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...
After many months of work, we proudly announce the first beta release of pytypes.
pytypes is a toolbox for PEP-484 style typing, explicitly supporting
Python >= 3.3, Python 2.7, Jython >= 2.7.1, PyPy 3 (others not tested, but might work).
Its main features are
- decorators and profiler for runtime typechecking
- decorators for typesafe override checking of methods
- easily pull type information from stubfiles into __annotations__
- get_type_hints: workable backport to Python 2.7 (based on type comments)
- get_type_hints: variant that also takes stubfiles into account
- utility functions is_of_type(obj, tp) and is_subtype(subtype, supertype) for use with PEP 484 types
- decorator and profiler for generating stubfiles from type information observed and logged at runtime
- all these features work equally well on Python 2.7
- all these features smoothly work with OOP, i.e. methods, static methods, class methods, properties
This beta release is intended to allow testing of pytypes' approaches in real-world code.
Please check to what extend it suites your needs and help us to improve it.
License: Apache 2.0
Pyspread 1.1.1 has been released.
Pyspread is a non-traditional spreadsheet that is based on and written
in the programming language Python.
The goal of pyspread is to be the most pythonic spreadsheet application.
Pyspread is free software. It is released under the GPL v3.
Project website: https://manns.github.io/pyspread/
Download page: https://pypi.python.org/pypi/pyspread
Source code: https://github.com/manns/pyspread
Release 1.1.1 is a bugfix release.
Major changes to 1.1:
* Pyspread color scheme now adapts better to most dark themes
* Shift-scroll now scrolls the grid sideways
* Undo and redo functionality made robust (now based on David
Townshend's functional undo framework)
* Table choice panel is now shown and hidden with F3
* Macro dialog changed to AUI panel (shown and hidden with F4)
* The entry line is now correctly updated
* Undo and redo buttons are now disabled if undo / redo is unavailable
* Current grid label highlights changed for better visibility on high
* The grid is now prevented from scrolling on focusing a cell editor
* Merged cells are now correctly drawn if the top left cell is
* Copy and paste format now ignores merged cell information
* If a merged area is shifted outside the grid via insert rows etc.
this is now correctly handled
* Chart dialog switched to AUI panel for better resizeability of sub
* GPG key choice now only allows choosing private keys without passwords
I've just released version 0.2.6 of distlib on PyPI . For newcomers,distlib is a library of packaging functionality which is intended to beusable as the basis for third-party packaging tools.
The main changes in this release are as follows:
* Fixed #99: Updated to handle a case where sys.getfilesystemencoding() returns None.
* Fixed #97: Eliminated a crash in EggInfoDistribution.list_distinfo_files() which was caused by trying to open a non-existent file.
* Fixed #96: SimpleScrapingLocator no longer fails prematurely when scraping links due to invalid versions.
* Improved error messages issued when interpreting markers.
* Improved the shebangs written into installed scripts when the interpreter path is very long or contains spaces (to cater for a limitation in shebang line parsing on Linux).
* Updated launcher binaries.
A more detailed change log is available at .
Please try it out, and if you find any problems or have any suggestions forimprovements, please give some feedback using the issue tracker! 
We’ve made a small change to the PEP process which may affect readers of python-list and python-ideas, so I’d like to inform you of it. This change was made to PEP 1 and PEP 12.
PEPs must have a Post-History header which records the dates at which the PEP is posted to mailing lists, in order to keep the general Python community in the loop as a PEP moves to final status. Until now, PEPs in development were supposed to be posted at least to python-dev and optionally to python-list. This guideline predated the creation of the python-ideas mailing list.
We’ve now changed this guideline so that Post-History will record the dates at which the PEP is posted to python-dev and optionally python-ideas. python-list is dropped from this requirement.
python-dev is always the primary mailing list of record for Python development, and PEPs under development should be posted to python-dev as appropriate. python-ideas is the list for discussion of more speculative changes to Python, and it’s often where more complex PEPs, and even proto-PEPs are first raised and their concepts are hashed out. As such, it makes more sense to change the guideline to include python-ideas and/or python-dev. In the effort to keep the forums of record to a manageable number, python-list is dropped.
