Changelog
### 0.29.21
```
====================
Bugs fixed
----------
* Fix a regression in 0.29.20 where ``__div__`` failed to be found in extension types.
(Github issue 3688)
* Fix a regression in 0.29.20 where a call inside of a finally clause could fail to compile.
Patch by David Woods. (Github issue 3712)
* Zero-sized buffers could fail to validate as C/Fortran-contiguous.
Patch by Clemens Hofreither. (Github issue 2093)
* ``exec()`` did not allow recent Python syntax features in Py3.8+ due to
https://bugs.python.org/issue35975.
(Github issue 3695)
* Binding staticmethods of Cython functions were not behaving like Python methods in Py3.
Patch by Jeroen Demeyer and Michał Górny. (Github issue 3106)
* Pythran calls to NumPy methods no longer generate useless method lookup code.
* The ``PyUnicode_GET_LENGTH()`` macro was missing from the ``cpython.*`` declarations.
Patch by Thomas Caswell. (Github issue 3692)
* The deprecated ``PyUnicode_*()`` C-API functions are no longer used, except for Unicode
strings that contain lone surrogates. Unicode strings that contain non-BMP characters
or surrogate pairs now generate different C code on 16-bit Python 2.x Unicode deployments
(such as MS-Windows). Generating the C code on Python 3.x is recommended in this case.
Original patches by Inada Naoki and Victor Stinner. (Github issues 3677, 3721, 3697)
* Some template parameters were missing from the C++ ``std::unordered_map`` declaration.
Patch by will. (Github issue 3685)
* Several internal code generation issues regarding temporary variables were resolved.
(Github issue 3708)
```
Links
- PyPI: https://pypi.org/project/cython
- Changelog: https://pyup.io/changelogs/cython/
- Homepage: http://cython.org/
Changelog
### 1.6.0
```
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and API changes
in this release, which are documented below. All users are encouraged to
upgrade to this release, as there are a large number of bug-fixes and
optimizations. Before upgrading, we recommend that users check that
their own code does not use deprecated SciPy functionality (to do so,
run your code with ``python -Wd`` and check for ``DeprecationWarning`` s).
Our development attention will now shift to bug-fix releases on the
1.6.x branch, and on adding new features on the master branch.
This release requires Python `3.7`+ and NumPy `1.16.5` or greater.
For running on PyPy, PyPy3 `6.0`+ is required.
Highlights of this release
----------------------------
- `scipy.ndimage` improvements: Fixes and ehancements to boundary extension
modes for interpolation functions. Support for complex-valued inputs in many
filtering and interpolation functions. New ``grid_mode`` option for
`scipy.ndimage.zoom` to enable results consistent with scikit-image's
``rescale``.
- `scipy.optimize.linprog` has fast, new methods for large, sparse problems
from the ``HiGHS`` library.
- `scipy.stats` improvements including new distributions, a new test, and
enhancements to existing distributions and tests
New features
============
`scipy.special` improvements
-----------------------------
`scipy.special` now has improved support for 64-bit ``LAPACK`` backend
`scipy.odr` improvements
-------------------------
`scipy.odr` now has support for 64-bit integer ``BLAS``
`scipy.odr.ODR` has gained an optional ``overwrite`` argument so that existing
files may be overwritten.
`scipy.integrate` improvements
-------------------------------
Some renames of functions with poor names were done, with the old names
retained without being in the reference guide for backwards compatibility
reasons:
- ``integrate.simps`` was renamed to ``integrate.simpson``
- ``integrate.trapz`` was renamed to ``integrate.trapezoid``
- ``integrate.cumtrapz`` was renamed to ``integrate.cumulative_trapezoid``
`scipy.cluster` improvements
-------------------------------
`scipy.cluster.hierarchy.DisjointSet` has been added for incremental
connectivity queries.
`scipy.cluster.hierarchy.dendrogram` return value now also includes leaf color
information in `leaves_color_list`.
`scipy.interpolate` improvements
---------------------------------
`scipy.interpolate.interp1d` has a new method ``nearest-up``, similar to the
existing method ``nearest`` but rounds half-integers up instead of down.
`scipy.io` improvements
------------------------
Support has been added for reading arbitrary bit depth integer PCM WAV files
from 1- to 32-bit, including the commonly-requested 24-bit depth.
`scipy.linalg` improvements
----------------------------
The new function `scipy.linalg.matmul_toeplitz` uses the FFT to compute the
product of a Toeplitz matrix with another matrix.
`scipy.linalg.sqrtm` and `scipy.linalg.logm` have performance improvements
thanks to additional Cython code.
Python ``LAPACK`` wrappers have been added for ``pptrf``, ``pptrs``, ``ppsv``,
``pptri``, and ``ppcon``.
