Python: Environments

Python environments can be confusing at first, but they are essential for developing and deploying Python applications.

Overview

At a high level, you can think of Python environments as isolated “containers” that provide a controlled environment for your code to run in.

They are similar in some ways to operating systems, in that they provide a layer of abstraction between the code and the underlying system, and allow multiple applications to run independently without interfering with each other.

Python Environments

In the case of Python environments, the “container” is a self-contained installation of the Python interpreter and associated packages and dependencies.

Environments allow you to install and manage different versions of Python and packages without affecting other environments or your system Python installation.

By creating separate environments for each project, you can ensure that each project has access to the correct versions of Python and packages, and that packages do not conflict with each other.

This can help ensure that your code works consistently across different machines and environments, and can make it easier to manage and deploy your code.

Importance

There are several reasons why Python environments are important:

Version management

Different projects may require different versions of Python or packages. By creating separate environments for each project, you can ensure that each project has access to the correct versions of Python and packages.

Dependency management

Python packages often have complex dependencies on other packages. By isolating each project in its own environment, you can avoid conflicts between different packages and ensure that each project has the correct dependencies installed.

Reproducibility

By using environments, you can ensure that your code works consistently across different machines and environments. This is important when collaborating with others or when deploying your code to a production environment.

Tools

There are several tools available for managing Python environments, including:

• virtualenv
• conda
• pipenv

These tools make it easy to create, manage, and switch between environments, and can be integrated with development tools like IDEs and text editors.

Create / Activate / Install

In practice, creating a new environment involves using a tool like virtualenv or conda to create a new environment directory, activating the environment, and then installing the required packages and dependencies using pip or conda.

Using The Active Environment

Once the environment is set up, you can run your code within that environment, and any packages you install will be isolated to that environment.