- Mastering Python
- Table of Contents
- Mastering Python
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Getting Started – One Environment per Project
- 2. Pythonic Syntax, Common Pitfalls, and Style Guide
- Code style – or what is Pythonic code?
- Formatting strings – printf-style or str.format?
- PEP20, the Zen of Python
- Beautiful is better than ugly
- Explicit is better than implicit
- Simple is better than complex
- Flat is better than nested
- Sparse is better than dense
- Readability counts
- Practicality beats purity
- Errors should never pass silently
- In the face of ambiguity, refuse the temptation to guess
- One obvious way to do it
- Now is better than never
- Hard to explain, easy to explain
- Namespaces are one honking great idea
- Conclusion
- Explaining PEP8
- Verifying code quality, pep8, pyflakes, and more
- Common pitfalls
- Summary
- Code style – or what is Pythonic code?
- 3. Containers and Collections – Storing Data the Right Way
- Time complexity – the big O notation
- Core collections
- Advanced collections
- ChainMap – the list of dictionaries
- counter – keeping track of the most occurring elements
- deque – the double ended queue
- defaultdict – dictionary with a default value
- namedtuple – tuples with field names
- enum – a group of constants
- OrderedDict – a dictionary where the insertion order matters
- heapq – the ordered list
- bisect – the sorted list
- Summary
- 4. Functional Programming – Readability Versus Brevity
- Functional programming
- list comprehensions
- dict comprehensions
- set comprehensions
- lambda functions
- functools
- itertools
- accumulate – reduce with intermediate results
- chain – combining multiple results
- combinations – combinatorics in Python
- permutations – combinations where the order matters
- compress – selecting items using a list of Booleans
- dropwhile/takewhile – selecting items using a function
- count – infinite range with decimal steps
- groupby – grouping your sorted iterable
- islice – slicing any iterable
- Summary
- 5. Decorators – Enabling Code Reuse by Decorating
- 6. Generators and Coroutines – Infinity, One Step at a Time
- 7. Async IO – Multithreading without Threads
- 8. Metaclasses – Making Classes (Not Instances) Smarter
- 9. Documentation – How to Use Sphinx and reStructuredText
- 10. Testing and Logging – Preparing for Bugs
- 11. Debugging – Solving the Bugs
- 12. Performance – Tracking and Reducing Your Memory and CPU Usage
- 13. Multiprocessing – When a Single CPU Core Is Not Enough
- 14. Extensions in C/C++, System Calls, and C/C++ Libraries
- 15. Packaging – Creating Your Own Libraries or Applications
- Index
Python has quite a few very useful collections built in. Since more and more collections are added regularly, the best thing to do is simply keep track of the collections manual. And do you ever wonder how or why any of the structures works? Just look at the source here:
https://hg.python.org/cpython/file/default/Lib/collections/__init__.py
After finishing this chapter, you should be aware of both the core collections and the most important collections from the collections module, but more importantly the performance characteristics of these collections in several scenarios. Selecting the correct data structure within your applications is by far the most important performance factor that your code will ever experience, making this essential knowledge for any programmer.
Next, we will continue with functional programming which covers lambda functions, list comprehensions, dict comprehensions, set comprehensions and an array of related topics. This includes some background information on the mathematics involved which could be interesting but can safely be skipped.
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