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Running Jupyter Notebook in a Virtual Environment: Installed Scikit-learn Module Not Available

In this blog, we will learn about the significance of the Jupyter Notebook, a crucial tool for data scientists and software engineers. This versatile platform empowers users to generate and disseminate documents encompassing live code, equations, visualizations, and explanatory text. Additionally, we’ll explore the necessity of establishing a virtual environment, a segregated Python environment ensuring the installation of project-specific packages and dependencies without impacting the global Python setup.
By Saturn Cloud | | Miscellaneous | Updated:

As a data scientist or software engineer, one of the most important tools in your arsenal is the Jupyter Notebook. It enables you to create and share documents that contain live code, equations, visualizations, and narrative text. However, when working on a project, it’s essential to create a virtual environment. A virtual environment is an isolated Python environment that allows you to install packages and dependencies specific to your project without affecting the global Python installation.

In this tutorial, we’ll show you how to run Jupyter Notebook in a virtual environment and how to troubleshoot the common error of a missing scikit-learn module.

Table of Contents

  1. Step-by-Step Installing scikit-learn in a Virtual Environment
  2. Troubleshooting: Scikit-learn Module Not Found
  3. Best Practices
  4. Conclusion

Step-by-Step Installing scikit-learn in a Virtual Environment

Step 1: Create a Virtual Environment

The first step is to create a virtual environment. To do this, you’ll need to have the virtualenv package installed.

pip install virtualenv

Once you have virtualenv installed, navigate to the directory where you want to create your virtual environment and run the following command:

virtualenv myenv

This will create a new directory called myenv, which contains a fresh Python installation and a pip executable.

Step 2: Activate the Virtual Environment

To activate your virtual environment, navigate into the directory and run:

source myenv/bin/activate

You should now see (myenv) in your command prompt, indicating that you’re working in your virtual environment.

Step 3: Install Jupyter Notebook

With your virtual environment activated, you can now install Jupyter Notebook. To do this, run:

pip install jupyter

This will install Jupyter Notebook and its dependencies into your virtual environment.

Step 4: Install Scikit-learn

Next, you’ll need to install the scikit-learn module. To do this, run:

pip install scikit-learn

This will install the scikit-learn module and its dependencies into your virtual environment.

Step 5: Launch Jupyter Notebook

With Jupyter Notebook and scikit-learn installed, you can now launch Jupyter Notebook. To do this, run:

jupyter notebook

This will launch Jupyter Notebook in your default web browser. You can now create a new notebook and start working on your project.

Troubleshooting: Scikit-learn Module Not Found

If you encounter the error ModuleNotFoundError: No module named 'sklearn', it’s likely that Jupyter Notebook is not running in your virtual environment. To fix this, you’ll need to install and enable the ipykernel package.

pip install ipykernel

Next, you’ll need to install the kernel for your virtual environment.

python -m ipykernel install --user --name=myenv

This will install the kernel for your virtual environment, which you can now select when creating a new notebook in Jupyter Notebook.

Best Practices

  1. Use Virtual Environments: Always work within a virtual environment to isolate your project dependencies and avoid conflicts with system-wide packages.

  2. Kernel Awareness: Ensure that the Jupyter Notebook kernel is aware of the virtual environment by installing Jupyter within the environment.

  3. Kernel Restart: If you encounter import errors, try restarting the Jupyter Notebook kernel after activating the virtual environment.

  4. Use Requirements.txt: Maintain a requirements.txt file listing all project dependencies. This makes it easy to recreate the environment on another machine.

Conclusion

Running Jupyter Notebook in a virtual environment is essential for any data scientist or software engineer working on a project. By following these simple steps, you can create a virtual environment, install Jupyter Notebook and scikit-learn, and start working on your project with confidence. If you encounter the common error of a missing scikit-learn module, simply install and enable the ipykernel package and install the kernel for your virtual environment.

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