Python Structure Starter Genie-Python Project Guidance

Example

Generating a file structure for a machine learning project, including directories for datasets, notebooks, scripts, and a `requirements.txt` file.

Scenario

When starting a new data science project, ensuring a clean separation of data, exploratory analysis, and scripts can vastly improve project clarity and collaboration.

Example

Advising on separating business logic, data access layers, and presentation code in a web application.

Scenario

In developing a complex web service, having distinct modules for handling database interactions, business processes, and the user interface promotes code reuse and simplifies debugging.

Example

Enforcing PEP 8 style guidelines, suggesting docstring formats for documentation, and setting up automated linting and formatting tools.

Scenario

For a team transitioning to Python from another language, adopting Pythonic conventions and maintaining a consistent coding style across the project can significantly enhance code readability and maintainability.

Example

Creating and managing a `pipenv` or `poetry` setup to handle project dependencies and virtual environments.

Scenario

Launching a new project with a complex set of dependencies, the Genie helps ensure that all team members are working within the same environment, preventing 'works on my machine' issues.

Example

Configuring a `pytest` framework, complete with unit and integration testing structures.

Scenario

Before adding new features to an existing application, establishing a comprehensive test suite allows developers to make changes confidently without breaking existing functionality.