Now, we know how to create classes and instantiate objects, but how do we organize them? For small programs, we can just put all our classes into one file and add a little script at the end of the file to start them interacting. However, as our projects grow, it can become difficult to find the one class that needs to be edited among the many classes we've defined. This is where modules come in. Modules are simply Python files, nothing more. The single file in our small program is a module. Two Python files are two modules. If we have two files in the same folder, we can load a class from one module for use in the other module.

For example, if we are building an e-commerce system, we will likely be storing a lot of data in a database. We can put all the classes and functions related to database access into a separate file (we'll call it something sensible: database.py). Then, our other modules (for example, customer models, product information, and inventory) can import classes from that module in order to access the database.

The import statement is used for importing modules or specific classes or functions from modules. We've already seen an example of this in our Point class in the previous section. We used the import statement to get Python's built-in math module and use its sqrt function in our distance calculation.

Here's a concrete example. Assume we have a module called database.py that contains a class called Database, and a second module called products.py that is responsible for product-related queries. At this point, we don't need to think too much about the contents of these files. What we know is that products.py needs to instantiate the Database class from database.py so that it can execute queries on the product table in the database.

There are several variations on the import statement syntax that can be used to access the class:

import database
db = database.Database()
# Do queries on db

This version imports the database module into the products namespace (the list of names currently accessible in a module or function), so any class or function in the database module can be accessed using the database.<something> notation. Alternatively, we can import just the one class we need using the from...import syntax:

from database import Database
db = Database()
# Do queries on db

If, for some reason, products already has a class called Database, and we don't want the two names to be confused, we can rename the class when used inside the products module:

from database import Database as DB
db = DB()
# Do queries on db

We can also import multiple items in one statement. If our database module also contains a Query class, we can import both classes using:

from database import Database, Query

Some sources say that we can import all classes and functions from the database module using this syntax:

from database import *

Don't do this. Every experienced Python programmer will tell you that you should never use this syntax. They'll use obscure justifications such as "it clutters up the namespace", which doesn't make much sense to beginners. One way to learn why to avoid this syntax is to use it and try to understand your code two years later. But we can save some time and two years of poorly written code with a quick explanation now!

When we explicitly import the database class at the top of our file using from database import Database, we can easily see where the Database class comes from. We might use db = Database() 400 lines later in the file, and we can quickly look at the imports to see where that Database class came from. Then, if we need clarification as to how to use the Database class, we can visit the original file (or import the module in the interactive interpreter and use the help(database.Database) command). However, if we use the from database import * syntax, it takes a lot longer to find where that class is located. Code maintenance becomes a nightmare.

In addition, most editors are able to provide extra functionality, such as reliable code completion, the ability to jump to the definition of a class, or inline documentation, if normal imports are used. The import * syntax usually completely destroys their ability to do this reliably.

Finally, using the import * syntax can bring unexpected objects into our local namespace. Sure, it will import all the classes and functions defined in the module being imported from, but it will also import any classes or modules that were themselves imported into that file!

Every name used in a module should come from a well-specified place, whether it is defined in that module, or explicitly imported from another module. There should be no magic variables that seem to come out of thin air. We should always be able to immediately identify where the names in our current namespace originated. I promise that if you use this evil syntax, you will one day have extremely frustrating moments of "where on earth can this class be coming from?".

parent_directory/
    main.py
    ecommerce/
        __init__.py
        database.py
        products.py
        payments/
            __init__.py
            square.py
            stripe.py

import ecommerce.products
product = ecommerce.products.Product()

from ecommerce.products import Product
product = Product()

from ecommerce import products
product = products.Product()

from .database import Database

from ..database import Database

from ..contact.email import send_mail

from .database import db

from ecommerce import db

Inside any one module, we can specify variables, classes, or functions. They can be a handy way to store the global state without namespace conflicts. For example, we have been importing the Database class into various modules and then instantiating it, but it might make more sense to have only one database object globally available from the database module. The database module might look like this:

class Database:
    # the database implementation
    pass

database = Database()

