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Learning Jupyter
Learning Jupyter

Learning Jupyter: Select, Share, Interact and Integrate with Jupyter Notebook

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Learning Jupyter

Chapter 1. Introduction to Jupyter

Jupyter is a tool that allows data scientists to record their complete analysis process, much in the same way other scientists use a lab notebook to record tests, progress, results, and conclusions.

The Jupyter product was originally developed as part of the IPython project. The IPython project was used to provide interactive online access to Python. Over time it became useful to interact with other data analysis tools, such as R, in the same manner. With this split from Python, the tool grew into its current manifestation of Jupyter. IPython is still an active tool that's available for use. The name Jupyter itself is derived from the combination of Julia, Python, and R.

Jupyter is available as a web application from a number of places. It can also be used locally over a wide variety of installations. In this book, we will be exploring using Jupyter on a Mac and a Windows PC and over the Internet with other providers.

In this chapter, we will cover the following topics:

  • First look at Jupyter
  • Installing Jupyter on Windows
  • Installing Jupyter on Mac
  • Notebook structure
  • Notebook workflow
  • Basic notebook operations
  • Security in Jupyter
  • Configuration options for Jupyter

First look at Jupyter

Here is a sample opening page when using Jupyter (this screenshot is on a Windows machine):

First look at Jupyter

You should get yourself acquainted with the environment. The Jupyter user interface has a number of components:

  • Product title, Jupyter, in the top left (as expected). The logo and the title name are clickable and will return you to the Jupyter Notebook home page.
  • There are three tabs displayed: Files, Running, and Clusters:

    First look at Jupyter

    • The Files tab shows the list of files in the current directory of the page (described later on in this section).
    • The Running tab presents another screen of the currently running processes and notebooks. The drop-down lists for Terminals and Notebooks are populated with their running members:

      First look at Jupyter

    • The Clusters tab presents another screen to display the list of clusters available. This topic is covered in a later chapter:

      First look at Jupyter

  • In the top right corner of the screen are three buttons: Upload, New (menu), and a Refresh button.
  • The Upload button is used to add files to the notebook space. You may also just drag and drop as you would when handling files. Similarly, you can drag and drop notebooks into specific folders as well.
  • The menu with New at the top presents a further menu of Text File, Folder, Terminals Unavailable, Notebooks, and Python 2:

    First look at Jupyter

    • The Text File option is used to add a text file to the current directory. Jupyter will open a new browser window for you running a text editor. The text entered is automatically saved and will be displayed in your notebook's Files display:

      First look at Jupyter

      Note

      The default filename, untitled.txt, is editable.

    • The Folder option creates a new folder with the name Untitled Folder. Remember, all of the file/folder names are editable:

      First look at Jupyter

    • The Terminals Unavailable option is disabled for Windows. On a Mac, the option allows you to start an IPython session.
    • The Notebooks option will be activated when additional notebooks are available in your environment.
    • The Python 2 option is used to begin a Python 2 session interactively in your notebook. The interface looks like the following screenshot. You have full file editing capabilities for your script, including saving as a new file. You also have a complete working IDE for your Python script:

      First look at Jupyter

      Note

      Like the Text File and Folder option, you have created a Python script file in your notebook and it is running!

      First look at Jupyter

  • The refresh button is used to update the display. It's not really necessary as the display is reactive to any changes in the underlying file structure.
  • At the top of the Files tab's item list is a checkbox, a drop-down menu, and a home button:
    • The checkbox is used to toggle all the checkboxes in the Items list
    • The drop-down menu presents a list of the choices available, Folders, All Notebooks, Running, and Files, as shown in the following screenshot:

      First look at Jupyter

    • The Folders selection will select all the folders in the display and present a count of the folders in the small box
    • The All Notebooks selection will change the count to the number of notebooks and provide you with three options:
      • Duplicate the current notebook
      • Shut down the current notebook
      • Trash the current notebook
    • You can see them in the following screenshot:

      First look at Jupyter

    • The Running selection will select any running scripts and update the count to the number selected

      First look at Jupyter

    • The Files selection will select all of the files in the notebook display and update the count accordingly
    • The home button brings you back to the home screen of the notebook.

On the left-hand side of every item is a checkbox, an icon, and the item's name:

First look at Jupyter

  • The checkbox is used to build a set of files to operate upon.
  • The icon is indicative of the type of item. In this case, all of the items are folders.
  • The name of the item corresponds to the name of the object. In this case, the filenames are as used on the disk.

Installing Jupyter on Windows

Jupyter requires Python to be installed (it is based on the Python language). There are a couple of tools that will automate the installation of Jupyter (and optionally Python) from a GUI. In this case, we are showing how to install using Anaconda, which is a Python tool for distributing software. You first have to install Anaconda. It is available on Windows and Mac environments. Download the executable from https://www.continuum.io/ (company that produces Anaconda) and run it to install Anaconda. The software provides a regular installation setup process, as shown in the following screenshot:

Installing Jupyter on Windows

The installation process goes through the regular steps of making you agree to the distribution rights license:

Installing Jupyter on Windows

The standard Windows installation allows you to decide whether all users on the machine can run the new software or not. If you are sharing a machine with different levels of users, then you can decide the appropriate action:

Installing Jupyter on Windows

After clicking on Next, it will ask for a destination for the software to reside (I almost always keep the default paths):

Installing Jupyter on Windows

And, most importantly, make sure that Python installed with Anaconda provides your Python basis going forward (by being placed in the execution path). Remember, Anaconda uses Python tool itself, so this is important.

Note

This process takes some time to download and install.

