防止我的RAM内存达到100％

Question

我有一个非常简单的python脚本，可读取CSV文件并根据时间戳对行进行排序。 但是，该文件足够大（16 GB），以至于其读取完全使用了内存。 当达到100％（即64 GB RAM内存）时，我的系统完全死机，我被迫重新启动计算机。

这是代码：

import pandas as pd
from time import time

filename = 'AKER_OB.csv'

start_ = time()
file_ = pd.read_csv(filename)
end_ = time()
duration = end_ - start_
print("The duration to load that file : {}".format(duration))

file_.to_datetime(df['TimeStamps'], format="%Y-%m-%d %H:%M:%S").sort_values()

AKER_OB.csv负责人：

TimeStamp,Bid1,BidSize1,Bid2,BidSize2,Bid3,BidSize3,Bid4,BidSize4,Bid5,BidSize5,Bid6,BidSize6,Bid7,BidSize7,Bid8,BidSize8,Bid9,BidSize9,Bid10,BidSize10,Bid11,BidSize11,Bid12,BidSize12,Bid13,BidSize13,Bid14,BidSize14,Bid15,BidSize15,Bid16,BidSize16,Bid17,BidSize17,Bid18,BidSize18,Bid19,BidSize19,Bid20,BidSize20,Ask1,AskSize1,Ask2,AskSize2,Ask3,AskSize3,Ask4,AskSize4,Ask5,AskSize5,Ask6,AskSize6,Ask7,AskSize7,Ask8,AskSize8,Ask9,AskSize9,Ask10,AskSize10,Ask11,AskSize11,Ask12,AskSize12,Ask13,AskSize13,Ask14,AskSize14,Ask15,AskSize15,Ask16,AskSize16,Ask17,AskSize17,Ask18,AskSize18,Ask19,AskSize19,Ask20,AskSize20
2016-10-08 00:00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
2016-10-08 00:00:01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
2016-10-08 00:00:02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
2016-10-08 00:00:03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
2016-10-08 00:00:04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
2016-10-08 00:00:05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
2016-10-08 00:00:06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
2016-10-08 00:00:07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
2016-10-08 00:00:08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

解决此问题的正确方法是什么？ 带有代码段的完整答案将不胜感激。

Answer 1

本质上，您必须实现自己的内存不足排序。

1. 使用Pandas CSV块分割器将文件分为两段或更多段，将每一段排序（一次一件！），将其保存到单独的CSV文件中，然后使用del释放内存。

2. 通过使用CSV块工具打开所有已保存的预排序文件，合并合并的文件，并根据需要组合块中的行，并将已排序的行附加到输出文件中。

Answer 2

只需按块拆分读取的文件。 类似的情况 。

还可以考虑将交换分区或文件添加到您的操作系统，这将有助于在其他情况下解决内存不足的问题。