Advanced Python Programming: Accelerate your Python programs using proven techniques and design patterns
Write fast, robust, and highly
reusable applications using Python's internal optimization, state-of-the-art
performance-benchmarking tools, and cutting-edge libraries
Key Features
·
Benchmark, profile,
and accelerate Python programs using optimization tools
·
Scale applications
to multiple processors with concurrent programming
·
Make applications
robust and reusable using effective design patterns
Book Description
Python's powerful capabilities for
implementing robust and efficient programs make it one of the most sought-after
programming languages.
In this book, you'll explore the
tools that allow you to improve performance and take your Python programs to
the next level.
This book starts by examining the
built-in as well as external libraries that streamline tasks in the development
cycle, such as benchmarking, profiling, and optimizing. You'll then get to
grips with using specialized tools such as dedicated libraries and compilers to
increase your performance at number-crunching tasks, including training machine
learning models.
The book covers concurrency, a major
solution to making programs more efficient and scalable, and various concurrent
programming techniques such as multithreading, multiprocessing, and
asynchronous programming.
You'll also understand the common
problems that cause undesirable behavior in concurrent programs.
Finally, you'll work with a wide
range of design patterns, including creational, structural, and behavioral
patterns that enable you to tackle complex design and architecture challenges,
making your programs more robust and maintainable.
By the end of the book, you'll be
exposed to a wide range of advanced functionalities in Python and be equipped
with the practical knowledge needed to apply them to your use cases.
What you will learn
·
Write efficient
numerical code with NumPy, pandas, and Xarray
·
Use Cython and
Numba to achieve native performance
·
Find bottlenecks in
your Python code using profilers
·
Optimize your
machine learning models with JAX
·
Implement
multithreaded, multiprocessing, and asynchronous programs
·
Solve common
problems in concurrent programming, such as deadlocks
·
Tackle architecture
challenges with design patterns
Who this book is for
This book is for intermediate to
experienced Python programmers who are looking to scale up their applications
in a systematic and robust manner. Programmers from a range of backgrounds will
find this book useful, including software engineers, scientific programmers,
and software architects.
Table of Contents
1.
Benchmarking and
Profiling
2.
Pure Python
Optimizations
3.
Fast Array
Operations with NumPy and Pandas
4.
C Performance with
Cython
5.
Exploring Compilers
6.
Automatic
Differentiation and Accelerated Linear Algebra for Machine Learning
7.
Implementing
Concurrency
8.
Parallel Processing
9.
Concurrent Web
Requests
10.
Concurrent Image
Processing
11.
Building
Communication Channels with asyncio
12.
Deadlocks
13.
Starvation
14.
Race Conditions
15.
The Global
Interpreter Lock
16.
The Factory Pattern
17.
The Builder Pattern
18.
Other Creational
Patterns
19.
The Adapter Pattern
20.
The Decorator
Pattern
21.
The Bridge Pattern
22.
The Facade Pattern
23.
Other Structural
Patterns
24.
The Chain of
Responsibility Pattern
25.
The Command Pattern
26.
The Observer
Pattern
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