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...
I'm happy to announce that iPOPO v0.7.0 has just been released!
What is iPOPO
iPOPO is a Service-Oriented Component Model (SOCM) based on Pelix,
a dynamic service platform. Both are inspired on two popular Java
technologies for the development of long-lived applications:
the iPOJO component model and the OSGi Service Platform.
iPOPO enables to conceive long-running and modular IT services.
It is based on the concepts specified by OSGi:
- Bundle: a Python module imported using Pelix and associated to a
context. A bundle has a life-cycle (install, start, updated, stop,
- Service: a Python object registered in a service registry,
associated to a specification and to properties.
- Component: the instance of a class described/manipulated by iPOPO
Components are bound together by the specification(s) of the service(s)
they provide. The required services are injected into components by iPOPO.
For more information about those concepts, see
iPOPO provides many services out-of-the-box, like an HTTP server,
local and remote shell, remote services...
iPOPO is released under the terms of Apache Software License 2.0
What's new in 0.7.0
This version mainly adds:
* Prototype Service Factories
* Automatic release of consumed services when a bundle stops.
This will avoid some stale references when using service
WARNING: This is an important change in behavior, which might
break some projects which use stale references to pass
information from one bundle version to another during an
update (which is a bad way to do it).
* Deprecation handling of the imp package
* Added a Framework.delete() method to avoid the need to know
about the FrameworkFactory class.
This release also removes some Python 2.6 compatibility code that was
remaining and which is not necessary anymore, as this version of
Python is not supported anymore by iPOPO.
Due to the behavior change caused by the automatic release of
consumed services, this release is version 0.7.0 instead of 0.6.6,
as it could break some existing code.
What's coming in 2018
2018 will be the year when iPOPO will get its Web Console. It will be
developed as a separate project (in fact, the project already exists
but is staled).
This might also be the year when Remote Service Admin will be added
to iPOPO, thanks to Scott Lewis!
See https://github.com/tcalmant/ipopo/issues/60 for more information.
You can take a look at the documentation at https://ipopo.readthedocs.io/
iPOPO is available on PyPI: https://pypi.python.org/pypi/iPOPO
Source is available on GitHub: https://github.com/tcalmant/ipopo
Feel free to send feedback on your experience of Pelix/iPOPO, via the mailing lists:
User list : http://groups.google.com/group/ipopo-users
Development list : http://groups.google.com/group/ipopo-dev
Have fun! and Happy New Year!
I'm happy to announce pandas 0.22.0 has been released.
This is a major release from 0.21.1 and includes a single, API-breaking
change. We recommend that all users upgrade to this version after carefully
reading the release note.
The only changes are:
- The sum of an empty or all-*NA* Series is now 0
- The product of an empty or all-*NA* Series is now 1
- We’ve added a min_count parameter to .sum() and .prod() controlling
the minimum number of valid values for the result to be valid. If fewer
than min_count non-*NA* values are present, the result is *NA*. The
default is 0. To return NaN, the 0.21 behavior, use min_count=1.
See the pandas 0.22.0 whatsnew
overview for further explanation of all the places in the library this
*What is it:*
pandas is a Python package providing fast, flexible, and expressive data
structures designed to make working with “relational” or “labeled” data
both easy and intuitive. It aims to be the fundamental high-level building
block for doing practical, real world data analysis in Python.
Additionally, it has the broader goal of becoming the most powerful and
flexible open source data analysis / manipulation tool available in any
*How to get it:*
Source tarballs and windows/mac/linux wheels are available on PyPI (thanks
to Christoph Gohlke for the Windows wheels, and to Matthew Brett for
setting up the Mac / Linux wheels).
Conda packages are available on the default and conda-forge channels.
Please report any issues on our issue tracker: https://github.com/py
Big Daddy's Python tkinter Journeyman Reference, version JR2, has been posted on www.wikipython.com.
When version 1 was posted we knew there was stuff left out but we had made a committment to just 8 pages – 4 pages front and back. It turns out there is a physics problem involved here – you just can’t stuff 10 pounds of poop in a 5 pound sack.
So version JR2 has expanded to 10 pages and now includes a small number of corrections – but a massively reformated attributes table with the additon of a notes column, a completely reformated methods table, almost 2 pages of operational commands that were missing completely, lots more vetted examples, almost a page on adding tkk, plus several more helpful filler items. If you think that sounds like a LOT more than just 2 additional pages, well, just take a look. As always, no registration, no fees, no charges, no cookies, no email list, no contribution accepted and no ads (for now). Toolboxes download from GitHub - for safety and larger file sizes. Comments and suggestions appreciated; I prefer email at oakey.john(a)yahoo.com.
I am pleased to announce release 2017.4 of SfePy.
SfePy (simple finite elements in Python) is a software for solving systems of
coupled partial differential equations by the finite element method or by the
isogeometric analysis (limited support). It is distributed under the new BSD
Home page: http://sfepy.org
Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/
Git (source) repository, issue tracker: https://github.com/sfepy/sfepy
Highlights of this release
- basic support for penalty-based contacts
- support for user-defined contexts in all solvers and preconditioners
- new example: dispersion analysis of heterogeneous periodic materials
For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1
(rather long and technical).
Contributors to this release in alphabetical order:
I am happy to announce the revival of the AggDraw project and the
release of version 1.3. This release is the first official PyPI release
in over 12 years. Most importantly this release adds Python 3 support.
AggDraw is a high-quality graphics engine for PIL, based on Maxim
Shemanarev's Anti-Grain Geometry library (from http://antigrain.com).
The original author, effbot, was unable to continue maintaining the
project quite a few years ago. After the project stopped being
maintained many developers forked the project and started their own
additions and modifications. In an effort consolidate and revive the
project for those that needed it the PyTroll group of developers have
taken over maintainership. If anyone has an interest in joining us in
maintaining the project let us know on our mailing list or slack:
Release notes: https://github.com/pytroll/aggdraw/blob/master/CHANGES
GitHub/Bug Tracker: https://github.com/pytroll/aggdraw
What is cx_Freeze?
cx_Freeze is a set of scripts and modules for freezing Python scripts into
executables, in much the same way that py2exe and py2app do. Unlike these
two tools, cx_Freeze is cross platform and should work on any platform that
Python itself works on. It supports Python 2.7 or Python 3.4 and higher.
More information can be found at the web site:
This release addresses a few reported issues. See the release notes:
To install, use the following command:
python -m pip install cx_Freeze --upgrade