python – 防止我的​​RAM内存达到100％

2019年7月28日 156次阅读

我有一个非常简单的
python脚本,它读取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),其读取完全使用RAM内存.当它达到100％(即64 GB RAM内存)时,我的系统完全冻结,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？我们将非常感谢代码片段的完整答案.

最佳答案 实质上,您必须实现自己的内存不足排序.

>使用Pandas CSV chunker将文件分成两个或多个部分,对每个部分进行排序(一次一个！),将其保存到单独的CSV文件中,然后使用del释放内存.
>通过使用CSV分组打开所有已保存的预排序文件,根据需要组合块中的行并将排序的行附加到输出文件来合并已排序的文件.

点赞