If you are a system administrator, it is likely that you have encountered Perl, Bash, ksh, or some other scripting language. You may have even used one or more yourself. Scripting languages are often used to do repetitive, tedious work at a rate and with an accuracy that far surpass what you could accomplish without them. All languages are tools. They are simply a means to get work done. They have value only insofar as they help you get your job done better. We believe that Python is a valuable tool, specifically because it enables you to get your work done efficiently.
So is Python better than Perl, Bash, Ruby, or any other language? Itâs really difficult to put that sort of qualitative label on a programming language, since the tool is so closely tied to the thought process of the programmer who is using it. Programming is a subjective, deeply personal activity. For the language to be excellent, it must fit the person using it. So weâre not going to argue that Python is better, but we will explain the reasons that we believe Python can be an excellent choice. Weâll also explain why it is a great fit for performing sysadmin tasks.
The first reason that we think that Python is excellent is that it is easy to learn. If a language canât help you become productive pretty quickly, the lure of that language is severely diminished. Why would you want to spend weeks or months studying a language before you are able to write a program that does something useful? This is especially the case for sysadmins. If you are a sysadmin, your work can pile up faster than you can unpile it. With Python, you can start writing useful scripts literally in hours rather than in days or weeks. If you canât learn a language quickly enough to start writing scripts with it almost immediately, you should strongly question whether you should be learning it.
However, a language that is easy to learn but doesnât allow you to do fairly complex tasks isnât worth much either. So the second reason that we consider Python to be an excellent programming language is that, while it lets you start simply, it also allows you to perform tasks that are as complex as you can imagine. Do you need to read through a logfile line by line and pull out some pretty basic information? Python can handle that. Or do you need to parse through a logfile, extract every piece of information that it provides, compare usage from each IP address in this logfile to usage in each logfile (which are stored in a relational database, by the way) from the past three months, and then store the results to a relational database? Sure, Python can do that as well. Python is being used on some pretty complex problems, such as analysis of genomic sequences, multithreaded web servers, and heavy duty statistical analysis. You may never have to work on anything like that, but itâs nice to know that when you need to do complex things, the language is able to work with you.
Additionally, if you are able to perform complex operations, but the maintainability of your code suffers along the way, that isnât a good thing. Python doesnât prevent code maintenance problems, but it does allow you to express complex ideas with simple language constructs. Simplicity is a huge factor in writing code that is easy to maintain later. Python has made it pretty simple for us to go back over our own code and work on it after we havenât touched it in months. It has also been pretty simple for us to work on code that we havenât seen before. So the language, that is the languageâs syntax and common idioms, are clear and concise and easy to work with over long periods of time.
The next reason we consider Python to be an excellent language is its readability. Python relies on whitespace to determine where code blocks begin and end. The indentation helps your eyes quickly follow the flow of a program. Python also tends to be âword-based.â By that we mean that while Python uses its share of special characters, features are often implemented as keywords or with libraries. The emphasis on words rather than special characters helps the reading and comprehension of code.
Now that weâve outlined a few of Pythonâs benefits, weâll show some comparisons of code examples in Python, Perl, and Bash. Along the way, weâll also look at a few more of Pythonâs benefits. Here is a simple example, in Bash, of showing all the combinations of 1, 2 and a, b:
#!/bin/bash
for a in 1 2; do
for b in a b; do
echo "$a $b"
done
doneAnd here is a comparable piece of Perl:
#!/usr/bin/perl
foreach $a ('1', '2') {
foreach $b ('a', 'b') {
print "$a $b\n";
}
}This is a pretty simple nested loop. Letâs compare these looping mechanisms with a for loop in Python:
#!/usr/bin/env python
for a in [1, 2]:
for b in ['a', 'b']:
print a, bNext, weâll demonstrate using conditionals in Bash, Perl, and
Python. We have a simple if/else condition check here. Weâre just checking
to see whether a certain file path is a directory:
#!/bin/bash
if [ -d "/tmp" ] ; then
echo "/tmp is a directory"
else
echo "/tmp is not a directory"
fiHere is the Perl equivalent of the same script:
#!/usr/bin/perl
if (-d "/tmp") {
print "/tmp is a directory\n";
}
else {
print "/tmp is not a directory\n";
}And here is the Python equivalent of the script:
#!/usr/bin/env python
import os
if os.path.isdir("/tmp"):
print "/tmp is a directory"
else:
