Python Environment 101 (2024)

FAQs

Should I use Conda or venv? ›

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.

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.

pipenv is a tool that combines virtualenv with pip . It allows you to create virtual environments and manage Python packages for your projects. It is designed to be more user-friendly than using virtualenv and pip separately.

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.

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.

Both pip and conda are included in Anaconda and Miniconda, so you do not need to install them separately.

The venv module supports creating lightweight “virtual environments”, each with their own independent set of Python packages installed in their site directories.

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.

ou don't need to create a virtual environment if Django is already installed on your PC. However, it is a good idea to do so to isolate dependencies, manage versions, and test your project. Here are the steps on how to create a virtual environment and install Django in it: Install the virtualenv package.

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.

Should I use virtualenv in production? ›

Is virtualenv recommended for django production server? Yes, it makes your project not depend on certain aspects of the system environment and also it allows you to make the deployment process more clear and configurable.

pyenv manages multiple versions of Python itself. virtualenv/venv manages virtual environments for a specific Python version.

PyCharm, Jupyter Notebook, IDLE, Visual Studio Code, and Spyder are top 5 python ide that are used by programmers to work efficiently.

Select and activate an environment

If you would prefer to select a specific environment, use the Python: Select Interpreter command from the Command Palette (Ctrl+Shift+P). Note: If the Python extension doesn't find an interpreter, it issues a warning.

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.

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.

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.

Data science and scientific computing: Conda excels in the data science and scientific computing domains. Its support for packages like NumPy, SciPy, pandas, and scikit-learn, along with its ability to manage complex dependencies, makes it a preferred choice in these fields.

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.