Using the pip freeze command we can generate the requirement.txt file based on the libraries that we installed in our virtual environment. Then run Terminal: Create New Terminal (Ctrl+Shift+`)) from the Command Palette, that opens a new python terminal and in parallel it activates the virtual environment.Ĭonfirm that that new environment is selected (Hint: look at the blue status bar at the bottom of the VS code) and then update the pip in the virtual environment: python -m pip install -upgrade pipįinally, let’s install the pandas and flask libraries python -m pip install flask Then, select the Python: Select Interpreter command and then the environment that we created “ myenv“: Then In VS Code, open the Command Palette ( View > Command Palette or (Ctrl+Shift+P)). Then, we can open the folder “ venv_example” from the VS Code using the File > Open Folder command. Sudo apt-get install python3-venv # If needed Within this folder, we can create a virtual environment called “ myvenv” by running the following command: # Linux Create a Project Environmentįor this example, we call our project “ venv_example“, and we have created a folder with the same name. Finally, it is less risky to mess with your other projects when you work with virtual environments. This is because with the virtual environments, the project is reproducible, and we will need to install only the required libraries as stated in the requirements.txt. Moreover, it is more efficient to work with the necessary only libraries. When we work on a Data Science project, which can include a Flask API, it is better to have full control over the libraries used in the project.

In this post, we will provide you a walk-through example of how to work with VS Code and virtual environments.

We have provided examples of how to work with conda environments.

Visual studio code python pip