#Conda virtual environment install

If it does, then there’s nothing more to be said - we can just use that. I don’t know if conda adheres to this approach, but if it doesn’t, then my first thought would be that they should (I’m taking your comment that conda envs are much more like virtualenvs than system envs as the basis of this view). Currently, and as part of the language definition, this is done by checking if sys.base_prefix != sys.prefix. But to work well, it would need a standard way to signal “this is a user-activated environment”. That sounds like a useful idea to have independently of this issue. Note that env_name is no longer in the list.By generalizing whatever you do to “user-activated environments” (I’d quite like the latter) Now view a list of your virtual environments again. Remove the virtual environment called env_name using the command. You need to exit out of the environment before you remove it.Ĭheck to see if the virtual environment you want to remove is present: > conda info -envs Remove a virtual environment with the command conda remove -name env_name -all. > conda info -envsĮnv_name /home/tribilium/anaconda3/envs/env_name That's because the env_name virtual environment is no longer active. If you run conda info -envs again, there is no * in front of env_name.

To exit the virtual environment, use the command conda deactivate. The virtual environment with the * is currently active. Notice the * asterisk on the line with env_name. Matplotlib /home/tribilium/anaconda3/envs/matplotlib > conda activate env_nameĮnv_name * /home/tribilium/anaconda3/envs/env_name View a list of your virtual environments using the command conda info -envs or conda env list. When the virtual environment is deactivated, the prompt looks normal (just >), with no environment name in parenthesis before it.

To deactivate an active environment, run the command: (env_name) > conda deactivate

#Conda virtual environment install

To install both NumPy and Jupyter at the same time use: (env_name) > conda install numpy jupyter Multiple packages can be installed with the same command. To install Matplotlib into a virtual environment type: (env_name) > conda install matplotlib Then package installation is accomplished with the conda install command followed by the package name. To install a package into the virtual environment env_name, first make sure the environment is active ( (env_name) before the prompt).

But a fresh new virtual environment will just have Python installed, no other packages. If you use the Anaconda distribution of Python, the base environment contains about 600 packages that come with Anaconda. When a new virtual environment is created, no packages are installed by default. The virtual environment is active when you see (env_name) > in parenthesis at the start of the prompt. To activate the environment env_name, issue the command: > conda activate env_name To use the new virtual environment env_name, the virtual environment needs to be activated.

Type y to confirm and create the new virtual environment. The following output or something similar results: The following NEW packages will be INSTALLED:Ĭa-certificates ca-certificates-2019.5.15-0 Including python=3.7 ensures the virtual environment has a current version of Python. The -name env_name flag gives the new virtual environment the name env_name. The conda create command builds the new virtual environment. To create the new virtual environment, open the Anaconda Prompt and issue the command: > conda create -name env_name python=3.7 Create a virtual environment with the Anaconda Prompt Two projects on the same computer can use different versions of Python and different versions of packages if virtual environments are used. Another project can use a different virtual environment and therefore have a different version of Python and a different set of packages. When you use virtual environments, one project can have a separate version of Python and packages. A virtual environment is an isolated installation of Python with associated packages. Using virtual environments is standard practice in Python.  Virtual Environments Virtual Environments Problem Solving with Python Book Construction Install packages in a virtual environment