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Jupyter Cookbook

Chapter 1. Installation and Setting up the Environment

In this chapter, we will cover the following recipes:

Installing Jupyter on Windows
Installing Jupyter on the Mac
Installing Jupyter on Linux
Installing Jupyter on a server

Installing Jupyter on Windows

The Windows environment suffers from a drawback: none of the standard Linux tools are available out of the box. This is a problem as Jupyter and many other programs were developed on a version of Unix and expect many developer tools normally used in Unix to be available.

Getting ready

Luckily, there is a company that has seen this problem and addressed it—Anaconda. Anaconda describes itself as a Python Data Science Platform, but its platform allows for a variety of solutions in data science that are not based on Python.

How to do it...

After installing Anaconda and starting Navigator, you get to a dashboard that presents the programs available, such as:

The Anaconda Navigator provides access to each of the programs you have installed (using Anaconda). Each of the programs can be started from Navigator (by clicking on the associated Launch button) and you can also start them individually (as they are standalone applications). As you install programs with Anaconda, additional menu items become available under the Anaconda menu tree for each of the applications to run directly. The menu item has coding to start the individual applications as needed.

As you can see in the preceding screen, the Home display shows the applications available. There are additional menu choices for:

Environments: This menu displays all the Python packages that have been installed. I don't think R packages are displayed, nor are other tools or packages included in this display.
Projects (beta): This menu is usually empty. I have been using/upgrading Anaconda for a while and have not seen anything displayed here.
Learning: This is a very useful feature, where a number of tutorials, videos, and write-ups have been included for the different applications that you (may) have installed.
Community: This lists some community groups for the different products you have installed.

The preceding screen shows Jupyter as an installed program. The standard install of Anaconda does include Jupyter. If you choose not to use Anaconda, you can install Jupyter directly.

Installing Jupyter directly

Jupyter, as a project, grew out of Python, so it is somewhat dependent on which version of Python you have installed. For Python 2 installations, the command line steps to install Jupyter are:

python -m pip install --upgrade pip
python -m pip install jupyter

This assumes you have pip installed. The pip system is a package management system written in Python. To install pip on your Windows machine, execute the following line:

python get-pip.py

As you can see, this is all Python (this code calls Python to execute a standard Python script).

Installing Jupyter through Anaconda

Anaconda provides the tools to install a number of programs, including Jupyter. Once you have installed Anaconda, Jupyter will be available to you already.

The only issue I found was that the engine installed was Python 2 instead of Python 3. There is a process that Anaconda uses to decide which version of Python to run on your machine. In my case, I started out with Python 2. To upgrade to Python 3, I used these commands:

conda create -n py3k python=3 
anaconda source activate py3k 
ipython kernelspec install-self

After this, when you start Jupyter, you will have the Python 3 engine choice.

You may prefer to have the Python 2 engine also available. This might be if you want to use scripts that were written using Python 2. The commands to add Python 2 back in as an engine choice are:

python2 -m pip install ipykernel 
python2 -m ipykernel install --user

You should now see Python 2 and Python 3 as engine choices when you start Jupyter:

Installing Jupyter on the Mac

Apple Macintosh provides a graphical interface that runs on the OS/X operating system. OS/X includes running BSD under the hood. BSD is a version of Unix originally developed at Berkeley. As a version of Unix, it has all the standard developer tools expected by Jupyter to install and upgrade the software; they are built-in tools.

Getting ready

Jupyter can be installed on the Mac using Anaconda (as before for Windows) or via the command line.

How to do it...

In this section, we will go through the steps for installing Jupyter on Mac.

Installing Jupyter on the Mac via Anaconda

Just as with Windows earlier, we download the latest version of Anaconda and run the installation program. One of the screens should look like this:

The Anaconda install is very typical for Mac installs: users can run the program and make sure they want to allocate so much storage for the application to install.

Once installed, Jupyter (and Anaconda Navigator) is available just like any other application on the system. You can run Jupyter directly, or you can launch Jupyter from the Anaconda Navigator display.

Installing Jupyter on the the Mac via the command line

Many Mac users will prefer using the command line to install Jupyter. Using the command line, you can decide whether to install Jupyter with the Python 2 or Python 3 engine. If you want to add the Python 2 engine as a choice in Jupyter, you can follow similar steps for doing so in the earlier Windows command line installation section.

The script to install Jupyter on Mac via the command line with the Python 3 engine is:

bash ~/Downloads/Anaconda3-5.0.0-MacOSX-x86_64.sh

Similarly, the command to install with the Python 2 engine is as follows:

bash ~/Downloads/Anaconda2-5.0.0-MacOSX-x86_64.sh

In either case, you will be prompted by some regular install questions:

Review and agree to the license agreement
Specify whether the standard install directory is OK (if not, you can specify where to install the software)
Specify whether to prepend the Anaconda location in your user path (Anaconda recommends this step)

At this point, you should be able to start Jupyter with the command line and see the appropriate engine choice available:

jupyter notebook

Installing Jupyter on a server

The term server has changed over time to mean several things. We are interested in a machine that will have multiple users accessing the same software concurrently. Jupyter Notebooks can be run by multiple users. However, there is no facility to separate the data for one user from another. Standard Jupyter installations only expect and account for one user. If we have a Notebook that allows for data input from the user, then the data from different users will be intermingled in one instance and possibly displayed incorrectly.

How to do it...

See the following example in this section.

Example Notebook with a user data collision

We can see an example of a collision with a Notebook that allows for data entry from a user and responds with incorrect results:

I call upon an example that I have used elsewhere for illustration. For this example, we will use a simple Notebook that asks the user for some information and changes the display to use that information:

from ipywidgets import interact
def myfunction(x):
    return x
interact(myfunction, x= "Hello World ");

The script presents a textbox to the user, with the original value of the box containing the Hello World string.
As the user interacts with the input field and changes the value, the value of the variable x in the script changes accordingly and is displayed on screen. For example, I have changed the value to the letter A:

We can see the multiuser problem if we open the same page in another browser window (copy the URL, open a new browser window, paste in the URL, and hit Enter). We get the exact same display—which is incorrect. We expected the new window to start with a new script, just prompting us with the default Hello World message. However, since the Jupyter software expects only one user, there is only one copy of the variable x; thus, it displays its value A.

We can have a Notebook server that expects multiple users and separates their instances from each other without the annoying collisions occurring. A Notebook server includes the standard Jupyter Notebook application that we have seen, but a server can also include software to distinguish the data of one user from another. We'll cover several examples of this solution in Chapter 8, Multiuser Environments.

Description

Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.

What you will learn

Install Jupyter and configure engines for Python, R, Scala and more Access and retrieve data on Jupyter Notebooks Create interactive visualizations and dashboards for different scenarios Convert and share your dynamic codes using HTML, JavaScript, Docker, and more Create custom user data interactions using various Jupyter widgets Manage user authentication and file permissions Interact with Big Data to perform numerical computing and statistical modeling Get familiar with Jupyter s next-gen user interface - JupyterLab

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