Python High Performance, 2/e (Paperback)
暫譯: Python 高效能,第二版 (平裝本)

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**

- 識別應用程式中的瓶頸,並使用最佳的性能分析技術解決它們
- 在 NumPy、Cython 和 Pandas 中編寫高效的數值代碼
- 通過並行編程使您的程式能夠在多個處理器和機器上運行

**書籍描述**

Python 是一種多用途的語言,已在許多行業中找到應用。其簡潔的語法、豐富的標準庫以及大量的第三方庫使得 Python 成為一種極受歡迎的語言。

《Python 高效能》是一本實用指南,展示如何利用本地和第三方 Python 庫的力量來構建穩健的應用程式。

本書解釋了如何使用各種性能分析工具來查找性能瓶頸,並應用正確的算法來修復它們。讀者將學習如何有效地使用 NumPy 和 Cython 來加速數值代碼。本書還解釋了並發編程的概念,以及如何使用反應式編程實現穩健和響應迅速的應用程式。讀者將學習如何為並行架構編寫代碼,使用 Tensorflow 和 Theano,並利用 Dask 和 PySpark 等技術在計算集群上進行大規模計算。

在本書結束時,讀者將學會如何從其 Python 應用程式中實現性能和擴展性。

**您將學到的內容**

- 使用 NumPy 和 Pandas 庫編寫高效的數值代碼
- 使用 Cython 和 Numba 實現本地性能
- 使用性能分析工具查找 Python 代碼中的瓶頸
- 使用 Asyncio 和 RxPy 編寫異步代碼
- 使用 Tensorflow 和 Theano 在 Python 中實現自動並行
- 使用 Dask 和 PySpark 在集群上設置和運行分佈式算法