In this chapter, we have considered the ability to work with multiple types of Python projects, virtual environments, and interpreters in PyCharm. Overall, PyCharm provides a wide range of options so that users are able to customize their projects, and they can do it in a dynamic way, even after a project has already been created.

In the Professional edition of PyCharm, you can have specific types of Python projects created, such as Django/Flask web development projects or scientific computing projects. This option will populate the new project with various boilerplate code and a directory structure that follows the convention of the corresponding project type. For example, a new Django project will have a templates folder and a manage.py file, while a scientific computing project will have commonly used folders such as data, models, and notebooks.

It is important to note that, while this functionality of generating boilerplate code allows users to save a significant amount of time when a new project is created, the whole process can be done manually in the Community edition.

We have also looked at how to create and customize a virtual environment and a Python interpreter for a PyCharm project. PyCharm's dynamic nature is demonstrated with the ability to switch the interpreter for a project freely, even after the project has already been associated with an interpreter of its own. This allows for easy and intuitive dependency-based testing processes in Python development.

Finally, we have seen how to import an external project into our PyCharm workspace and install all of its package and library requirements using the requirements.txt file. PyCharm actively combs through this file and looks for any unmet requirements in the current interpreter, effectively taking the pain of installing project dependencies away from the developer.

As you have probably noticed, the first few chapters have concerned themselves with setting up various aspects of the usage of PyCharm in order to create an optimal workspace for our projects.

In the next chapter, we will go into details about the assistance PyCharm provides when it actually comes to programming and writing code. Specifically, we will consider PyCharm's real-time smart coding assistance, together with the refactoring and documentation processes and how PyCharm streamlines them.