Python: Programming Efficiently

Course details

Cut down on your development time by learning how to write elegant code in an efficient manner, specifically in Python—the popular and pragmatic computer language. Michele Vallisneri explains how to make your Python code more expressive and concise, and leverage the most powerful features of the language. He shines a spotlight on some of the strongest third-party packages you can take advantage of, discusses object-oriented and functional programming, and shares strategies for improving the performance of your code. Michele illustrates these concepts with many intriguing examples, showing how to make 3D images and animations of the sun, draw fractals, implement a graphical programming language, and more.

Concepts

0. Introduction

01 - Programming efficiently with Python
02 - Base knowledge

1. Installation and Setup

03 - Install the Anaconda Python distribution on macOS X
04 - Install the Anaconda Python distribution on Windows
05 - Work with Jupyter Notebooks

2. Writing Python Efficiently

06 - Writing Python efficiently
07 - Python vs. C
08 - Design efficient loops
09 - Comprehensions and generators
10 - Exploit Python collections
11 - Write Pythonic code
12 - Challenge - Analyze Olympic medalist data
13 - Solution - Analyze Olympic medalist data

3. Leveraging Python Libraries

14 - Choose the best libraries for your task
15 - Download webpages with requests
16 - Manipulate images with Pillow
17 - Parse HTML with Beautiful Soup
18 - Make movies with Matplotlib
19 - Serve webpages with Flask
20 - Challenge - Image-editing application
21 - Solution - Image-editing application

4. Efficient Python with Object-Oriented and Functional Programming

22 - Object-oriented and functional Python
23 - Divide and conquer with Python classes
24 - Exploit class inheritance
25 - Functional techniques in Python
26 - Function decorators
27 - Challenge - Bale of turtles
28 - Solution - Bale of turtles

5. Performance Optimization in Python

29 - Computer architecture and optimization techniques
30 - Time profiling
31 - Memory profiling
32 - Algorithm complexity
33 - Introduction to parallel programming
34 - Challenge - Inverted index
35 - Solution - Inverted index

Conclusion

36 - Keep taking advantage of Python