To activate this environment, use

In the previous article, we discussed in detail how to use Anaconda to uninstall packages no longer needed, thereby keeping your environment clean and organized. In this article, we’ll learn how to create virtual environments and install required packages within them. Virtual environments allow you to independently manage dependencies for different projects on the same machine—preventing version conflicts between packages.

Creating a Virtual Environment

With Anaconda, you can quickly create a new virtual environment using the conda command. Here is the basic syntax:

conda create --name <env_name> [python=<version>]

Replace <env_name> with your desired environment name, and <version> with the Python version you wish to use in that environment (the default is the latest available version). For example, to create an environment named myenv using Python 3.8, run:

conda create --name myenv python=3.8

During environment creation, conda automatically resolves package dependencies and installs essential base packages. Upon successful creation, you’ll see output similar to the following:

Proceed ([y]/n)? y

# To activate this environment, use
#
#     $ conda activate myenv

Activating the Virtual Environment

After creating the virtual environment, you must activate it to install and run packages within it. Use the following command to activate your environment:

conda activate myenv

Once activated, your command-line prompt will change to reflect the active environment—typically appearing as:

(myenv) user@hostname:~$

Installing Packages

Once your virtual environment is activated, you can use the conda install command to install required packages. The basic syntax is:

conda install <package_name>

For instance, to install numpy into the myenv environment, run:

conda install numpy

conda automatically handles dependencies and installs numpy along with all its required dependencies.

Example — Creating a Virtual Environment and Installing Multiple Packages

Let’s walk through a complete example to illustrate these steps. Suppose you’re developing a data analysis project requiring the following libraries:

• numpy
• pandas
• matplotlib

Follow these steps to create the environment and install the libraries:

1. Create the environment:

   conda create --name data_analysis python=3.9
   

2. Activate the environment:

   conda activate data_analysis
   

3. Install the required packages:

   conda install numpy pandas matplotlib
   

After installation completes, verify the installed packages using:

conda list

This command displays all packages installed in the current virtual environment, along with their versions.

Summary

In this article, we learned how to use conda to create virtual environments and install necessary packages within them. This approach ensures each project operates in an isolated environment with its own set of dependencies—avoiding issues caused by incompatible package versions. In the next article, we’ll explore how to manage environment dependencies to ensure consistent, reproducible project execution.