Python has become the prime language for application development in the Data Science and Machine Learning domains. However, data scientists are not necessarily experienced programmers. While Python lets them quickly implement their algorithms, when moving at scale, computation efficiency becomes inevitable. Thus, harnessing high-performance devices such as multicore processors and Graphical Processing Units (GPUs) to their potential is generally not trivial. The present narrative survey was thought as a reference document for such practitioners to help them make their way in the wealth of tools and techniques available for the Python language. Our document revolves around user scenarios, which are meant to cover most situations they may face. We believe that this document may also be of practical use to tool developers, who may use our work to identify potential lacks in existing tools and help them motivate their contributions.

翻译：Python已成为数据科学和机器学习领域应用开发的主要语言。然而，数据科学家未必具备丰富的编程经验。尽管Python能让他们快速实现算法，但在大规模应用时，计算效率问题不可避免。因此，充分发挥多核处理器和图形处理单元（GPU）等高性能设备的潜力通常并非易事。本文的叙述性综述旨在为这类实践者提供参考文献，帮助他们在Python语言可用的众多工具与技术中明确方向。本文围绕用户场景展开，旨在覆盖他们可能面临的大多数情况。我们相信，本文对工具开发者同样具有实用价值——他们可借本研究识别现有工具的潜在不足，并为其贡献提供理论依据。