在ShellPy库中可以使用python而不是bash。

下面是一个从Github下载Python用户头像的例子:

import json
import os
import tempfile

# get the api answer with curl
answer = `curl https://api.github.com/users/python
# syntactic sugar for checking returncode of executed process for zero
if answer:
    answer_json = json.loads(answer.stdout)
    avatar_url = answer_json['avatar_url']

    destination = os.path.join(tempfile.gettempdir(), 'python.png')

    # execute curl once again, this time to get the image
    result = `curl {avatar_url} > {destination}
    if result:
        # if there were no problems show the file
        p`ls -l {destination}
    else:
        print('Failed to download avatar')

    print('Avatar downloaded')
else:
    print('Failed to access github api')

如您所见，所有在grave重音(')符号内的表达式都在shell中执行。在Python代码中，您可以捕获此执行的结果并对其执行操作。例如:

log = `git log --pretty=oneline --grep='Create'

这一行首先在shell中执行git log——pretty=oneline——grep='Create'，然后将结果赋值给log变量。结果具有以下属性:

从已执行进程的Stdout中Stdout整个文本

Stderr从已执行进程的Stderr得到的整个文本

Returncode执行的返回码

这是该库的总体概述，更详细的描述和示例可以在这里找到。

以下是我的一些经验之谈:

外壳:

Shell可以很容易地生成只读代码。把它写下来，当你回头看的时候，你永远也不会知道你又做了什么。这很容易做到。 shell可以用管道在一行中做大量的文本处理、分割等。 当涉及到集成不同编程语言的程序调用时，它是最好的粘合语言。

python:

如果你想要Windows的可移植性，请使用python。 当您必须处理的不仅仅是文本，比如数字的集合时，Python可能会更好。为此，我推荐python。

我通常选择bash来处理大多数事情，但当我有一些必须跨越窗口边界的东西时，我就使用python。

在ShellPy库中可以使用python而不是bash。

下面是一个从Github下载Python用户头像的例子:

import json
import os
import tempfile

# get the api answer with curl
answer = `curl https://api.github.com/users/python
# syntactic sugar for checking returncode of executed process for zero
if answer:
    answer_json = json.loads(answer.stdout)
    avatar_url = answer_json['avatar_url']

    destination = os.path.join(tempfile.gettempdir(), 'python.png')

    # execute curl once again, this time to get the image
    result = `curl {avatar_url} > {destination}
    if result:
        # if there were no problems show the file
        p`ls -l {destination}
    else:
        print('Failed to download avatar')

    print('Avatar downloaded')
else:
    print('Failed to access github api')

如您所见，所有在grave重音(')符号内的表达式都在shell中执行。在Python代码中，您可以捕获此执行的结果并对其执行操作。例如:

log = `git log --pretty=oneline --grep='Create'

这一行首先在shell中执行git log——pretty=oneline——grep='Create'，然后将结果赋值给log变量。结果具有以下属性:

从已执行进程的Stdout中Stdout整个文本

Stderr从已执行进程的Stderr得到的整个文本

Returncode执行的返回码

这是该库的总体概述，更详细的描述和示例可以在这里找到。

如果你的文本文件操作通常是一次性的，可能在shell提示符下完成，你从python中不会得到更好的东西。

另一方面，如果你经常不得不一遍又一遍地做同样的(或类似的)任务，并且你必须为此编写脚本，那么python是很棒的——你可以很容易地创建自己的库(你也可以用shell脚本来做，但它更麻烦)。

这是一个很简单的例子。

import popen2
stdout_text, stdin_text=popen2.popen2("your-shell-command-here")
for line in stdout_text:
  if line.startswith("#"):
    pass
  else
    jobID=int(line.split(",")[0].split()[1].lstrip("<").rstrip(">"))
    # do something with jobID

还要检查sys和getopt模块，它们是您首先需要的。

In the beginning there was sh, sed, and awk (and find, and grep, and...). It was good. But awk can be an odd little beast and hard to remember if you don't use it often. Then the great camel created Perl. Perl was a system administrator's dream. It was like shell scripting on steroids. Text processing, including regular expressions were just part of the language. Then it got ugly... People tried to make big applications with Perl. Now, don't get me wrong, Perl can be an application, but it can (can!) look like a mess if you're not really careful. Then there is all this flat data business. It's enough to drive a programmer nuts.

进入Python、Ruby等。这些都是非常好的通用语言。它们支持文本处理，并且做得很好(尽管可能与语言的基本核心没有那么紧密地交织在一起)。但它们也可以很好地扩展，并且在一天结束时仍然有漂亮的代码。他们还发展了相当庞大的社区，有大量的图书馆，几乎可以提供任何东西。

Now, much of the negativeness towards Perl is a matter of opinion, and certainly some people can write very clean Perl, but with this many people complaining about it being too easy to create obfuscated code, you know some grain of truth is there. The question really becomes then, are you ever going to use this language for more than simple bash script replacements. If not, learn some more Perl.. it is absolutely fantastic for that. If, on the other hand, you want a language that will grow with you as you want to do more, may I suggest Python or Ruby.

不管怎样，祝你好运!

我刚刚发现了如何结合bash和ipython的最佳部分。到目前为止，这似乎比使用subprocess等更舒服。你可以很容易地复制现有bash脚本的大部分内容，例如以python的方式添加错误处理:) 这是我的结果:

#!/usr/bin/env ipython3

# *** How to have the most comfort scripting experience of your life ***
# ######################################################################
#
# … by using ipython for scripting combined with subcommands from bash!
#
# 1. echo "#!/usr/bin/env ipython3" > scriptname.ipy    # creates new ipy-file
#
# 2. chmod +x scriptname.ipy                            # make in executable
#
# 3. starting with line 2, write normal python or do some of
#    the ! magic of ipython, so that you can use unix commands
#    within python and even assign their output to a variable via
#    var = !cmd1 | cmd2 | cmd3                          # enjoy ;)
#
# 4. run via ./scriptname.ipy - if it fails with recognizing % and !
#    but parses raw python fine, please check again for the .ipy suffix

# ugly example, please go and find more in the wild
files = !ls *.* | grep "y"
for file in files:
  !echo $file | grep "p"
# sorry for this nonsense example ;)

请参阅IPython文档，了解系统shell命令并将其用作系统shell。