IPython provides a rich toolkit to help you make the most of using Python interactively. It provides a Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends. 
 
IPython’s interactive shell (ipython), has the following goals, amongst others:
<ol>
<li>Provide an interactive shell superior to Python’s default. IPython
has many features for tab-completion, object introspection, system shell
access, command history retrieval across sessions, and its own special
command system for adding functionality when working interactively. It
tries to be a very efficient environment both for Python code development
and for exploration of problems using Python objects (in situations like
data analysis).</li>
<li>Serve as an embeddable, ready to use interpreter for your own
programs. An interactive IPython shell can be started with a single call
from inside another program, providing access to the current namespace.
This can be very useful both for debugging purposes and for situations
where a blend of batch-processing and interactive exploration are needed.</li>
<li>Offer a flexible framework which can be used as the base
environment for working with other systems, with Python as the underlying
bridge language. Specifically scientific environments like Mathematica,
IDL and Matlab inspired its design, but similar ideas can be
useful in many fields.</li>
<li>Allow interactive testing of threaded graphical toolkits. IPython
has support for interactive, non-blocking control of GTK, Qt, WX, GLUT, and
OS X applications via special threading flags. The normal Python
shell can only do this for Tkinter applications.</li>
</ol>

IPython provides a rich toolkit to help you make the most of using Python interactively. It provides a Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends. 

IPython - Interactive Computing

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.
 
It works in Python close to all the PyData tools you are already familiar with. Plots, dashboards, and apps can be published in web pages or Jupyter notebooks.

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.

Bokeh -  Interactive Data Visualization in the browser, from Python

AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on recent AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models.

AdaNet - Fast and flexible AutoML with learning guarantees

PyGDF implements the Python interface to access and manipulate the GPU Dataframe of GPU Open Analytics Initialive (GOAI). We aim to provide a simple interface that similar to the Pandas dataframe and hide the details of GPU programming. Continuum Analytics, H2O.ai, and MapD Technologies have announced the formation of the GPU Open Analytics Initiative (GOAI) to create common data frameworks enabling developers and statistical researchers to accelerate data science on GPUs. GOAI will foster the development of a data science ecosystem on GPUs by allowing resident applications to interchange data seamlessly and efficiently.

 PyGDF implements the Python interface to access and manipulate the GPU Dataframe of GPU Open Analytics Initialive (GOAI). We aim to provide a simple interface that similar to the Pandas dataframe and hide the details of GPU programming. 

pygdf - GPU Data Frame

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IPython - Interactive Computing

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pygdf - GPU Data Frame

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IPython - Interactive Computing

Bokeh - Interactive Data Visualization in the browser, from Python

AdaNet - Fast and flexible AutoML with learning guarantees

pygdf - GPU Data Frame

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