When you are done working in your virtual environment, you will want to deactivate it using the 'deactivate' command. Once you have activated the environment, you can install packages and run any Python scripts associated with the project. conda activate Project1Īfter activating the virtual environment, the command prompt will be preceded by '(Project1)' instead of '(base)' to indicate that you are now in the Project1 virtual environment. To use the virtual environment, use the conda command to activate it. In this example, we will create a Python 3.6 virtual environment named 'Project1'. Instead, you will want to use the 'conda' command to create your own virtual environment to keep your projects isolated from each other. This indicates that you are in the default environment named 'base.' You don't want to install programs into the base environment. In the Anaconda Prompt window, you will notice that the prompt is preceded with (base). This will open up a command window that is configured to use Python, conda, and other tools. The virtual environments are isolated from each other, allowing you to install dependencies for one project without worrying about breaking something in the other project.Ĭonnect to the CLA WTS servers and select Start → All Programs → Anaconda$version → Anaconda Prompt (where $version denotes the Anaconda Python version you want to use).

An easy way around this is to create a separate virtual environment for each of your projects. For example, if you are working on two different projects and Project 1 requires version 1.0 of a library whereas Project 2 requires version 2.0 of that same library, installing the dependency for Project 2 will likely break Project 1. While doing a 'pip install –user some_package' will allow you to install Python packages in your home directory, you may run into issues later on with package dependencies. When using Python, you might need to install packages and modules that aren't a part of the standard library. Setting up a Python virtual environments for each project ensures that it will have the requirements it needs without interference from other projects. A Python virtual environment is an isolated Python environment that allows you to maintain separate dependencies for different Python projects.

Conda list environments