Скачать или смотреть How to Install Python 3.9 and PyPy 3.7 in the Same Conda Environment

Discover the solution to the common issue of installing `Python 3.9` alongside `PyPy 3.7` in a Conda environment. Learn the steps in a user-friendly way!
---
This video is based on the question https://stackoverflow.com/q/67570535/ asked by the user 'Sirion' ( https://stackoverflow.com/u/3333319/ ) and on the answer https://stackoverflow.com/a/67575152/ provided by the user 'merv' ( https://stackoverflow.com/u/570918/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Install Python3.9 and PyPy3.7 in same conda environment

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Install Python 3.9 and PyPy 3.7 in the Same Conda Environment: A Comprehensive Guide

When working with Python in data science, machine learning, or web development, you might find yourself wanting to leverage different implementations of Python, such as Python 3.9 and PyPy 3.7, in the same environment. However, this can lead to dependency conflicts, as highlighted in a common issue faced by users attempting to install these Python versions simultaneously in Conda.

The Problem at Hand

A user reported that after installing Python 3.9, they encountered difficulties while trying to install PyPy 3.7 in the same Conda environment. The error messages were revealing:

Conflicts with the versions of openssl

Issues surrounding ncurses and libcxx

Despite having the required versions installed, the Conda package manager generated dependency errors, leading to frustration on how to proceed.

Here's a sample of the error output:

[[See Video to Reveal this Text or Code Snippet]]

This situation raises a significant question: Is it possible to install both versions in the same Conda environment?

The Solution

Here's the straightforward answer: It is not possible to have Python 3.9 and PyPy 3.7 in the same Conda environment. This limitation arises from PyPy's strict version constraints, which specify that it only works with Python 3.7.

Why This Happens

Version Constraints: PyPy 3.7 is specifically designed to run with Python 3.7.*, making it incompatible with Python 3.9.

Conda's Conflict Resolution: While Conda’s error messages can sometimes be misleading, they reflect underlying incompatibilities accurately.

To illustrate, if you attempt to run:

[[See Video to Reveal this Text or Code Snippet]]

Conda fails to resolve the dependencies because it realizes that:

pypy3.7 requires a Python version of 3.7

Python 3.9 conflicts with that requirement.

A Better Approach with Mamba

To handle the installation in a more efficient way, consider using mamba, which is a reimplementation of the Conda package manager, providing better dependency resolution. Here's the command to try:

[[See Video to Reveal this Text or Code Snippet]]

However, upon running this, you would still get an output indicating constraint issues:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

In conclusion, you cannot install Python 3.9 and PyPy 3.7 in the same Conda environment due to their conflicting version requirements. Emphasis on understanding package constraints and using tools like mamba can help clarify the resolution process when dependencies arise. Should you require both environments, consider creating separate ones: one for Python 3.9 and another for PyPy 3.7.

By mastering these concepts, you can navigate Python’s diverse ecosystem more effectively and make informed decisions about managing your programming environments.