How Can You Structure Your Python Script?

You may have begun your Python journey interactively, exploring ideas within Jupyter Notebooks or through the Python REPL. While that’s great for quick experimentation and immediate feedback, you’ll likely find yourself saving code into .py files. However, as your codebase grows, your Python script structure efficiency becomes increasingly important.

Transitioning from interactive environments to structured scripts helps promote readability, enabling better collaboration and more robust development practices. This tutorial transforms messy scripts into well-organized, shareable code. Along the way, you’ll learn standard Python practices and tools. These techniques bridge the gap between quick scripting and disciplined software development.

By the end of this tutorial, you’ll know how to:

• Organize your Python scripts logically with functions, constants, and appropriate import practices.
• Efficiently manage your script’s state using data structures such as enumerations and data classes.
• Enhance interactivity through command-line arguments and improve robustness with structured feedback using logging and libraries like Rich.
• Create self-contained, shareable scripts by handling dependencies inline using PEP 723.

Without further ado, it’s time to start working through a concrete script that interacts with a web server to obtain and manipulate a machine learning dataset.

Setting the Stage for Scripting

Throughout this tutorial, you’ll apply the structuring concepts by building a Python script step-by-step. The goal of this script will be to work with the well-known Iris dataset, a classic dataset in machine learning containing measurements for three species of Iris flowers.

Your script, called iris_summary.py, will evolve through several stages, demonstrating different structural improvements. These stages are:

1. Set Up the Initial Script: Begin with a functional script using standard language features. Apply a foundational structure using named constants for clarity and the entry-point guard to separate executable code from importable definitions.

2. Integrate External Libraries and Dependencies: Incorporate third-party libraries when needed to leverage specialized functionality or simplify complex tasks. Declare and manage script dependencies within the file using standards like PEP 723 for better reproducibility.

3. Handle Command-Line Arguments: Add command-line arguments using helper libraries to make the script interactive and configurable. Define a clear main() function to encapsulate the core script logic triggered by the command-line interface (CLI).

4. Structure Internal Data: Improve how data is represented by selecting appropriate data structures. Move beyond basic types and use constructs like enum for fixed choices, or dataclass and namedtuple for structured records.

5. Enhance Feedback and Robustness: Refine how the script communicates its progress and results. Implement structured logging instead of relying solely on print(). Use assert statements for internal consistency checks during development, and improve the terminal output presentation, potentially using libraries designed for richer interfaces, like Rich.

By following these steps, you’ll see how structure transforms a basic script into something more robust, readable, and shareable. Each new concept will be introduced and immediately applied to the evolving Iris script.

Before diving into the specifics of script structure, it’s important to understand some foundational elements that make your Python scripts executable and well-organized.

Using the Shebang Line

On Unix-like systems, such as Linux and macOS, you can make your Python script directly executable from the command line, like ./iris_summary.py, instead of always typing python iris_summary.py. This involves making the file executable with chmod +x iris_summary.py, and adding a shebang line at the top of your file.

The shebang tells the system which interpreter to use. The recommended, portable shebang for Python is:

#!/usr/bin/env python3
# Your script logic goes here...

This small addition signals that your file is intended to be run as a standalone script.

Now that you know how to tell the operating system how to run your script, you can focus on organizing the code within the script, starting with imports.

Organizing the Import Statements

As your script starts interacting with more modules, the import statements at the top of your file become important for clarity and code quality. Python’s official style guide, PEP 8, recommends specific conventions for ordering imports, which significantly improves readability. Following these conventions is standard practice, and there are modern tools like Ruff to enforce these conventions.

Following a standard order helps anyone reading your code quickly understand its dependencies. The recommended grouping is:

1. Standard Library Imports: Modules included with Python, like pathlib.
2. Third-Party Imports: Libraries you’ve installed with pip, like requests.
3. Local Imports: Local modules, either application files or libraries, such as when importing another .py file you wrote.

A good scripting practice for sharing code is to avoid local or library-specific imports and to ensure that only cross-platform third-party packages are used.

Note that for simple, standalone scripts intended for easy sharing—for example, as a GitHub gist—minimizing dependencies is often a goal. This might mean sticking primarily to the standard library and avoiding local imports if possible.