nairobi-os 0.4.0

Nairobi OS: Heavy Iron Data Science Infrastructure with Lagos Vision (Hardware-Accelerated Zero-Copy Rendering)

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Nairobi Python

Overview

Nairobi Python provides the high-level bridge to the Nairobi OS infrastructure. It enables data scientists to harness the power of Rust-based, hardware-accelerated analytics through a familiar, Pythonic interface. The package handles daemon management, IPC coordination, and memory-mapping, allowing users to focus on data analysis.

Key Features

• SovereignFrame: A fluent, Pandas-like interface for managing remote data handles.
• Lazy Ignition: Automatically starts and configures the refinery daemon upon first data access.
• Jupyter Integration: First-class support for interactive visualizations using the Lagos Vision widget.
• Zero-Copy Bridge: Directly consumes memfd handles from the Rust refinery with sub-millisecond overhead.

Installation

From PyPI

pip install nairobi-os

From Source

cd crates/nairobi-python
pip install -e .

Note: Building from source requires the Rust toolchain and maturin to be installed.

Usage

Quick Start

import nairobi_os

# Connect to the refinery (automatically handles D-Bus and daemon startup)
nairobi_os.connect()

# Ingest a CSV file
df = nairobi_os.read_csv("data.csv")

# Fluent API for statistics
mean_val = df.column_name.mean()
p99_val = df.column_name.p99()

# Run SQL queries directly on the engine
tall_players = df.query("SELECT * FROM dataset WHERE height > 80")

# Plot using Lagos Vision
tall_players.plot()

API Reference

nairobi_os.connect()

Initializes the environment, starts the D-Bus session if necessary, and ignites the refinery daemon.

nairobi_os.read_csv(path, delimiter=",", encoding="utf-8")

Ingests a CSV file using the refinery's zero-copy pipeline. Returns a SovereignFrame.

SovereignFrame Methods

• df.column.mean(): Compute the arithmetic mean.
• df.column.std_dev(): Compute the standard deviation.
• df.column.p95(), df.column.p99(): Compute percentiles.
• df.column.skewness(), df.column.kurtosis(): Compute statistical moments.
• df.query(sql_string): Execute Polars-SQL on the dataset.
• df.correlate("col1,col2"): Compute Pearson and Spearman correlation.
• df.plot(width, height): Display an interactive anywidget visualization.

Development

Adding New Python Bindings

Nairobi Python uses PyO3 to interface with Rust. New core functions should be added to crates/nairobi-python/src/lib.rs and exposed through the nairobi_os._core.data module.

Testing

Integration tests for the Python package can be run using pytest (if configured) or the provided test script:

python3 test_nairobi.py

To test in isolation without the full refinery, you can mock the _core.data module or use the SovereignFrame with pre-existing handles.

Troubleshooting

• Refinery Failed to Register on D-Bus: This often happens in headless environments. Ensure dbus-launch is available or call nairobi_os.connect() which attempts to fix the environment.
• Handle Not Found: Data handles are session-bound. If the refinery restarts, previous SovereignFrame handles will become invalid.

License

This project is licensed under the Apache License 2.0.

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© 2026 Kevin Chege. All Rights Reserved.