Methods to Change and Downgrade Python Version in Colab

Apart from the generic method, there are other methods that can be used to downgrade Python in Colab:

• Using Virtual Environments
• Using Conda

Downgrade Python Version Using Virtual Environments

Virtual environments are isolated Python environments that allow you to manage different Python versions and packages for your projects.

Step 1: Create a Virtual Environment:

Start by creating a virtual environment with your desired Python version. For example, to create a Python 3.6 environment, run:

!pip install virtualenv

create the virtual environment by running this command

!virtualenv myenv

Step 2: Activate the Virtual Environment:

Activate the virtual environment:

!source myenv/bin/activate

Step 3: Install Jupyter:

Install Jupyter Notebook or JupyterLab within your virtual environment:

!pip install jupyter

Step 4: Start a Jupyter Notebook:

Launch Jupyter Notebook from within your virtual environment:

!jupyter notebook

Output:

 To access the notebook, open this file in a browser:
        file:///root/.local/share/jupyter/runtime/nbserver-2408-open.html
    Or copy and paste one of these URLs:
        http://localhost:8888/?token=34dbdc5487d062805c5642d0780e9b4610854309cd3ca79b
     or http://127.0.0.1:8888/?token=34dbdc5487d062805c5642d0780e9b4610854309cd3ca79b

Copy paste one of the URLs to access the jupyter notebooks.

Change Python Version Using Conda

If you prefer Conda for package and environment management, you can also use it to switch Python versions.

Step 1: Installing Conda: If Conda is not already installed, you can install it by running:

!conda install python=3.6

This command installs Python 3.6 using Conda.

Step 2: Creating and Activating Conda Environments:

You can create Conda environments with specific Python versions and activate them as needed.

To create a Conda environment with Python 3.6, use:

!conda create --name myenv python=3.6

Activate the environment with

!conda activate myenv

You can then install packages within the Conda environment.

Step 3: Deactivating the Conda Environment:

To deactivate the Conda environment and return to the default Python version, use:

!conda deactivate

Note: While the generic method and virtual environments provide flexibility in managing Python versions, Conda offers additional features for environment and package management.

3. Handling Package Compatibility

When downgrading Python in Colab, you may encounter package compatibility issues. Here’s how to handle them:

Step 1: Update Packages:

Before downgrading Python, ensure that your packages are up-to-date. Run `!pip install –upgrade package_name` to update individual packages.

Example:

!pip install --upgrade numpy

Output:

Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.23.5)
Collecting numpy
  Downloading numpy-1.26.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 18.2/18.2 MB 85.3 MB/s eta 0:00:00
Installing collected packages: numpy
  Attempting uninstall: numpy
    Found existing installation: numpy 1.23.5
    Uninstalling numpy-1.23.5:
      Successfully uninstalled numpy-1.23.5

Step 2: Check Compatibility:

Verify if your required packages are compatible with the older Python version. Some libraries may not support older Python versions. This can be done through running some library specific functions.

Step 3: Install Specific Versions:

If a package is not compatible with your downgraded Python version, consider installing an older version of the package that is compatible. Use `!pip install package_name==version`.

!pip install numpy==1.21.0

Google Colab is an incredibly versatile platform for data science and machine learning. It offers a virtual environment equipped with a variety of pre-installed software tools, and the best part is, that it’s free! In this guide, we’ll take you through the process of downgrading the Python version in Google Colab.

Typically, Google Colab comes bundled with the latest Python version, which is generally a good thing. However, there are situations where you might need an older Python version due to compatibility issues with specific libraries or project requirements. Here, we will change and downgrade Python version.