FireDucks

Compiler Accelerated DataFrame Library for Python with fully-compatible pandas API

import fireducks.pandas as pd

News

Release fireducks-1.1.3 (Dec 10, 2024)
Updated TPC-H Benchmark: 125x average speedup over pandas, 2.2x average speedup over polars (Dec 06, 2024)
"How to run polars-tpch benchmark with FireDucks" is now available(blog) (Dec 06, 2024)
"Unveiling the Optimization Benefit of FireDucks Lazy Execution: Part #2" is now available(blog) (Dec 05, 2024)
"Unveiling the Optimization Benefit of FireDucks Lazy Execution: Part #1" is now available(blog) (Dec 05, 2024)
"What to do when FireDucks is slow" is now available(blog) (Nov 15, 2024)

Accelerate pandas without any manual code changes

Do you have a pandas-based program that is slow? FireDucks can speed-up your programs without any manual code changes. You can accelerate your data analysis without worrying about slow performance due to single-threaded execution in pandas.

Sustainable and Economical

Concerned about the cost and environmental impact of cloud computing? Our acceleration technology reduces cloud usage fees, while minimizing the CO2 emissions at the same time, making FireDucks an environment-friendly and wallet-friendly choice.

Reliability and Performance

FireDucks is developed by infusing the essence of supercomputers that NEC has refined over the years. Made in Japan, high-quality FireDucks promises reliability and high performance.

Features

Multithreaded ✨

FireDucks is multi-threaded, enabling higher speeds on multi-core CPUs. More

JIT Compile ⚡

A runtime compiler embedded in the library optimizes your code. More

pandas API 🐼

FireDucks is fully compatible with pandas API. The only difference is the import statement. No additional learning is required to start with FireDucks.Get started

AS IS Execution 🚀

You can run your pandas program directly with FireDucks. Its import-hook functionality will automatically replace the import statement for pandas with the import statement for FireDucks. More

Benchmarks 📈

FireDucks shows high performance gain while executing various queries included in the TPC-H and TPCx-BB benchmarks. More.