- Scientific Computing
- Technical level
To understand libraries, techniques and benchmarks to help you speed up your data storage, retrieval and processing.
Working with data in Python requires making a number of choices, ranging from the simple to the complex.
- Should I use pickle, CSV or JSON? (Ans: CSV).
- What do I read it with: csv.DictReader or csv.reader? (Ans: Pandas).
- How should I parse dates? (Ans: Anything but Pandas / dateutil)
- How do I optimise numpy calculations? (Ans: Learn vector algebra)
- How do I run a function in parallel?
- How to make my program restartable?
- How do I use multiple cores?
This session will explain how to benchmark code and share insights on the patterns of programming that make your application faster.
A good working knowledge of Python and the standard libraries
He blogs at http://www.s-anand.net/