Virtual Environment Introduction
Virtual environments are isolated or private spaces within a computer system where software and dependencies can be installed and managed independently of the system-wide environment. They are frequently employed in software development to establish separate environments for various projects or applications, each with unique dependencies, configurations, and Python packages.
To ensure that the dependencies and configurations used during development and testing are consistent across various stages of the software development lifecycle, developers can use virtual environments to create a controlled and reproducible environment for their projects.
They help to avoid conflicts between different versions of Python packages or dependencies that different projects may require. Virtual environments are especially helpful when working on multiple Python projects with various dependencies or collaborating with other developers. They offer a clean, isolated environment that makes results more consistent and reproducible while being simple to share or duplicate across various machines.
Virtual Environment Managers like Conda
Conda, virtualenv, or Python's built-in venv module are a few examples of tools that can be used to create virtual environments. A virtual environment can be created, and once it is, it functions as a stand-alone Python environment with its own isolated Python interpreter, site-packages directory, and environment variables. Then, without affecting the system-wide Python installation or other virtual environments, developers can install and manage Python packages and dependencies specific to their project within the virtual environment.
Virtual Environment Management Softwares offer a means of establishing independent environments, independent from the system-wide environment or other virtual environments, that can have their own sets of dependencies, configurations, and packages. This fosters consistency and reproducibility in software development workflows.
The Anaconda distribution is a complete data science and machine learning platform that offers a well-known virtual environment manager - Conda.
Conda in addition to installing and managing packages, dependencies, and configurations inside of those environments, enables users to create and manage virtual environments with various Python versions.
Conda does not just allow creation of virtual environment but it also allows facility for Environment creation and management, Package management, Cross-platform support, Conda channels, Conda environments as code, and Integration with other tools.
To summarize, virtual environment managers like Conda provide a powerful and flexible way to create and manage isolated environments or virtual environments for Python development, helping ensure consistency, reproducibility, and efficient collaboration among developers working on a similar project or for developers that are working on multiple projects that has different dependencies required.
Let us Deep Dive into the topic in the upcoming articles.