If you have been watching for new PEPs to be posted to python-list, you are invited to follow either python-dev or python-ideas.
-Barry (on behalf of the Python development community)
Both python-dev and python-ideas are available via Gmane.
 PEPs may have a Discussions-To header which changes the list of forums where the PEP is discussed. In that case, Post-History records the dates that the PEP is posted to those forums. See PEP 1 for details.
I am happy to announce the release of VisPy 0.5. It has taken a while
for some of us new maintainers to get spun up on every component of this
project, but after more than two years since the last release VisPy is
back. Many components have been refactored, new visuals and other
features added, and over 177 pull requests merged.
What is VisPy?
VisPy is a Python library for interactive scientific visualization that
is designed to be fast, scalable, and easy to use. VisPy leverages the
computational power of modern Graphics Processing Units (GPUs) through
the OpenGL library to display very large datasets. Applications of VisPy
High-quality interactive scientific plots with millions of points.
Direct visualization of real-time data.
Fast interactive visualization of 3D models (meshes, volume rendering).
OpenGL visualization demos.
Scientific GUIs with fast, scalable visualization widgets (Qt or IPython
notebook with WebGL).
See the Gallery and many other example scripts on the VisPy website
VisPy supports Python 2.7 and 3.x on Linux, Mac OSX, and Windows.
VisPy's heavy use of the GPU means that users will need to have modern
and up-to-date video drivers for their system. VisPy can use one of many
backends, see the documentation for details. Due to the large refactor
of VisPy, users of the previous 0.4 release will likely have to change
Gitter (for chat): https://gitter.im/vispy/vispy
Mailing list: https://groups.google.com/forum/#!forum/vispy
Help is always welcome. We have over 250 GitHub issues and pull requests
that we are still sorting through. Feel free to talk to us on Gitter or
send in a pull request.
We are extremely pleased to announce the release of SciPy 1.0, 16 years
version 0.1 saw the light of day. It has been a long, productive journey to
get here, and we anticipate many more exciting new features and releases in
Why 1.0 now?
A version number should reflect the maturity of a project - and SciPy was a
mature and stable library that is heavily used in production settings for a
long time already. From that perspective, the 1.0 version number is long
Some key project goals, both technical (e.g. Windows wheels and continuous
integration) and organisational (a governance structure, code of conduct
roadmap), have been achieved recently.
Many of us are a bit perfectionist, and therefore are reluctant to call
something "1.0" because it may imply that it's "finished" or "we are 100%
with it". This is normal for many open source projects, however that
make it right. We acknowledge to ourselves that it's not perfect, and there
are some dusty corners left (that will probably always be the case).
that, SciPy is extremely useful to its users, on average has high quality
and documentation, and gives the stability and backwards compatibility
guarantees that a 1.0 label imply.
Some history and perspectives
- 2001: the first SciPy release
- 2005: transition to NumPy
- 2007: creation of scikits
- 2008: scipy.spatial module and first Cython code added
- 2010: moving to a 6-monthly release cycle
- 2011: SciPy development moves to GitHub
- 2011: Python 3 support
- 2012: adding a sparse graph module and unified optimization interface
- 2012: removal of scipy.maxentropy
- 2013: continuous integration with TravisCI
- 2015: adding Cython interface for BLAS/LAPACK and a benchmark suite
- 2017: adding a unified C API with scipy.LowLevelCallable; removal of
- 2017: SciPy 1.0 release
**Pauli Virtanen** is SciPy's Benevolent Dictator For Life (BDFL). He says:
*Truthfully speaking, we could have released a SciPy 1.0 a long time ago,
happy we do it now at long last. The project has a long history, and during
years it has matured also as a software project. I believe it has well
its merit to warrant a version number starting with unity.*
*Since its conception 15+ years ago, SciPy has largely been written by and
scientists, to provide a box of basic tools that they need. Over time, the
of people active in its development has undergone some rotation, and we have
evolved towards a somewhat more systematic approach to development.