Then we can use any of the import methods we've discussed to access the database object, for example:

from ecommerce.database import database

A problem with the preceding class is that the database object is created immediately when the module is first imported, which is usually when the program starts up. This isn't always ideal since connecting to a database can take a while, slowing down startup, or the database connection information may not yet be available. We could delay creating the database until it is actually needed by calling an initialize_database function to create the module-level variable:

class Database:
    # the database implementation
    pass

database = None

def initialize_database():
    global database
    database = Database()

The global keyword tells Python that the database variable inside initialize_database is the module level one we just defined. If we had not specified the variable as global, Python would have created a new local variable that would be discarded when the method exits, leaving the module-level value unchanged.

As these two examples illustrate, all module-level code is executed immediately at the time it is imported. However, if it is inside a method or function, the function will be created, but its internal code will not be executed until the function is called. This can be a tricky thing for scripts (such as the main script in our e-commerce example) that perform execution. Often, we will write a program that does something useful, and then later find that we want to import a function or class from that module in a different program. However, as soon as we import it, any code at the module level is immediately executed. If we are not careful, we can end up running the first program when we really only meant to access a couple functions inside that module.

To solve this, we should always put our startup code in a function (conventionally, called main) and only execute that function when we know we are running the module as a script, but not when our code is being imported from a different script. But how do we know this?

class UsefulClass:
    '''This class might be useful to other modules.'''
    pass

def main():
    '''creates a useful class and does something with it for our module.'''
    useful = UsefulClass()
    print(useful)

if __name__ == "__main__":
    main()

Every module has a __name__ special variable (remember, Python uses double underscores for special variables, such as a class's __init__ method) that specifies the name of the module when it was imported. When the module is executed directly with python module.py, it is never imported, so the __name__ is arbitrarily set to the string "__main__". Make it a policy to wrap all your scripts in an if __name__ == "__main__": test, just in case you write a function you will find useful to be imported by other code someday.

So, methods go in classes, which go in modules, which go in packages. Is that all there is to it?

Actually, no. This is the typical order of things in a Python program, but it's not the only possible layout. Classes can be defined anywhere. They are typically defined at the module level, but they can also be defined inside a function or method, like this:

def format_string(string, formatter=None):
    '''Format a string using the formatter object, which
    is expected to have a format() method that accepts
    a string.'''
    class DefaultFormatter:
        '''Format a string in title case.'''
        def format(self, string):
            return str(string).title()

    if not formatter:
        formatter = DefaultFormatter()

    return formatter.format(string)

hello_string = "hello world, how are you today?"
print(" input: " + hello_string)
print("output: " + format_string(hello_string))

The output will be as follows:

input: hello world, how are you today?
output: Hello World, How Are You Today?

The format_string function accepts a string and optional formatter object, and then applies the formatter to that string. If no formatter is supplied, it creates a formatter of its own as a local class and instantiates it. Since it is created inside the scope of the function, this class cannot be accessed from anywhere outside of that function. Similarly, functions can be defined inside other functions as well; in general, any Python statement can be executed at any time.

These inner classes and functions are occasionally useful for one-off items that don't require or deserve their own scope at the module level, or only make sense inside a single method. However, it is not common to see Python code that frequently uses this technique.

Most object-oriented programming languages have a concept of access control. This is related to abstraction. Some attributes and methods on an object are marked private, meaning only that object can access them. Others are marked protected, meaning only that class and any subclasses have access. The rest are public, meaning any other object is allowed to access them.

Python doesn't do this. Python doesn't really believe in enforcing laws that might someday get in your way. Instead, it provides unenforced guidelines and best practices. Technically, all methods and attributes on a class are publicly available. If we want to suggest that a method should not be used publicly, we can put a note in docstrings indicating that the method is meant for internal use only (preferably, with an explanation of how the public-facing API works!).

By convention, we should also prefix an attribute or method with an underscore character, _. Python programmers will interpret this as "this is an internal variable, think three times before accessing it directly". But there is nothing inside the interpreter to stop them from accessing it if they think it is in their best interest to do so. Because if they think so, why should we stop them? We may not have any idea what future uses our classes may be put to.

