Mastering Python High Performance

Cover
Copyright
Credits
About the Author
About the Reviewers
www.PacktPub.com
Table of Contents
Preface
Chapter 1: Profiling 101
	What is profiling?
		Event-based profiling
		Statistical profiling
	The importance of profiling
	What can we profile?
		Execution time
		Where are the bottlenecks?
	Memory consumption and memory leaks
	The risk of premature optimization
	Running time complexity
		Constant time – O(1)
		Linear time – O(n)
		Logarithmic time – O(log n)
		Linearithmic time – O(nlog n)
		Factorial time – O(n!)
		Quadratic time – O(n^)
	Profiling best practices
		Build a regression-test suite
		Mind your code
		Be patient
		Gather as much data as you can
		Preprocess your data
		Visualize your data
	Summary
Chapter 2: The Profilers
	Getting to know our new best friends: the profilers
		cProfile
		A note about limitations
		The API provided
		The Stats class
		Profiling examples
			Fibonacci again
			Tweet stats
	line_profiler
		kernprof
		Some things to consider about kernprof
		Profiling examples
			Back to Fibonacci
			Inverted index
	Summary
Chapter 3: Going Visual: GUIs to Help Understand Profiler Output
	KCacheGrind – pyprof2calltree
		Installation
		Usage
		A profiling example – TweetStats
		A profiling example – Inverted Index
	RunSnakeRun
		Installation
		Usage
		Profiling examples – the lowest common multiplier
		A profiling example – search using the inverted index
	Summary
Chapter 4: Optimize Everything
	Memoization / lookup tables
		Performing a lookup on a list or linked list
		Simple lookup on a dictionary
		Binary search
		Use cases for lookup tables
	Usage of default arguments
	List comprehension and generators
	ctypes
		Loading your own custom C library
		Loading a system library
	String concatenation
	Other tips and tricks
	Summary
Chapter 5: Multithreading versus Multiprocessing
	Parallelism versus concurrency
		Multithreading
			Threads
		Multiprocessing
			Multiprocessing with Python
	Summary
Chapter 6: Generic Optimization Options
	PyPy
		Installing PyPy
		A Just-in-time compiler
		Sandboxing
		Optimizing for the JIT
			Think of functions
			Consider using cStringIO to concatenate strings
			Actions that disable the JIT
		Code sample
	Cython
		Installing Cython
		Building a Cython module
		Calling C functions
			Solving naming conflicts
		Defining types
		Defining types during function definitions
		A Cython example
		When to define a type
		Limitations
			Generator expressions
			Comparison of char* literals
			Tuples as function arguments
			Stack frames
	How to choose the right option
		When to go with Cython
		When to go with PyPy
	Summary
Chapter 7: Lightning Fast Number Crunching with Numba, Parakeet, and pandas
	Numba
		Installation
		Using Numba
			Numba\'s code generation
			Running your code on the GPU
	The pandas tool
		Installing pandas
		Using pandas for data analysis
	Parakeet
		Installing Parakeet
		How does Parakeet work?
	Summary
Chapter 8: Putting It All into Practice
	The problem to solve
		Getting data from the Web
		Postprocessing the data
	The initial code base
		Analyzing the code
			Scraper
		Analyzer
	Summary
Index