerubo | 4500 | Nairobi |
| 2 | Peter Njoroge | 3200 | Mombasa |
| 3 | Mercy Akinyi | 5100 | Nairobi |
How this tool works
Data profiling is the first step in any serious data quality initiative. Before cleaning, transforming, or modelling a dataset, analysts need to understand its shape: how many unique values each column has, what the distribution of numeric columns looks like, which columns are mostly empty, and whether the inferred type matches the expected type.
DataFix generates a compact profile for every column covering: inferred type, null count and percentage, unique value count, min/max (for numerics and dates), mean, and the top 5 most frequent values. The result is a JSON report you can save and version alongside your pipeline.
Before beginning data engineering or analysis, obtaining an automated data quality profile reveals structural health, data types, null rates, and distribution statistics. DataFix's automated CSV profiler analyzes column datatypes, counts total rows and columns, detects empty cell ratios, measures distinct value counts, and computes min, max, median, and quartile distributions for numeric columns. All profiling calculations execute locally inside your browser within seconds, delivering deep insights into your dataset.
Automated profiling delivers essential baseline health metrics for tabular files, calculating sparsity percentages, uniqueness ratios, inferred data types, and min/max distributions across each column. This profiling report allows analysts to spot schema drifts and anomalies before writing ingestion transformations.
Things that usually go wrong
- Profiling very wide files (200+ columns) may be slow on older browsers.
- Columns with mixed types (some rows numeric, some text) are reported as 'mixed'.
Edge cases we tested
🧪 Column with 100% null values
Flagged as empty column candidate; type listed as 'unknown'.
🧪 Numeric column with 1 outlier at 10× median
Mean and median both reported so the skew is visible.
🧪 Free-text column with high cardinality (10,000 unique values)
Top-5 listed; unique count reported; word frequency skipped for performance.
Frequently Asked Questions
What statistics are computed for numeric columns?
Min, max, mean, median, standard deviation, and count of nulls.
What is reported for text columns?
Most common values (top 5), total unique count, null count, and average string length.
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