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IPython & Jupyter in depth: high productivity interactive and parallel python
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IPython & Jupyter in depth: high productivity interactive python
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# Category
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@@ -16,64 +16,87 @@ Introductory
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# Description
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IPython and Jupyter provide tools for interactive and parallel computing that are widely
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IPython and Jupyter provide tools for interactive computing that are widely
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used in scientific computing, but can benefit any Python developer.
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We will show how to use IPython in different ways, as: an interactive shell,
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a graphical console, a network-aware VM in GUIs, a web-based
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notebook with code, graphics and rich HTML.
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We will show how to use IPython in different ways, as: an interactive shell, a
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graphical console, a network-aware VM in GUIs, a web-based notebook with code,
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graphics and rich HTML. We will demonstrate how to deploy a custom environment
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with Docker that not only contain multiple Python kernels as well as a couple
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of other languages.
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# Audience
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# Audience
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Programmers interested in using Python interactively, especially in data
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analysis environments.
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# Objectives
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At the end of this tutorial, attendees will have an understanding of the overall design of Jupyter as a suite of applications they can use and combine in multiple ways in the course of their development work with Python and other languages. They will learn:
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At the end of this tutorial, attendees will have an understanding of the
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overall design of Jupyter (and IPython) as a suite of applications they can use
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and combine in multiple ways in the course of their development work with
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Python and other languages. They will learn:
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* Tricks from the IPython machinery that are useful in everyday development,
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* What the high-level applications in Jupyter, the web-based notebooks and the graphical Qt console, can do and how they can be used.
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* What the high-level applications in Jupyter, the web-based notebooks can do
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and how they can be used.
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* How the IPython's architecture forms a natural foundation for high-level parallel computing with low latency and high throughput.
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* How the overall picture of IPython and Jupyter fits together, so that they
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can better use its components for the problem at hand.
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* How the overall picture of IPython and Jupyter fits together, so that they can better use its components for the problem at hand.
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# Detailed Abstract
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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.
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IPython started in 2001 simply as a better interactive Python shell. Over the
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last decade it has grown into a powerful set of interlocking tools that
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maximize developer productivity in Python while working interactively.
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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:
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Today, Jupyter consists of an IPython kernel that executes user code, provides
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many features for introspection and namespace manipulation, and tools to
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control this kernel either in-process or out-of-process thanks to a well
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specified communications protocol implemented over ZeroMQ. This architecture
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allows the core features to be accessed via a variety of clients, each
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providing unique functionality tuned to a specific use case:
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* An interactive, terminal-based shell with capabilities beyond the default Python interactive interpreter (this is the classic application opened by the `ipython` command that most users are familiar with).
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* An interactive, terminal-based shell with capabilities beyond the default
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Python interactive interpreter (this is the classic application opened by the
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`ipython` command that most users are familiar with).
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* A [graphical, Qt-based console](http://ipython.org/ipython-doc/stable/interactive/qtconsole.html) that provides the look and feel of a terminal, but adds support for inline figures, graphical calltips, a persistent session that can survive crashes of the kernel process, and more. A user-based review of some of these features can be found [here](http://stronginference.com/weblog/2011/7/15/innovations-in-ipython.html).
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* A [web-based notebook](http://jupyter.org/) that can execute
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code and also contain rich text and figures, mathematical equations and
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arbitrary HTML. This notebook presents a document-like view with cells where
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code is executed but that can be edited in-place, reordered, mixed with
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explanatory text and figures, etc. The notebook provides an interactive
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experience that combines live code and results with literate documentation
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and the rich media that modern browsers can display:
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* A [web-based notebook](http://ipython.org/notebook.html) 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 language allowing to mix Python
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with Cython, C, R and other languages to access features hard to obain from
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Python.
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* A high-performance, low-latency system for [parallel computing](http://ipython.org/ipython-doc/stable/parallel/parallel_intro.html) that supports the control of a cluster of IPython engines communicating over ZeroMQ, with optimizations that minimize unnecessary copying of large objects (especially numpy arrays). These engines can be controlled interactively while developing and doing exploratory work, or can run in batch mode either on a local machine or in a large cluster/supercomputing environment via a batch scheduler.
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These tools also increasingly work with languages other than Python, and we are renaming 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*.
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These tools also increasingly work with languages other than Python, and we are
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renaming the language independent frontend components to *Jupyter* in order to
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make this clearer. The Python kernel we provide and the original terminal-based
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shell will continue to be called *IPython*.
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In this hands-on, in-depth tutorial, we will briefly describe IPython's
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architecture and will then show how to use the above tools for a highly
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productive workflow in Python.
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# Outline
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**IPython: Interactivity beyond Python**
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**IPython: Interactivity beyond Python**
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- Introducing the IPython Notebook as an interactive environment.
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