`scipy.linalg.norm` and the ``svd`` family of functions will now use 64-bit
integer backends when available.
`scipy.ndimage` improvements
-----------------------------
`scipy.ndimage.convolve`, `scipy.ndimage.correlate` and their 1d counterparts
now accept both complex-valued images and/or complex-valued filter kernels. All
convolution-based filters also now accept complex-valued inputs
(e.g. ``gaussian_filter``, ``uniform_filter``, etc.).
Multiple fixes and enhancements to boundary handling were introduced to
`scipy.ndimage` interpolation functions (i.e. ``affine_transform``,
``geometric_transform``, ``map_coordinates``, ``rotate``, ``shift``, ``zoom``).
A new boundary mode, ``grid-wrap`` was added which wraps images periodically,
using a period equal to the shape of the input image grid. This is in contrast
to the existing ``wrap`` mode which uses a period that is one sample smaller
than the original signal extent along each dimension.
A long-standing bug in the ``reflect`` boundary condition has been fixed and
the mode ``grid-mirror`` was introduced as a synonym for ``reflect``.
A new boundary mode, ``grid-constant`` is now available. This is similar to
the existing ndimage ``constant`` mode, but interpolation will still performed
at coordinate values outside of the original image extent. This
``grid-constant`` mode is consistent with OpenCV's ``BORDER_CONSTANT`` mode
and scikit-image's ``constant`` mode.
Spline pre-filtering (used internally by ``ndimage`` interpolation functions
when ``order >= 2``), now supports all boundary modes rather than always
defaulting to mirror boundary conditions. The standalone functions
``spline_filter`` and ``spline_filter1d`` have analytical boundary conditions
that match modes ``mirror``, ``grid-wrap`` and ``reflect``.
`scipy.ndimage` interpolation functions now accept complex-valued inputs. In
this case, the interpolation is applied independently to the real and
imaginary components.
The ``ndimage`` tutorials
(https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been
updated with new figures to better clarify the exact behavior of all of the
interpolation boundary modes.
`scipy.ndimage.zoom` now has a ``grid_mode`` option that changes the coordinate
of the center of the first pixel along an axis from 0 to 0.5. This allows
resizing in a manner that is consistent with the behavior of scikit-image's
``resize`` and ``rescale`` functions (and OpenCV's ``cv2.resize``).
`scipy.optimize` improvements
------------------------------
`scipy.optimize.linprog` has fast, new methods for large, sparse problems from
the ``HiGHS`` C++ library. ``method='highs-ds'`` uses a high performance dual
revised simplex implementation (HSOL), ``method='highs-ipm'`` uses an
interior-point method with crossover, and ``method='highs'`` chooses between
the two automatically. These methods are typically much faster and often exceed
the accuracy of other ``linprog`` methods, so we recommend explicitly
specifying one of these three method values when using ``linprog``.
`scipy.optimize.quadratic_assignment` has been added for approximate solution
of the quadratic assignment problem.
`scipy.optimize.linear_sum_assignment` now has a substantially reduced overhead
for small cost matrix sizes
`scipy.optimize.least_squares` has improved performance when the user provides
the jacobian as a sparse jacobian already in ``csr_matrix`` format
`scipy.optimize.linprog` now has an ``rr_method`` argument for specification
of the method used for redundancy handling, and a new method for this purpose
is available based on the interpolative decomposition approach.
`scipy.signal` improvements
----------------------------
`scipy.signal.gammatone` has been added to design FIR or IIR filters that
model the human auditory system.
`scipy.signal.iircomb` has been added to design IIR peaking/notching comb
filters that can boost/attenuate a frequency from a signal.
`scipy.signal.sosfilt` performance has been improved to avoid some previously-
observed slowdowns
`scipy.signal.windows.taylor` has been added--the Taylor window function is
commonly used in radar digital signal processing
`scipy.signal.gauss_spline` now supports ``list`` type input for consistency
with other related SciPy functions
`scipy.signal.correlation_lags` has been added to allow calculation of the lag/
displacement indices array for 1D cross-correlation.
`scipy.sparse` improvements
----------------------------
A solver for the minimum weight full matching problem for bipartite graphs,
also known as the linear assignment problem, has been added in
`scipy.sparse.csgraph.min_weight_full_bipartite_matching`. In particular, this
provides functionality analogous to that of
`scipy.optimize.linear_sum_assignment`, but with improved performance for sparse
inputs, and the ability to handle inputs whose dense representations would not
fit in memory.
The time complexity of `scipy.sparse.block_diag` has been improved dramatically
from quadratic to linear.