print "/tmp is not a directory"Another point in favor of Pythonâs excellence is its simple support for object-oriented programming (OOP). And, actually, the converse of that is that you donât have to do OOP if you donât want to. But if you do, itâs dead simple in Python. OOP allows you to easily and cleanly break problems apart and bundle pieces of functionality together into single âthingsâ or âobjects.â Bash doesnât support OOP, but both Perl and Python do. Here is a module in Perl that defines a class:
package Server;
use strict;
sub new {
my $class = shift;
my $self = {};
$self->{IP} = shift;
$self->{HOSTNAME} = shift;
bless($self);
return $self;
}
sub set_ip {
my $self = shift;
$self->{IP} = shift;
return $self->{IP};
}
sub set_hostname {
my $self = shift;
$self->{HOSTNAME} = shift;
return $self->{HOSTNAME};
}
sub ping {
my $self = shift;
my $external_ip = shift;
my $self_ip = $self->{IP};
my $self_host = $self->{HOSTNAME};
print "Pinging $external_ip from $self_ip ($self_host)\n";
return 0;
}
1;And here is a piece of code that uses it:
#!/usr/bin/perl
use Server;
$server = Server->new('192.168.1.15', 'grumbly');
$server->ping('192.168.1.20');The code that makes use of the OO module is straightforward and simple. The OO module may take a bit more mental parsing if youâre not familiar with OOP or with the way that Perl tackles OOP.
A comparable Python class and use of the class looks something like this:
#!/usr/bin/env python
class Server(object):
def __init__(self, ip, hostname):
self.ip = ip
self.hostname = hostname
def set_ip(self, ip):
self.ip = ip
def set_hostname(self, hostname):
self.hostname = hostname
def ping(self, ip_addr):
print "Pinging %s from %s (%s)" % (ip_addr, self.ip, self.hostname)
if __name__ == '__main__':
server = Server('192.168.1.20', 'bumbly')
server.ping('192.168.1.15')Both the Perl and Python examples demonstrate some of the fundamental pieces of OOP. The two examples together display the different flavors that each respective language provides while reaching toward its respective goals. They both do the same thing, but are different from one another. So, if you want to use OOP, Python supports it. And itâs quite simple and clear to incorporate it into your programming.
Another element of Pythonâs excellence comes not from the language
itself, but from the community. In the Python community, there is
much consensus about the way to accomplish certain tasks and the idioms
that you should (and should not) use. While the language itself may
support certain phrasings for accomplishing something, the consensus of
the community may steer you away from that phrasing. For example,
from module import * at the top of a
module is valid Python. However, the community frowns upon this and
recommends that you use either: import
module or: from module import
resource. Importing all the contents of a module into another
moduleâs namespace can cause serious annoyance when you try to figure
out how a module works, what functions it is calling, and where those
functions come from. This particular convention will help you write code
that is clearer and will allow people who work on your code after you to
have a more pleasant maintenance experience. Following common
conventions for writing your code will put you on the path of best
practices. We consider this a good thing.
The Python Standard Library is another excellent attribute of Python. If you ever hear the phrase âbatteries includedâ in reference to Python, it simply means that the standard library allows you to perform all sorts of tasks without having to go elsewhere for modules to help you get it done. For example, though it isnât built-in to the language directly, Python includes regular expression functionality; sockets; threads; date/time functionality; XML parsers; config file parser; file and directory functionality; data persistence; unit test capabilities; and http, ftp, imap, smpt, and nntp client libraries; and much more. So once Python is installed, modules to support all of these functions will be imported by your scripts as they are needed. You have all the functionality we just listed here. It is impressive that all of this comes with Python without requiring anything else. All of this functionality will help you out immensely as you write Python programs to do work for you.
Easy access to numerous third-party packages is another real advantage of Python. In addition to the many libraries in the Python Standard Library, there are a number of libraries and utilities that are easily accessible on the internet that you can install with a single shell command. The Python Package Index, PyPI (http://pypi.python.org), is a place where anyone who has written a Python package can upload it for others to use. At the time we are writing this book, there are over 3,800 packages available for download and use. Packages include IPython, which we cover in the following chapter; Storm (an object-relational mapper, which we cover in Chapter 12); and Twisted, a network framework, which we cover in Chapter 5âjust to name 3 of the over 3,800 packages. Once you start using PyPI, youâll find it nearly indispensible for finding and installing useful packages.
Many of the benefits that we see in Python stem from the central
philosophy of Python. When you type import
this at a Python prompt, you will see The Zen of
Python by Tim Peters. Here it is:
In [1]: import this The Zen of Python, by Tim Peters Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you're Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let's do more of those!
