You have seen how you can reuse code in your program by defining functions once. What if you wanted to reuse a number of functions in other programs that you write? As you might have guessed, the answer is modules.
There are various methods of writing modules, but the simplest way is to create a file with a .py
extension that contains functions and variables.
Another method is to write the modules in the native language in which the Python interpreter itself was written. For example, you can write modules in the C programming language and when compiled, they can be used from your Python code when using the standard Python interpreter.
A module can be imported by another program to make use of its functionality. This is how we can use the Python standard library as well. First, we will see how to use the standard library modules.
Example (save as module_using_sys.py):
link:programs/module_using_sys.py[role=include]Output:
link:programs/module_using_sys.txt[role=include]
First, we import the sys module using the import statement. Basically, this translates to us
telling Python that we want to use this module. The sys module contains functionality related to
the Python interpreter and its environment i.e. the system.
When Python executes the import sys statement, it looks for the sys module. In this case, it is
one of the built-in modules, and hence Python knows where to find it.
If it was not a compiled module i.e. a module written in Python, then the Python interpreter will
search for it in the directories listed in its sys.path variable. If the module is found, then
the statements in the body of that module are run and the module is made available for you
to use. Note that the initialization is done only the first time that we import a module.
The argv variable in the sys module is accessed using the dotted notation i.e. sys.argv. It
clearly indicates that this name is part of the sys module. Another advantage of this approach is
that the name does not clash with any argv variable used in your program.
The sys.argv variable is a list of strings (lists are explained in detail in a
later chapter. Specifically, the sys.argv contains the list of command line
arguments i.e. the arguments passed to your program using the command line.
If you are using an IDE to write and run these programs, look for a way to specify command line arguments to the program in the menus.
Here, when we execute python module_using_sys.py we are arguments, we run the module
module_using_sys.py with the python command and the other things that follow are arguments
passed to the program. Python stores the command line arguments in the sys.argv variable for us
to use.
Remember, the name of the script running is always the first argument in the sys.argv list. So,
in this case we will have 'module_using_sys.py' as sys.argv[0], 'we' as sys.argv[1],
'are' as sys.argv[2] and 'arguments' as sys.argv[3]. Notice that Python starts counting
from 0 and not 1.
The sys.path contains the list of directory names where modules are imported from. Observe that
the first string in sys.path is empty - this empty string indicates that the current directory is
also part of the sys.path which is same as the PYTHONPATH environment variable. This means that
you can directly import modules located in the current directory. Otherwise, you will have to place
your module in one of the directories listed in sys.path.
Note that the current directory is the directory from which the program is launched. Run import
os; print os.getcwd() to find out the current directory of your program.
Importing a module is a relatively costly affair, so Python does some tricks to make it faster. One
way is to create byte-compiled files with the extension .pyc which is an intermediate form that
Python transforms the program into (remember the introduction section on how Python
works?). This .pyc file is useful when you import the module the next time from a different
program - it will be much faster since a portion of the processing required in importing a module
is already done. Also, these byte-compiled files are platform-independent.
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Note
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These .pyc files are usually created in the same directory as the corresponding .py
files. If Python does not have permission to write to files in that directory, then the .pyc
files will not be created.
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If you want to directly import the argv variable into your program (to avoid typing the sys.
everytime for it), then you can use the from sys import argv statement.
In general, you should avoid using this statement and use the import statement instead since
your program will avoid name clashes and will be more readable.
Example:
from math import sqrt
print "Square root of 16 is", sqrt(16)Every module has a name and statements in a module can find out the name of their module. This is
handy for the particular purpose of figuring out whether the module is being run standalone or
being imported. As mentioned previously, when a module is imported for the first time, the code it
contains gets executed. We can use this to make the module behave in different ways depending on
whether it is being used by itself or being imported from another module. This can be achieved
using the name attribute of the module.
Example (save as module_using_name.py):
link:programs/module_using_name.py[role=include]Output:
link:programs/module_using_name.txt[role=include]
Every Python module has its name defined. If this is 'main', that implies that the
module is being run standalone by the user and we can take appropriate actions.
Creating your own modules is easy, you’ve been doing it all along! This is because every Python
program is also a module. You just have to make sure it has a .py extension. The following
example should make it clear.
