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Understanding the Different Python Versions in Jupyter Notebook

Discover why you're seeing different Python versions in Jupyter Notebook and how to manage Python environments effectively. --- This video is based on the question https://stackoverflow.com/q/74124899/ asked by the user 'Wick' ( https://stackoverflow.com/u/15820854/ ) and on the answer https://stackoverflow.com/a/74126792/ provided by the user 'H4iku' ( https://stackoverflow.com/u/1825964/ ) 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: Different Python version in jupyter 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. --- Understanding the Different Python Versions in Jupyter Notebook If you've ever run into the issue of finding different Python versions in your Jupyter Notebook, you're not alone. This common problem can create confusion, especially when you're trying to ensure consistency in your coding environment. In this guide, we’ll explore why you might find multiple Python versions in Jupyter and how to address this issue effectively. The Problem: Different Python Versions in JupyterLab When you execute commands like !python -V, !py -V, or check the version in PowerShell, you may notice discrepancies in the Python version reported. For instance: In JupyterLab, you might see Python 3.8.13 when using !python -V. On the same platform, !py -V shows Python 3.10.7. In PowerShell, both python -V and py -V return Python 3.10.7. This situation raises an important question: Why is there a difference between the Python versions? Understanding the Root Cause Conda vs Non-Conda Installations The discrepancy in Python versions is primarily due to how Python environments are managed on your system. Here's a breakdown of the situation: Conda Environment: JupyterLab is launched from a specific Conda environment that has Python 3.8.13 installed. When you use !python -V, it reflects the Python version from this Conda environment. Non-Conda Installation: You also have a separate installation of Python 3.10.7, which is a non-Conda version. The command !py -V is associated with this non-Conda installation and uses the Python launcher specific to Windows. PowerShell Commands: When you run python -V and py -V in PowerShell, those commands utilize the non-Conda Python version (3.10.7), leading to consistent results in that environment. Verifying Your Python Environment To further understand the Python version in use, you can run the following code snippet inside your Jupyter Notebook: [[See Video to Reveal this Text or Code Snippet]] This will provide you with detailed information about the Python executable and version being used in your JupyterLab environment. For instance, you might see output like: [[See Video to Reveal this Text or Code Snippet]] Managing Your Python Versions Effectively To avoid confusion with multiple Python versions, consider the following best practices: Use Virtual Environments: Create dedicated environments for different projects using Conda or venv to manage dependencies and Python versions separately. Activate the Correct Environment: Always ensure that you activate the relevant Conda environment before launching Jupyter Notebook or JupyterLab to maintain consistency in the Python version. Check Kernel Selection: In Jupyter, verify that you are using the correct kernel that corresponds to your desired Python environment. By following these guidelines, you can ensure that you're using the appropriate Python version for your projects, minimizing conflicts and enhancing productivity. Conclusion Finding different Python versions across platforms can be perplexing, but with an understanding of Conda environments versus non-Conda installations, you can navigate these issues. By managing your Python environments wisely and ensuring you're activating the correct settings, you can streamline your workflow and maintain a consistent coding environment. If you're grappling with this issue or wish to share your own tips on managing Python versions, feel free to leave a comment below!