# 🌟 NorthStar: Blazingly Fast Data Processing 🚀
<center>
<img src="./assets/northstar-logo.png" height=300>
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! 😎
</center>
## 🌈 Features
- 🦀 Rust-powered core for lightning-fast computations
- 🐍 Seamless Python integration
- 📊 Efficient DataFrame and Series operations
- 🚄 Optimized for speed and memory usage
## 🛠️ Installation
Get started with NorthStar in seconds:
```bash
pip install northstar-data
```
## 🚀 Quick Start
Here's a taste of what NorthStar can do:
```python
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]))
# Filter the data
young_df = df.filter("ages", lambda x: x < 25)
# Select specific columns
selected_df = df.select_columns(["names", "scores"])
# Apply a function to a series
boosted_scores = df.apply("scores", lambda x: x * 1.1)
```
## 🏎️ Performance
NorthStar is designed for speed. Here's a quick comparison:
```python
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!")
```
## 🛣️ Roadmap
We're just getting started! Here's what's coming:
- [ ] Advanced grouping and aggregation operations
- [ ] More statistical functions (median, mode, standard deviation)
- [ ] DataFrame join operations
- [ ] Enhanced data visualization capabilities
- [ ] Even more performance optimizations
## 🤝 Contributing
We'd love your help to make NorthStar even more awesome! Check out our [Contributing Guide](CONTRIBUTING.md) to get started.
## 📜 License
NorthStar is MIT licensed. See the [LICENSE](LICENSE) file for details.
## 🌟 Star Us!
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! 🚀📊🦀
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"description": "# \ud83c\udf1f NorthStar: Blazingly Fast Data Processing \ud83d\ude80\n\n<center>\n<img src=\"./assets/northstar-logo.png\" height=300>\n\nNorthStar 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! \ud83d\ude0e\n\n</center>\n\n## \ud83c\udf08 Features\n\n- \ud83e\udd80 Rust-powered core for lightning-fast computations\n- \ud83d\udc0d Seamless Python integration\n- \ud83d\udcca Efficient DataFrame and Series operations\n- \ud83d\ude84 Optimized for speed and memory usage\n\n## \ud83d\udee0\ufe0f Installation\n\nGet started with NorthStar in seconds:\n\n```bash\npip install northstar-data\n```\n\n## \ud83d\ude80 Quick Start\n\nHere's a taste of what NorthStar can do:\n\n```python\nimport northstar as ns\n\n# Create a DataFrame\ndf = ns.PyDataFrame()\ndf.add_series(ns.PySeries(\"ages\", [25, 30, 22, 28, 33]))\ndf.add_series(ns.PySeries(\"names\", [\"Alice\", \"Bob\", \"Charlie\", \"David\", \"Eve\"]))\ndf.add_series(ns.PySeries(\"scores\", [88.5, 92.0, 79.5, 95.5, 87.0]))\n\n# Filter the data\nyoung_df = df.filter(\"ages\", lambda x: x < 25)\n\n# Select specific columns\nselected_df = df.select_columns([\"names\", \"scores\"])\n\n# Apply a function to a series\nboosted_scores = df.apply(\"scores\", lambda x: x * 1.1)\n```\n\n## \ud83c\udfce\ufe0f Performance\n\nNorthStar is designed for speed. Here's a quick comparison:\n\n```python\nimport northstar as ns\nimport time\n\ndata = list(range(1_000_000))\n\nstart_time = time.time()\nns_series = ns.PySeries(\"big_data\", data)\nns_sum = ns_series.sum()\nns_time = time.time() - start_time\n\nprint(f\"NorthStar time: {ns_time:.4f} seconds\")\n\nstart_time = time.time()\npy_sum = sum(data)\npy_time = time.time() - start_time\n\nprint(f\"Python time: {py_time:.4f} seconds\")\n\nspeedup = py_time / ns_time\nprint(f\"NorthStar is {speedup:.2f}x faster than pure Python!\")\n```\n\n## \ud83d\udee3\ufe0f Roadmap\n\nWe're just getting started! Here's what's coming:\n\n- [ ] Advanced grouping and aggregation operations\n- [ ] More statistical functions (median, mode, standard deviation)\n- [ ] DataFrame join operations\n- [ ] Enhanced data visualization capabilities\n- [ ] Even more performance optimizations\n\n## \ud83e\udd1d Contributing\n\nWe'd love your help to make NorthStar even more awesome! Check out our [Contributing Guide](CONTRIBUTING.md) to get started.\n\n## \ud83d\udcdc License\n\nNorthStar is MIT licensed. See the [LICENSE](LICENSE) file for details.\n\n## \ud83c\udf1f Star Us!\n\nIf 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.\n\nHappy data crunching! \ud83d\ude80\ud83d\udcca\ud83e\udd80\n",
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