How to Change Python Version in an Existing Conda Virtual Environment
Why Change Python Version?
Before we dive into the how, let’s discuss the why. Different Python versions may have different features, performance improvements, or bug fixes. Additionally, some libraries or packages may only be compatible with certain Python versions. Therefore, being able to switch between Python versions in your Conda environment can be a game-changer for your data science projects.
What is Conda?
Conda is an open-source package management system and environment management system. It allows you to install multiple versions of software packages and their dependencies and switch between them. It’s particularly popular among data scientists because it makes it easy to manage Python and R packages.
Step-by-Step Guide to Changing Python Version
Now, let’s get to the meat of the matter. Here’s a step-by-step guide to changing the Python version in an existing Conda environment.
Step 1: Check the Current Python Version
First, you need to check the current Python version in your Conda environment. You can do this by activating the environment and then running the Python version command:
conda activate myenv
Step 2: Reinstall the desired Python version
Inside the enviornment, you can install the target python (for example 3.8)
conda install python=3.8
Step 3: Verify the Python Version
Finally, verify that the Python version has been changed in the environment:
You can specify the desired python version when creating a new enviornment so you might try creating a new enviornemnt if you need to work with a specific python version.
- You need to deactivate your current environment using the following:
- Create a New Environment with the Desired Python Version. For example, if you want to switch to Python 3.8, you would use the following command:
conda create --name myenv python=3.8
- Verify that the Python Version has been changed in the new environment:
conda activate myenv
Changing the Python version in an existing Conda environment is a straightforward process. It allows you to take advantage of the features, improvements, and bug fixes in different Python versions, and ensures compatibility with different libraries and packages. With this guide, you should be able to manage your Python versions effectively and make your data science projects more efficient and productive.
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