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
import sys print('The command line arguments are:') for i in sys.argv: print(i) print('nnThe PYTHONPATH is', sys.path, 'n')
$ python3 using_sys.py we are arguments The command line arguments are: using_sys.py we are arguments The PYTHONPATH is ['/Users/swaroop/byte_of_python', '/usr/local/Cellar/python3/3.3.2/Frameworks/Python.framework/Versions/3.3/lib/python3.3', '/usr/local/Cellar/python3/3.3.2/Frameworks/Python.framework/Versions/3.3/lib/python3.3/plat-darwin', '/usr/local/Cellar/python3/3.3.2/Frameworks/Python.framework/Versions/3.3/lib/python3.3/lib-dynload', '/usr/local/lib/python3.3/site-packages']
How It Works:
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.
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.
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 using_sys.py we are arguments, we run the 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
sys.argv. Notice that Python starts counting from 0 and not 1.
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
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.
Byte-compiled .pyc files
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.
.pycfiles are usually created in the same directory as the corresponding
.pyfiles. If Python does not have permission to write to files in that directory, then the
.pycfiles will not be created.
The from ... import statement
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.
from math import sqrt print("Square root of 16 is", sqrt(16))
A module's name
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
if __name__ == '__main__': print('This program is being run by itself') else: print('I am being imported from another module')
$ python3 using_name.py This program is being run by itself $ python3 >>> import using_name I am being imported from another module >>>
How It Works:
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.
Making Your Own Modules
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
def sayhi(): print('Hi, this is mymodule speaking.') __version__ = '0.1'
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
Another module (save as
import mymodule mymodule.sayhi() print ('Version', mymodule.__version__)
$ python3 mymodule_demo.py Hi, this is mymodule speaking. Version 0.1
How It Works:
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
from mymodule import sayhi, __version__ sayhi() print('Version', __version__)
The output of
mymodule_demo2.py is same as the output of
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
sayhi but would not import
__version__ because it starts with double underscores.
- Zen of Python
- One of Python's guiding principles is that "Explicit is better than Implicit". Run
import thisto learn more and see this StackOverflow discussion which lists examples for each of the principles.
The dir function
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.
$ python3 >>> import sys # get list of attributes, in this case, for the sys module >>> dir(sys) ['__displayhook__', '__doc__', '__excepthook__', '__name__', '__package__', '__s tderr__', '__stdin__', '__stdout__', '_clear_type_cache', '_compact_freelists', '_current_frames', '_getframe', 'api_version', 'argv', 'builtin_module_names', ' byteorder', 'call_tracing', 'callstats', 'copyright', 'displayhook', 'dllhandle' , 'dont_write_bytecode', 'exc_info', 'excepthook', 'exec_prefix', 'executable', 'exit', 'flags', 'float_info', 'getcheckinterval', 'getdefaultencoding', 'getfil esystemencoding', 'getprofile', 'getrecursionlimit', 'getrefcount', 'getsizeof', 'gettrace', 'getwindowsversion', 'hexversion', 'intern', 'maxsize', 'maxunicode ', 'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache', 'platfor m', 'prefix', 'ps1', 'ps2', 'setcheckinterval', 'setprofile', 'setrecursionlimit ', 'settrace', 'stderr', 'stdin', 'stdout', 'subversion', 'version', 'version_in fo', 'warnoptions', 'winver'] >>> dir() # get list of attributes for current module ['__builtins__', '__doc__', '__name__', '__package__', 'sys'] >>> a = 5 # create a new variable 'a' >>> dir() ['__builtins__', '__doc__', '__name__', '__package__', 'a', 'sys'] >>> del a # delete/remove a name >>> dir() ['__builtins__', '__doc__', '__name__', '__package__', 'sys'] >>>
How It Works:
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
dirand 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
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.
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