This statement isnât a dogmatic imperative that is strictly enforced at all levels of development of the language, but the spirit of it seems to permeate much of what happens in and with the language. And we have found this spirit to be a beautiful thing. This is perhaps the essence of why we choose to use Python day after day. This philosophy resonates within us as what we want and expect from a language. And if this resonates with you, then Python is probably a good choice for you as well.
If you justpicked up this book in a bookstore or are reading an introduction online somewhere, you may be asking yourself, how hard it is going to be to learn Python and if it is even worth it. Although Python is catching on like wildfire, there are many sysadmins who have been exposed to Bash and Perl only. If you find yourself in this category, you should take comfort in knowing that Python is very easy to learn. In fact, although it is a matter of opinion, Python is considered by many to be the easiest language to learn and teach, period!
If you already know Python, or are a programming guru in another language, you will probably be able to jump right into any of the following chapters without reading this intro and immediately start being productive using our examples. We made a concerted effort to create examples that will actually help you get your job done. There are examples of ways to discover and monitor subnets automatically with SNMP, to convert to an interactive Python shell called IPython, to build data processing pipelines, to write custom metadata management tools with object-relational mappers, to perform network programming, to write command-line tools, and much more.
If you are coming from a shell programming/scripting background, though, donât worry at all. You, too, can learn Python quite easily. You need only motivation, curiosity, and determination, the same factors that led you to pick up this book and look at the introduction in the first place.
We sense there are still a few skeptics out there. Maybe some of the things you have heard about programming have scared you. One common, and horribly false, misconception is that only some people can learn to program, and they are a mysterious and elite few. The frank truth is that anyone can learn how to program. A second, equally false, misconception is that earning a computer science degree is the only way a person can truly become a software engineer. But some of the most prolific software developers do not have engineering degrees. There are people with philosophy, journalism, nutritional science, and English degrees who are competent Python programmers. Having a degree in computer science is not a requirement to learn Python, although it certainly doesnât hurt.
Another funny, and false, misconception is that you must have started to program in your teenage years, or you will never learn to program. While this makes people who were lucky enough to have someone in their life that encouraged them to program at a young age feel good, it is another myth. It is very helpful to have started learning programming at a young age, but age is not a requirement to learn Python. Learning Python is most certainly not a âyoung personâs game,â as we have heard some people say. There are countless cases of developers who learned to program in their late 20s, 30s, 40s, and onward.
If you have gotten this far, we should point out that you, the reader, have an advantage many people do not. If you decided to pick up a book on Python for Unix and Linux system administration, then you most likely know something about how to execute commands from a shell. This is a tremendous advantage to learning to become a Python programmer. Having an understanding of the way to execute commands from a terminal is all that is required for this introduction to Python. If you truly believe you will learn how to program with Python, then read the next section immediately. If you donât believe it yet, then reread this section again, and convince yourself it really is just a matter of getting your mind to understand you do have the power to learn how to program in Python. It is really that simple; if you make this decision, it will change your life.
This introduction to Python is going to be very different from any other one
weâve seen, as it will use an interactive shell called IPython and a
regular Bash shell. You will need to open two terminal windows, one with
IPython and one with Bash. In every example, we will compare what we do
in Python with a Bash example. The first steps are to download the correct version of IPython for your platform
and install it. You can get a copy at http://ipython.scipy.org/moin/Download. If for some
reason, you canât get IPython to install you can also just use a regular
Python shell. You can also download a copy of the virtual machine that
includes all of the software for the book, as we have a copy of IPython
preconfigured and ready to run. You just need to type in ipython, and you will get a prompt.
Once you have installed IPython and have an IPython shell prompt, it should look something like this:
[ngift@Macintosh-7][H:10679][J:0]# ipython
Python 2.5.1 (r251:54863, Jan 17 2008, 19:35:17)
Type "copyright", "credits" or "license" for more information.
IPython 0.8.2 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object'. ?object also works, ?? prints more.
In [1]:An IPython shell is quite a bit like a regular Bash
shell and can execute commands such as ls, cd, and
pwd, but you can read the next
chapter for more of a scoop on IPython. This chapter is about learning
Python, so on to the tutorial.
In your Python terminal, type in the following:
In [1]: print "I can program in Python" I can program in Python
In your Bash terminal, type in the following:
[ngift@Macintosh-7][H:10688][J:0]# echo "I can program in Bash" I can program in Bash
In these two examples, there isnât much of a difference in Python and Bash; we hope it takes some of the mystery out of Python.