Once Anaconda is installed, you need to run a command-line instruction to install Jupyter. The command is as follows:

conda install jupyter

This will invoke a process to download all the necessary components for Jupyter onto your PC. Your output should look something like this:

C:\Users\Dan>conda install jupyter
Using Anaconda Cloud api site https://api.anaconda.org
Fetching package metadata: ....
Solving package specifications: .........
# packages in environment at C:\Users\Dan\Anaconda2:
#
jupyter                   1.0.0                    py27_2

Note

Additional lines will be present for an install. I have abbreviated the output. You now have Jupyter installed on your machine. You can start the process using the following command:

C:\Users\Dan>jupyter notebook

This command is starting a Jupyter Notebook server on your machine. Once the server is started, a browser instance will be opened at the starting point of the notebook. You should see logging statements similar to the following on your machine as the server starts:

[I 16:21:59.144 NotebookApp] Writing notebook server cookie secret to C:\Users\Dan\AppData\Roaming\jupyter\runtime\notebook_cookie_secret
[I 16:21:59.846 NotebookApp] Serving notebooks from local directory: C:\Users\Dan
[I 16:21:59.846 NotebookApp] 0 active kernels
[I 16:21:59.846 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/
[I 16:21:59.862 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).

Once Jupyter is running, you will notice a running icon for Jupyter (two inverted crescents) at the bottom of your screen:

Installing Jupyter on Windows

Note, the last line of the log is the instruction you must use to stop the server (press Ctrl + C in the command-line window where the server is running).

If you press Ctrl + C in that window, the Jupyter server will shut down gracefully:

[W 17:26:36.688 NotebookApp] 404 GET /favicon.ico (::1) 62.00ms referer=None
[W 17:26:36.750 NotebookApp] 404 GET /favicon.ico (::1) 0.00ms referer=None
[I 17:28:24.891 NotebookApp] Interrupted...
[I 17:28:24.891 NotebookApp] Shutting down kernels

You will notice that the Anaconda package has been installed on your application menu for further use:

Installing Jupyter on Windows

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Key benefits

  • Learn to write, execute, and comment your live code and formulae all under one roof using this unique guide
  • This one-stop solution on Project Jupyter will teach you everything you need to know to perform scientific computation with ease
  • This easy-to-follow, highly practical guide lets you forget your worries in scientific application development by leveraging big data tools such as Apache Spark, Python, R etc

Description

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more. This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we’ll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode. Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.

Who is this book for?

This book caters to all developers, students, or educators who want to execute code, see output, and comment all in the same document, in the browser. Data science professionals will also find this book very useful to perform technical and scientific computing in a graphical, agile manner.

What you will learn

  • Install and run the Jupyter Notebook system on your machine
  • Implement programming languages such as R, Python, Julia, and JavaScript with Jupyter Notebook
  • Use interactive widgets to manipulate and visualize data in real time
  • Start sharing your Notebook with colleagues
  • Invite your colleagues to work with you in the same Notebook
  • Organize your Notebook using Jupyter namespaces
  • Access big data in Jupyter

Product Details

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Publication date : Nov 30, 2016
Length: 238 pages
Edition : 1st
Language : English
ISBN-13 : 9781785884870
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Length: 238 pages
Edition : 1st
Language : English
ISBN-13 : 9781785884870
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Tools :

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Table of Contents

10 Chapters
1. Introduction to Jupyter Chevron down icon Chevron up icon
2. Jupyter Python Scripting Chevron down icon Chevron up icon
3. Jupyter R Scripting Chevron down icon Chevron up icon
4. Jupyter Julia Scripting Chevron down icon Chevron up icon
5. Jupyter JavaScript Coding Chevron down icon Chevron up icon
6. Interactive Widgets Chevron down icon Chevron up icon
7. Sharing and Converting Jupyter Notebooks Chevron down icon Chevron up icon
8. Multiuser Jupyter Notebooks Chevron down icon Chevron up icon
9. Jupyter Scala Chevron down icon Chevron up icon
10. Jupyter and Big Data Chevron down icon Chevron up icon

Customer reviews

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1 star 100%
Axel Willy Jan 28, 2017
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Selbst für Einsteiger nicht zu gebrauchen. Ein paar Seiten Text kopiert aus Manpages, dazu sehr viele übergroße Screenshots. Informationsgehalt fast nicht vorhanden. Buch zurück geschickt.
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Drew Dec 04, 2018
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There are things that could be useful theoretically. Starting with my first gripe - The book states that it is using Python 2. It is November, 2016 - and Python 2 is dead. I don't have a problem with using Python2, but I do have a problem with not identifying that you use Python 2. It just comes across as false advertising.Don't worry, that isn't my only gripe. There are a few things, like commands on how to use Jupyter that you may find useful, but a quick internet search may prove more useful. The word vomit continually shows little understanding of what is going on. That means this isn't even a language barrier, it is just plain bad. I included to screenshots. It shows commentary that conveys that he doesn't understand what is going on, while using built in datasets. I was using those datasets on day 1 of my exposure to the material. 'I understand that he doesn't understand why numbers don't add up to 100%. But he needs to follow it up with details on whether there is data actually missing (which represents an unaddressed category), whether there is an error in the math (like floating point error), or what is going on. As it is, I assume that this is his first exposure, and he just tells the readers that sometimes there are problems. Not that the writer will take any action to resolve them.The statistics one is a great expression of incompetence. If you are going to use a statistical methodology, at least understand statistics. The Standard Deviation won't drift a lot when rolling two six-sided dice. The theoretical odds of the sum are always between 2 and 12. The small domain limits the statistical data quite nicely. There are specific patterns which we don't need to go into, but needless to say, it makes the writer appear incompetent about his chosen content. I could read more, but why?
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Matt Mar 28, 2017
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Only Windows and Mac OS. No Linux. Not helpful.
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