limulus 0.5.0

DataStep for Your Workspace

limulus — Data Step for Your Workspace

日本語

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limulus is a Python library for data processing using Data Step syntax.
Its goal is to bring the simplicity and long-term stability of Data Step into Python workflows.

This is currently an alpha release. Please note that breaking changes to the API and other interfaces may occur before the beta release.

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Installation

pip install limulus

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Usage

1. Prepare Your Data

Load a DataFrame (Arrow / Polars / Pandas) from a CSV or other source.

import pandas as pd
import limulus

health_df = pd.DataFrame({
	"name": ["Alice", "Bob", "Charlie", "David"],
	"age": [25, 30, 35, 40],
	"height": [65, 70, 68, 72],  # inches
	"weight": [140, 180, 130, 200]  # pounds
})

# Load data into a Session
session = limulus.Session()
session.loads({"health": health_df})

2. Run a Data Step

session.submit("""
data result;
  set health;
  where age > 25;
  height_m = height * 0.0254;
  weight_kg = weight * 0.454;
  bmi = round(weight_kg / (height_m**2), 0.1);
  keep name age bmi;
run;
""")

Multiple Data Steps can be submitted at once. Datasets created by earlier steps can be referenced by subsequent steps.

3. Retrieve Results

The simplest way is to retrieve the result from the session as an Arrow table using subscript notation.
You can convert it to pandas using Arrow's methods.

df_out = session["result"].to_pandas()
print(df_out)

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Documentation

https://k-nkmt.github.io/limulus/

License

PolyForm Noncommercial License 1.0.0

This project is distributed under the PolyForm Noncommercial License. Creative Commons licenses are not used because they are generally not recommended for software distribution.

The software may be used for personal, educational, academic, and noncommercial research purposes. Commercial use is not permitted under the current license terms.

A more permissive license may be considered in the future as the project gains a more stable foundation.

contact: [e-mail]

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Notices

Project Positioning
limulus is a modern data-step–inspired data transformation framework implemented independently in Python and Rust. It is not affiliated with or endorsed by SAS Institute Inc.

Trademark Notice
SAS® is a registered trademark of SAS Institute Inc. All other trademarks are the property of their respective owners.

Independence Statement
This is an independent implementation. No SAS source code or proprietary materials have been used.

Compatibility Disclaimer
Compatibility with SAS software is not guaranteed and is not a project goal. Certain behaviors intentionally differ to provide modern semantics.