Example (save as mymodule.py):
link:programs/mymodule.py[role=include]The above was a sample module. As you can see, there is nothing particularly special about it compared to our usual Python program. We will next see how to use this module in our other Python programs.
Remember that the module should be placed either in the same directory as the program from which we
import it, or in one of the directories listed in sys.path.
Another module (save as mymodule_demo.py):
link:programs/mymodule_demo.py[role=include]Output:
link:programs/mymodule_demo.txt[role=include]
Notice that we use the same dotted notation to access members of the module. Python makes good reuse of the same notation to give the distinctive 'Pythonic' feel to it so that we don’t have to keep learning new ways to do things.
Here is a version utilising the from..import syntax (save as mymodule_demo2.py):
link:programs/mymodule_demo2.py[role=include]The output of mymodule_demo2.py is same as the output of mymodule_demo.py.
Notice that if there was already a version name declared in the module that imports mymodule,
there would be a clash. This is also likely because it is common practice for each module to
declare it’s version number using this name. Hence, it is always recommended to prefer the import
statement even though it might make your program a little longer.
You could also use:
from mymodule import *This will import all public names such as say_hi but would not import version because it
starts with double underscores.
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Warning
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Remember that you should avoid using import-star, i.e. from mymodule import *.
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One of Python’s guiding principles is that "Explicit is better than Implicit". Run import this in
Python to learn more and see this
StackOverflow discussion which lists examples for each of the principles.
You can use the built-in dir function to list the identifiers that an object defines. For
example, for a module, the identifiers include the functions, classes and variables defined in that
module.
When you supply a module name to the`dir()` function, it returns the list of the names defined in that module. When no argument is applied to it, it returns the list of names defined in the current module.
Example:
$ python >>> import sys # get names of attributes in sys module >>> dir(sys) ['__displayhook__', '__doc__', 'argv', 'builtin_module_names', 'version', 'version_info'] # only few entries shown here # get names of attributes for current module >>> dir() ['__builtins__', '__doc__', '__name__', '__package__'] # create a new variable 'a' >>> a = 5 >>> dir() ['__builtins__', '__doc__', '__name__', '__package__', 'a'] # delete/remove a name >>> del a >>> dir() ['__builtins__', '__doc__', '__name__', '__package__']
First, we see the usage of dir on the imported sys module. We can see the huge list of
attributes that it contains.
Next, we use the dir function without passing parameters to it. By default, it returns the list
of attributes for the current module. Notice that the list of imported modules is also part of this
list.
In order to observe the dir in action, we define a new variable a and assign it a value and
then check dir and we observe that there is an additional value in the list of the same name. We
remove the variable/attribute of the current module using the del statement and the change is
reflected again in the output of the dir function.
A note on del - this statement is used to delete a variable/name and after the statement has
run, in this case del a, you can no longer access the variable a - it is as if it never existed
before at all.
Note that the dir() function works on any object. For example, run dir('print') to learn
about the attributes of the print function, or dir(str) for the attributes of the str class.
There is also a vars() function which can
potentially give you the attributes and their values, but it will not work for all cases.
By now, you must have started observing the hierarchy of organizing your programs. Variables usually go inside functions. Functions and global variables usually go inside modules. What if you wanted to organize modules? That’s where packages come into the picture.
Packages are just folders of modules with a special init.py file that indicates to Python
that this folder is special because it contains Python modules.
Let’s say you want to create a package called 'world' with subpackages 'asia', 'africa', etc. and these subpackages in turn contain modules like 'india', 'madagascar', etc.
This is how you would structure the folders:
- <some folder present in the sys.path>/
- world/
- __init__.py
- asia/
- __init__.py
- india/
- __init__.py
- foo.py
- africa/
- __init__.py
- madagascar/
- __init__.py
- bar.py
Packages are just a convenience to hierarchically organize modules. You will see many instances of this in the standard library.
Just like functions are reusable parts of programs, modules are reusable programs. Packages are another hierarchy to organize modules. The standard library that comes with Python is an example of such a set of packages and modules.
We have seen how to use these modules and create our own modules.
Next, we will learn about some interesting concepts called data structures.