Fast, Polars-native data profiling with interactive HTML reports and data quality alerts.
Project description
dataxid-profiling
Fast, Polars-native data profiling with interactive HTML reports and data quality alerts.
Quickstart
import polars as pl
from dataxid_profiling import ProfileReport
df = pl.read_csv("data.csv")
report = ProfileReport(df)
report.to_html("report.html")
Pandas works too:
report = ProfileReport(pd.read_csv("data.csv"))
Report Preview
Dataset overview — row/column counts, missing cells, duplicates, memory usage, and column type distribution at a glance.
Column details — per-column statistics, top value distribution, and word clouds for categorical data.
Correlations — interactive heatmap showing relationships between numeric columns.
Interactions — scatter plots for numeric pairs and box plots for categorical × numeric pairs, with dynamic column selection.
Highlights
- Built on Polars — fast, memory-efficient, Rust-powered
- 3 lines to profile any dataset
- Programmatic-first:
.to_dict(),.stats,.alerts - Interactive HTML reports with ECharts
- Accepts Polars, Pandas, CSV, and Parquet
- 5 column types: numeric, categorical, boolean, datetime, text
- 7 data quality alerts out of the box
- 5 correlation types: Pearson, Spearman, Kendall, Cramér's V, Phi K
- Interactions: scatter plot + box plot with dynamic column selection
- Two modes:
"complete"for deep analysis,"overview"for speed - Fully typed
Installation
pip install dataxid-profiling
Usage
Programmatic access
report = ProfileReport(df, title="Customer Data Profile")
stats = report.to_dict()
alerts = report.alerts
column_stats = report.stats["age"]
correlations = report.correlations
JSON export
report.to_json("report.json")
Configuration
from dataxid_profiling import ProfileReport, ProfileConfig
config = ProfileConfig(
title="Customer Data Profile",
mode="overview",
missing_threshold=0.1,
histogram_bins=30,
)
report = ProfileReport(df, config=config)
Modes
| Feature | "complete" |
"overview" |
|---|---|---|
| Basic stats | ✓ | ✓ |
| Histograms & value counts | ✓ | ✓ |
| Correlations | ✓ | ✗ |
| Interactions | ✓ | ✗ |
| Character analysis | ✓ | ✗ |
| Duplicate rows table | ✓ | ✗ |
Output formats
| Format | Method | Use case |
|---|---|---|
| HTML | report.to_html("report.html") |
Interactive report |
| JSON | report.to_json("report.json") |
Machine-readable |
| Dict | report.to_dict() |
Python-native |
Contributing
Contributions are welcome. See CONTRIBUTING.md for details.
Links
License
Release history Release notifications | RSS feed
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