this underlying drive has stayed the same, and I think it will also continue
propelling the project forward in future. This is all good, since not long
after 1.0 comes 1.1.*
**Travis Oliphant** is one of SciPy's creators. He says:
*I'm honored to write a note of congratulations to the SciPy developers and
entire SciPy community for the release of SciPy 1.0. This release
a dream of many that has been patiently pursued by a stalwart group of
for nearly 2 decades. Efforts have been broad and consistent over that
from many hundreds of people. From initial discussions to efforts coding
packaging to documentation efforts to extensive conference and community
building, the SciPy effort has been a global phenomenon that it has been a
privilege to participate in.*
*The idea of SciPy was already in multiple people’s minds in 1997 when I
joined the Python community as a young graduate student who had just fallen
love with the expressibility and extensibility of Python. The internet was
just starting to bringing together like-minded mathematicians and
nascent electronically-connected communities. In 1998, there was a
discussion on the matrix-SIG, python mailing list with people like Paul
Barrett, Joe Harrington, Perry Greenfield, Paul Dubois, Konrad Hinsen, David
Ascher, and others. This discussion encouraged me in 1998 and 1999 to
procrastinate my PhD and spend a lot of time writing extension modules to
Python that mostly wrapped battle-tested Fortran and C-code making it
to the Python user. This work attracted the help of others like Robert
Pearu Peterson and Eric Jones who joined their efforts with mine in 2000 so
that by 2001, the first SciPy release was ready. This was long before
simplified collaboration and input from others and the "patch" command and
email was how you helped a project improve.*
*Since that time, hundreds of people have spent an enormous amount of time
improving the SciPy library and the community surrounding this library has
dramatically grown. I stopped being able to participate actively in
the SciPy library around 2010. Fortunately, at that time, Pauli Virtanen
Ralf Gommers picked up the pace of development supported by dozens of other
contributors such as David Cournapeau, Evgeni Burovski, Josef Perktold, and
Warren Weckesser. While I have only been able to admire the development of
SciPy from a distance for the past 7 years, I have never lost my love of the
project and the concept of community-driven development. I remain driven
even now by a desire to help sustain the development of not only the SciPy
library but many other affiliated and related open-source projects. I am
extremely pleased that SciPy is in the hands of a world-wide community of
talented developers who will ensure that SciPy remains an example of how
grass-roots, community-driven development can succeed.*
**Fernando Perez** offers a wider community perspective:
*The existence of a nascent Scipy library, and the incredible --if tiny by
today's standards-- community surrounding it is what drew me into the
scientific Python world while still a physics graduate student in 2001.
I am awed when I see these tools power everything from high school
the research that led to the 2017 Nobel Prize in physics.*
*Don't be fooled by the 1.0 number: this project is a mature cornerstone of
modern scientific computing ecosystem. I am grateful for the many who have
made it possible, and hope to be able to contribute again to it in the
My sincere congratulations to the whole team!*
Highlights of this release
Some of the highlights of this release are:
- Major build improvements. Windows wheels are available on PyPI for the
first time, and continuous integration has been set up on Windows and OS X
in addition to Linux.
- A set of new ODE solvers and a unified interface to them
- Two new trust region optimizers and a new linear programming method, with
improved performance compared to what `scipy.optimize` offered previously.
- Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are
Upgrading and compatibility
There have been a number of deprecations and API changes in this release,
are documented below. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so, run
code with ``python -Wd`` and check for ``DeprecationWarning`` s).
This release requires Python 2.7 or >=3.4 and NumPy 1.8.2 or greater.
This is also the last release to support LAPACK 3.1.x - 3.3.x. Moving the
lowest supported LAPACK version to >3.2.x was long blocked by Apple
providing the LAPACK 3.2.1 API. We have decided that it's time to either
Accelerate or, if there is enough interest, provide shims for functions
in more recent LAPACK versions so it can still be used.
`scipy.cluster.hierarchy.optimal_leaf_ordering`, a function to reorder a
linkage matrix to minimize distances between adjacent leaves, was added.
N-dimensional versions of the discrete sine and cosine transforms and their
inverses were added as ``dctn``, ``idctn``, ``dstn`` and ``idstn``.
