Python High Performance, 2/e (Paperback)

Gabriele Lanaro

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商品描述

Key Features

  • 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 programming

Book Description

Python 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.

What 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 PySpark

商品描述(中文翻譯)

主要特點


  • 使用最佳的分析技術,找出應用程式中的瓶頸並解決問題

  • 在 NumPy、Cython 和 Pandas 中撰寫高效能的數值程式碼

  • 使用平行程式設計將程式適應多個處理器和機器

書籍描述

Python 是一種多用途的語言,在許多行業中都有應用。其簡潔的語法、豐富的標準庫和廣泛的第三方庫使 Python 成為一種非常受歡迎的語言。

《Python 高效能》是一本實用指南,展示如何利用本地和第三方 Python 库來構建強大的應用程式。

本書解釋了如何使用各種分析工具找出性能瓶頸並應用正確的算法來解決問題。讀者將學習如何有效地使用 NumPy 和 Cython 來加速數值程式碼。本書還解釋了並行程式設計的概念,以及如何使用反應式程式設計來實現強大和反應靈敏的應用程式。讀者將學習如何使用 Tensorflow 和 Theano 在平行架構上編寫程式碼,並使用像 Dask 和 PySpark 這樣的技術在一個計算集群上進行大規模計算。

通過閱讀本書,讀者將學會如何從他們的 Python 應用程式中實現性能和擴展性。

你將學到什麼


  • 使用 NumPy 和 Pandas 库撰寫高效的數值程式碼

  • 使用 Cython 和 Numba 實現本地性能

  • 使用分析工具找出 Python 程式碼中的瓶頸

  • 使用 Asyncio 和 RxPy 撰寫非同步程式碼

  • 使用 Tensorflow 和 Theano 在 Python 中實現自動平行處理

  • 使用 Dask 和 PySpark 在集群上設置和執行分散式算法