When the command is executed a new browser tab opens with the Jupyter notebook interface. Once installed, you can launch the browser by entering the command jupyter notebook from the command line.
Create jupyter notebook tutorial install#
How Jupyter Notebook works – A very simple tutorialįirst, be sure to first install Jupyter Notebook if it’s not already on your machine. I’ll show you examples in both Python and PowerShell since those are commonly used to interface with Cisco compute API’s.
Create jupyter notebook tutorial code#
It runs as an interactive web application supporting markdown and code execution with support for over 100 programming languages (called “kernels”) including Python, PowerShell, and Matlab just to name a few. IDLE does the job but it has limitations when it comes to documenting and sharing ideas. Enter Jupyter Notebook, an interactive way of documenting and executing live code from a web-based interface. What if you wanted to share what you tried with someone else? Assuming you copy/pasted the contents of your shell session, would someone else be able to understand what the flow of what you were trying? Could you refer to it later and understand the flow of what you did? You run your line(s) of code and verify things work and when you are finished, you close your session with and you move on with life. IDLE lets you execute Python code directly from the command line without needing to run a *.py file. If you have experience with Python you may be familiar with IDLE (see the screenshot below). It’s used extensively in academia especially by data scientists. Jupyter Notebook is an open-source web application used to create and share code along with narrative text. In this example, the items in the inventory are UCS X-Series compute nodes managed by IMM (Cisco Intersight Managed Mode). This post shows a simple Python example that retrieves physical compute inventory claimed in Cisco Intersight. Once verified, it is documented, saved, and the code is then added to the IDE, such as VSCode, where the main Python code is developed. As an example, a developer writes small blocks of Python to vet an algorithm, a function, or syntax. Once the problem is broken down and implemented, however, how can we convey what we learned to others? One way is using Jupyter Notebook to combine documentation authored in markdown together with live code.
It’s a practice that certainly applies in the world of programming. But we can all agree that breaking large problems into smaller pieces is a valuable arrow in the quiver of problem-solving techniques. Admittedly, I don’t know of anyone who eats whale one bite at a time (or any way otherwise for that matter). A: One bite at a time… or so the saying goes.
0 kommentar(er)
