TLDR: There are three main options for creating and managing virtual environments in Python: pipenv , conda and venv . pipenv is superior to venv and conda . Learn to use pipenv first and you will not have to waste time learning the other two.A virtual environment is a tool that helps to keep dependencies required by different projects separate by creating isolated Python virtual environments for them. This is one of the most important tools that most Python developers use.Choosing the right environment management tool depends on your needs. If you need a simple, easy-to-use tool, venv might be the best choice. If you're dealing with complex dependencies, Conda env is the way to go. If you need to switch between different Python versions, consider pyenv or virtualenv.

What is the best way to create a virtual environment in Python : Getting Started

1. Create a virtual environment in your current directory for a project with the command: virtualenv my_project. "my_project" is whatever name you would like to give this environment.
2. To create a virtual environment with a specific version of python use the command: virtualenv -p /usr/bin/python2.7 my_project.

Why is poetry better than conda

Poetry emerges as a modern and organized solution for Python dependency management, offering improved organization, version control, and flexibility compared to traditional tools like Pip and Conda.

Should I always use a venv : Always use a Virtual Environment

Always. Virtual environments let you have a stable, reproducible, and portable environment.

The decision to use either Anaconda or Python highly depends on the specific requirements and goals of a project. If the project involves data analysis, machine learning, or scientific computing, then Anaconda may be the more suitable choice due to its pre-installed packages and data science tools.

If you're a developer working on multiple Python projects in parallel, you should use distinct virtual environments for each project. This practice makes working on multiple projects more organized and also reduces the risks of code execution errors.

Should I install conda or Anaconda

If Anaconda doesn't include a package that you need, you use conda to download and install it. If Anaconda doesn't have the version of a package you need, you use conda to update it.Anaconda comes with over 1,500 pre-installed packages, making it a comprehensive solution for data science projects. On the other hand, Miniconda allows you to install only the packages you need, reducing unnecessary clutter in your environment.Any time you're working on a Python project that uses external dependencies that you're installing with pip , it's best to first create a virtual environment: Windows. Linux. macOS.

Always use a Virtual Environment

You are in control of which packages versions are installed and when they are upgraded. You can have as many venvs as you want.

Should I use poetry or Pip : Not everyone might use Poetry, but since Pip has been around forever, it may be worth the ease of use to just use Pip. But if your project and your workload value the importance of organization and are willing to explore new tools to improve your process, Poetry is a tool you should consider.

Should I use venv or Docker : Docker excels in deploying complex applications across different environments, while virtual environments are more focused on managing package dependencies in Python projects. By understanding their differences and use cases, developers can choose the most suitable solution for their specific needs.

Where should I create my venv

Contained in a directory, conventionally either named venv or .venv in the project directory, or under a container directory for lots of virtual environments, such as ~/.virtualenvs .

If Anaconda doesn't include a package that you need, you use conda to download and install it. If Anaconda doesn't have the version of a package you need, you use conda to update it.Disadvantages of Anaconda

Some disadvantages may include the following: Large size: Anaconda comes with a large number of pre-installed packages, which can make the installation file quite large. This can make it slower to download and install and also take up more storage space on your computer.

Should you use venv or virtualenv : These are almost completely interchangeable, the difference being that virtualenv supports older python versions and has a few more minor unique features, while venv is in the standard library.