• author: The PyCoach

Expanding your Jupiter Lab Experience with Jupiter AI

This video is sponsored by Brilliant.

Today, I stumbled upon a fascinating extension called Jupiter AI that dramatically enhances the user interface of Jupiter Lab. Not only does it provide a sleek chat interface for asking questions about your notebook, but it also integrates magic commands to interact with AI models like GPT-3.5, generating entire notebooks from a simple text prompt. Since incorporating this extension into my workflow, productivity during data analysis has skyrocketed. In this article, I will guide you through the setup process and showcase the myriad of possibilities Jupiter AI brings to the table.

Setting Up Jupiter AI

The first step is visiting the official documentation where you will find the code necessary to install Jupiter AI. You have the option of using either Conda or Pip for installation. However, for Mac users with M1 or M2 chips, I highly recommend using Conda to ensure optimal performance. Simply follow the given code snippet, and you'll be good to go.

Note: Jupiter AI requires Python 3.10 or 3.8 and Jupiter Lab 3. If you have Jupiter Lab 4, unfortunately, Jupiter AI is not compatible.

Now, let's walk through the installation process step by step:

  1. Create a virtual environment with Python 3.10 (or 3.8) by running the provided code snippet in your terminal.
  2. Activate the virtual environment using the given code snippet.
  3. Install Jupiter AI by executing the installation command.
    • If you are using Conda, copy the provided command and run it in the terminal.
    • If you are using Pip, copy the appropriate command and execute it similarly.
  4. For Mac users with M1 or M2 chips, there is an additional step: uninstall the grpcio package and reinstall it using Conda. Execute the given commands to perform this extra step.

Once the installation is complete, launch Jupiter Lab by typing "Jupiter Lab" in your terminal. After a few seconds, you should notice a new icon, the chat icon, in the left panel. This signifies that the chat interface has been successfully installed.

Pro-tip: To avoid repetition, create a virtual environment and install Jupiter AI beforehand. This way, you can quickly get started once you've obtained and pasted your Open AI API key.

Exploring the Chat Interface

The chat icon allows you to configure various settings. To set up the interface, follow these steps:

  1. Copy your Open AI API key (if you don't have one, a link to generate a new key is provided in the description).
  2. Open the chat interface and paste your API key into the designated field.
  3. Save the changes and return to the main interface.

You are now ready to interact with the AI-powered chatbot, called Jupyter Note. Simply type your query, such as "Hello world," and hit Shift+Enter to initiate the conversation. However, please note that using the Open AI API key requires sufficient credits. If you do not have enough credits, the chatbot will not function properly.

In the following sections, we will delve deeper into the capabilities of the Jupiter AI extension, both within the chat interface and within notebook cells.

Unleashing the Power of Jupiter AI in Notebooks

To fully utilize Jupiter AI within notebooks, we must first load the extension. Execute the provided code snippet to load Jupiter_AI.

To access a list of available AI models and providers, use the following command:


By running this command within the AI magic function, an extensive list of providers and models will be displayed. It is worth mentioning that Jupiter AI is not limited to Open AI and boasts numerous other providers and models.

If you intend to use Open AI models, you need to install the Open AI package. Run the following command:


Once you have installed the Open AI package, go ahead and import the OS module and set your Open AI API key as an environment variable. The provided code snippet demonstrates how to do this.

For instance, with the magic command "%%AI chat_gpt," you can generate customized code based on prompts. The code below showcases an example:


Upon running the command, you will observe the desired code generated according to your prompt. By default, the output is formatted as markdown. However, you can modify the format to suit your requirements. The available options include code, markdown, math, HTML, JSON, and plain text. Utilize the "F" argument to specify the desired format, as shown in the provided examples below:

  1. Output in code format:
  1. Output in math format:

Furthermore, you are not limited to working only with prompts within the same cell. The magic command can also access code from other notebook cells. This gives you the flexibility to interact with diverse sections of your notebook, all within the same chat interface.

To demonstrate this functionality, let's create a simple for loop that prints the numbers from 1 to 9 in a separate cell. Then, execute the following command within the chat interface:


This will generate an explanation of the code contained within the designated cell.

