Functional Python
summary
Hands-on Jupyter notebooks use JupyterLab with conda and the codebase imports more_itertools, toolz, functools, itertools, operator; notebooks and HTML/PDF exports land on the conference site. Concrete claims: closures and currying produce callable factories, functools.partial pre-binds args, generators and map/filter create lazy pipelines, and recursion shows a real cost — recursion becomes ~2–4× slower and hits Python's recursion limit (~3000). Exercises plus solutions and runnable examples let one reproduce timings, iterator recipes, and the small composable utilities that actually work.
AI-written summary · about
chapters (44)
- 0:00Start
- 3:51How Long Are You Using Python
- 7:07Basics about Functional Programming
- 8:26Programming Basics
- 8:59Imperative Programming
- 9:20Object-Oriented Programming
- 9:49Declarative Programming
- 11:21Features
- 12:15Immutable Data Structures
- 13:48No Global State
- 14:18Advantages
- 16:07Parallel Programming
- 16:39Live Updates
- 17:23Disadvantages
- 19:13Python Function Features
- 20:01Decorators
- 20:24Modules
- 28:27Side Effects
- 29:14Functions Are Objects
- 30:15Functions as Keys and Dictionaries
- 35:26Closure
- 47:49Partial Function
- 48:15Partial Function Application
- 51:34Print Function
- 56:49Mutable Side Effects
- 1:01:49Recursion
- 1:20:07Lambda
- 1:24:56Map Filter Entries
- 1:26:38Iterators
- 1:30:17Operator Module
- 1:30:38Filter
- 1:38:21Mapreduce
- 1:41:17Add Function
- 1:42:33Python Keywords
- 1:43:08Operators
- 1:44:57Attribute Gator
- 1:48:48Exercises
- 1:49:15Comprehensions
- 1:49:42Nested Comprehension
- 1:50:18Set Comprehensions
- 1:53:29Iterator
- 2:04:51Repeat
- 2:07:57Slicing
- 2:11:21Dictionary Comprehensions
