Cocalc 01 Jupyter Notebook 101 Ipynb

Let's build up from the basics: what is a Jupyter Notebook? A notebook is a document made of cells. You can write in some of them (markdown cells) or you can perform calculations in Python (code cells) and run them like this: Cool, huh? This combination of prose and code makes Jupyter Notebook ideal for experimentation: we can see the rationale for each experiment, the code, and the results in one comprehensive document. Try it yourself now.

Click "Copy & Edit" in the top right to get your own editable version of this notebook, then click the cell above and hit Shift-Enter. Other renowned institutions in academia and industry use Jupyter Notebook, including Google, Microsoft, IBM, Bloomberg, Berkeley and NASA among others. Even Nobel-winning economists use Jupyter Notebooks for their experiments and some suggest that Jupyter Notebooks will be the new format for research papers. A type of cell in which you can write text is called a Markdown cell. Markdown is a very popular markup language. To specify that a cell is Markdown you need to click in the drop-down menu in the toolbar and select Markdown.

By the end of this lecture you will be able to: Define variables and differentiate between global and local variables. Identify and use different object types in python. Use some of the python's default functions and define your own functions. Introduction to numpy and matplotlib libraries Welcome to the first optional lab!

Optional labs are available to: provide information - like this notebook reinforce lecture material with hands-on examples provide working examples of routines used in the graded labs Get a brief introduction to Jupyter notebooks The Jupyter notebook is a platform for using and writing code in a dynamic way that allows users to combine cells of code snippets that are executed with a persistent namespace and kernel alongside...

In this notebook, we cover some basics of Jupyter functionality along with a discussion of some details of how you can use python in this environment and elsewhere. For this workshop, we've constructed an online environment for everyone to use in order to smooth out any platform-dependent installation issues, but you'll probably want to install the tools we use today locally on... To do this, we recommend Anaconda, which is an effective tool for python package management that can create virtual environments, comes with a pre-installed IDE, and includes all of the Jupyter functionality that you'll... The Anaconda installer should be detailed on the page linked above, but here's another resource for installation that might be helpful. Jupyter notebooks are broken down into "cells" which might contain either code or markdown. If you select a cell with your mouse, it should be highlighted with a green border indicating that you are in "edit," mode and can edit the contents of the cell.

If text reading In [ ]: is on the left hand side of the cell, it's a "code" cell. For example, type the following and press "Shift + Enter" Pressing "Shift + Enter" executes the code in the cell, prints the output below the cell, and creates a new cell below that one. In addition to code cells, you can also write your own markdown cells by converting a cell using either the dropdown menu in the toolbar or pressing "Esc + m". In general, pressing escape enters "command mode" for which you can issue a number of commands, including Jupyter notebook atms391geodata/Week 1/Week 1 Exercise 1 scripts_examples_notebook_tour.ipynb

This document will give you a brief tour of the capabilities of the IPython notebook. You can view its contents by scrolling around, or execute each cell by typing Shift-Enter. After you conclude this brief high-level tour, you should read the accompanying notebook titled 01_notebook_introduction, which takes a more step-by-step approach to the features of the system. The rest of the notebooks in this directory illustrate various other aspects and capabilities of the IPython notebook; some of them may require additional libraries to be executed. NOTE: This notebook must be run from its own directory, so you must cd to this directory and then start the notebook, but do not use the --notebook-dir option to run it from another... You can run shell aliases and magic commands:

Over the next five weeks you will learn to code in Python. Python is an easy-to-learn, but very powerful, programming language. As well as powering many of the apps on your phone, Python is widely used in many areas of science including the Biological and Medical Sciences. You will use Jupyter Notebooks (like the one you're reading this in now) to type in Python code and immediately run it to see the output. Jupyter Notebooks are widely used in biological data analysis as well as within the field of data science, partly owing to its ability to incorporate code and graphs all within a single notebook. A Notebook consists of a series of cells.

For example, this text is in a Markdown cell. The following cell is a Code cell in which Python code is written and executed. You can tell the type of a cell by selecting the cell, and looking at the toolbar at the top of the page. For example, try clicking on this cell. You should see the cell type menu displaying Markdown. Also the left margin is empty.

In writing Python code for data science, I generally go between three modes of working: I use the IPython shell for trying out short sequences of commands, the Jupyter Notebook for longer interactive analysis... This chapter focuses on the first two modes: the IPython shell and the Jupyter Notebook. Use of an IDE for software development is an important third tool in the data scientist's repertoire, but we will not directly address that here. The text in this part, like most of this book, is not designed to be absorbed passively. I recommend that as you read through it, you follow along and experiment with the tools and syntax we cover: the muscle memory you build through doing this will be far more useful than... Start by launching the IPython interpreter by typing ipython on the command line; alternatively, if you've installed a distribution like Anaconda or EPD, there may be a launcher specific to your system (we'll discuss...

Once you do this, you should see a prompt like the following: With that, you're ready to follow along. The Jupyter Notebook is a browser-based graphical interface to the IPython shell, and builds on it a rich set of dynamic display capabilities. As well as executing Python/IPython statements, notebooks allow the user to include formatted text, static and dynamic visualizations, mathematical equations, JavaScript widgets, and much more. Furthermore, these documents can be saved in a way that lets other people open them and execute the code on their own systems. As the official page of the Jupyter project states:

A notebook is a shareable document that combines computer code, plain language descriptions, data, rich visualizations like 3D models, charts, graphs and figures, and interactive controls. A notebook, along with an editor (like JupyterLab), provides a fast interactive environment for prototyping and explaining code, exploring and visualizing data, and sharing ideas with others. Nowadays a Jupyter Notebook is a de facto standard document format, typically stored in .ipynb files, which records interactive sessions with a kernel. It is made up of cells, which can either store one or more lines of code or formatted text. When you run a code cell, the active kernel session evaluates the piece of code in it and the resulting output is shown below. The combination of communicating back and forth with a kernel and adding descriptive text makes this form of document very attractive.

CoCalc has its own implementation of the user interface to work with Jupyter Notebooks, which supports our unique features such as real-time collaboration, AI Assistant, and TimeTravel. However, the underlying format of .ipynb files is exactly the same. You can bring your existing notebooks to CoCalc and resume working, or you can download your work from CoCalc to a local computer or another cloud platform, there is no vendor-lock-in! Most of the time you don’t have to think much about the Jupyter kernel which you are using, because your notebook already has the right kernel selected or your default kernel is picked automatically... But when you want or need to, you can choose the programming language and environment by selecting a kernel explicitly. See Jupyter Kernel Selection

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