![]() The Python extension for VS Code goes a long way to making working with Python in a code editor as simple and easy as possible. Conclusions: Using Python in VS Code Made Easy For example, in the screenshot above “andrewd-activestate/PythonWindows3’:ActiveState” is shown. You can always see which interpreter you’re currently running by checking the bottom of the VS Code window. This will automatically generate a list of all of the Python interpreters available on your machine, including those provided by ActiveState. From the dropdown displayed, click on Python: Select Interpreter.Select your newly installed Python interpreter by pressing Shift+Ctrl+P on Windows or Linux.Boot up/restart VS Code and open or create a Python file.If you already have a version of ActiveState Python installed, just run “ state update” to ensure you have the latest version of the state tool installed.Run the install command to download the Python runtime environment for your project and install it into a virtual environment on your local machine.The Platform will automatically build all the dependencies in your project securely from source code, and package them for deployment on Windows, Linux and Mac.Create a Python project on the ActiveState Platform.This list now includes virtual environments created by installing a version of ActiveState Python. There are far too many ways to create virtual installations of Python (including virtualenv, venv, pipenv, pyenv, etc), which can cause issues as I’ve discussed in a previous blog post, but luckily the VS Code extension will recognize and work with all of them. While VS Code will work with any version of Python you have installed on your local system, it’s recommended you always work with a virtual installation of Python to ensure against dependency conflicts and contamination between projects. Refactoring: restructure code with variable extraction and method extraction. ![]() Jupyter Notebooks: create and edit Jupyter Notebooks, add and run code cells, render plots, visualize variables through the variable explorer, visualize dataframes with the data viewer, and more.Testing: run and debug tests through the Test Explorer with unittest or pytest.Debugging: script, web app, remote or multi-threaded process debugging support. ![]()
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