A set of new ODE solvers have been added to `scipy.integrate`. The
function `scipy.integrate.solve_ivp` allows uniform access to all solvers.
The individual solvers (``RK23``, ``RK45``, ``Radau``, ``BDF`` and
can also be used directly.
The BLAS wrappers in `scipy.linalg.blas` have been completed. Added
are ``*gbmv``, ``*hbmv``, ``*hpmv``, ``*hpr``, ``*hpr2``, ``*spmv``,
``*tbmv``, ``*tbsv``, ``*tpmv``, ``*tpsv``, ``*trsm``, ``*trsv``, ``*sbmv``,
Wrappers for the LAPACK functions ``*gels``, ``*stev``, ``*sytrd``,
``*sytf2``, ``*hetrf``, ``*sytrf``, ``*sycon``, ``*hecon``, ``*gglse``,
``*stebz``, ``*stemr``, ``*sterf``, and ``*stein`` have been added.
The function `scipy.linalg.subspace_angles` has been added to compute the
subspace angles between two matrices.
The function `scipy.linalg.clarkson_woodruff_transform` has been added.
It finds low-rank matrix approximation via the Clarkson-Woodruff Transform.
The functions `scipy.linalg.eigh_tridiagonal` and
`scipy.linalg.eigvalsh_tridiagonal`, which find the eigenvalues and
eigenvectors of tridiagonal hermitian/symmetric matrices, were added.
Support for homogeneous coordinate transforms has been added to
The ``ndimage`` C code underwent a significant refactoring, and is now
a lot easier to understand and maintain.
The methods ``trust-region-exact`` and ``trust-krylov`` have been added to
function `scipy.optimize.minimize`. These new trust-region methods solve the
subproblem with higher accuracy at the cost of more Hessian factorizations
(compared to dogleg) or more matrix vector products (compared to ncg) but
usually require less nonlinear iterations and are able to deal with
Hessians. They seem very competitive against the other Newton methods
implemented in scipy.
`scipy.optimize.linprog` gained an interior point method. Its performance
superior (both in accuracy and speed) to the older simplex method.
An argument ``fs`` (sampling frequency) was added to the following
``firwin``, ``firwin2``, ``firls``, and ``remez``. This makes these
consistent with many other functions in `scipy.signal` in which the sampling
frequency can be specified.
`scipy.signal.freqz` has been sped up significantly for FIR filters.
Iterating over and slicing of CSC and CSR matrices is now faster by up to
The ``tocsr`` method of COO matrices is now several times faster.
The ``diagonal`` method of sparse matrices now takes a parameter, indicating
which diagonal to return.
A new iterative solver for large-scale nonsymmetric sparse linear systems,
`scipy.sparse.linalg.gcrotmk`, was added. It implements ``GCROT(m,k)``, a
flexible variant of ``GCROT``.
`scipy.sparse.linalg.lsmr` now accepts an initial guess, yielding
SuperLU was updated to version 5.2.1.
Many distance metrics in `scipy.spatial.distance` gained support for
The signatures of `scipy.spatial.distance.pdist` and
`scipy.spatial.distance.cdist` were changed to ``*args, **kwargs`` in order
support a wider range of metrics (e.g. string-based metrics that need extra
keywords). Also, an optional ``out`` parameter was added to ``pdist`` and
``cdist`` allowing the user to specify where the resulting distance matrix
to be stored
The methods ``cdf`` and ``logcdf`` were added to
`scipy.stats.multivariate_normal`, providing the cumulative distribution
function of the multivariate normal distribution.
New statistical distance functions were added, namely
`scipy.stats.wasserstein_distance` for the first Wasserstein distance and
`scipy.stats.energy_distance` for the energy distance.
The following functions in `scipy.misc` are deprecated: ``bytescale``,
``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``,
``imsave``, ``imshow`` and ``toimage``. Most of those functions have
behavior (like rescaling and type casting image data without the user asking
for that). Other functions simply have better alternatives.
``scipy.interpolate.interpolate_wrapper`` and all functions in that
are deprecated. This was a never finished set of wrapper functions which is
not relevant anymore.
