The Ultimate Guide To Run A Python Script In Jupyter Notebook Securely

Jupyter Notebooks is a popular tool for data analysis and visualization in Python. It provides a web-based interface and allows you to write code in separate blocks or “cells,” execute each code block individually, make changes, and rerun the program, all in the same window. But knowing how to run a Python script in Jupyter Notebook is not enough. Those Python scripts must be secure. Web proxies can be used to secure Notebooks, as they allow running arbitrary external processes and Web applications to run alongside your Notebook and provide authenticated web access.

As a note, one ultimate form of web proxies are residential proxies. And here’s how to get started securely running a Python script in Jupyter Notebook using web proxies.

What Are Jupyter Notebooks?

The first step to learning how to run a Python script in Jupyter Notebook is knowing what Jupyter Notebook is. It is a web-based interactive computing platform that allows you to create and share documents that contain live code, equations, visualizations, and text.

Jupyter Notebook was spun out of IPython, an interactive way of running Python code in the terminal using the “Read, Evaluate, Print, and Loop” (REPL) model. The IPython Kernel runs the computations and communicates with the Jupyter Notebook front-end interface. It also allows Jupyter Notebook to support over a hundred programming languages, including Python, R, Julia, Java, Matlab, and Scala.

Jupyter Notebook also extends IPython through additional features, like storing your code and output and allowing you to keep markdown notes, both very useful while learning how to run a Python script in Jupyter Notebook.

What Are Jupyter Notebooks Used For?

Jupyter Notebooks are versatile tools used for various purposes in data science, programming, and education. Here are some common uses of Jupyter Notebooks:

• Data Analysis and Visualization: Jupyter Notebooks allow data scientists to perform data cleaning, transformation, exploratory data analysis, and visualization. They provide an interactive environment to analyze and visualize data using libraries like Pandas, Matplotlib, and Seaborn. The interactive environment contributes significantly to understanding how to run a Python script in Jupyter Notebook.
• Machine Learning and Data Modeling: Jupyter Notebooks are widely used for developing and prototyping machine learning models. They provide a convenient way to experiment with different algorithms, preprocess data, and evaluate model performance. Popular machine-learning libraries like Scikit-learn and TensorFlow can be used within Jupyter Notebooks.
• Teaching and Learning: Jupyter Notebooks are valuable tools for teaching and learning programming and data science concepts. They allow educators to create interactive lessons and tutorials that combine code, explanations, and visualizations. Students can run and modify code examples, making learning how to run a Python script in Jupyter Notebook more engaging.
• Collaboration and Documentation: Jupyter Notebooks facilitate collaboration among team members by allowing them to share and work on the same Notebook simultaneously. Notebooks can be shared via email, Dropbox, GitHub, or the Jupyter Notebook Viewer. They serve as self-contained documents that combine code, visualizations, and explanatory text, making it easy to document and share data analysis workflows and knowledge on how to run a Python script in Jupyter Notebook.
• Presentations and Reports: Jupyter Notebooks can be used to create interactive presentations and reports. Notebooks can be converted to various formats, including HTML, PDF, and slides, making it convenient to prepare materials for presenting/sharing findings and guiding users on how to run a Python script in Jupyter Notebook.

However, note that there are security concerns to be aware of. These are:

• Access to PC files: By default, Jupyter Notebook allows access to your PC files, which means anyone else can also access your PC files. This could lead to data breaches and unauthorized access.
• Malware and viruses: There is a risk of transmitting viruses and malware from the server to your PC. It is important to ensure that the servers you are using are secure and up to date.
• Security vulnerabilities: Like any software, Jupyter Notebook may have vulnerabilities that malicious actors can exploit. It is important to keep your software up-to-date and to report security vulnerabilities to the Jupyter team.
• Confidential data: Jupyter Notebook may not be the best tool for collaborating on confidential data. The security vulnerabilities mentioned above can lead to data breaches and unauthorized access. It is important to handle confidential data carefully and use appropriate security measures.

