The choice between venv and Anaconda depends on your needs: For minimal environments: If you prefer lightweight, minimal environments, venv is the better choice. For data science projects: If you're working on data science projects and need a wide range of pre-installed packages, Anaconda is more suitable.Python virtual environments are the light version of the conda environments. Virtual environments are a native tool to Python developers, and they provide a functionality similar to that of the conda environments. The main difference is that they rely on the Python package manager.Always use a Virtual Environment

Always. Virtual environments let you have a stable, reproducible, and portable environment. You are in control of which packages versions are installed and when they are upgraded.

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.

Is Anaconda still useful

Anaconda is widely used by companies in different industries, particularly those that rely on data science and machine learning. Here are some examples of companies that use Anaconda: IBM uses Anaconda to power their IBM Watson Studio, a cloud-based platform for data science and machine learning.

Should I use venv in Python : The great thing about this is that there are no limits to the number of environments you can have since they're just directories containing a few scripts. A virtual Environment should be used whenever you work on any Python-based project.

Key Differences at a Glance

Scope: Venv is Python-exclusive, while Conda is language-agnostic. Package Management: Venv doesn't handle package dependencies itself, whereas Conda excels in this area.

Using Anaconda Virtual Environments

Conda quickly installs, runs, and updates packages and their dependencies. Conda easily creates, saves, loads, and switches between environments on your local computer.

Should I create a venv for each project

The solution is to create a virtual environment, or “venv”, for each project which will isolate it from the other environment and, most importantly isolate it from your OS global env.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.A virtual Environment should be used whenever you work on any Python-based project. It is generally good to have one new virtual environment for every Python-based project you work on. So the dependencies of every project are isolated from the system and each other.

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.

Should I use Anaconda or just Python : While Python is a versatile programming language that can be used for a wide range of applications, Anaconda provides a more specialized environment for machine learning and data science, with pre-installed packages and a package manager that make it easier to manage dependencies and resolve conflicts between packages.

Should I get Anaconda or Python : 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.

Should I make a new venv for every project

A virtual Environment should be used whenever you work on any Python-based project. It is generally good to have one new virtual environment for every Python-based project you work on. So the dependencies of every project are isolated from the system and each other.

In general, it is best to use Conda for managing packages and environments, and Docker for deploying your machine learning models in a production environment.Generally, Anaconda does not significantly affect the performance of Python programs. It primarily acts as a package manager and an environment manager, and any performance differences would likely be due to the specific packages and libraries used rather than Anaconda itself.

Should I install Miniconda or Anaconda : 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.