Environments

Use Conda. It can do everything and more! Its mature, maintained, secure and battle tested at large! Conda provides package, dependency, and environment management for any language. So not only Python.

Conda provides many commands for managing packages and environments.

The default Python way for managing environments is venv — Creation of virtual environments. Its part of the standard library. The venv module supports creating lightweight “virtual environments”, each with their own independent set of Python packages installed in their site directories.

Whenever you check online for the best way of creating and managing environments for Python you will get an opionated answer. There is no best way. Every method has pro’s and cons. It depends of you use case and what works best for you!

I use mostly Conda. Conda is well supported in all IDEs and has some extra advantages over the default Python ‘venv` library. Especially for hacking on ML applications. Also many large FOSS Python projects use Conda.

Tip

Use the Conda Cheatsheet

Background

Some background info on common used tool and their differences: Conda, pip, virtualenv and venv:

• Conda: A powerful and versatile tool for managing both Python and non-Python packages and environments across various platforms. Excellent for data science and machine learning projects.

• pip: The standard package installer for Python. Primarily used for installing packages from PyPI.

• virtualenv: A popular third-party tool for creating isolated Python environments.

• venv: The built-in Python module for creating virtual environments, often considered a more modern and streamlined option.

Feature	Conda	pip	virtualenv	venv
Primary Function	Package and environment management for any software	Package installer for Python packages	Creates isolated Python environments	Built-in Python module for creating virtual environments
Focus	Cross-platform package and environment management	Python-specific package management	Python-specific environment isolation	Python-specific environment isolation
Package Sources	Conda repositories (Anaconda, conda-forge)	Python Package Index (PyPI)	Primarily relies on PyPI	Primarily relies on PyPI
Environment Creation	Creates isolated environments for different projects or dependencies	Can be used within virtual environments to install packages	Creates isolated environments for different projects or dependencies	Creates isolated Python environments
Dependencies	Handles dependencies for both Python and non-Python packages	Primarily focuses on Python package dependencies	Primarily focuses on Python package dependencies	Primarily focuses on Python package dependencies
Cross-platform Support	Excellent cross-platform support (Windows, macOS, Linux)	Excellent cross-platform support	Excellent cross-platform support	Excellent cross-platform support
Common Use Cases	Data science, machine learning, deep learning (due to support for libraries like TensorFlow, PyTorch, NumPy)	General Python development, web development, scripting	General Python development, avoiding conflicts between projects	General Python development, avoiding conflicts between projects