- 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
Just as you can create a set using curly brackets ({}), you can also create a set using set comprehensions. These work in a way similar to list comprehensions, but the values are unique (and without sort order):
>>> [x*y for x in range(3) for y in range(3)] [0, 0, 0, 0, 1, 2, 0, 2, 4] >>> {x*y for x in range(3) for y in range(3)} {0, 1, 2, 4}
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