本页面中的信息并不完全以您的首选语言展示，我们正在完善其他语言版本。想要以您的首选语言了解相关信息，可以点击这里下载PDF。
发布于 : Apr 24, 2019
不在本期内容中
这一条目不在当前版本的技术雷达中。如果它出现在最近几期中，那么它很有可能仍然具有相关参考价值。如果这一条目出现在更早的雷达中，那么它很有可能已经不再具有相关性，我们的评估将不再适用于当下。很遗憾我们没有足够的带宽来持续评估以往的雷达内容。
了解更多
Apr 2019
Assess
在了解它将对你的企业产生什么影响的前提下值得探索
Data scientists and engineers often use libraries such as pandas to perform ad hoc data analysis. Although expressive and powerful, these libraries have one critical limitation: they only work on a single CPU and don't provide horizontal scalability for large data sets. Dask, however, includes a lightweight, high-performance scheduler that can scale from a laptop to a cluster of machines. And because it works with NumPy, pandas and Scikit-learn, Dask looks promising for further assessment.