There's another thing you can do to strongly suggest that outside objects don't access a property or method: prefix it with a double underscore, __. This will perform name mangling on the attribute in question. This basically means that the method can still be called by outside objects if they really want to do it, but it requires extra work and is a strong indicator that you demand that your attribute remains private. For example:

class SecretString:
    '''A not-at-all secure way to store a secret string.'''

    def __init__(self, plain_string, pass_phrase):
        self.__plain_string = plain_string
        self.__pass_phrase = pass_phrase

    def decrypt(self, pass_phrase):
        '''Only show the string if the pass_phrase is correct.'''
        if pass_phrase == self.__pass_phrase:
            return self.__plain_string
        else:
            return ''

If we load this class and test it in the interactive interpreter, we can see that it hides the plain text string from the outside world:

>>> secret_string = SecretString("ACME: Top Secret", "antwerp")
>>> print(secret_string.decrypt("antwerp"))
ACME: Top Secret
>>> print(secret_string.__plain_text)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'SecretString' object has no attribute
'__plain_text'

It looks like it works; nobody can access our plain_text attribute without the passphrase, so it must be safe. Before we get too excited, though, let's see how easy it can be to hack our security:

>>> print(secret_string._SecretString__plain_string)
ACME: Top Secret

Oh no! Somebody has hacked our secret string. Good thing we checked! This is Python name mangling at work. When we use a double underscore, the property is prefixed with _<classname>. When methods in the class internally access the variable, they are automatically unmangled. When external classes wish to access it, they have to do the name mangling themselves. So, name mangling does not guarantee privacy, it only strongly recommends it. Most Python programmers will not touch a double underscore variable on another object unless they have an extremely compelling reason to do so.

However, most Python programmers will not touch a single underscore variable without a compelling reason either. Therefore, there are very few good reasons to use a name-mangled variable in Python, and doing so can cause grief. For example, a name-mangled variable may be useful to a subclass, and it would have to do the mangling itself. Let other objects access your hidden information if they want to, just let them know, using a single-underscore prefix or some clear docstrings, that you think this is not a good idea.

Python ships with a lovely standard library, which is a collection of packages and modules that are available on every machine that runs Python. However, you'll soon find that it doesn't contain everything you need. When this happens, you have two options:

We won't be covering the details about turning your packages into libraries, but if you have a problem you need to solve and you don't feel like coding it (the best programmers are extremely lazy and prefer to reuse existing, proven code, rather than write their own), you can probably find the library you want on the Python Package Index (PyPI) at http://pypi.python.org/. Once you've identified a package that you want to install, you can use a tool called pip to install it. However, pip does not come with Python, but Python 3.4 contains a useful tool called ensurepip, which will install it:

python -m ensurepip

This may fail for you on Linux, Mac OS, or other Unix systems, in which case, you'll need to become root to make it work. On most modern Unix systems, this can be done with sudo python -m ensurepip.

Once pip is installed and you know the name of the package you want to install, you can install it using syntax such as:

pip install requests

However, if you do this, you'll either be installing the third-party library directly into your system Python directory, or more likely, get an error that you don't have permission to do so. You could force the installation as an administrator, but common consensus in the Python community is that you should only use system installers to install the third-party library to your system Python directory.

Instead, Python 3.4 supplies the venv tool. This utility basically gives you a mini Python installation called a virtual environment in your working directory. When you activate the mini Python, commands related to Python will work on that directory instead of the system directory. So when you run pip or python, it won't touch the system Python at all. Here's how to use it:

cd project_directory
python -m venv env
source env/bin/activate  # on Linux or MacOS
env/bin/activate.bat     # on Windows

Typically, you'll create a different virtual environment for each Python project you work on. You can store your virtual environments anywhere, but I keep mine in the same directory as the rest of my project files (but ignored in version control), so first we cd into that directory. Then we run the venv utility to create a virtual environment named env. Finally, we use one of the last two lines (depending on the operating system, as indicated in the comments) to activate the environment. We'll need to execute this line each time we want to use that particular virtualenv, and then use the command deactivate when we are done working on this project.

