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Building Robust Object-Oriented Python Applications and Libraries

Organizing Code in Modules

Explore how to organize Python code into modules to manage variables, classes, and functions cleanly. Understand type hints, module-level variables, and best practices like using the main function guard. Learn when to use module imports and how inner classes and functions can encapsulate functionality within scopes. This lesson helps you write cleaner, modular Python applications.

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The Python module is an important focus. Every application or web service has at least one module. Even a seemingly “simple” Python script is a module. 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.

Example

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:

Python 3.10.4
class Database:
    """The Database Implementation"""
    def __init__(self, connection: Optional[str] = None) -> None:
        """Create a connection to a database."""
        pass
database = Database("path/to/data")

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

from ecommerce.database import database

Use of Optional type hint

A drawback of 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 because we need to read a configuration file. We could delay creating the database until it is actually needed by calling an initialize_database() function to create a module-level variable:

db: Optional[Database] = None
def initialize_database(connection:
...