PyData London 2018
Generators were a compelling feature when added to Python 2.2 in seventeen years ago with PEP-255. When they were enhanced to become coroutines with PEP-342, they gained additional capabilities for modelling common problems.
But, surprisingly, we don't often see generators discussed as a core tool in the data scientist, computational scientist, and data engineer's toolkit. Why not?
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0:00 About PyData and introducing generators
1:50 Hacking the Python print function
4:36 Overview of the talk
5:32 Review
6:25 Three different implementations of the add function
8:15 Adding a state variable, the difference between class and function
11:05 A compute function with sleep
13:55 Using the generator pattern with _iter_ and _next_
15:15 Differences in approaches: laziness, storage
17:27 What generators are
19:40 Pipelines and nwise function
21:05 Writing a basic scheduler with generators
23:05 Why they are not what you think they are
23:40 Timing code and Python not being optimal vs Perl for scientists
27:55 Have some fun, computing graphs
29:10 Implementing a graph structure with generators
31:20 Improving the design with more laziness
38:00 Final version of the code (propagation in nodes of a graph)
39:00 Conclusion
S/o to github.com/mycaule for the video timestamps!
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12 авг 2024