Virtual environments are a terrific way to keep your third-party dependencies separate. It is common to have different projects that depend on different versions of a particular library (for example, an older website might run on Django 1.5, while newer versions run on Django 1.8). Keeping each project in separate virtualenvs makes it easy to work in either version of Django. Further, it prevents conflicts between system-installed packages and pip installed packages if you try to install the same package using different tools.

To tie it all together, let's build a simple command-line notebook application. This is a fairly simple task, so we won't be experimenting with multiple packages. We will, however, see common usage of classes, functions, methods, and docstrings.

Let's start with a quick analysis: notes are short memos stored in a notebook. Each note should record the day it was written and can have tags added for easy querying. It should be possible to modify notes. We also need to be able to search for notes. All of these things should be done from the command line.

The obvious object is the Note object; less obvious one is a Notebook container object. Tags and dates also seem to be objects, but we can use dates from Python's standard library and a comma-separated string for tags. To avoid complexity, in the prototype, let's not define separate classes for these objects.

Note objects have attributes for memo itself, tags, and creation_date. Each note will also need a unique integer id so that users can select them in a menu interface. Notes could have a method to modify note content and another for tags, or we could just let the notebook access those attributes directly. To make searching easier, we should put a match method on the Note object. This method will accept a string and can tell us if a note matches the string without accessing the attributes directly. This way, if we want to modify the search parameters (to search tags instead of note contents, for example, or to make the search case-insensitive), we only have to do it in one place.

The Notebook object obviously has the list of notes as an attribute. It will also need a search method that returns a list of filtered notes.

But how do we interact with these objects? We've specified a command-line app, which can mean either that we run the program with different options to add or edit commands, or we have some kind of a menu that allows us to pick different things to do to the notebook. We should try to design it such that either interface is supported and future interfaces, such as a GUI toolkit or web-based interface, could be added in the future.

As a design decision, we'll implement the menu interface now, but will keep the command-line options version in mind to ensure we design our Notebook class with extensibility in mind.

If we have two command-line interfaces, each interacting with the Notebook object, then Notebook will need some methods for those interfaces to interact with. We need to be able to add a new note, and modify an existing note by id, in addition to the search method we've already discussed. The interfaces will also need to be able to list all notes, but they can do that by accessing the notes list attribute directly.

We may be missing a few details, but that gives us a really good overview of the code we need to write. We can summarize all this in a simple class diagram:

Before writing any code, let's define the folder structure for this project. The menu interface should clearly be in its own module, since it will be an executable script, and we may have other executable scripts accessing the notebook in the future. The Notebook and Note objects can live together in one module. These modules can both exist in the same top-level directory without having to put them in a package. An empty command_option.py module can help remind us in the future that we were planning to add new user interfaces.

parent_directory/
    notebook.py
    menu.py
    command_option.py

Now let's see some code. We start by defining the Note class as it seems simplest. The following example presents Note in its entirety. Docstrings within the example explain how it all fits together.

import datetime

# Store the next available id for all new notes
last_id = 0

class Note:
    '''Represent a note in the notebook. Match against a
    string in searches and store tags for each note.'''

    def __init__(self, memo, tags=''):
        '''initialize a note with memo and optional
        space-separated tags. Automatically set the note's
        creation date and a unique id.'''
        self.memo = memo
        self.tags = tags
        self.creation_date = datetime.date.today()
        global last_id
        last_id += 1
        self.id = last_id

    def match(self, filter):
        '''Determine if this note matches the filter
        text. Return True if it matches, False otherwise.
        
