We are extremely pleased to announce the release of SciPy 1.0, 16 years after
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 the
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 and a
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% happy
with it". This is normal for many open source projects, however that doesn't
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). Despite
that, SciPy is extremely useful to its users, on average has high quality code
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 scipy.weave
- 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, so I'm
happy we do it now at long last. The project has a long history, and during the
years it has matured also as a software project. I believe it has well proved
its merit to warrant a version number starting with unity.
Since its conception 15+ years ago, SciPy has largely been written by and for
scientists, to provide a box of basic tools that they need. Over time, the set
of people active in its development has undergone some rotation, and we have
evolved towards a somewhat more systematic approach to development. Regardless,
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 the
entire SciPy community for the release of SciPy 1.0. This release represents
a dream of many that has been patiently pursued by a stalwart group of pioneers
for nearly 2 decades. Efforts have been broad and consistent over that time
from many hundreds of people. From initial discussions to efforts coding and
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 first
joined the Python community as a young graduate student who had just fallen in
love with the expressibility and extensibility of Python. The internet was
just starting to bringing together like-minded mathematicians and scientists in
nascent electronically-connected communities. In 1998, there was a concerted
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 available
to the Python user. This work attracted the help of others like Robert Kern,
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 Github
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 developing
the SciPy library around 2010. Fortunately, at that time, Pauli Virtanen and
Ralf Gommers picked up the pace of development supported by dozens of other key
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. Today,
I am awed when I see these tools power everything from high school education to
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 the
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 future.
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
- Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are now
Upgrading and compatibility
There have been a number of deprecations and API changes in this release, which
are documented below. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so, run your
python -Wd and check for
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 Accelerate
providing the LAPACK 3.2.1 API. We have decided that it's time to either drop
Accelerate or, if there is enough interest, provide shims for functions added
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
A set of new ODE solvers have been added to
scipy.integrate. The convenience
scipy.integrate.solve_ivp allows uniform access to all solvers.
The individual solvers (
can also be used directly.
The BLAS wrappers in
scipy.linalg.blas have been completed. Added functions
Wrappers for the LAPACK functions
*stein have been added.
scipy.linalg.subspace_angles has been added to compute the
subspace angles between two matrices.
scipy.linalg.clarkson_woodruff_transform has been added.
It finds low-rank matrix approximation via the Clarkson-Woodruff Transform.
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
ndimage C code underwent a significant refactoring, and is now
a lot easier to understand and maintain.
trust-krylov have been added to the
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 indefinite
Hessians. They seem very competitive against the other Newton methods
implemented in scipy.
scipy.optimize.linprog gained an interior point method. Its performance is
superior (both in accuracy and speed) to the older simplex method.
fs (sampling frequency) was added to the following functions:
remez. This makes these functions
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 ~35%.
tocsr method of COO matrices is now several times faster.
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
flexible variant of
scipy.sparse.linalg.lsmr now accepts an initial guess, yielding potentially
SuperLU was updated to version 5.2.1.
Many distance metrics in
scipy.spatial.distance gained support for weights.
The signatures of
scipy.spatial.distance.cdist were changed to
*args, **kwargs in order to
support a wider range of metrics (e.g. string-based metrics that need extra
keywords). Also, an optional
out parameter was added to
cdist allowing the user to specify where the resulting distance matrix is
to be stored
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:
toimage. Most of those functions have unexpected
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 submodule
are deprecated. This was a never finished set of wrapper functions which is
not relevant anymore.
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.
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 recommended
to use the new version of
scipy.spatial.distance.minkowski that implements
the correct behaviour.
Positional arguments of
scipy.spatial.distance.cdist should be replaced with their keyword version.
Backwards incompatible changes
The following deprecated functions have been removed from
The following deprecated functions have been removed from
reta keywords have been removed from
The deprecated functions
sparse.linalg.symeig have been removed from
The following deprecated keywords have been removed in
The deprecated functions
expm3 have been removed from
scipy.linalg. The deprecated keyword
q was removed from
scipy.linalg.expm. And the deprecated submodule
The deprecated functions
K2F have been removed from
ppform class was removed from
The deprecated keyword
iprint was removed from
The default value for the
zero_phase keyword of
has been changed to True.
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_inkn have been removed. Users should instead use
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 - (
gain) - (
- TransferFunction (
gain) - (
- ZerosPolesGain (
den) - ()
signal.freqz(b, a) with
a >1-D raises a
was a corner case for which it was unclear that the behavior was well-defined.
scipy.stats.dirichlet now returns a scalar rather than
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 <https://github.com/scipy/scipy/blob/master/doc/source/dev/governance/governance.rst>_
It is now possible to build SciPy on Windows with MSVC + gfortran! Continuous
integration has been set up for this build configuration on Appveyor, building
Continuous integration for OS X has been set up on TravisCI.
The SciPy test suite has been migrated from
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 +
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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 complete.