If you spend a lot of your day typing commands into a terminal, then you are used to executing statements and, perhaps, redirecting the output to a file or to another Unix command. Letâs look at the way we would execute a command in Bash and then compare that to the way it works in Python. In the Bash terminal, type the following:
[ngift@Macintosh-7][H:10701][J:0]# ls -l /tmp/ total 0 -rw-r--r-- 1 ngift wheel 0 Apr 7 00:26 file.txt
In the Python terminal, type the following:
In [2]: import subprocess
In [3]: subprocess.call(["ls","-l ","/tmp/"])
total 0
-rw-r--r-- 1 ngift wheel 0 Apr 7 00:26 file.txt
Out[3]: 0The Bash example shouldnât need any explanation as
it is a simple ls command, but if you
have never seen Python code before, the Python example probably looks a
bit strange. You might be thinking, âWhat the heck is this import subprocess thing?â One of the powerful
features of Python is its ability to import modules or other files that
contain code and reuse them in a new program. If you are familiar with
âsourcingâ a file in Bash, then you will recognize some similarities. In
this particular situation, all that is important to know is that you
import the subprocess and use it in the syntax that is shown. We will
get into the particulars of how subprocess and import work later, but for now, ignore why it
works and copy the code:
subprocess.call(["some_command", "some_argument", "another_argument_or_path"])
You can run any shell command in Python just as it would
be run with Bash. Given this bit of information, you can now create a
Python version of ls. Just open up
your favorite text editor in another terminal tab or window and place this in a file named
pyls.py, and make it executable by using
chmod +x pyls.py. See Example 1-1.
Example 1-1. Python wrapper for ls command
#!/usr/bin/env python
#Python wrapper for the ls command
import subprocess
subprocess.call(["ls","-l"])Now if you run this script, you will get the exact same output
that you would get if you ran ls -ls
from the command line:
[ngift@Macintosh-7][H:10746][J:0]# ./pyls.py total 8 -rwxr-xr-x 1 ngift staff 115 Apr 7 12:57 pyls.py
While this may seem silly, (and it is silly actually), it gives you a good idea of a common use of Python in systems programming. Often, you use Python to âwrapâ other scripts or Unix commands. Given this new bit of information, you could happily start writing some basic scripts if you just put one command after another in a file and ran it. Letâs take a look at something pretty simple that does just that. To follow along from home, either cut and paste the code in Example 1-2, or run the scripts pysysinfo.py and bashsysinfo.sh located in the source code that is included with this chapter. See Examples 1-2 and 1-3.
Example 1-2. System information scriptâPython
#!/usr/bin/env python
#A System Information Gathering Script
import subprocess
#Command 1
uname = âunameâ
uname_arg = â-aâ
print "Gathering system information with %s command:\n" % uname
subprocess.call([uname, uname_arg])
#Command 2
diskspace = "df"
diskspace_arg = "-h"
print "Gathering diskspace information %s command:\n" % diskspace
subprocess.call([diskspace, diskspace_arg])Example 1-3. System information scriptâBash
#!/usr/bin/env bash
#A System Information Gathering Script
#Command 1
UNAME="uname -a"
printf âGathering system information with the $UNAME command: \n\n"
$UNAME
#Command 2
DISKSPACE="df -h"
printf "Gathering diskspace information with the $DISKSPACE command: \n\n"
$DISKSPACEIf we look at both of the scripts, we see that they look a lot a
like. And if we run them, we see that the output of each is identical.
One quick note though: splitting the command from the argument is
completely optional using subprocess.call. You can also use
this syntax:
subprocess.call("df -h", shell=True)So
far so good, but we still havenât explained import and subprocess completely. In the Python version
of the script, we imported the subprocess module because it already contained
the code to make system calls in Python.
As we mentioned earlier, importing a module like subprocess is
just importing a file that contains code you can use. You can create
your own module or file and reuse code you have written in the same way
you import subprocess. Importing is not magic at
all, it is just a file with some code in it. One of the nice things
about the IPython shell that you have open is its ability to inspect
inside modules and files, and see the attributes that are available
inside them. In Unix terms, this is a lot like running the ls command inside of
/usr/bin. If you happen to be on a new system such
as Ubuntu or Solaris, and you are used to Red Hat, you might do an
ls of /usr/bin
to see if tools such as wget, curl, or lynx are available. If you want
to use a tool you find inside /usr/bin, you would
simply type /usr/bin/wget, for
example.
Modules such as subprocess are very similar.
With IPython you can use tab complete to look at the tools that are available inside a module. Letâs
walk through subprocess using tab complete to look
at the attributes available inside of it. Remember, a module is just a
file with some code in it. Here is what a tab complete looks like with
the subprocess module in
IPython:
In [12]: subprocess. subprocess.CalledProcessError subprocess.__hash__ subprocess.call subprocess.MAXFD subprocess.__init__ subprocess.check_call subprocess.PIPE subprocess.__name__ subprocess.errno subprocess.Popen subprocess.__new__ subprocess.fcntl subprocess.STDOUT subprocess.__reduce__ subprocess.list2cmdline subprocess.__all__ subprocess.__reduce_ex__ subprocess.mswindows subprocess.__builtins__ subprocess.__repr__ subprocess.os subprocess.__class__ subprocess.__setattr__ subprocess.pickle subprocess.__delattr__ subprocess.__str__ subprocess.select subprocess.__dict__ subprocess._active subprocess.sys subprocess.__doc__ subprocess._cleanup subprocess.traceback subprocess.__file__ subprocess._demo_posix subprocess.types subprocess.__getattribute__ subprocess._demo_windows
To replicate this same behavior, you simply need to type:
import subprocess
subprocess.
and press Tab
to get a tab completion of the attributes available. In the third column
of our example, notice that you see
subprocess.call. Now, to see more information about
how to use subprocess.call, type:
In [13]: subprocess.call?
Type: function
Base Class: <type 'function'>
String Form: <function call at 0x561370>
Namespace: Interactive
File: /System/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/
subprocess.py
Definition: subprocess.call(*popenargs, **kwargs)
Docstring:
Run command with arguments. Wait for command to complete, then
return the returncode attribute.
The arguments are the same as for the Popen constructor. Example:
retcode = call(["ls", "-l"])Think of the special question mark syntax as a manpage query. If you want to know how a tool works in Unix, simply type:
man name_of_tool
It
is the same with an attribute located inside a module such as subprocess.call. In IPython, when you type a
question mark after the attribute you want to find information about,
the documentation that has been included with the attribute will print
out. If you do this on most attributes in the standard library, you
should find some helpful information to properly use them. Keep in mind
that you can also refer to the Python Standard Library documentation as
well.
When we look at this documentation, âDocstringâ is the official
term, we see an example of the way to use
subprocess.call and a description of what it
does.
You now have enough information to call yourself a Python programmer. You know how to write a simple Python script, how to translate simple scripts from Bash and call them with Python, and, finally, how to find documentation about new modules and attributes. In the next section, youâll see how to better organize these flat sequences of commands into functions.
In the previous section we went through executing statements one after another, which is pretty useful, because it means we were able to automate something that we would normally have to do manually. The next step to automating our code execution is to create functions. If you are not already familiar with writing functions in Bash or another language, then one way to think about functions is as miniscripts. A function allows you to create blocks of statements that get called in groups that live inside of the function. This is quite a bit like the Bash script we wrote in which there were two commands enclosed in a script. One of the differences between a Bash script and a function is that you can include many function scripts. Ultimately, you can have multiple functions that group statements together in a script, and then that group of statements can be called to run a miniprogram at the proper time in your script.
At this point, we need to talk about the topic of whitespace. In Python, a uniform level of indentation must be maintained in nesting code. In another language, like Bash, when you define a function you put brackets around the code inside of a function. With Python, you must indent the code inside of the bracket. This can trip up newcomers to the language, at first, but after a while it will grow on you, and you will realize that this encourages readable code. If you have trouble getting any of these examples to work interactively, make sure you refer to the actual source code to see the proper indentation level. The most common practice is to set a tab to indent exactly four spaces.
Letâs take a look at how this works in Python and Bash. If you still have the IPython shell open, you donât need to create a Python script file, although you can if you like. Just type the following into the interactive IPython prompt:
In [1]: def pyfunc():
...: print "Hello function"
...:
...:
In [2]: pyfunc
Out[2]: <function pyfunc at 0x2d5070>
In [3]: pyfunc()
Hello function
In [4]: for i in range(5):
...: pyfunc()
...:
...:
Hello function
Hello function
Hello function
Hello function
Hello functionIn this example, you can see that putting a print statement in a function allows you not only to call the function later but also to call it as many times as we want. In line [4], we use a programming idiom, or technique, that executes the function five times. If you havenât seen that technique before, understand that it calls the function five times.