`scipy.sparse.linalg` improvements
-----------------------------------
The vendored version of ``SuperLU`` has been updated
`scipy.fft` improvements
-------------------------
The vendored ``pocketfft`` library now supports compiling with ARM neon vector
extensions and has improved thread pool behavior.
`scipy.spatial` improvements
-----------------------------
The python implementation of ``KDTree`` has been dropped and ``KDTree`` is now
implemented in terms of ``cKDTree``. You can now expect ``cKDTree``-like
performance by default. This also means ``sys.setrecursionlimit`` no longer
needs to be increased for querying large trees.
``transform.Rotation`` has been updated with support for Modified Rodrigues
Parameters alongside the existing rotation representations (PR gh-12667).
`scipy.spatial.transform.Rotation` has been partially cythonized, with some
performance improvements observed
`scipy.spatial.distance.cdist` has improved performance with the ``minkowski``
metric, especially for p-norm values of 1 or 2.
`scipy.stats` improvements
---------------------------
New distributions have been added to `scipy.stats`:
- The asymmetric Laplace continuous distribution has been added as
`scipy.stats.laplace_asymmetric`.
- The negative hypergeometric distribution has been added as `scipy.stats.nhypergeom`.
- The multivariate t distribution has been added as `scipy.stats.multivariate_t`.
- The multivariate hypergeometric distribution has been added as `scipy.stats.multivariate_hypergeom`.
The ``fit`` method has been overridden for several distributions (``laplace``,
``pareto``, ``rayleigh``, ``invgauss``, ``logistic``, ``gumbel_l``,
``gumbel_r``); they now use analytical, distribution-specific maximum
likelihood estimation results for greater speed and accuracy than the generic
(numerical optimization) implementation.
The one-sample Cramér-von Mises test has been added as
`scipy.stats.cramervonmises`.
An option to compute one-sided p-values was added to `scipy.stats.ttest_1samp`,
`scipy.stats.ttest_ind_from_stats`, `scipy.stats.ttest_ind` and
`scipy.stats.ttest_rel`.
The function `scipy.stats.kendalltau` now has an option to compute Kendall's
tau-c (also known as Stuart's tau-c), and support has been added for exact
p-value calculations for sample sizes ``> 171``.
`stats.trapz` was renamed to `stats.trapezoid`, with the former name retained
as an alias for backwards compatibility reasons.
The function `scipy.stats.linregress` now includes the standard error of the
intercept in its return value.
The ``_logpdf``, ``_sf``, and ``_isf`` methods have been added to
`scipy.stats.nakagami`; ``_sf`` and ``_isf`` methods also added to
`scipy.stats.gumbel_r`
The ``sf`` method has been added to `scipy.stats.levy` and `scipy.stats.levy_l`
for improved precision.
`scipy.stats.binned_statistic_dd` performance improvements for the following
computed statistics: ``max``, ``min``, ``median``, and ``std``.
We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source
Software for Science program for supporting many of these improvements to
`scipy.stats`.
Deprecated features
===================
`scipy.spatial` changes
------------------------
Calling ``KDTree.query`` with ``k=None`` to find all neighbours is deprecated.
Use ``KDTree.query_ball_point`` instead.
``distance.wminkowski`` was deprecated; use ``distance.minkowski`` and supply
weights with the ``w`` keyword instead.
Backwards incompatible changes
==============================
`scipy` changes
----------------
Using `scipy.fft` as a function aliasing ``numpy.fft.fft`` was removed after
being deprecated in SciPy ``1.4.0``. As a result, the `scipy.fft` submodule
must be explicitly imported now, in line with other SciPy subpackages.
`scipy.signal` changes
-----------------------
The output of ``decimate``, ``lfilter_zi``, ``lfiltic``, ``sos2tf``, and
``sosfilt_zi`` have been changed to match ``numpy.result_type`` of their inputs.
The window function ``slepian`` was removed. It had been deprecated since SciPy
``1.1``.
`scipy.spatial` changes
------------------------
``cKDTree.query`` now returns 64-bit rather than 32-bit integers on Windows,
making behaviour consistent between platforms (PR gh-12673).
`scipy.stats` changes
----------------------
The ``frechet_l`` and ``frechet_r`` distributions were removed. They were
deprecated since SciPy ``1.0``.
Other changes
=============
``setup_requires`` was removed from ``setup.py``. This means that users
invoking ``python setup.py install`` without having numpy already installed
will now get an error, rather than having numpy installed for them via
``easy_install``. This install method was always fragile and problematic, users
are encouraged to use ``pip`` when installing from source.
- Fixed a bug in `scipy.optimize.dual_annealing` ``accept_reject`` calculation
that caused uphill jumps to be accepted less frequently.
