Simplicity and conciseness of R,
with the blazing speed of Rust.

Chainable • Composable • Declarative • Semantic • Agent-Ready

pydplyr is a modern, expressive, and chainable data manipulation library built for humans and machines. Think:

• 🧠 Like dplyr in R
• ⚡️ With Python's ecosystem
• 🚀 On a path to a Rust-powered backend
• 🤖 Agent-ready for autonomous systems

Whether you're a data scientist, a developer building intelligent systems, or a machine 🤖 looking for clarity—this library speaks your language.

• ✅ Composable Verbs – clear, expressive syntax for fast prototyping & serious work
• 🧬 Chainable API – minimal boilerplate, max readability
• 🎨 Grammar of Graphics – familiar design for layered visualizations (Coming Soon!)
• 🔍 Simplified Regex – harness RegEx without the brain melt
• 🔗 Agent-First Thinking – semantically rich, logic-oriented operations
• 🦀 Rust Ambition – future versions will compile to Rust for blazing performance

Each verb is purpose-built and plays well with others. Start small, scale infinitely.

from pydplyr import *

result = (
    Panel(df)
    .arrange(desc("score"))
    .filter("score > 80")
    .mutate(score_plus_age="score + age")
    .select("name", "score_plus_age")
    .collect()
)

Intuitive, readable, chainable. One thought per line.

Just like in ggplot2, our graphics philosophy follows this layered system:

• Data – the DataFrame
• Aesthetics – x/y mappings, color, shape
• Geoms – bar, point, line, etc.
• Stats – transformations like count or smooth
• Facets – split plots by category
• Coords – coordinate systems (polar, cartesian, etc.)
• Themes – polish for publication or dashboard

📊 Visuals should tell, not yell.

We believe RegEx shouldn't be a dark art.

Panel(df).filter_col("email", pattern=".*@example.com")

You get the full power of re, simplified into expressive helpers for real-world usage.

The future of data is semantic, composable, and intelligent.

pydplyr is being designed with agentic AI frameworks in mind — where the code can be read and written by both humans and agents.

Whether it’s embedded in LLM-based agents or running as the logic core of autonomous data pipelines, pydplyr is made to be interpretable, traceable, and chainable.

• Core verbs (arrange, select, filter, mutate, summarize, distinct)
• Grouped operations
• Grammar of Graphics module
• Rust backend (via pyo3 or polars)
• Natural-language Regex builder
• LLM prompt-to-code interface
• Optional async API for distributed computing
• Plugin system for custom verbs and visual geoms

pip install pydplyr

We welcome contributors who care about:

• elegant APIs 🧼
• expressive code 💬
• performance 🔥
• semantic richness 🌐
• and dreaming big 💡

To get started, clone the repo and check the CONTRIBUTING.md guidelines.

Got ideas? Found bugs? Want to build the future of data science?

• Open an issue
• Start a discussion
• Or just drop by with a star ⭐️

"The art of data science is not in the numbers — it’s in the story they tell, and the tools that let them speak."

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