We can do the same thing in a live Bash shell as well. Here is one way:
bash-3.2$ function shfunc()
> {
> printf "Hello function\n"
> }
bash-3.2$ for (( i=0 ; i < 5 ; i++))
> do
> shfunc
> done
Hello function
Hello function
Hello function
Hello function
Hello functionIn the Bash example, we created a simple function
shfunc, and then called it five times, just like we
did with the Python function earlier. One thing to notice is that the
Bash example requires more âbaggageâ to do the same thing that Python
does. Notice the difference between the Bash for
loop and the Python for loop. If this is your first
exposure to a function in Bash or
Python, you should make some other functions in your IPython window
before you continue.
Functions are not magic, and writing multiple functions interactively is a great way to take away the mystery if this is your first experience with them. Here are a couple of examples of simple functions:
In [1]: def print_many(): ...: print "Hello function" ...: print "Hi again function" ...: print "Sick of me yet" ...: ...: In [2]: print_many() Hello function Hi again function Sick of me yet
In [3]: def addition(): ...: sum = 1+1 ...: print "1 + 1 = %s" % sum ...: ...: In [4]: addition() 1 + 1 = 2
Now we have a few silly examples under our belt, in addition to the silly examples that you tried out on your own as well, right? So we can go back to the script we wrote that prints system information and convert those statements into functions. See Example 1-4.
Example 1-4. Converted Python system info script: pysysinfo_func.py
#!/usr/bin/env python
#A System Information Gathering Script
import subprocess
#Command 1
def uname_func():
uname = "uname"
uname_arg = "-a"
print "Gathering system information with %s command:\n" % uname
subprocess.call([uname, uname_arg])
#Command 2
def disk_func():
diskspace = "df"
diskspace_arg = "-h"
print "Gathering diskspace information %s command:\n" % diskspace
subprocess.call([diskspace, diskspace_arg])
#Main function that call other functions
def main():
uname_func()
disk_func()
main()Given our experiments with functions, this converted example of our previous script that we simply placed these statements inside functions and then used the main function to call them all at once. If you are not familiar with this style, you might not have known that it is common to create several functions inside a script and then call them all with one main function. One of many reasons for this is that if you decide to reuse this script for another program, you can either call the functions independently or together with the main method. The key is that you decide after the module is imported.
When there is no control flow, or main function, then all of the code gets executed immediately when it is imported. This may be OK for a one-off script, but if you plan to create reusable tools, and you should, then it is a good practice to create functions that encapsulate specific actions, and then have a main function that executes the whole program.
For comparisonâs sake, letâs convert our previous Bash system information script to use functions as well. See Example 1-5.
Example 1-5. Converted Bash system info script: bashsysinfo_func.sh
#!/usr/bin/env bash
#A System Information Gathering Script
#Command 1
function uname_func ()
{
UNAME="uname -a"
printf "Gathering system information with the $UNAME command: \n\n"
$UNAME
}
#Command 2
function disk_func ()
{
DISKSPACE="df -h"
printf "Gathering diskspace information with the $DISKSPACE command: \n\n"
$DISKSPACE
}
function main ()
{
uname_func
disk_func
}
mainLooking at our Bash example, you can see it has quite a bit in common with its Python cousin. We created two functions and then called those two functions by calling the main function. If this is your first experience with functions, then we would highly recommend that you comment out the main method by placing a pound sign in front of both the Bash and the Python scripts and running them again. You should get absolutely nothing when you run both scripts, because the program should execute, but wonât call the two functions inside.
At this point, you are now a programmer capable of writing simple functions in both Bash and Python. Programmers learn by doing, though, so at this point we highly recommend that you change the system calls in these two Bash and Python programs and make them your own. Give yourself some bonus points if you add several new functions to the script and call them from a main function.
One problem with learning something new is that, if it is abstract, like calculus, for example, it is hard to justify caring about it. When was the last time you used the math you learned in high school at the grocery store? In our previous examples, we showed you how to create functions as an alternative to executing shell commands one after another in a script. We also told you that a module is really just a script, or some lines of code in a file. It isnât anything tricky, but it does need to be arranged in a particular way so that it can be reused in another future program. Here is the point where we show you why you should care. Letâs import the previous system information scripts in both Bash and Python and execute.
