Advanced Python Programming

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Title Page

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Advanced Python Programming

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Preface

Who This Book Is For

What This Book Covers

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Download the Example Code Files

Conventions Used

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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