Here are some best practices for securing your Notebooks:

• Using web proxies: Web proxies offer authentication, encryption, access control, and running of external processes with Notebooks. Use proxies while learning how to run a Python script in Jupyter Notebook.
• Restricting access to the Notebook server: Since access to the Jupyter Notebook server means access to running arbitrary code, it is important to restrict access to the Notebook server. Notebook 4.3 introduces token-based authentication that is on by default. If you enable a password for your Notebook server, token authentication is not enabled by default, and the behavior of the Notebook server is unchanged from versions earlier than 4.3.
• Keeping your software updated: Keep your software up-to-date and report any security vulnerabilities to the Jupyter team while practicing running a Python script in Jupyter Notebook.
• Using secure passwords: Practice using strong and unique passwords while learning to run a Python script in Jupyter Notebook, and avoid sharing them with others. You can also use a password manager to generate and store secure passwords.
• Using HTTPS: Practice using HTTPS because it helps encrypt the communication between your browser and the Notebook server, preventing eavesdropping and man-in-the-middle attacks.
• Use extensions and plugins: Jupyter Notebook is highly extensible, allowing you to customize and enhance its functionality. You can install extensions and plugins to add new features and capabilities to the Notebook. However, be cautious when installing third-party extensions and plugins, as they may introduce security vulnerabilities.

What Is a Script in Python?

The second step to learning how to run a Python script in Jupyter Notebook is to have a thorough understanding of what a Python script is. A script is a file containing instructions or lines of code that perform a specific task. Scripts are designed to be executed like a program and can be run from the command line or within a Python interactive shell. They are called scripts because they are read and interpreted by Python line-by-line in order from the first line to the last.

A Python script file usually has the “.py” suffix file extension, and file names can be anything your operating system allows. Still, it is recommended that you make them short and descriptive. Scripts can contain all the information required to run, but you may often have to provide that information yourself. A script can also contain functions and import various modules to perform a specific task.

What Is Python Scripting Used For?

Python scripting is used for a wide range of applications, including:

• Automation: Python is a great tool for writing scripts to automate repetitive tasks. You can automate tasks such as data cleaning, file manipulation, and web scraping.
• Utility software: Python started as a language for writing utility scripts and is still widely used for this purpose. Many of the built-in modules in Python are designed for utility tasks, such as working with files, directories, and strings.
• Web development: Python is used to build websites and web applications. It has several frameworks, such as Django and Flask, which are great for building web applications.
• Scientific and numeric computing: Python is commonly used in scientific computing and data analysis. Its libraries, such as NumPy, SciPy, and Pandas, make it easy to work with large data sets and perform complex computations.
• Machine learning and artificial intelligence: Python is widely used in machine learning and artificial intelligence. Libraries such as TensorFlow, Keras, and PyTorch make building and training machine-learning models easy.
• Business applications: Python is also used to build business applications such as ERP systems, CRM systems, and accounting software.

These are just some of the applications. In the following sections, you can learn how to run a Python script in Jupyter Notebook and try applying it to your specific use case.

How To Change the Python Version in Jupyter Notebook

Changing the Python version is another part of learning how to run a Python script in Jupyter Notebook. You can follow these steps.

1. Confirm the current Python version

Before changing the Python version, it’s important to know which version Jupyter Notebook is currently using. You can check the Python version by running the following code in a Jupyter Notebook cell:

python

import sys

print(sys.version)

2. Install the desired Python version

Install your desired version of Python. You can use a package manager like conda or pip to install the specific Python version you want.

3. Install the “ipykernel” package

The “ipykernel” package allows you to manage Jupyter settings, including changing the default Python version. You can install it by running the following command in your terminal or command prompt:

shell

pip install ipykernel

4. Create a new kernel

Once you have installed the ipykernel package, you can create a new kernel with the desired Python version. Run the following command in your terminal or command prompt:

shell

python -m ipykernel install –user –name myenv –display-name “Python (desired version)”

Replace “myenv” with a name for your kernel and desired version with the version number of the Python you want to use.