Open the IPython and Bash windows if you closed them so that we can demonstrate very quickly why functions are important for code reuse. One of the first scripts we created in Python was a sequence of commands in a file named pysysinfo.py. In Python because a file is a module and vice versa, we can import this script file into IPython. Keep in mind that you never need to specify the .py portion of the file you are importing. In fact if you do this, the import will not work. Here is what it looks like when we do that on Noahâs Macbook Pro laptop:
In [1]: import pysysinfo Gathering system information with uname command: Darwin Macintosh-8.local 9.2.2 Darwin Kernel Version 9.2.2: / Tue Mar 4 21:17:34 PST 2008; root:xnu-1228.4.31~1/RELEASE_I386 i386 Gathering diskspace information df command: Filesystem Size Used Avail Capacity Mounted on /dev/disk0s2 93Gi 88Gi 4.2Gi 96% / devfs 110Ki 110Ki 0Bi 100% /dev fdesc 1.0Ki 1.0Ki 0Bi 100% /dev map -hosts 0Bi 0Bi 0Bi 100% /net map auto_home 0Bi 0Bi 0Bi 100% /home /dev/disk1s2 298Gi 105Gi 193Gi 36% /Volumes/Backup /dev/disk2s3 466Gi 240Gi 225Gi 52% /Volumes/EditingDrive
Wow, that is pretty cool, right? If you import a file full of Python code it seems to runs great. But, actually, there are a few problems with this. If you plan to run Python code, it should always be executed from the command line as a part of a script or program you write. Using import is to help with this âreusing codeâ idea we keep throwing around. Here is the punch line: what if you only wanted to print the output of the diskspace portion of the script? The answer is you canât. That is why you use functions. They allow you to control when and how parts of your program run so that they donât all run at once, as they do in this example. Donât just take our word for it, though. If you import the example of a script that puts these commands into functions, youâll see what we mean.
Here is the output from the IPython terminal:
In [3]: import pysysinfo_func Gathering system information with uname command: Darwin Macintosh-8.local 9.2.2 Darwin Kernel Version 9.2.2: Tue Mar 4 21:17:34 PST 2008; root:xnu-1228.4.31~1/RELEASE_I386 i386 Gathering diskspace information df command: Filesystem Size Used Avail Capacity Mounted on /dev/disk0s2 93Gi 88Gi 4.1Gi 96% / devfs 110Ki 110Ki 0Bi 100% /dev fdesc 1.0Ki 1.0Ki 0Bi 100% /dev map -hosts 0Bi 0Bi 0Bi 100% /net map auto_home 0Bi 0Bi 0Bi 100% /home /dev/disk1s2 298Gi 105Gi 193Gi 36% /Volumes/Backup /dev/disk2s3 466Gi 240Gi 225Gi 52% /Volumes/EditingDrive
Now
we get the exact same output that we get from script that doesnât
contain functions. If you are puzzled, this is a good sign. To see the
reason we get the same exact output, we just need to look at the source
code. If you are following along at home, open up another terminal tab
or window and look at the script
pysysinfo_func:
#Main function that call other functions
def main():
uname_func()
disk_func()
main()The problem is that main function
we created at the end of the last chapter is coming back to bite us. On
one hand we want to be able to run our script on the command line to get
the output, but on the other hand when we import it we donât want all of
the output all at once. Fortunately, the need to use a module as both a
script that gets executed from the command line and as a reusable module
is very common in Python. The solution is to change the way the main
method gets called by replacing the last part of the script to look like
this:
#Main function that call other functions
def main():
uname_func()
disk_func()
if __name__ == "__main__":
main()This is an âidiom,â a technique that is commonly
used to solve a problem. Any code that you indent underneath this
statement gets run only when it is executed from the command line. To
see this, either replace this in your copy of the script or import the
fixed version of the script
pysysinfo_func_2.py.
Now, if we go back to our IPython interpreter and import this new script, we should see this:
In [1]: import pysysinfo_func_2
This
time, the main method is not called, because of the
fix we made. So, to return to our original point about reusable code, we
have three functions that we can use in other programs or use to
interact with the IPython shell. Remember earlier we said how it would
be nice to call only the function that prints the disk usage without
having to call the function that calls the other commands, too. First,
though, we need to review an IPython trick that we showed you before.
