Remember that this article was written in a Jupyter notebook, so all of the narrative text and images you have seen so far was achieved in Markdown.
In the menu just above the code, you have options to play around with the cells: add, edit, cut, move cells up and down, run the code in the cell, stop the code, save your work and restart the kernel.
Click on it and you will see the notebook file opened exactly like you open it from local dashboard of notebook server on your local machine.
In this way, you can think of a notebook document as being somewhat comparable to a script file, except that it is multimedia.
If you import a file while running Jupyter Notebook on your machine (not in a Kaggle kernel or some other dockerized environment), the imported code will be executed on your machine!
It allows a more flexible and powerful way of working on projects, but with the same components that Jupyter notebooks have.
Command mode binds the keyboard to notebook level commands and is indicated by a grey cell border with a blue left margin.
Anaconda also comes with Jupyter Notebook and several other tools that are useful for working in Python.
If you wish to share your notebooks with a small private group, this functionality may well be all you need.
You can download and run this document as a Jupyter notebook using the link at the bottom of this page.
If your code is on Github (or any online repository), you can also clone your repository using the git clone command.
We've now learned all about what these notebooks are and how to go about setting them up on our own machines.
Use a local URL to an image that you will be keeping alongside your notebook, such as in the same git repo.
In the screenshot of a new notebook in the section above, that box with the green outline is an empty cell.
NOTE: If you have a lot of stuff in your dropbox it may be too big to sync without using a interactive job.
Notebooks and dashboard are web apps, and Jupyter starts up a local Python server to serve these apps to your web browser, making it essentially platform independent and opening the door to easier sharing on the web.
For example: if you are working on a Jupyter notebook in a CPU instance, you can switch to a GPU instance to speed up your training.
If your data is available on the internet, you can also download it directly into your workspace using the Terminal.
You can also use the nbconvert option to manually convert your notebook into a different format like HTML or PDF.
Notebooks, Jupyter has put together a gallery of interesting Jupyter Notebooks that you may find helpful and the Nbviewer homepage links to some really fancy examples of quality notebooks.
