Python High Performance - Second Edition

Python High Performance - Second Edition

by Lanaro, Gabriele

Type Book
Rate 0.0 /5

Learn how to use Python to create efficient applicationsAbout This Book* Identify the bottlenecks in your applications and solve them using the best profiling techniques* Write efficient numerical code in NumPy, Cython, and Pandas* Adapt your programs to run on multiple processors and machines with parallel programmingWho This Book Is ForThe book is aimed at Python developers who want to improve the performance of their application. Basic knowledge of Python is expectedWhat You Will Learn* Writ…

Read More ↓
Details
Files
Reviews
Download

Title Statements

  • Full title :Python High Performance - Second Edition.

Summary

Learn how to use Python to create efficient applicationsAbout This Book* Identify the bottlenecks in your applications and solve them using the best profiling techniques* Write efficient numerical code in NumPy, Cython, and Pandas* Adapt your programs to run on multiple processors and machines with parallel programmingWho This Book Is ForThe book is aimed at Python developers who want to improve the performance of their application. Basic knowledge of Python is expectedWhat You Will Learn* Write efficient numerical code with the NumPy and Pandas libraries* Use Cython and Numba to achieve native performance* Find bottlenecks in your Python code using profilers* Write asynchronous code using Asyncio and RxPy* Use Tensorflow and Theano for automatic parallelism in Python* Set up and run distributed algorithms on a cluster using Dask and PySparkIn DetailPython is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language. Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications. The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. By the end of the book, readers will have learned to achieve performance and scale from their Python applications. Style and approachA step-by-step practical guide filled with real-world use cases and examples.

Read More ↓

Edition Statements

  • Publication Date:2017.
  • Edition:2nd ed

Publication Statements

  • Publisher(s):Packt Publishing
  • Place of Publication:Birmingham
  • Language:eng
  • Publication Date:2017.

Notes

Chapter 9: Designing for High Performance.

The Physical Object

  • Description:1 online resource (264 pages)

Edition Identifiers

  • ISBN:9781787282438, 1787282430, 9781787282896, 1787282899
  • oclc:ocn988867272
  • LC Classification:T55.4-60.8
  • Dewey Decimal:006.8

Table of Contents

Cover ; Copyright ; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: 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; Chapter 2: Pure Python Optimizations ; Useful algorithms and data structures; Lists and deques; Dictionaries.

Building an in-memory search index using a hash mapSets; Heaps; Tries; Caching and memoization; Joblib; Comprehensions and generators; Summary; Chapter 3: 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.

Chapter 4: C Performance with C…

Read More ↓

Subjects

Python

Writers

Lanaro, Gabriele

Rate & Review

0.0/5
(0 people Voted)

No reviews yet. Be the first to rate this work.

Files & Downloads

  • Python High Performance -- (.pdf)

    PDF · 4.5 MB · updated
    Login →

  • Python High Performance -- (.epub)

    EPUB · 1.6 MB · updated
    Login →

  • Python high performance: build robust application…

    AZW3 · 1.7 MB · updated
    Login →

  • Python High Performance - Second Edition -- (.pdf)

    PDF · 4.5 MB · updated
    Login →

  • Python High Performance - Second Edition -- (.epu…

    EPUB · 2.5 MB · updated
    Login →

  • Python High Performance - Second Edition -- (.pdf)

    PDF · 5.0 MB · updated
    Login →

  • Python High Performance -- (.pdf)

    PDF · 4.5 MB · updated
    Login →