Python Generators
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Slides accompanying the Generators meetup for HyPy. https://www.meetup.com/Hyderabad-Python-Meetup-Group/events/245065908/
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Just like a virtual machine, a Linux Container can run (almost) anywhere. But containers have many advantages over VMs: they are lightweight and easier to manage. After operating a large-scale PAAS for a few years, dotCloud realized that with those advantages, containers could become the perfect format for software delivery, since that is how dotCloud delivers from their build system to their hosts. To make it happen everywhere, dotCloud open-sourced Docker, the next generation of the containers engine powering its PAAS. Docker has been extremely successful so far, being adopted by many projects in various fields: PAAS, of course, but also continuous integration, testing, and more.
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Python Generators
- 1. Generators
- 2. Why generators? ● Writing memory optimized code. ● Understanding how Python web framework works. Eg: Django, Flask ● Writing simpler, abstracted code. We will see an example at the end.
- 3. What is a generator? ● Function ● Function having yield keyword ● Lazy evaluation
- 4. Example generator In [65]: def example_generator(): # Is a function ...: yield 1 # Has **yield** ...: yield 2 # Can have more than 1 yield ...: In [66]: for each in example_generator(): # Generator instances are mostly used with for ...: print each ...:
- 5. Without generator ● Memory consuming code ● Custom implementation of range, custom_range() ● 4 million numbers would take ~ 100 MB
- 6. With generator ● Memory efficient code ● Custom implementation of xrange, custom_xrange(). ● 4 million numbers would take 24 bytes
- 7. Generator control flow ● Execution suspends on encountering yield. ● Execution resumes with next iteration. ● Execution stops on encountering return.
- 8. Real world usage ● xrange() ● os.walk() ● File operations: Reading, searching etc. ● itertools.permutations()
- 9. Generators and Iterators ● Every generator is an iterator but not vice versa. ● Generator instances are iterables. They can be used with iter()
- 10. Recap ● Iterable must implement __iter__(). No need to implement next(). ● Iterator must implement next(). No need to implement __iter__() ● iter() must be used with Iterable. ● next() must be used with iterator.
- 11. How for loop works ● **for** works with an iterable which must have an underlying iterator. ● iter(iterable) is called which returns a iterator. ● next() of iterator is repeatedly called until StopIteration is raised from next().
- 12. Why generator over iterators ● Generator functions are convenient shortcuts to build iterators. ● Generators remove boilerplate. ● Compare class xrange_iterator() with generator custom_xrange().