August 2015
Intermediate to advanced
384 pages
7h 42m
Chinese
Overview
「儘管在學界與業界都非常普遍，但 Python 經常因為在實際應用上過於緩慢而遭人詬病，這本書透過詳盡的策略介紹，將那些誤解與疑慮通通掃除掉，讓我們能夠利用 Python 建立快速且具高度擴充性的計算機制。」
— Jake VanderPlas, 華盛頓大學
你的 Python 程式碼可能運作無誤，但你需要它執行得更快速。透過探索設計選項背後的基礎理論，這本實用指南幫助你對 Python 實作獲得更深入的瞭解。你將學習如何找出效能瓶頸，並且在高資料量(high-data-volume)的程式中大幅加快你的程式碼運作。
如何善用多核心架構或叢集？或者建立能夠擴充及縮小規模，但又不失可靠性的系統？有經驗的 Python 程式人員將學到這些議題與其他問題的具體解法，並且獲悉各家公司利用高效能 Python 從事社群媒體分析(social media analytics)，機器學習(machine learning)，以及各種議題的英雄史。
‧更深入理解 numpy，Cython 及效能分析器
‧熟悉 Python 如何抽象化底層的電腦架構
‧利用效能分析尋找 CPU 時間與記憶體使用量的瓶頸
‧透過選擇合適的資料結構撰寫有效率的程式
‧加速矩陣與向量計算
‧使用工具將 Python 編譯成機器碼
‧並行管理多個 I/O 與計算操作
‧將 multiprocessing 程式碼轉換成執行在本地或遠端的叢集上
‧使用較少的 RAM 解決大型的問題
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