We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

A faster Python? You Have These Choices

Formal Metadata

Title
A faster Python? You Have These Choices
Title of Series
Number of Parts
160
Author
License
CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
Identifiers
Publisher
Release Date
Language

Content Metadata

Subject Area
Genre
Abstract
A faster Python? You Have These Choices [EuroPython 2017 - Talk - 2017-07-13 - Arengo] [Rimini, Italy] Python was never intended as a fast language but many modern uses of Python require high performance computing, particularly in data science. This talk explores your options for squeezing maximum performance out of critical Python code. This talk provides a succinct summary of the options you have: C extensions, Cython, CFFI, PyPy and many others. It also shows the trade-offs between execution performance and the cost of writing and maintaining code with each choice. Each option is also explored for maturity and ease of use for Python programmers. A real world programming problem is coded and benchmarked using each of these techniques. All the code used in the talk is available on GitHub. At the end of this talk you will be better place to decide on which technique to use to make your code run 100x faster