For now, we'll just check to make sure you have a working Python distribution. In the Jupyter Notebook Tutorial we cover these buttons in detail. You will see a "code cell" (grey rectangle) along with a bunch of other boxes above it.
The right-ha d side of your screen is the actual notebook. Right-click the "Untitled.ipynb" in the sidebar and rename it to something more informative, say testing_out_python.ipynb. One of them will be a "Python 3 Notebook".Ĭlicking this will open a new Jupyter Nook named Untitled.ipynb. This will open a new "Launcher" window where a variety of new filetypes can be opened.
Caltech jupyter notebook tutorial code#
Navigate to the folder where you store your code files ( aph161_in_class/code in my case) and click the + in the sidebar. This should automatically open a browser window with the JupyterLab interface, Launch the JupyterLab IDE by clicking the 'launch' button.
Where I have boxed in the JupyterLab prompt with a red box. When you open up Anaconda Navigator, you should see a screen that looks like this, When you installed Anaconda, you also installed the Anaconda Navigator, an app that allows you to easily launch a JupyterLab instance. Even better, JupyterLab comes prepackaged with your Anaconda Python distribution. JupyterLab allows omne to write code in notebooks, navigate around your file system, write isolated python scripts, and even access a UNIX terminal, all of which we will do throughout this class. While Jupyter Notebooks are fantastic alone, we will be using them throughout the course via the JupyterLab Integrated Development Environment (IDE). In fact, you are reading a Jupyter Notebook right now!
Caltech jupyter notebook tutorial download#
If you want to use the interactivity to explore probability distributions, you will need to download the. This tutorial was generated from an Jupyter notebook. This document was prepared at Caltech with financial support from the Donna and Benjamin M. This acts lkike an interactive script which allows one to interweave code, mathematics, and text to create a complete narrative around your computational project. Tutorial 3b: Probability distributions and their stories. The key component of the Jupyter interactive programming environment is the Jupyter Notebook. As you've guessed by this point, we will be focusing on using Python through the Jupyter Environment. Jupyter allows for interactive programming in a large array of programming languages including Julia, R, and MATLAB. This environment is incredibly useful for interactive programming and development and is widely used across scientific computing. Packaged with the Anaconda Python distribution is the Jupyter project.