how to change python version in conda environment

Daniel Parker

2 min read

·

24-01-2025

1

0

Share

Conda is a powerful package and environment manager, especially useful for managing multiple Python versions alongside their respective dependencies. This article details how to change the Python version within an existing Conda environment, covering both creating a new environment with a specified Python version and updating the Python version within an existing one.

Creating a New Conda Environment with a Specific Python Version

The simplest way to use a different Python version is to create a new Conda environment from scratch. This keeps your existing environments untouched.

1. List Available Python Versions:

First, check which Python versions Conda can install. Use the following command:

conda search "^python{{content}}quot;

This command searches for packages starting with "python". The output will list available versions.

2. Create the New Environment:

Once you've chosen your desired Python version (e.g., 3.9), create a new environment using the conda create command:

conda create -n myenv python=3.9

Replace myenv with your desired environment name and 3.9 with your target Python version. This command creates a new environment named myenv with the specified Python version and essential packages.

3. Activate the Environment:

After creation, activate the new environment:

conda activate myenv

You should now see (myenv) prepended to your command prompt, indicating that the environment is active.

4. Verify Python Version:

Confirm that the correct Python version is active:

python --version

Updating the Python Version in an Existing Conda Environment

Modifying the Python version within an existing environment is more complex and potentially risky. Incorrectly updating Python can break dependencies. Proceed with caution and back up your important work.

1. Deactivate the Environment:

Before making any changes, deactivate the current environment:

conda deactivate

2. Update Python within the Environment:

Use the conda update command to update Python to your desired version:

conda update -n myenv python=3.9

Replace myenv with your environment name and 3.9 with your target version. Conda will resolve any dependencies and attempt to update Python to the specified version while keeping other packages compatible, if possible.

3. Resolve Conflicts (If Necessary):

If dependency conflicts arise, you might need to manually resolve them. This often involves identifying conflicting packages and choosing which to keep or remove. The conda list command within the environment will show installed packages. You might need to use conda remove to remove problematic packages. Always carefully consider the consequences of removing packages.

4. Reactivate and Verify:

After the update, reactivate the environment and check the Python version:

conda activate myenv
python --version

Troubleshooting and Best Practices

• Backup: Before making significant changes, back up your environment using conda env export > environment.yml. This allows you to easily restore your environment if something goes wrong.
• conda update --all: Avoid using conda update --all indiscriminately within an environment, as it can lead to unexpected issues and break your environment. Focus on updating only the packages you need.
• Virtual Environments: While Conda provides excellent environment management, consider using Python's built-in venv for simpler projects. venv is lighter weight if you don't need Conda's extensive package management capabilities.
• Specific Package Versions: If you have very strict requirements on package versions (beyond just the Python version), carefully manage them with conda install <package>=<version> to ensure compatibility.

Changing Python versions in Conda environments can be a valuable skill for managing projects with different dependency requirements. By following these steps and utilizing best practices, you can efficiently and safely manage your Python versions within Conda. Remember to always proceed cautiously and back up your work to mitigate potential issues.