Best Practices to Organize Python Modules

Organizing Python modules efficiently is important to maintain scalability and collaboration. Following best practices can make your project easier to understand and use. In this article, we will discuss about an structure approach to organize Python modules with a sample project and some of the best practices to organize.

Sample Project Structure

Samplemod is an example of a well-structured project that focuses on creating sample module. Below is the directory structure of this project -

README.rst
LICENSE
setup.py
requirements.txt
sample/__init__.py
sample/core.py
sample/helpers.py
docs/conf.py
docs/index.rst
tests/test_basic.py
tests/test_advanced.py

Let's look at these one by one -

• The README.rst file: This file provides a brief description of the module, installation instructions, and relevant information like how to set it up, how to use it, etc.
• setup.py: It is Python's approach to create multi-platform installer and make file. If you're familiar with command line installations, then make python setup.py build and python setup.py install translates to make && make install in other environments.
• requirements.txt:A Pip requirements file should specify the dependencies required to contribute to the project which includes testing, building, and generating documentation. If your project has no development dependencies or you prefer development environment setup via setup.py, this file is unnecessary.
• docs/: This directory contains the documentation for your project.
• LICENSE: Contains license terms and any copyright claims which states how it protects your work and how others can use it.
• tests/:All your tests should reside in this directory. Initially, you'll have a single test file. As they start to grow, you can structure your tests like your module directory.
• sample/: This directory contains your actual module code. If your module consists of only a single file, you can place it directly in the root of your repository as sample.py. Your library does not belong in an ambiguous src or python subdirectory. This will contain a __init__.py file if you want this module to reside within a package.

Practices for Organizing Python Projects

Below is the list of some of the best practices for organizing Python projects -

Meaningful Module Names

Each module name should be concise, lowercase and mainly should explain their functionality. This will allow you to understand what each module does based on its name so that you can easily structure the project and also reuse/import it based on your requirement. For example,

# Good naming
data_processor.py  # Processes data
user_authentication.py  # Handles user authentication
config_manager.py  # Manages configuration

# Poor naming
stuff.py  # Unclear purpose
functions.py  # Too generic
misc.py  # Ambiguous content
my_code.py  # Uninformative

Organizing by Functionality

Grouping related code into logical packages based on their functionality is also a better technique to structure code as this way each package is clear and focuses on a particular purpose. For example,

weather_app/
??? data/
?   ??? __init__.py
?   ??? api_client.py  # Handles API requests to weather services
?   ??? parser.py      # Parses weather data responses
??? models/
?   ??? __init__.py
?   ??? forecast.py    # Forecast data models
?   ??? location.py    # Location data models
??? views/
?   ??? __init__.py
?   ??? console.py     # Console output formatting
?   ??? plots.py       # Weather data visualization
??? utils/
    ??? __init__.py
    ??? validators.py  # Input validation functions
    ??? converters.py  # Unit conversion utilitiess

Use init.py

The _init_.py files is usually used to mark a directory as a package. Additionally, it controls what is exposed when a package is imported and also simplifies imports throughout the project.

This approach hides the implementation details and provides a clean public API.

Avoid Circular Imports

It is better to avoid circular imports to avoid cluster, to be precise, circular imports occur when module A imports from module B, which imports from module A. This creates mess, debugging issues, and indicates poor organization. For example,

#PROBLEMATIC STRUCTURE
# user.py
from .permissions import has_permission

class User:
    def can_access(self, resource):
        return has_permission(self, resource)

# permissions.py
from .user import User  # Circular import!

def has_permission(user, resource):
    if not isinstance(user, User):
        return False
    # Permission logic

Following are the different ways to avoid circular imports -

Import where needed

In this method, we delay imports i.e., instead of mentioning at the module level, we mention it inside functions when they are actually needed.

# permissions.py
def has_permission(user, resource):
    # Import inside function to avoid circular import
    from .user import User
    
    if not isinstance(user, User):
        return False
    # Permission logic

Use Dependency Injection

This method eliminates the need to import the User class entirely, thus preventing circular dependencies.

# permissions.py
def has_permission(user, resource):
    # Check for duck typing instead of explicit import
    if not hasattr(user, 'role'):
        return False
    # Permission logic

Restructure Modules

In this method, we organize code into a hierarchy where related functionality are categorized into different directories.

app/
??? models/
?   ??? __init__.py
?   ??? user.py  # User class without permission logic
??? services/
?   ??? __init__.py
?   ??? permissions.py  # Functions that work with user instances