A naukri.com initiative
Towards Data Science
1M
151
NumExpr: The “Faster than Numpy” Library Most Data Scientists Have Never Used
- NumExpr is a library that claims to be faster than NumPy for complex numerical calculations, offering up to 15 times faster performance in some cases.
- NumExpr is designed to accelerate expressions operating on arrays, using less memory compared to performing similar calculations in Python with other numerical libraries like NumPy.
- Due to its multithreaded nature, NumExpr can efficiently utilize all CPU cores, resulting in substantial performance scaling in comparison to NumPy.
- Users can create a separate Python environment for NumExpr development and install the necessary software using tools like conda before starting coding.
- A comparison between NumPy and NumExpr performance includes examples like array addition calculations, Monte Carlo simulation for estimating Pi, and implementing a Sobel image filter.
- In various benchmarks, NumExpr showcased notable speed improvements, such as a 6 times faster runtime in array addition calculations and close to double the speed in some complex applications like Sobel filter implementation.
- While NumExpr did not always reach the claimed 15x speed increase over NumPy, it demonstrated significant performance gains in tasks such as Fourier series approximation where it showed a 5 times improvement.
- Overall, NumExpr presents a viable option for data scientists and developers looking to optimize numerical computations and extract higher performance levels compared to traditional libraries like NumPy.
- Users interested in exploring the capabilities of NumExpr further can refer to the library's GitHub page for more information on its functionalities and potential use cases.
- NumExpr offers a compelling option for those striving to maximize performance in numerical computations and may surprise users with its speed improvements over NumPy in various scenarios.
Read Full Article
8 Likes
For uninterrupted reading, download the app