August 2015
Intermediate to advanced
384 pages
7h 42m
Chinese
Content preview from 高效能PYTHON程式設計
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第十一章
節省 RAM 的訣竅
通常，就把資料放進 RAM 這件事而言，能省則省。你所載入的一切都會耗用你的
RAM。你或許可以只載入一部分資料，例如，使用記憶體對映檔（memory-mapped
file）（
https://docs.python.org/2/library/mmap.html
）；或者，你也許能夠使用生成器，僅載
入進行部分計算所需的資料，而不是一次通通都載入。
如果你正在處理數值資料，那麼，你幾乎肯定會想要改用
numpy
陣列—該套件提供許多
快速的演算法，直接操作底層的基本型別物件，相較於使用數字串列，這會節省龐大的
RAM，而且節省的時間可能同樣驚人。
在這本書裡，你應該會注意到我們通常使用
xrange
，而不是
range
，原因很簡單，因為
（在 Python 2.x 中）
xrange
是生成器，而
range
建構整個串列。只是為了迭代正確的次
數就建造 100,000,000 個整數，實在太不經濟。Python 3.x 將
range
改變成生成器，所以
你不需要再做這個改變。
如果你正在使用Python 2.x 操作字串，並且想要節省RAM，盡量使用
str
代替
unicode
。如果你的整個程式需要大量的 Unicode 物件，或許直接升級到 Python 3.3+ 會
有比較好的結果。如果你正在將大量 Unicode 物件儲存在靜態結構中，那麼，你可能會
想要研究一下剛剛所討論的 DAWG 與 trie 結構。
如果你正在處理大量位元字串，請研究一下
numpy
和
bitarray
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