microeda 0.5.0

Ultra-lightweight micro EDA (exploratory data analysis) tool for small datasets

🚀 microeda

microeda is an ultra-lightweight Python library for Exploratory Data Analysis (EDA) on small datasets (<10k rows). Get deep insights into your data instantly—detect column types, summarize statistics, spot missing values & outliers, and explore relationships.

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✨ Features

• Automatic Column Typing: numeric, categorical, boolean, datetime, text, ID.
• Smart Summaries:
  • Numeric: mean, median, quartiles, missing values, outliers
  • Categorical: unique counts, top values
  • Datetime: min, max, range, missing
  • Text: token counts, top words
• Missing Data Analysis:
  • Column-level missing percentages
  • Pairwise missing correlations
• Outlier Detection:
  • IQR method
  • Z-score method
• Pairwise Relationships:
  • Pearson for numeric
  • Mutual Information & Cramer's V for categorical
• CLI Support: Generate Markdown or HTML reports
• Dependency-light: Only pandas & numpy required, optional rich for pretty CLI
• Semi-structured Data Support: Detect columns with JSON-like or list-like structures

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📦 Installation

From PyPI:

pip install microeda

Or install from source:

git clone https://github.com/SaptarshiMondal123/microeda.git
cd microeda
pip install .

Usage

Python API

import pandas as pd
from microeda import analyze

df = pd.read_csv("your_data.csv")
report = analyze(df, name="My Dataset")

# Inspect your data
print(report["column_types"])
print(report["summaries"])
print(report["missingness"])
print(report["pairwise_hints"])

…will only give you raw dicts, no table.

If you want a readable table like the demo output, you should do:

from microeda import analyze_table

analyze_table(df, name="My Dataset")

CLI

Generate a Markdown report directly from the terminal:

microeda path/to/data.csv --style md --out report.md
microeda path/to/data.csv --style html --out report.html

Options:

--style: md (Markdown) or html (HTML)

--out: output file path

🌟 Example Output

Dataset: 100 rows x 5 cols

Column Summary:

Column	Type	Unique	Missing	Sample Stats
Age	numeric	30	0	mean=29.8
Gender	categorical	2	5	Male:55, Female:40
Name	text	95	0	avg_tokens=2
Salary	numeric	50	2	mean=55000
City	text	10	0	avg_tokens=1

Contributing

Contributions are welcome! Feel free to submit issues or pull requests.

• Fork the repo

• Create a new branch (git checkout -b feature-name)

• Make your changes

• Run tests (pytest)

• Submit a pull request

License

MIT License © 2025 Saptarshi Mondal

Links

GitHub: https://github.com/SaptarshiMondal123/microeda

PyPI: https://pypi.org/project/microeda/