Name: Python High Performance（Second Edition）
Author: Gabriele Lanaro
ISBN: 9781787282438

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Python High Performance（Second Edition）

Gabriele Lanaro

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Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
Benchmarking and Profiling
Designing your application
Writing tests and benchmarks
Timing your benchmark
Better tests and benchmarks with pytest-benchmark
Finding bottlenecks with cProfile
Profile line by line with line_profiler
Optimizing our code
The dis module
Profiling memory usage with memory_profiler
Summary
Pure Python Optimizations
Useful algorithms and data structures
Lists and deques
Dictionaries
Building an in-memory search index using a hash map
Sets
Heaps
Tries
Caching and memoization
Joblib
Comprehensions and generators
Summary
Fast Array Operations with NumPy and Pandas
Getting started with NumPy
Creating arrays
Accessing arrays
Broadcasting
Mathematical operations
Calculating the norm
Rewriting the particle simulator in NumPy
Reaching optimal performance with numexpr
Pandas
Pandas fundamentals
Indexing Series and DataFrame objects
Database-style operations with Pandas
Mapping
Grouping, aggregations, and transforms
Joining
Summary
C Performance with Cython
Compiling Cython extensions
Adding static types
Variables
Functions
Classes
Sharing declarations
Working with arrays
C arrays and pointers
NumPy arrays
Typed memoryviews
Particle simulator in Cython
Profiling Cython
Using Cython with Jupyter
Summary
Exploring Compilers
Numba
First steps with Numba
Type specializations
Object mode versus native mode
Numba and NumPy
Universal functions with Numba
Generalized universal functions
JIT classes
Limitations in Numba
The PyPy project
Setting up PyPy
Running a particle simulator in PyPy
Other interesting projects
Summary
Implementing Concurrency
Asynchronous programming
Waiting for I/O
Concurrency
Callbacks
Futures
Event loops
The asyncio framework
Coroutines
Converting blocking code into non-blocking code
Reactive programming
Observables
Useful operators
Hot and cold observables
Building a CPU monitor
Summary
Parallel Processing
Introduction to parallel programming
Graphic processing units
Using multiple processes
The Process and Pool classes
The Executor interface
Monte Carlo approximation of pi
Synchronization and locks
Parallel Cython with OpenMP
Automatic parallelism
Getting started with Theano
Profiling Theano
Tensorflow
Running code on a GPU
Summary
Distributed Processing
Introduction to distributed computing
An introduction to MapReduce
Dask
Directed Acyclic Graphs
Dask arrays
Dask Bag and DataFrame
Dask distributed
Manual cluster setup
Using PySpark
Setting up Spark and PySpark
Spark architecture
Resilient Distributed Datasets
Spark DataFrame
Scientific computing with mpi4py
Summary
Designing for High Performance
Choosing a suitable strategy
Generic applications
Numerical code
Big data
Organizing your source code
Isolation, virtual environments, and containers
Using conda environments
Virtualization and Containers
Creating docker images
Continuous integration
Summary

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Python High Performance（Second Edition）