Remember that you can use Tab to complete a module, and it will show you
all of the attributes that are available to use. Hereâs
what that looks like:
In [2]: pysysinfo_func_2. pysysinfo_func_2.__builtins__ pysysinfo_func_2.disk_func pysysinfo_func_2.__class__ pysysinfo_func_2.main pysysinfo_func_2.__delattr__ pysysinfo_func_2.py pysysinfo_func_2.__dict__ pysysinfo_func_2.pyc pysysinfo_func_2.__doc__ pysysinfo_func_2.subprocess pysysinfo_func_2.__file__ pysysinfo_func_2.uname_func pysysinfo_func_2.__getattribute__ pysysinfo_func_2.__hash__
In this example, we can ignore anything with double underscores,
because these are special methods that are beyond the scope of this
introduction. Because IPython is also a regular shell, it picks up the
filename and the byte-compiled Python file with the .pyc extension. Once we filter past all of
those names, we can see that there is a pysysinfo_func_2.disk_func. Letâs go ahead and
call that function:
In [2]: pysysinfo_func_2.disk_func() Gathering diskspace information df command: Filesystem Size Used Avail Capacity Mounted on /dev/disk0s2 93Gi 89Gi 4.1Gi 96% / devfs 111Ki 111Ki 0Bi 100% /dev fdesc 1.0Ki 1.0Ki 0Bi 100% /dev map -hosts 0Bi 0Bi 0Bi 100% /net map auto_home 0Bi 0Bi 0Bi 100% /home /dev/disk1s2 298Gi 105Gi 193Gi 36% /Volumes/Backup /dev/disk2s3 466Gi 240Gi 225Gi 52% /Volumes/EditingDrive
You
might have realized by now that functions are always âcalledâ or run by
attaching the â()â after the name. In this case, we ran just that one
function inside of a file that contained three functions: the function
we just called disk_func, the
uname_func, and finally the
main function. Aha! We finally have our code reuse.
We were able to import something we wrote earlier and interactively run
just the part of it we needed. Of course, we can also run the other two
functions we wrote separately. Letâs take a look at that:
In [3]: pysysinfo_func_2.uname_func() Gathering system information with uname command: Darwin Macintosh-8.local 9.2.2 Darwin Kernel Version 9.2.2: Tue Mar 4 21:17:34 PST 2008; root:xnu-1228.4.31~1/RELEASE_I386 i386 In [4]: pysysinfo_func_2.main() Gathering system information with uname command: Darwin Macintosh-8.local 9.2.2 Darwin Kernel Version 9.2.2: Tue Mar 4 21:17:34 PST 2008; root:xnu-1228.4.31~1/RELEASE_I386 i386 Gathering diskspace information df command: Filesystem Size Used Avail Capacity Mounted on /dev/disk0s2 93Gi 89Gi 4.1Gi 96% / devfs 111Ki 111Ki 0Bi 100% /dev fdesc 1.0Ki 1.0Ki 0Bi 100% /dev map -hosts 0Bi 0Bi 0Bi 100% /net map auto_home 0Bi 0Bi 0Bi 100% /home /dev/disk1s2 298Gi 105Gi 193Gi 36% /Volumes/Backup /dev/disk2s3 466Gi 240Gi 225Gi 52% /Volumes/EditingDrive
If
you look carefully, youâll see that we ran both of the other functions.
Remember, the main function runs everything at
once.
Often, the point of writing a reusable module is so that you can take some of the code and use it over and over again in a new script. So practice that by writing another script that uses one of the functions. See Example 1-6.
Example 1-6. Reusing code with import: new_pysysinfo
#Very short script that reuses pysysinfo_func_2 code
from pysysinfo_func_2 import disk_func
import subprocess
def tmp_space():
tmp_usage = "du"
tmp_arg = "-h"
path = "/tmp"
print "Space used in /tmp directory"
subprocess.call([tmp_usage, tmp_arg, path])
def main():
disk_func()
tmp_space()
if __name__ == "__main__":
main()In this example, not only do we reuse the code we wrote earlier,
but we use a special Python syntax that allows us to import the exact
function we need. Whatâs fun about reusing code is that it is possible
to make a completely different program just by importing the function
from our previous program. Notice that in the main method we mix the
function from the other module we created,
disk_func(), and the new one we just created in
this file.
In this section, we learned the power of code reuse and how simple
it really is. In a nutshell, you put a function or two in a file and
then, if you also want it to run as script, place that special
if__name__ == "__main__":
syntax. Later you can either import those functions into IPython or
simply reuse them in another script. With the information you have just
learned, you are truly dangerous. You could write some pretty sophisticated Python modules and
reuse them over and over again to create new tools.
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