databeacon

A Python library for exploratory data analysis, data cleaning, feature engineering, and visualization.

Installation

pip install databeacon

Or install from source:

git clone https://github.com/Adityasharma-6782/databeacon.git
cd databeacon
pip install -e ".[dev]"

Quick Start

import pandas as pd
from databeacon import (
    summarize, describe_numerics, describe_categoricals, correlation_matrix,
    drop_duplicates, handle_missing, remove_outliers, fix_dtypes,
    encode_categoricals, scale_features, apply_transforms,
    plot_distributions, plot_correlations, plot_missing,
)

df = pd.read_csv("your_data.csv")

# ── EDA ──────────────────────────────────────────────────────────
info = summarize(df)
print(info["shape"], info["missing"])

num_stats = describe_numerics(df)
cat_stats = describe_categoricals(df)
corr = correlation_matrix(df, method="spearman")

# ── Cleaning ─────────────────────────────────────────────────────
df = drop_duplicates(df)
df = handle_missing(df, strategy="mean")          # or "median", "mode", "drop", "constant"
df = remove_outliers(df, method="iqr")            # or "zscore"
df = fix_dtypes(df, datetime_cols=["date_col"])

# ── Feature Engineering ──────────────────────────────────────────
df = encode_categoricals(df, method="onehot")     # or "label", "ordinal"
df = scale_features(df, method="standard")        # or "minmax", "robust"
df = apply_transforms(df, columns=["price"], method="log")

# ── Visualization ────────────────────────────────────────────────
plot_distributions(df)
plot_correlations(df)
plot_missing(df)

Modules

databeacon.eda

databeacon.cleaning

databeacon.features

databeacon.viz

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Run tests with coverage
pytest tests/ --cov=databeacon --cov-report=term-missing

# Lint
ruff check databeacon/

# Format
black databeacon/ tests/

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/my-feature
3. Commit your changes: git commit -m "Add my feature"
4. Push to the branch: git push origin feature/my-feature
5. Open a Pull Request

Please add tests for any new functionality and ensure all tests pass.

License

MIT — see LICENSE for details.