IPython and Jupyter in Depth: High productivity, interactive Python - PyCon 2017

"Speakers: Matthias Bussonnier, Mike Bright, Min Ragan-Kelley

Description

IPython and Jupyter provide tools for interactive computing that are widely
used in scientific computing, education, and data science, but can benefit any
Python developer.

You will learn how to use IPython in different ways, as:

an interactive shell,
a graphical console,
a network-aware VM (Virtual machine) in GUIs,
a web-based notebook combining code, graphics and rich HTML.

We will demonstrate how to deploy a custom environment
with Docker that not only contains multiple Python kernels but also a couple
of other languages.

Objectives

At the end of this tutorial, attendees will have an understanding of the
overall design of Jupyter (and IPython) as a suite of applications they can use
and combine in multiple ways in the course of their development work with
Python and other programming languages. They will learn:

Tricks from the IPython machinery that are useful in everyday development,

What high-level applications in Jupyter, the web-based notebooks, can do and
how these applications can be used.

How to use IPython and Jupyter together so that they can be best used for the
problem at hand.

Python Level

Intermediate

Domain Level

Introductory

Detailed Abstract

IPython started in 2001 simply as a better interactive Python shell. Over the
last decade it has grown into a powerful set of interlocking tools that
maximize developer productivity in Python while working interactively.

Today, Jupyter consists of an IPython kernel that executes user code, provides
many features for introspection and namespace manipulation, and tools to
control this kernel either in-process or out-of-process thanks to a well
specified communications protocol implemented over ZeroMQ. This architecture
allows the core features to be accessed via a variety of clients, each
providing unique functionality tuned to a specific use case:

An interactive, terminal-based shell with capabilities beyond the default
Python interactive interpreter (this is the classic application opened by the
`ipython` command that many users have worked with)

A [web-based notebook](http://jupyter.org/) that can execute
code and also contain rich text and figures, mathematical equations and
arbitrary HTML. This notebook presents a document-like view with cells where
code is executed but that can be edited in-place, reordered, mixed with
explanatory text and figures, etc. The notebook provides an interactive
experience that combines live code and results with literate documentation
and the rich media that modern browsers can display:


The notebooks also allow for code in multiple languages allowing to mix Python
with Cython, C, R and other programming languages to access features hard to obain from
Python.

These tools also increasingly work with languages other than Python, and we
renamed the language independent frontend components to Jupyter in order to
make this clearer. The Python kernel we provide and the original terminal-based
shell will continue to be called *IPython*.

In this hands-on, in-depth tutorial, we will briefly describe IPython's
architecture and will then show how to use the above tools for a highly
productive workflow in Python.

The materials for this tutorial are
[available on a github repository](https://github.com/ipython/ipython-in....


Slides can be found at: https://speakerdeck.com/pycon2017 and https://github.com/PyCon/2017-slides"