This talk discusses generators as a mechanism for modelling data-centric problems. The techniques suggested focus on simplifying the semantics of processing code, adding flexibility by inverting control structures, and allowing performance optimisations through caching, laziness, and targeted specialisations.

* This would be a continuation of the material I presented at PyData NYC 2012. I would incorporate feedback from that presentation to cover areas of particular interest. It would also use material developed since then, including some illustrative examples of how generators could be used to model certain problems in finance (the benchmark pricing problem, the refdata problem, &c.)

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