penwings 0.3.0.dev3

Lightweight library to handle data and reproduce workflows

Penwings

Penwings is a lightweight Python library for building reproducible data workflows.

It provides simple, composable tools to:

• manage project structure
• standardize data access
• cache SQL queries efficiently

The goal is to reduce boilerplate and make data pipelines faster, cleaner, and reproducible by default.

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✨ Features

🗂️ Project Structure Management

• Standardized folder setup for data science projects
• Automatic project root detection
• Flexible, extensible path system

🧠 SQL → Parquet Caching

• Execute SQL queries via SQLAlchemy
• Automatically cache results as Parquet
• Reuse cached data to avoid unnecessary database hits
• Configurable refresh logic

⚡ Lightweight & Modular

• Minimal core dependencies (pandas, numpy)
• Optional SQL support
• Designed to scale into larger workflows

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📦 Installation

pip install penwings

Optional SQL support

pip install penwings[sql]

Requires Python 3.11+

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🚀 Quick Start

1. Project structure

from penwings import ProjectPaths

paths = ProjectPaths()

print(paths.data)
print(paths.models)

Creates a standardized structure like:

configs/
data/
  raw/
  processed/
  external/
features/
logs/
models/
notebooks/
reports/
  figures/
  tables/
sql/

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2. SQL caching

from sqlalchemy import create_engine
from penwings import SQLParquetCache

engine = create_engine("sqlite:///example.db")

cache = SQLParquetCache(
    sql_dir="sql",
    parquet_dir="cache",
    conn=engine,
    refresh_days=1
)

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📊 Usage

Using SQL files

df = cache.get("sales.sql")

• Loads from Parquet if cached
• Otherwise executes SQL and caches result

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Using raw SQL

query = "SELECT * FROM sales WHERE month = '2026-02'"

df = cache.get(
    sql=query,
    parquet_name="sales_feb"
)

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Cache behavior

df = cache.get("sales.sql", force=True)

• force=True → always re-run SQL

• refresh_days=N → cache expires after N days

• returns:

  • DataFrame

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🧩 ProjectPaths

Create only specific parts of a project:

paths = ProjectPaths(folders=["data", "ml"])

Custom directories:

paths = ProjectPaths(
    custom_dirs={
        "modules": "src/modules",
        "views": "src/views"
    }
)

Access paths:

paths.data
paths["models"]
paths.as_dict()

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🧠 Design Philosophy

Penwings is built around a few core ideas:

• Reproducibility first → deterministic data access via caching
• Convention over configuration → sensible defaults for structure
• Composable building blocks → small tools that work well together
• Lightweight core → no heavy framework overhead

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🛣️ Roadmap

• Pipeline abstraction (data workflows as steps)
• Improved SQL utilities and query management
• Integration with feature engineering workflows
• Better caching strategies and metadata tracking

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🔢 Versioning

Penwings follows semantic versioning:

• MAJOR → breaking changes
• MINOR → new features
• PATCH → bug fixes

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a branch (feature/my-feature)
3. Commit your changes
4. Open a pull request

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📄 License

MIT License — see LICENSE

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💡 Example Workflow

from sqlalchemy import create_engine
from penwings import ProjectPaths, SQLParquetCache

# Setup project structure
paths = ProjectPaths()

# Setup SQL cache
engine = create_engine("sqlite:///example.db")

cache = SQLParquetCache(
    sql_dir=paths.sql,
    parquet_dir=paths.data,
    conn=engine
)

# Load data
df_sales = cache.get("sales.sql")

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⭐ Why Penwings?

Penwings sits between:

• ad-hoc scripts ❌
• heavy frameworks ❌

It gives you just enough structure to:

• stay organized
• move fast
• keep workflows reproducible

without getting in your way.