Skip to content

Latest commit

 

History

History
173 lines (120 loc) · 6.66 KB

pycon-submission.md

File metadata and controls

173 lines (120 loc) · 6.66 KB
Raw

Authors/Presenter.

  • Matthias Bussonnier: bussonniermatthias AT gmail.com , Core Developer of IPython/Jupyter.
  • Mike Bright: pycon AT mjbright.net, Solution Architect at HPE OpenNFV Lab Grenoble, long time IPython and Jupyter User and Contributor.
  • Min RK: benjaminrk AT gmail.com Core Developer of IPython/Jupyter

Notes: Box on the Pycon Proposal website are

  • Description
  • Audience
  • Outline
  • Additional Notes

Title

IPython and Jupyter in Depth: High productivity, interactive Python

Category

Python Libraries

Python Level

Intermediate

Domain Level

Introductory

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.

Audience

Programmers interested in using Python interactively, especially in data analysis environments. Prior knowledge of Python is best. Some prior knowledge of Python is helpful. Some experience with Docker would be helpful but not required for the last quarter of the tutorial.

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.

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 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:

    Notebook screenshot

    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.

Outline

Note to reviewers: Each section will take 1/4 of the teaching time, taking into account the scheduled snack break. Each section will provide takeaway slides and notebooks for the attendee. There will be hands-on time of 5-10 minutes during each section for attendees to try out concepts.

IPython: Interactivity beyond Python

  • Introducing the IPython Notebook as an interactive environment.
  • Beyond Python: magic commands, shell access, object introspection, variable caching.
  • Development workflow: integrating IPython with scripts via the %run command.
  • Tools for typical development tasks: timing, profiling, debugging.

We will leave 1 to 2 minutes hands-on for simple subjects like object introspection and variable caching. We'll give a couple of 5 minutes hands-on exercises for profiling and debugging.

Back to the terminal(s)

  • Demo and discussion of the last added features of the command line interpreter.
  • IPython.embed: a useful 'microscope' into your own scripts.
  • Control the namespace of your GUI codes with an IPython kernel.
  • Customizing IPython with profiles.

We'll leave 5-10 minutes at the end of this section for user to play with multiple profiles and embeded IPython.

The IPython/Jupyter Notebook

  • Basic concepts: the server, the dashboard, your notebooks.
  • A notebook as a rich document: text, code, results and multimedia.
  • The IPython display protocol: __repr__ for more than just text.
  • Converting notebooks to other formats for sharing, blogging and publication.
  • Sharing your notebooks: nbviewer.

We'll leave 10 minutes for user to create a custom representation for an object of their choice and publish their notebook online on nbviewer.

Cloud hosting and multilanguage

  • Deploying with docker (locally or in the cloud).
  • have the attendees deploy a image that contains the latest development versions.
  • show how to write various extensions, and multi language integration.
  • introduce JupyterHub and its use for groups

More info

For full details about IPython including documentation, previous presentations and videos of talks, please see the project website.

The materials for this tutorial are available on a github repository.

Additional Notes

Versions of this tutorial have been presented at PyCon 2012, 2014, 2015 and also EuroPython 2016. It has been well received so far, and we would like to keep teaching about IPython and Jupyter!

https://www.youtube.com/watch?v=XFw1JVXKJss (2012) https://www.youtube.com/watch?v=bP8ydKBCZiY (2013) https://www.youtube.com/watch?v=05fA_DXgW-Y (2015)