        Search is case sensitive and matches both text and
        tags.'''
        return filter in self.memo or filter in self.tags

Before continuing, we should quickly fire up the interactive interpreter and test our code so far. Test frequently and often because things never work the way you expect them to. Indeed, when I tested my first version of this example, I found out I had forgotten the self argument in the match function! We'll discuss automated testing in Chapter 10, Python Design Patterns I. For now, it suffices to check a few things using the interpreter:

>>> from notebook import Note
>>> n1 = Note("hello first")
>>> n2 = Note("hello again")
>>> n1.id
1
>>> n2.id
2
>>> n1.match('hello')
True
>>> n2.match('second')
False

It looks like everything is behaving as expected. Let's create our notebook next:

class Notebook:
    '''Represent a collection of notes that can be tagged,
    modified, and searched.'''

    def __init__(self):
        '''Initialize a notebook with an empty list.'''
        self.notes = []

    def new_note(self, memo, tags=''):
        '''Create a new note and add it to the list.'''
        self.notes.append(Note(memo, tags))

    def modify_memo(self, note_id, memo):
        '''Find the note with the given id and change its
        memo to the given value.'''
        for note in self.notes:
            if note.id == note_id:
                note.memo = memo
                break

    def modify_tags(self, note_id, tags):
        '''Find the note with the given id and change its
        tags to the given value.'''
        for note in self.notes:
            if note.id == note_id:
                note.tags = tags
                break

    def search(self, filter):
        '''Find all notes that match the given filter
        string.'''
        return [note for note in self.notes if
                note.match(filter)]

We'll clean this up in a minute. First, let's test it to make sure it works:

>>> from notebook import Note, Notebook
>>> n = Notebook()
>>> n.new_note("hello world")
>>> n.new_note("hello again")
>>> n.notes
[<notebook.Note object at 0xb730a78c>, <notebook.Note object at
  0xb73103ac>]
>>> n.notes[0].id
1
>>> n.notes[1].id
2
>>> n.notes[0].memo
'hello world'
>>> n.search("hello")
[<notebook.Note object at 0xb730a78c>, <notebook.Note object at
  0xb73103ac>]
>>> n.search("world")
[<notebook.Note object at 0xb730a78c>]
>>> n.modify_memo(1, "hi world")
>>> n.notes[0].memo
'hi world'

It does work. The code is a little messy though; our modify_tags and modify_memo methods are almost identical. That's not good coding practice. Let's see how we can improve it.

Both methods are trying to identify the note with a given ID before doing something to that note. So, let's add a method to locate the note with a specific ID. We'll prefix the method name with an underscore to suggest that the method is for internal use only, but of course, our menu interface can access the method if it wants to:

    def _find_note(self, note_id):
        '''Locate the note with the given id.'''
        for note in self.notes:
            if note.id == note_id:
                return note
        return None

    def modify_memo(self, note_id, memo):
        '''Find the note with the given id and change its
        memo to the given value.'''
        self._find_note(note_id).memo = memo

This should work for now. Let's have a look at the menu interface. The interface simply needs to present a menu and allow the user to input choices. Here's our first try:

import sys
from notebook import Notebook, Note

class Menu:
    '''Display a menu and respond to choices when run.'''
    def __init__(self):
        self.notebook = Notebook()
        self.choices = {
                "1": self.show_notes,
                "2": self.search_notes,
                "3": self.add_note,
                "4": self.modify_note,
                "5": self.quit
                }

    def display_menu(self):
        print("""
Notebook Menu

1. Show all Notes
2. Search Notes
3. Add Note
4. Modify Note
5. Quit
""")

    def run(self):
        '''Display the menu and respond to choices.'''
        while True:
            self.display_menu()
            choice = input("Enter an option: ")
            action = self.choices.get(choice)
            if action:
                action()
            else:
                print("{0} is not a valid choice".format(choice))

    def show_notes(self, notes=None):
        if not notes:
            notes = self.notebook.notes
        for note in notes:
            print("{0}: {1}\n{2}".format(
                note.id, note.tags, note.memo))

    def search_notes(self):
        filter = input("Search for: ")
        notes = self.notebook.search(filter)
        self.show_notes(notes)

    def add_note(self):
        memo = input("Enter a memo: ")
        self.notebook.new_note(memo)
        print("Your note has been added.")

