@Marvin how can I restructure my project so that I can make my code more modular. I have a lot of repetitive code that should be reused as opposed to copy and pasted. My project might looks something like this:

my_project/
├── flow1/
│   └── flow1.py
├── flow2/
│   └── flow2.py
└── futilities/
    ├── __init__.py
    └── utilities.py

To make your project more modular and avoid repetitive code, you can consider the following steps: 1. Identify Common Code: Look for patterns and commonalities in your code. If you find yourself copying and pasting code, that's a good sign that it could be abstracted into a function or class. 2. Create Utility Modules: In your

futilities

directory, you can create different Python modules (.py files) each responsible for a specific functionality. For example, you could have a

data_processing.py

for data-related functions,

file_operations.py

for file I/O operations, etc. 3. Use Classes and Functions: Encapsulate repetitive code into functions or classes. This not only reduces redundancy but also makes your code more readable and maintainable. 4. Use Sub-packages: If your project grows larger, you can group related modules into sub-packages. This helps to keep your project structure clean and understandable. 5. Import Modules Correctly: Use

import

statements to use your utility functions in your flows. For example, if you have a function

foo

in

utilities.py

, you can use it in

flow.py

like this:

from futilities.utilities import foo

6. Consider Using a Framework: Depending on the complexity of your project, you might want to consider using a framework or library that supports modularity and code reuse. For example, Prefect is a great tool for building data workflows in a modular way. Here's how your project structure might look after restructuring:

my_project/
├── flows/
│   ├── __init__.py
│   ├── flow1.py
│   └── flow2.py
└── futilities/
    ├── __init__.py
    ├── utilities.py
    ├── data_processing.py
    └── file_operations.py

Remember, the goal of modularity is to make code more reusable, maintainable, and understandable. The exact structure will depend on the specifics of your project.

ahh i need to fix that, when you edit messages, slack sends another event, marvin should check that he did not already give an answer 🙂

I immediatley regretted editing it lol

When deploying a Python project that uses modules, there are a few additional considerations to keep in mind: 1. Dependencies: Ensure that all dependencies required by your modules are properly declared in your

requirements.txt

or

Pipfile

(if you're using pipenv). This includes any third-party libraries that your modules rely on. 2. Relative Imports: If you're using relative imports in your modules (e.g.,

from ..futilities.utilities import my_function

), make sure that the deployed environment maintains the same directory structure as your development environment. Relative imports depend on the location of the script that's being run, so changes in directory structure can cause these imports to fail. 3. init.py Files: Make sure each of your directories contains an

__init__.py

file. This file, which can be empty, indicates to Python that the directory should be treated as a package. This is necessary for Python to correctly resolve your internal imports. 4. Environment Variables: If your modules rely on environment variables (for example, for configuration), ensure these variables are properly set in the deployment environment. 5. Testing: Before deploying, test your application in an environment that closely mirrors the deployment environment. This can help catch any issues related to module imports or dependencies before they cause problems in production. In general, deploying a Python project with modules doesn't require much more work than deploying a project without modules. The key is to ensure that your deployment environment is properly set up to handle your project's structure and dependencies.