On demand data in Python, Part 1: Python iterators and generators

The oldest known way to process data in Python is building up data in
lists, dictionaries and other such data structures. Though such techniques
work well in many cases, they cause major problems when dealing with large
quantities of data. It’s easy to find that your code is running painfully
slowly or running out of memory. Generators and iterators help address this
problem. These techniques have been around in Python for a while but are not
well understood. Used properly, they can bring big data tasks down to size so
that they don’t require a huge hardware investment to complete.

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