The ``fillvalue`` of `scipy.signal.convolve2d` will be cast directly to the
dtypes of the input arrays in the future and checked that it is a scalar or
an array with a single element.
``scipy.spatial.distance.matching`` is deprecated. It is an alias of
`scipy.spatial.distance.hamming`, which should be used instead.
Implementation of `scipy.spatial.distance.wminkowski` was based on a wrong
interpretation of the metric definition. In scipy 1.0 it has been just
deprecated in the documentation to keep retro-compatibility but is
to use the new version of `scipy.spatial.distance.minkowski` that implements
the correct behaviour.
Positional arguments of `scipy.spatial.distance.pdist` and
`scipy.spatial.distance.cdist` should be replaced with their keyword
Backwards incompatible changes
The following deprecated functions have been removed from `scipy.stats`:
``betai``, ``chisqprob``, ``f_value``, ``histogram``, ``histogram2``,
``pdf_fromgamma``, ``signaltonoise``, ``square_of_sums``, ``ss`` and
The following deprecated functions have been removed from
``betai``, ``f_value_wilks_lambda``, ``signaltonoise`` and ``threshold``.
The deprecated ``a`` and ``reta`` keywords have been removed from
The deprecated functions ``sparse.csgraph.cs_graph_components`` and
``sparse.linalg.symeig`` have been removed from `scipy.sparse`.
The following deprecated keywords have been removed in
``drop_tol`` from ``splu``, and ``xtype`` from ``bicg``, ``bicgstab``,
``cgs``, ``gmres``, ``qmr`` and ``minres``.
The deprecated functions ``expm2`` and ``expm3`` have been removed from
`scipy.linalg`. The deprecated keyword ``q`` was removed from
`scipy.linalg.expm`. And the deprecated submodule ``linalg.calc_lwork`` was
The deprecated functions ``C2K``, ``K2C``, ``F2C``, ``C2F``, ``F2K`` and
``K2F`` have been removed from `scipy.constants`.
The deprecated ``ppform`` class was removed from `scipy.interpolate`.
The deprecated keyword ``iprint`` was removed from
The default value for the ``zero_phase`` keyword of `scipy.signal.decimate`
has been changed to True.
The ``kmeans`` and ``kmeans2`` functions in `scipy.cluster.vq` changed the
method used for random initialization, so using a fixed random seed will
not necessarily produce the same results as in previous versions.
`scipy.special.gammaln` does not accept complex arguments anymore.
The deprecated functions ``sph_jn``, ``sph_yn``, ``sph_jnyn``, ``sph_in``,
``sph_kn``, and ``sph_inkn`` have been removed. Users should instead use
the functions ``spherical_jn``, ``spherical_yn``, ``spherical_in``, and
``spherical_kn``. Be aware that the new functions have different
The cross-class properties of `scipy.signal.lti` systems have been removed.
The following properties/setters have been removed:
Name - (accessing/setting has been removed) - (setting has been removed)
* StateSpace - (``num``, ``den``, ``gain``) - (``zeros``, ``poles``)
* TransferFunction (``A``, ``B``, ``C``, ``D``, ``gain``) - (``zeros``,
* ZerosPolesGain (``A``, ``B``, ``C``, ``D``, ``num``, ``den``) - ()
``signal.freqz(b, a)`` with ``b`` or ``a`` >1-D raises a ``ValueError``.
was a corner case for which it was unclear that the behavior was
The method ``var`` of `scipy.stats.dirichlet` now returns a scalar rather
an ndarray when the length of alpha is 1.
SciPy now has a formal governance structure. It consists of a BDFL (Pauli
Virtanen) and a Steering Committee. See `the governance document
It is now possible to build SciPy on Windows with MSVC + gfortran!
integration has been set up for this build configuration on Appveyor,
Continuous integration for OS X has been set up on TravisCI.
The SciPy test suite has been migrated from ``nose`` to ``pytest``.
``scipy/_distributor_init.py`` was added to allow redistributors of SciPy to
add custom code that needs to run when importing SciPy (e.g. checks for
hardware, DLL search paths, etc.).
