Jupyter Tutorial - Read the Docs
Jupyter Tutorial
Release 1.1.0 Veit Schiele
Jun 26, 2023
CONTENTS
1 Introduction
3
1.1 Target group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Why Jupyter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Jupyter infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Workspace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 What's new?
5
3 Notebook
7
3.1 Install Jupyter Notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Create notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.3 Keyboard shortcuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.4 Jupyter paths and configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.5 Parameterisation and scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.6 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 JupyterLab
29
4.1 Install JupyterLab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 JupyterLab extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.3 JupyterLab on JupyterHub . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4 Real-time collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.5 Scheduler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5 JupyterHub
35
5.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.2 Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.3 systemdspawner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.4 Create service nbviewer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.5 ipyparallel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
6 Binder
61
7 nbconvert
63
7.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
7.2 Use on the command line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
7.3 nb2xls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
7.4 Own exporters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
8 nbviewer
67
8.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
8.2 Extending the Notebook Viewer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
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8.3 Access control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
9 Kernels
69
9.1 Install, view and start the kernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
9.2 Python2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
9.3 What's new in Python 3.8? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
9.4 What's new in Python 3.9? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
9.5 What's New In Python 3.10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
9.6 R-Kernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
10 ipywidgets
83
10.1 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
10.2 Widget list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
10.3 Widget events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
10.4 Custom widget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
10.5 ipywidgets libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
10.6 Embed Jupyter widgets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
11 nbextensions
131
11.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
11.2 List of extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
11.3 Create plugin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 11.4 setup.ipynb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 11.5 ipylayout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
12 Visualise data
145
13 Dashboards
147
13.1 Jupyter Dashboards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
13.2 Appmode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
13.3 Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
13.4 Voil? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
13.5 jupyter-flex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
14 Sphinx
213
14.1 nbsphinx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
14.2 Executable Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
15 Use cases
219
16 Index
221
Index
223
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Jupyter Tutorial, Release 1.1.0
Jupyter notebooks are growing in popularity with data scientists and have become the de facto standard for rapid prototyping and exploratory analysis. They inspire experiments and innovations enormously and as well they make the entire research process faster and more reliable. In addition, many additional components are created that expand the original limits of their use and enable new uses.
What do you want to do?
JupyterNotebook
Single user
Teamwork
Convert Java, R, Julia etc. Extend notebooks
Embed notebooks
nbconvert Kernels
Conversion service
JupyterLab JupyterHub
Binder nbviewer Widgets nbextensions Visualise
data
Examples
Dashboards in HTML
Enterprise applications
nbsphinx Executable Books
CONTENTS
1
Jupyter Tutorial, Release 1.1.0
2
CONTENTS
CHAPTER
ONE
INTRODUCTION
1.1 Target group
The users of Jupyter notebooks are diverse, from data scientists to data engineers and analysts to system engineers. Their skills and workflows are very different. However, one of the great strengths of Jupyter notebooks is that they allow these different experts to work closely together in cross-functional teams. Data scientists
explore data with different parameters and summarise the results. Data engineers
check the quality of the code and make it more robust, efficient and scalable. Data analysts
use the code provided by data engineers to systematically analyse the data. System engineers
provide the research platform based on the JupyterHub on which the other roles can perform their work. In this tutorial we address system engineers who want to build and run a platform based on Jupyter notebooks. We then explain how this platform can be used effectively by data scientists, data engineers and analysts.
1.2 Why Jupyter?
How can these diverse tasks be simplified? You will hardly find a tool that covers all of these tasks, and several tools are often required even for individual tasks. Therefore, on a more abstract level, we are looking for more general patterns for tools and languages with which data can be analysed and visualised and a project can be documented and presented. This is exactly what we are aiming for with Project Jupyter. The Jupyter project started in 2014 with the aim of creating a consistent set of open source tools for scientific research, reproducible workflows, computational narratives and data analysis. In 2017, Jupyter received the ACM Software Systems Award ? a prestigious award which, among other things, shares with Unix and the web. To understand why Jupyter notebooks are so successful, let's take a closer look at the core functions: Jupyter Notebook Format
Jupyter Notebooks are an open, JSON-based document format with full records of the user's sessions and the code they contain. Interactive Computing Protocol The notebook communicates with the computing kernel via the Interactive Computing Protocol, an open network protocol based on JSON data via ZMQ and WebSockets.
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Jupyter Tutorial, Release 1.1.0
Kernels Kernels are processes that execute interactive code in a specific programming language and return the output to the user.
See also: ? Jupyter celebrates 20 years
1.3 Jupyter infrastructure
A platform for the above-mentioned use cases requires an extensive infrastructure that not only allows the provision of the kernel and the parameterisation, time control and parallelisation of notebooks, but also the uniform provision of resources. This tutorial provides a platform that enables fast, flexible and comprehensive data analysis beyond Jupyter notebooks. At the moment, however, we are not yet going into how it can be expanded to include streaming pipelines and domaindriven data stores. However, you can also create and run the examples in the Jupyter tutorial locally.
1.4 Workspace
Setting up the workspace includes installing and configuring IPython and Jupyter notebooks, nbextensions and ipywidgets.
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Chapter 1. Introduction
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