Feel free to experiment and explore the wide range of possibilities Jupiter AI offers, such as working with different formats, accessing code from other cells, and engaging with diverse AI models.

Enhancing Code Understanding and Efficiency with ChatDPT

As data analysts and data scientists, it is crucial to have strong coding skills and analytical thinking abilities. These skills not only help us understand and optimize our code but also are essential in navigating the ever-evolving landscape of AI tools and technologies. In this article, we will explore how the ChatDPT extension for Jupyter Notebook can assist us in code comprehension, error debugging, and even generate entire notebooks from scratch.

Explaining Code with Magic Command

ChatDPT introduces a magic command that allows us to obtain explanations for code snippets within Jupyter Notebook. By simply using the magic command and enclosing the code in curly braces, we can easily acquire insights into the functionality and purpose of the code.

To illustrate this, let's consider a for loop that prints numbers from 1 to 9. Using the magic command AI chat, we can explain the code embedded in a specific code cell. For instance, if we want to understand the code in the cell with the input of 8, we can execute AI chat explain the code below {in:8}. This will provide us with a detailed explanation of the code's operation, including information such as iterating through a sequence of numbers using the for loop.

Additionally, we can leverage other specialized explanations such as errors. By utilizing the err keyword and specifying the error input, we can retrieve detailed explanations for encountered errors. For example, if we want to understand a given error (e.g., missing colon in for loop syntax), we can employ the command AI chat explain the error below {err:10}. This will enlighten us about the reason behind the error, allowing us to promptly rectify it.

Code Explanation via Chat Interface

Apart from using the magic command, ChatDPT offers a convenient chat interface built directly into the left panel of Jupyter Notebook. This interface grants similar functionality while fostering a more interactive and conversational approach to exploring code.

To obtain explanations through the chat interface, we can highlight the code in question and use the "include selection" option. By merely selecting the code and typing a relevant question such as "What does this code do?", the chat interface will automatically include the selected code in the prompt and provide us with an explanation.

Generating Notebooks

One of the most remarkable capabilities of ChatDPT is the ability to generate entire notebooks from scratch. The generate command allows us to specify the type of notebook we wish to create. For instance, if we want a demonstration of using the pandas library, we can execute generate demo of how to use the pandas Library. The AI will then create a fully functional notebook dedicated to demonstrating pandas functionality, including sections on exploration, cleaning, manipulation, and other relevant topics.

To access the generated notebook, we simply click on the folder icon located within the chat interface. From there, we can easily select the Python 3 kernel and explore the comprehensive notebook entirely generated by ChatDPT.

It is worth mentioning that while the AI-generated notebooks are incredibly useful, they require a solid foundation in coding and analytical thinking to fully comprehend and utilize effectively. These skills can be honed through platforms like brilliant.org, which offers interactive data science and analytical courses. By developing these skills, data analysts and data scientists can navigate the evolving AI landscape and maximize the benefits offered by tools like ChatDPT.

Jupiter ai is more than just a chat interface. it enhances your jupiter lab experience by seamlessly integrating ai capabilities and boosting your productivity when working with notebooks. whether you want to generate code snippets, explore mathematical equations, or collaborate with external cells, jupiter ai provides a versatile environment to meet your needs. give it a try and witness how this extension can revolutionize your data analysis workflows.

remember to protect your open ai api key and use it responsibly.

In conclusion, ChatDPT's integration into Jupyter Notebook empowers data analysts and data scientists by enhancing code understanding and efficiency. By leveraging the magic command and the chat interface, we can easily obtain explanations for code snippets and troubleshoot errors. Moreover, the ability to generate entire notebooks from scratch streamlines the development process, allowing us to create functional notebooks at an unprecedented speed.

As we embrace the AI revolution, it is crucial to cultivate our coding skills and analytical thinking abilities continuously. Platforms like brilliant.org provide invaluable resources to develop these skills, enabling us to thrive in a world where tools like ChatDPT continuously evolve and improve. Try out ChatDPT today and take your code comprehension and development efficiency to new heights.

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