5. Change the kernel in Jupyter Notebook

Open Jupyter Notebook and navigate to the notebook where you want to change the Python version. Go to “Kernel” in the top menu and select “Change kernel.” Choose the kernel with the desired Python version from the drop-down menu.

These steps will help you change the version of Python before learning how to run a Python script in Jupyter Notebook.

How To Code With Python in Jupyter Notebook

Coding is an important part of learning how to run a Python script in Jupyter Notebook. Here’s some common actions:

Launching Jupyter Notebook

After installing Jupyter Notebook, you can launch it by running the following command in your terminal or command prompt:

bash

jupyter notebook

Your web browser will open a new tab displaying the Jupyter dashboard.

Creating a new Python notebook

Click the New button at the top right and select the Python version you want to use. A new tab will open with a blank notebook.

Writing code

In a blank cell, you can start typing your Python code. To write comments, you can use the # symbol.

Running code

Press Shift + Enter to run the code in the current cell and move to the next one. You can also click the Run button on the toolbar.

Adding new cells

Click the + button on the toolbar or press B (while not in edit mode) to create a new cell below the current one. You can use A to insert a cell above the current cell.

Changing cell types

You can switch between code and markdown cells (for text and documentation) from the toolbar’s drop-down menu or by using M for Markdown and Y for Code (while not in edit mode).

Using Markdown for text

You can use Markdown syntax to add headings, lists, links, images, etc., in text cells.

Saving your Notebook

Click the Save button or press Ctrl + S to save your notebook.

Exporting your Notebook

You can export your notebook to different formats (like HTML, PDF) by going to File > Download as.

Keyboard shortcuts

Press H (while not in edit mode) to view a list of keyboard shortcuts for common actions.

Shutting down

You can close the browser tab and shut down the Jupyter Notebook server by pressing Ctrl + C in the terminal.

These steps should help you effectively write and run Python code in Jupyter Notebook and allow you to explore and analyze data, create visualizations, and develop data science projects.

Additional tips while learning how to run a Python script in Jupyter Notebook:

• Use the %matplotlib in-line magic command to display plots directly within the notebook.
• Consider breaking longer code into separate cells for clarity.
• If a cell is stuck executing, you can interrupt it by clicking the Stop button in the toolbar.

How To Run a Python Script in Jupyter Notebook

Follow the instructions below to learn how to run a Python script in Jupyter Notebook:

1. Open Jupyter Notebook in your web browser.
2. Create a new Notebook or open an existing one.
3. In a new cell, type %run followed by the path to your Python script. Example: %run /path/to/your/script.py.
4. Run the cell to execute the script.

If you want to import functions or variables from your Python script into your Jupyter Notebook, use the %run -I command instead of %run. To work with pure Python files in Jupyter Notebook, open the “.py” files in JupyterLab in the code editor and run snippets from the files in an associated console.

Alternatively, you can open a terminal in Jupyter Notebook and run your Python scripts in the terminal as described below:

1. Install Jupyter Notebooks

If you haven’t already installed Jupyter, you can do so by running the following command in your terminal or command prompt:

bash

pip install jupyterlab

2. Launch Jupyter Notebooks

In your terminal or command prompt, navigate to the directory where your Python script is located and run:

bash

jupyter lab

3. Create a new Notebook

Once Jupyter Lab is open in your web browser, click the + sign to create a new notebook or go to File > New > Notebook.

4. Select Python kernel

Ensure that the proper Python kernel is selected in the top-right corner of the notebook.

5. Write or paste code:

You can write your Python code directly into the cells or paste your Python script into a new cell.

6. Run the code

You can execute the code in a cell by clicking the “Run” button (a play symbol) in the toolbar or pressing Shift + Enter. The output will appear below the cell.

7. Save your work

If you want to save your work, go to File > Save Notebook.

