Creating and Managing Python Environments

Python is an extremely popular programming language, widely used in diverse domains like web development, artificial intelligence, data science, machine learning, and more. One of the significant advantages of Python is the rich assortment of specialized libraries and frameworks it hosts. But, dealing with multiple Python packages can sometimes be a complex task. The complexity increases when we need different versions of the same package for different projects. This is where Python environments come in handy.

In this comprehensive article, we will discuss how to create and manage Python environments using tools like virtualenv and conda. We will begin with the basics, covering why we need Python environments, and will subsequently dive into hands-on examples of creating and managing these environments.

Table of Contents

• Why Python Environments
• Getting Started: The Basic Python Environment
• Introducing Virtual Environments
• Creating a Virtual Environment
• Managing Packages in a Virtual Environment
• Introducing Conda Environments
• Creating a Conda Environment
• Managing Packages in a Conda Environment
• Concluding Thoughts

Why Python Environments?

When writing Python code for different projects, you’ll often need different sets of libraries, sometimes requiring different versions of the same package. Installing all packages globally can lead to version clashes and messy maintenance. In such cases, you need isolated “environments,” each tailored with specific packages required for a specific project.

Python environments enable this feature. You can think of a Python environment as an isolated workspace, with its own Python binary and independently managed packages. Therefore, you can have various environments with distinct package versions, all living happily on the same system.

Python has several tools for managing such environments, including venv (builtin since Python 3.3), virtualenv, and conda. We will focus on virtualenv and conda in this guide because of their extensive usage and flexibility.

Getting Started: The Basic Python Environment

The Basic Python Environment is the primary Python installation on your system. After installing Python, you’ll have access to the Python interpreter and pip, the package manager for Python.

python ––version
Python 3.7.5

pip ––version
pip 19.3.1

You can install Python libraries globally using pip. For instance, you can install the widely used ‘requests’ library as follows:

pip install requests

However, installing all your needed libraries like this could lead to problems, paving the way for the need for virtual Python environments.

Introducing Virtual Environments

Virtual Environments, as discussed earlier, are isolated Python workspaces. They are an integral tool in every Python developer’s toolkit because they can help manage dependencies neatly. The easiest way to start using Virtual Environments is through virtualenv.

virtualenv works by creating a folder in the desired directory, which then hosts the Python binary and the pip manager. Any package installed in this environment will be isolated from the global Python installation.

Before starting with virtualenv, we need to install it globally. Use the following command for the same:

pip install virtualenv

Creating a Virtual Environment

Creating a new virtual environment is a breeze with virtualenv. Let’s create one called my_env:

virtualenv my_env

This command creates a new directory my_env. This new directory works like a self-contained Python installation. To activate this environment, use the following command:

source my_env/bin/activate

Now you are inside your new Python environment, my_env. Any pip installation done now will not affect the global Python installation.

Managing Packages in a Virtual Environment

Once in a virtual environment, you can use pip to install packages as you would do normally. Suppose, we install the requests library:

pip install requests

You can check the list of installed packages:

pip freeze
requests==2.25.1

To deactivate the environment, just type:

deactivate

Introducing Conda Environments

Conda is a popular package, dependency, and environment manager, especially favored in the Data Science community. Conda is part of the Anaconda Distribution, particularly useful when handling Python packages linked with C libraries like NumPy, SciPy, etc.

Creating a Conda Environment

Creating a new Conda environment is very straightforward. To create an environment named conda_env, use:

conda create -n conda_env

To activate the environment, use:

conda activate conda_env

Managing Packages in a Conda Environment

You can install packages in a conda environment using both conda install and pip install. Let’s install the numpy package:

conda install numpy

To list the installed packages, use:

conda list

And to deactivate the environment:

conda deactivate

Concluding Thoughts

Python environments, be it virtualenv or conda, are an integral part of Python programming, especially when juggling between different projects. It takes a bit of practice getting used to these tools, but once acquainted, they can do wonders in streamlining your Python development process.

Remember, the efficient use of Python environments helps in maintaining clean project states, avoiding package version clash, and reproducing project states easily, leading to a considerable boost in your programming productivity. Happy coding!

Python for Beginners provides a great starting point for those new to Python or programming. You can also find in-depth, comprehensive resources about Python environments in the Python Packaging User Guide.