Python / IDLE CPU 无故使用

发布于 2024-11-04 02:05:32 字数 494 浏览 5 评论 0原文

这是我在 Windows 上使用 IDLE（版本 2.6.5，具有相同 Python 版本）时遇到的一个奇怪问题。

我尝试运行以下三个命令：

fid= open('file.txt', 'r')
lines=fid.readlines()
print lines

当执行 printlines 命令时，pythonw.exe 进程的 CPU 疯狂，消耗 100% 的 CPU，并且 IDLE 似乎没有响应。 file.txt 大约 130 kb - 我不认为该文件很大！

当这些行最终打印出来时（几分钟后），如果我尝试向上滚动查看它们，我会再次遇到同样非常大的 CPU 使用率。

pythonw.exe 的内存使用量始终在 15-16 MB 左右。

任何人都可以向我解释这种行为 - 显然这不可能是 IDLE 中的错误，因为它会被发现......另外，我能做些什么来抑制这种行为？我喜欢使用 IDLE 来执行类似涉及文件数据转换的脚本任务。

原文

Here is a strange problem I have with IDLE (version 2.6.5 with the same Python version) on windows.

I try to run the following three commands:

fid= open('file.txt', 'r')
lines=fid.readlines()
print lines

When the print lines command is executed, the pythonw.exe process is going CPU crazy, consuming 100% of CPU and the IDLE seems to not be responding. The file.txt is around 130 kb - I don't consider that file very large !

When the lines finally print (after some minutes), if I try to scroll up to see them, I once again experience the same very large CPU usage.

The memory usage of pythonw.exe is around 15-16 MB all the time.

Can anybody explain to me this behaviour - obviously this can't be a bug in IDLE since it would have been discovered ... Also, what can I do to supress that behavior ? I like using IDLE for script like tasks involving data transformations from files.

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最舍不得你 2024-11-11 02:05:32

尝试逐行阅读：

fid = open('file.txt', 'r')

for line in fid:
  print line

来自输入输出，读取文件的方式好像有两种：

print f.read() # This reads the *whole* file. Might be bad to do this for large files.

for l in f:    # This reads it line by line
  print l      # and prints it. Might be better for big files.

Try reading it line by line:

fid = open('file.txt', 'r')

for line in fid:
  print line

From the documentation on Input Output, there seem to be two ways to read files:

print f.read() # This reads the *whole* file. Might be bad to do this for large files.

for l in f:    # This reads it line by line
  print l      # and prints it. Might be better for big files.

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