8. Run external scripts

If you have an existing Python script (e.g., myscript.py) and you want to run it inside the notebook, you can use the %run magic command in a cell:

python

%run myscript.py

9. Shut down

When you’re done, you can close the browser tab and shut down the Jupyter Notebook server by pressing Ctrl + C in the terminal.

Practice following the steps above to master running a Python script in Jupyter Notebook. Specific steps may vary depending on your operating system and network setup for running a Python script in Jupyter Notebook.

The Python kernel selected in the Jupyter Notebook must match the version of Python you want to use for running your script. If you encounter issues, make sure your Python environment is correctly configured.

How to comment multiple lines with Python in Jupyter Notebook

Commenting multiple lines is a part of learning how to run a Python script in Jupyter Notebook. You can use the keyboard shortcut Ctrl + / (for Windows and Linux) or Cmd + / (for Mac). This will add a hash symbol (#) at the beginning of each selected line, turning them into comments. To uncomment the lines, simply repeat the shortcut.

The other way to add multiple comment lines is to use triple quotes (”’) to create a multiline string, which will effectively comment out the code. However, this method is not recommended as it can cause issues with indentation and is not as clear as using the hash symbol.

Add comments to explain the purpose of the code and how it works, which can help others understand and modify the code when needed. Future learners can also benefit from it when studying how to run a Python script in Jupyter Notebook.

Configuring Jupyter Notebook to run Python scripts behind a web proxy

Web proxies are not required for running a Python script in Jupyter Notebook. However, depending on your network setup and security configurations, you may need to know how to run a Python script in Jupyter Notebook with one or more web proxies.

Follow the steps below to run a Python script in Jupyter Notebook behind a web proxy:

1. Connect to a secure network

Ensure you are connected to a trusted secure network, like a VPN or your office network.

2. Configure proxy settings

You’ll need to set up your system to use the proxy. This may vary depending on your operating system and the type of proxy.

For running a Python script in Jupyter Notebook on Windows

You can set the environment variables by opening a command prompt and typing:

bash

set HTTP_PROXY=http://proxy_url:port

set HTTPS_PROXY=https://proxy_url:port

For running a Python script in Jupyter Notebook on macOS and Linux

Open a terminal and type:

bash

export HTTP_PROXY=http://proxy_url:port

export HTTPS_PROXY=https://proxy_url:port

Replace proxy_url and port with your proxy’s URL and port number.

3. Configure Jupyter Notebook

You can configure Jupyter Notebook to use the proxy settings by editing its configuration file.

Create a Jupyter configuration file (if it doesn’t exist):

bash

jupyter notebook –generate-config

Edit the configuration file

Find the configuration file, usually located at ~/.jupyter/jupyter_notebook_config.py, and open it with a text editor.

Add proxy settings

Add the following lines. Adjust the URL and port to match your proxy for learning how to run a Python script in Jupyter Notebook.

python

c.NotebookApp.NotebookApp.http_proxy = ‘http://proxy_url:port’

c.NotebookApp.NotebookApp.https_proxy = ‘https://proxy_url:port’

4. Start Jupyter Notebook

Launch Jupyter Notebook as you usually would:

bash

jupyter notebook

Exercise caution with proxy configurations while learning how to run a Python script in Jupyter Notebook, especially if they involve authentication details, as improper handling can pose security risks. If you’re in doubt, consult the official Jupyter Notebook documentation or seek assistance from your network administrator for more specific instructions based on your setup for learning how to run a Python script in Jupyter Notebook.

Wrapping Up

Jupyter Notebooks is a versatile and powerful tool widely used in scientific computing, machine learning, and web development. It supports data analysis, interactive visualization, collaboration, documentation, teaching, and learning, making it a favorite for running Python scripts.

Learning how to run a Python script in Jupyter Notebook is a valuable skill, but you should use proxies as part of your security best practices. Try Rayobyte Proxies with Rayobyte’s Web Scraping API to optimize and secure the execution of your web scraping and Python processes in Jupyter Notebooks.

The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.