    def modify_note(self):
        id = input("Enter a note id: ")
        memo = input("Enter a memo: ")
        tags = input("Enter tags: ")
        if memo:
            self.notebook.modify_memo(id, memo)
        if tags:
            self.notebook.modify_tags(id, tags)

    def quit(self):
        print("Thank you for using your notebook today.")
        sys.exit(0)

if __name__ == "__main__":
    Menu().run()

This code first imports the notebook objects using an absolute import. Relative imports wouldn't work because we haven't placed our code inside a package. The Menu class's run method repeatedly displays a menu and responds to choices by calling functions on the notebook. This is done using an idiom that is rather peculiar to Python; it is a lightweight version of the command pattern that we will discuss in Chapter 10, Python Design Patterns I. The choices entered by the user are strings. In the menu's __init__ method, we create a dictionary that maps strings to functions on the menu object itself. Then, when the user makes a choice, we retrieve the object from the dictionary. The action variable actually refers to a specific method, and is called by appending empty brackets (since none of the methods require parameters) to the variable. Of course, the user might have entered an inappropriate choice, so we check if the action really exists before calling it.

Each of the various methods request user input and call appropriate methods on the Notebook object associated with it. For the search implementation, we notice that after we've filtered the notes, we need to show them to the user, so we make the show_notes function serve double duty; it accepts an optional notes parameter. If it's supplied, it displays only the filtered notes, but if it's not, it displays all notes. Since the notes parameter is optional, show_notes can still be called with no parameters as an empty menu item.

If we test this code, we'll find that modifying notes doesn't work. There are two bugs, namely:

The latter bug can be solved by modifying the Notebook class's _find_note method to compare the values using strings instead of the integers stored in the note, as follows:

    def _find_note(self, note_id):
        '''Locate the note with the given id.'''
        for note in self.notes:
            if str(note.id) == str(note_id):
                return note
        return None

We simply convert both the input (note_id) and the note's ID to strings before comparing them. We could also convert the input to an integer, but then we'd have trouble if the user had entered the letter "a" instead of the number "1".

The problem with users entering note IDs that don't exist can be fixed by changing the two modify methods on the notebook to check whether _find_note returned a note or not, like this:

    def modify_memo(self, note_id, memo):
        '''Find the note with the given id and change its
        memo to the given value.'''
        note = self._find_note(note_id)
        if note:
            note.memo = memo
            return True
        return False

This method has been updated to return True or False, depending on whether a note has been found. The menu could use this return value to display an error if the user entered an invalid note. This code is a bit unwieldy though; it would look a bit better if it raised an exception instead. We'll cover those in Chapter 4, Expecting the Unexpected.

Write some object-oriented code. The goal is to use the principles and syntax you learned in this chapter to ensure you can use it, instead of just reading about it. If you've been working on a Python project, go back over it and see if there are some objects you can create and add properties or methods to. If it's large, try dividing it into a few modules or even packages and play with the syntax.

If you don't have such a project, try starting a new one. It doesn't have to be something you intend to finish, just stub out some basic design parts. You don't need to fully implement everything, often just a print("this method will do something") is all you need to get the overall design in place. This is called top-down design, in which you work out the different interactions and describe how they should work before actually implementing what they do. The converse, bottom-up design, implements details first and then ties them all together. Both patterns are useful at different times, but for understanding object-oriented principles, a top-down workflow is more suitable.

If you're having trouble coming up with ideas, try writing a to-do application. (Hint: It would be similar to the design of the notebook application, but with extra date management methods.) It can keep track of things you want to do each day, and allow you to mark them as completed.

Now, try designing a bigger project. It doesn't have to actually do anything, but make sure you experiment with the package and module importing syntax. Add some functions in various modules and try importing them from other modules and packages. Use relative and absolute imports. See the difference, and try to imagine scenarios where you would want to use each one.

In this chapter, we learned how simple it is to create classes and assign properties and methods in Python. Unlike many languages, Python differentiates between a constructor and an initializer. It has a relaxed attitude toward access control. There are many different levels of scope, including packages, modules, classes, and functions. We understood the difference between relative and absolute imports, and how to manage third-party packages that don't come with Python.

In the next chapter, we'll learn how to share implementation using inheritance.