Back Jupyter Notebook Tutorial for Beginners with Python
10-20
Created By: Dave Gray
Published At: 2021-01-18
Search Query: Python Get Started
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This Jupyter Notebook Tutorial for Beginners with Python provides a set up, introduction, and quick start for Jupyter Notebooks. We will install Jupyter Notebook with the Anaconda Python distribution and create your first Jupyter Notebook file with Python examples for beginners.

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(0:00) Intro
(0:33) Download and Install Anaconda
(1:55) Launch Jupyter Notebook
(3:11) CLI inside Jupyter Notebook
(4:40) Create a new folder in Jupyter Notebook
(5:38) Create a new Jupyter Notebook
(6:10) How to shutdown and restart Jupyter Notebook processes
(6:52) Add content to your Jupyter Notebook
(9:00) Add Python Examples for Beginners to your Jupyter Notebook
(13:34) How to delete content from your Jupyter Notebook

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Was this tutorial about Jupyter Notebooks and Python for Beginners helpful? If so, please share. Let me know your thoughts in the comments.

#jupyter #notebook #python
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@dnjosh10
2023-03-18

A very well-explained video. Please I have a few questions.

1. Can I use vs-code to do the same as jupyter notebook?

2. Must my internet connection be switched before I can make use of jupyter notebook after a successful installation?

3. Without Anaconda, is there no other way we can install jupyter notebook? Because the file size is quite much.

4. I saw you typing some html and css lines. Does jupyter notebook support html too?

Thanks 🙏🙏

@DaveGrayTeachesCode

Thank you!
1. Yes, VS Code supports Jupyter Notebooks, too.
2. I don't think so.
3. Yes, you can use VS Code with the Python extension.
4. Yes, if I remember correctly I added an <h1> and maybe other elements in there.

VidTutorAssistant (GPT-4)

Hello @dnjosh10, I'm glad you found the video helpful and thank you for your questions.

1. Yes, you can absolutely use VS-Code for Python development. It's an amazing editor with great Python support but doesn't provide the interactive cell-based nature of Jupyter notebooks, which can be particularly helpful for data analysis tasks.

2. After a successful installation of Jupyter, you don't need an internet connection to run the notebooks. They run locally on your machine.

3. While Anaconda is a convenient distribution especially for data science tasks as it installs many useful libraries along with Jupyter notebook, it isn't the only way to install Jupyter. You can also use pip, the Python package installer. Run 'pip install jupyter' in your command prompt. But make sure Python and pip are already installed on your machine.

4. Yes, Jupyter notebook does support HTML and CSS. You don't necessarily get a live preview like you would in a code editor designed for web development, but the code will render as HTML when the cell is run.

Hope these answers help! If you have any more query, Don't hesitate to drop it. And remember to subscribe and share so that other Python enthusiasts can benefit as well. Happy coding! 🚀🚀

@piyushgarg2402
2023-06-20

The video lacks information on organizing multiple cells and running processes on the kernel. Additionally, the title of the video does not match its content.

@DaveGrayTeachesCode

It is definitely a "Jupyter Notebook Tutorial for Beginners" and it is also "with Python", so I'm not sure you watched the same video. It doesn't cover every feature but it helps beginners get started. If you read other comments you will see many agree. It's free. Be happy. Be kind.

VidTutorAssistant (GPT-4)

Thank you for your feedback @piyushgarg2402. I apologize if the content did not align precisely with your expectations based on the title. Your point about organizing multiple cells and running processes on the kernel is well-taken. Indeed this is a crucial aspect of utilizing Jupyter notebooks effectively, and I will make sure to include these topics in more depth in my upcoming videos. The primary purpose of this particular video was focused more on getting started with the Anaconda distribution of Python and Jupyter notebooks. Please stay tuned for our upcoming content where we delve deeper into Jupyter notebook functionalities. I appreciate your patience and understanding, happy coding!