Building Python Applications with smaller memory & time foot-print

[atifadib]

Building Python Applications with a Smaller Memory & Time foot-print

Description

Python has a very limited set of native types namely dict, int, bool, float, tuple, str , etc but a plethora of other defined types with similar function to these but a smaller memory foot-print like named tuples, frozen dict, frozen set, ImmutableDict, etc.
Along with these data types there are many nuggets of helper functions in functools, cache tools and itertools to improve the running time of the functions in your application.
we'll largely focus on the three standard libraries: functools, cachetools and itertools.

Duration

• 30 min

Audience

Pre-requisite: Love for Python and Optimisation

Outline

0-5 minutes: How to calculate the memory footprint of your function
5-10 minutes: Replacing native objects with memory efficient objects
5-15 minutes: Using functools and cachetools to improve your functions memory and time footprint.
15-20 minutes: Writing Idiomatic python code using memory efficient design patterns; Adapter and Decorator Design Patterns.
20-25 minutes: Iterators and generators to the Rescue.
25-30 minutes: Q & A.

Additional notes

View Github Repo: https://github.com/atifadib/python_best_practises for more details.

[TrigonaMinima]

Hi @atifadib will you be available for the talk on 29th Feb?