This course will help you to expertise the usage of Python in Data Science world.
Carter your Python Knowledge so that it can be utilized to get the Insights of Data using Methodologies and Techniques of Data Science...
Understand the concepts of Data science and Python
You will be able to use Python in Discovering Data.
You will have an idea of Statistical and Analytical methods to deal with huge data sets.
You will gain an expertise on Regular Expressions, looping functions and concepts of Object Oriented Programming.
You will be able to create business algorithms and data models using Python and it's techniques.
Work on Real-life Projects will help you to get a practical experience of real scenarios of IT Industry.
Start learning Python for Data Science from basics to advance levels here...
On behalf of the Bokeh team, I am pleased to announce the release of version 0.12.1 of Bokeh!
This is a minor, incremental update that adds a few new small features and fixes several bugs.
Please see the announcement post at:
which has much more information as well as live demonstrations. And as always, see the CHANGELOG and Release Notes for full details.
If you are using Anaconda/miniconda, you can install it with conda:
conda install -c bokeh bokeh
Alternatively, you can also install it with pip:
pip install bokeh
Full information including details about how to use and obtain BokehJS are at:
Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh
Documentation is available at http://bokeh.pydata.org/en/0.12.1
Questions can be directed to the Bokeh mailing list: bokeh(a)continuum.io or the Gitter Chat room: https://gitter.im/bokeh/bokeh
Bryan Van de Ven
Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
PyQt v5.7 has been released. These are the Python bindings for the Qt application toolkit and runs on Linux, OS X, Windows, iOS and Android.
Also released for the first time under the GPL are PyQtChart, PyQtDataVisualization and PyQtPurchasing.
PyQtChart are the bindings for the Qt Charts library. This implements a set of classes for creating and manipulating 2D charts.
PyQtDataVisualization are the bindings for the Qt Data Visualization library. This implements a set of classes for representing data in 3D and allowing the user to interact with the view.
PyQtPurchasing are the bindings for the Qt Purchasing library. This implements a set of classes that allow applications to support in-app purchases from the Mac App Store on OS X, the App Store on iOS, and Google Play on Android.
Wheels are available from PyPI and include the relevent Qt libraries - nothing else needs to be installed.
Source packages and more information can be found at https://www.riverbankcomputing.com/.
Im very happy to announce the release of Pymunk 5.0!
Pymunk is a easy-to-use pythonic 2d physics library that can be used
you need 2d rigid body physics from Python. Perfect when you need 2d
in your game, demo or other application! It is built on top of the very
capable 2d physics library.
See http://www.pymunk.org for more details and instructions on what Pymunk
and how to use it.
This is a BIG release of Pymunk! Just in time before Pymunk turns 10 next
* Support for 64 Python on Windows
* Updated to use Chipmunk 7 which includes lots of great improvements
* Updated to use CFFI for wrapping, giving improved development and
* New util module with draw help for matplotlib (with example Jupyter
* Support for automatically generate geometry. Can be used for such things
deformable terrain (example included).
* Deprecated obsolete submodule pymunk.util.
* Lots of smaller improvements
New in this release is also testing on Travis and Appveyor to ensure good
I hope you will enjoy this new release!
Pynotebook is a command shell for interactive computing following the
“notebook” concept which originally appeared with the program Mathematica
and became famous for python with the iPython/Jupyter project. Unlike
Jupyter, pynotebook runs inside an ordinary window and does not use a
webbrowser. Pynotebook has no external dependencies (except python and wx).
See https://youtu.be/BflI5W760mI for a short demo.
* syntax highlighting
* command completion (tab-key)
* matplotlib plotting
For comments or bug reports contact me under textmodelview(a)gmail.com.
Announcing Numexpr 2.6.1
This is a maintenance release that fixes a performance regression in
some situations. More specifically, the BLOCK_SIZE1 constant has been
set to 1024 (down from 8192). This allows for better cache utilization
when there are many operands and with VML. Fixes #221.
Also, support for NetBSD has been added. Thanks to Thomas Klausner.
In case you want to know more in detail what has changed in this
Numexpr is a fast numerical expression evaluator for NumPy. With it,
expressions that operate on arrays (like "3*a+4*b") are accelerated
and use less memory than doing the same calculation in Python.
It wears multi-threaded capabilities, as well as support for Intel's
MKL (Math Kernel Library), which allows an extremely fast evaluation
of transcendental functions (sin, cos, tan, exp, log...) while
squeezing the last drop of performance out of your multi-core
processors. Look here for a some benchmarks of numexpr using MKL:
Its only dependency is NumPy (MKL is optional), so it works well as an
easy-to-deploy, easy-to-use, computational engine for projects that
don't want to adopt other solutions requiring more heavy dependencies.
Where I can find Numexpr?
The project is hosted at GitHub in:
You can get the packages from PyPI as well (but not for RC releases):
Share your experience
Let us know of any bugs, suggestions, gripes, kudos, etc. you may
The EuroPython Society (EPS) is happy to announce the Call for
Interest (CFI) for EuroPython 2017:
The purpose of this call is to get to know teams willing to help
organize the EuroPython conference on site at a suitable location
and determine the Call for Participation (CFP) candidates in the
second phase of the selection process.
If your team is interested in submitting a CFP in phase two of
the process, please send in a CFI proposal.
Some important dates:
CFIs received until this day will be announced in the
conference closing session
Deadline for CFI submissions
PS: Please forward or retweet to help us reach all interested parties: