⚡ PyQuery Core: The Engine ⚙️

Pure Logic. Zero Fluff. The Backend of the Data OS.

🧠 The Brain Behind the Operation

PyQuery Core is the headless, high-performance ETL and Analytics engine that powers the PyQuery ecosystem.

Previously hidden inside a generic monorepo, it has now been extracted into its own pure-python library. It handles the heavy lifting: File I/O, Data Transformation, Statistical Analytics, and Machine Learning.

It has no UI. It has no CLI. It is just raw, unadulterated Polars power wrapped in a strict, type-safe architecture.

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⚡ Key Features

• 🚀 Lazy-First Architecture: Built on Polars LazyFrames. Nothing executes until you say so.
• 🛡️ Strict Type Safety: Every transform, every parameter, and every I/O operation is validated with Pydantic models. No more stringly-typed chaos.
• 🔌 Universal I/O:
  • Readers: CSV, Parquet, Excel, JSON, IPC.
  • Healers: Auto-detects encoding issues and "heals" broken CSVs on the fly.
• 🧪 Analytics Module:
  • Built-in scikit-learn integration for Clustering and Regression.
  • Automatic "What-If" simulation engines.
• 🔧 Transform Registry: A modular plugin system for registering data transformation steps.

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📦 Installation

pip install pyquery-core

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💻 Usage (The SDK)

This is a library for builders. Use it to construct your own data pipelines.

1. The Engine

The PyQueryEngine is the orchestrator.

from pyquery_core.core import PyQueryEngine
from pyquery_core.io.files import FileLoader

# Initialize
engine = PyQueryEngine()

# Load Data (Lazy)
df = FileLoader.read_csv("massive_data.csv")

# Register a Pipeline
pipeline = [
    {"type": "filter", "params": {"column": "revenue", "operator": ">", "value": 1000}},
    {"type": "group_by", "params": {"by": "region", "agg": {"revenue": "sum"}}}
]

# Execute
result = engine.run(df, pipeline)
print(result.collect())

2. Analytics & ML

Run complex statistical analysis without the boilerplate.

from pyquery_core.analytics.ml import ClusterEngine

# Auto-Clustering
model = ClusterEngine(data=df, n_clusters=3)
segments = model.fit_predict()
print(segments)

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📂 Architecture

The library is structured for modularity:

Module	Description
pyquery_core.io	Input/Output. Smart loaders for Excel, CSV, Parquet, and SQL.
pyquery_core.transforms	Logic. Atomic data manipulation steps (Filter, Sort, Mutate).
pyquery_core.analytics	Intelligence. Statistical tests, ML models, and forecasting.
pyquery_core.recipes	Orchestration. JSON-serializable pipeline definitions.
pyquery_core.jobs	Async Workers. Background task management for long-running ops.

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🤝 Contributing

This is the Core. Code quality here is paramount.

1. Fork it.
2. Branch it (git checkout -b feature/fancy-algo).
3. Test it. (If it breaks the engine, we break your PR).
4. Push it.

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📜 License

GPL-3.0. Open source forever. 💖

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Made with ☕, 🦀 (Rust), and 💖 by Sudharshan TK