meti-profil 0.1.0

Modern, fast data profiling in Rust with Python bindings

meti_profil

A modern, Rust-powered data profiling library with Python bindings. It reads CSV, Parquet, and Excel files (or pandas / polars DataFrames) and generates a hybrid Markdown report that is readable by humans and structured for consumption by code agents.

Installation

pip install meti_profil

Quick start

import meti_profil as mp

# From a file
report = mp.ProfileReport("data.csv", title="My dataset")

# Interactive HTML report (self-contained, works offline)
report.to_html("profile.html")

# Markdown report (great for diffs and code agents)
report.to_file("profile.md")

# From a pandas DataFrame
import pandas as pd
df = pd.read_csv("data.csv")
report = mp.ProfileReport(df)

# Programmatic access
print(report.get_summary())          # dataset-level metrics
print(report.get_column_info("age")) # per-column schema info
markdown = report.to_markdown()
html = report.to_html()              # returns the HTML as a string

In a notebook

In Jupyter / VSCode, just display the report — it renders inline as an interactive dashboard (sandboxed, no external resources):

report = mp.ProfileReport(df)
report  # interactive histograms, bar charts, correlation heatmap, ...

ProfileReport parameters

Parameter	Type	Default	Description
source	str, Path, pandas/polars DataFrame	required	Data source.
title	str	"Dataset Profile"	Report title (written to the frontmatter).
minimal	bool	False	Reserved: reduce heavy analyses.
explorative	bool	True	Reserved: enable advanced analyses.

Report format

The Markdown report starts with a YAML frontmatter block (rows, columns, missing cells, duplicates, version) followed by normalized ## sections: Overview, Schema, Numeric Columns, Categorical Columns, Missing Values, Duplicate Rows, and Correlations.

Features

• Fast Rust engine backed by Apache Arrow.
• Reads CSV, Parquet (snappy/zstd/lz4/brotli/gzip), and Excel files.
• Accepts pandas and polars DataFrames.
• Schema/type detection, descriptive numeric statistics, categorical frequencies, missing-value and duplicate-row analysis, and Pearson correlations.
• Interactive HTML report: a single self-contained file (embedded CSS/JS, no CDN) with histograms, categorical bar charts, a missing-value overview and a correlation heatmap — all with hover tooltips.
• Native notebook rendering in Jupyter / VSCode via _repr_html_.
• Clean Markdown reports optimized for both humans and code agents.

Output formats

Method	Output
to_html(path)	Write a self-contained interactive HTML file.
to_html()	Return the HTML document as a string.
to_file(path)	Write the Markdown report.
to_markdown()	Return the Markdown report as a string.
get_summary()	Dataset-level metrics as a dict.
get_column_info(name)	Per-column schema info as a dict.
display in a notebook	Inline interactive dashboard (_repr_html_).

Development

Requires a Rust toolchain (1.78+) and Python 3.10+.

python3 -m venv .venv
source .venv/bin/activate
pip install maturin pytest pandas polars pyarrow

# Build the extension in-place
maturin develop

# Run the test suites
cargo test --workspace
pytest tests/python -v

License

MIT