NorthStar is a high-performance data processing library implemented in Rust with Python bindings. It's designed to make your data science workflows faster and cooler! 😎

• 🦀 Rust-powered core for lightning-fast computations
• 🐍 Seamless Python integration
• 📊 Efficient DataFrame and Series operations
• 🚄 Optimized for speed and memory usage

Get started with NorthStar in seconds:

pip install northstar-data

Find the package here!!!

Here's a taste of what NorthStar can do:

import northstar as ns

# Create a DataFrame
df = ns.PyDataFrame()
df.add_series(ns.PySeries("ages", [25, 30, 22, 28, 33]))
df.add_series(ns.PySeries("names", ["Alice", "Bob", "Charlie", "David", "Eve"]))
df.add_series(ns.PySeries("scores", [88.5, 92.0, 79.5, 95.5, 87.0]))

NorthStar is designed for speed. Here's a quick comparison:

import northstar as ns
import time

data = list(range(1_000_000))

start_time = time.time()
ns_series = ns.PySeries("big_data", data)
ns_sum = ns_series.sum()
ns_time = time.time() - start_time

print(f"NorthStar time: {ns_time:.4f} seconds")

start_time = time.time()
py_sum = sum(data)
py_time = time.time() - start_time

print(f"Python time: {py_time:.4f} seconds")

speedup = py_time / ns_time
print(f"NorthStar is {speedup:.2f}x faster than pure Python!")

We're just getting started! Here's what's coming:

• Trying to make it much faster 🏎️
• Advanced grouping and aggregation operations
• More statistical functions (median, mode, standard deviation)
• DataFrame join operations
• Enhanced data visualization capabilities
• Even more performance optimizations

We'd love your help to make NorthStar even more awesome! Check out our Contributing Guide to get started.

NorthStar is MIT licensed. See the LICENSE file for details.

If you find NorthStar helpful, give us a star on GitHub! It helps us know we're on the right track and encourages us to keep improving.

Happy data crunching! 🚀📊🦀