Support for PEP 518 (specifying build system requirements) was added - see
``pyproject.toml`` in the root of the SciPy repository.
In order to have consistent function names, the function
``scipy.linalg.solve_lyapunov`` is renamed to
`scipy.linalg.solve_continuous_lyapunov`. The old name is kept for
* @arcady +
* @xoviat +
* Anton Akhmerov
* Dominic Antonacci +
* Alessandro Pietro Bardelli
* Ved Basu +
* Michael James Bedford +
* Ray Bell +
* Juan M. Bello-Rivas +
* Sebastian Berg
* Felix Berkenkamp
* Jyotirmoy Bhattacharya +
* Matthew Brett
* Jonathan Bright
* Bruno Jiménez +
* Evgeni Burovski
* Patrick Callier
* Mark Campanelli +
* CJ Carey
* Robert Cimrman
* Adam Cox +
* Michael Danilov +
* David Haberthür +
* Andras Deak +
* Philip DeBoer
* Anne-Sylvie Deutsch
* Cathy Douglass +
* Dominic Else +
* Guo Fei +
* Roman Feldbauer +
* Yu Feng
* Jaime Fernandez del Rio
* Orestis Floros +
* David Freese +
* Adam Geitgey +
* James Gerity +
* Dezmond Goff +
* Christoph Gohlke
* Ralf Gommers
* Dirk Gorissen +
* Matt Haberland +
* David Hagen +
* Charles Harris
* Lam Yuen Hei +
* Jean Helie +
* Gaute Hope +
* Guillaume Horel +
* Franziska Horn +
* Yevhenii Hyzyla +
* Vladislav Iakovlev +
* Marvin Kastner +
* Mher Kazandjian
* Thomas Keck
* Adam Kurkiewicz +
* Ronan Lamy +
* J.L. Lanfranchi +
* Eric Larson
* Denis Laxalde
* Gregory R. Lee
* Felix Lenders +
* Evan Limanto
* Julian Lukwata +
* François Magimel
* Syrtis Major +
* Charles Masson +
* Nikolay Mayorov
* Tobias Megies
* Markus Meister +
* Roman Mirochnik +
* Jordi Montes +
* Nathan Musoke +
* Andrew Nelson
* M.J. Nichol
* Juan Nunez-Iglesias
* Arno Onken +
* Nick Papior +
* Dima Pasechnik +
* Ashwin Pathak +
* Oleksandr Pavlyk +
* Stefan Peterson
* Ilhan Polat
* Andrey Portnoy +
* Ravi Kumar Prasad +
* Aman Pratik
* Eric Quintero
* Vedant Rathore +
* Tyler Reddy
* Joscha Reimer
* Philipp Rentzsch +
* Antonio Horta Ribeiro
* Ned Richards +
* Kevin Rose +
* Benoit Rostykus +
* Matt Ruffalo +
* Eli Sadoff +
* Pim Schellart
* Nico Schlömer +
* Klaus Sembritzki +
* Nikolay Shebanov +
* Jonathan Tammo Siebert
* Scott Sievert
* Max Silbiger +
* Mandeep Singh +
* Michael Stewart +
* Jonathan Sutton +
* Deep Tavker +
* Martin Thoma
* James Tocknell +
* Aleksandar Trifunovic +
* Paul van Mulbregt +
* Jacob Vanderplas
* Aditya Vijaykumar
* Pauli Virtanen
* James Webber
* Warren Weckesser
* Eric Wieser +
* Josh Wilson
* Zhiqing Xiao +
* Evgeny Zhurko
* Nikolay Zinov +
* Zé Vinícius +
A total of 121 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully
PyCA cryptography 2.1.2 has been released to PyPI. cryptography includes
both high level recipes and low level interfaces
to common cryptographic algorithms such as symmetric ciphers, message
digests, and key derivation functions. We support Python 2.7, Python 3.4+,
* This release corrects a bug with the manylinux1 wheels where OpenSSL's
stack was marked executable.
-Paul Kehrer (reaperhulk)