- The time required for (un)pickling of `scipy.stats.rv_continuous`,
`scipy.stats.rv_discrete`, and `scipy.stats.rv_frozen` has been significantly
reduced (gh12550). Inheriting subclasses should note that ``__setstate__`` no
longer calls ``__init__`` upon unpickling.
Authors
=======
* endolith
* vkk800
* aditya +
* George Bateman +
* Christoph Baumgarten
* Peter Bell
* Tobias Biester +
* Keaton J. Burns +
* Evgeni Burovski
* Rüdiger Busche +
* Matthias Bussonnier
* Dominic C +
* Corallus Caninus +
* CJ Carey
* Thomas A Caswell
* chapochn +
* Lucía Cheung
* Zach Colbert +
* Coloquinte +
* Yannick Copin +
* Devin Crowley +
* Terry Davis +
* Michaël Defferrard +
* devonwp +
* Didier +
* divenex +
* Thomas Duvernay +
* Eoghan O'Connell +
* Gökçen Eraslan
* Kristian Eschenburg +
* Ralf Gommers
* Thomas Grainger +
* GreatV +
* Gregory Gundersen +
* h-vetinari +
* Matt Haberland
* Mark Harfouche +
* He He +
* Alex Henrie
* Chun-Ming Huang +
* Martin James McHugh III +
* Alex Izvorski +
* Joey +
* ST John +
* Jonas Jonker +
* Julius Bier Kirkegaard
* Marcin Konowalczyk +
* Konrad0
* Sam Van Kooten +
* Sergey Koposov +
* Peter Mahler Larsen
* Eric Larson
* Antony Lee
* Gregory R. Lee
* Loïc Estève
* Jean-Luc Margot +
* MarkusKoebis +
* Nikolay Mayorov
* G. D. McBain
* Andrew McCluskey +
* Nicholas McKibben
* Sturla Molden
* Denali Molitor +
* Eric Moore
* Shashaank N +
* Prashanth Nadukandi +
* nbelakovski +
* Andrew Nelson
* Nick +
* Nikola Forró +
* odidev
* ofirr +
* Sambit Panda
* Dima Pasechnik
* Tirth Patel +
* Paweł Redzyński +
* Vladimir Philipenko +
* Philipp Thölke +
* Ilhan Polat
* Eugene Prilepin +
* Vladyslav Rachek
* Ram Rachum +
* Tyler Reddy
* Martin Reinecke +
* Simon Segerblom Rex +
* Lucas Roberts
* Benjamin Rowell +
* Eli Rykoff +
* Atsushi Sakai
* Moritz Schulte +
* Daniel B. Smith
* Steve Smith +
* Jan Soedingrekso +
* Victor Stinner +
* Jose Storopoli +
* Diana Sukhoverkhova +
* Søren Fuglede Jørgensen
* taoky +
* Mike Taves +
* Ian Thomas +
* Will Tirone +
* Frank Torres +
* Seth Troisi
* Ronald van Elburg +
* Hugo van Kemenade
* Paul van Mulbregt
* Saul Ivan Rivas Vega +
* Pauli Virtanen
* Jan Vleeshouwers
* Samuel Wallan
* Warren Weckesser
* Ben West +
* Eric Wieser
* WillTirone +
* Levi John Wolf +
* Zhiqing Xiao
* Rory Yorke +
* Yun Wang (Maigo) +
* Egor Zemlyanoy +
* ZhihuiChen0903 +
* Jacob Zhong +
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.
```
### 1.5.4
```
compared to `1.5.3`. Importantly, wheels are now available
for Python `3.9` and a more complete fix has been applied for
issues building with XCode `12`.
Authors
=====
* Peter Bell
* CJ Carey
* Andrew McCluskey +
* Andrew Nelson
* Tyler Reddy
* Eli Rykoff +
* Ian Thomas +
A total of 7 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.
```
### 1.5.3
```
compared to `1.5.2`. In particular, Linux ARM64 wheels are now
available and a compatibility issue with XCode 12 has
been fixed.
Authors
=======
* Peter Bell
* CJ Carey
* Thomas Duvernay +
* Gregory Lee
* Eric Moore
* odidev
* Dima Pasechnik
* Tyler Reddy
* Simon Segerblom Rex +
* Daniel B. Smith
* Will Tirone +
* Warren Weckesser
A total of 12 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.
```
### 1.5.2
```
compared to `1.5.1`.
Authors
=====
* Peter Bell
* Tobias Biester +
* Evgeni Burovski
* Thomas A Caswell
* Ralf Gommers
* Sturla Molden
* Andrew Nelson
* ofirr +
* Sambit Panda
* Ilhan Polat
* Tyler Reddy
* Atsushi Sakai
* Pauli Virtanen
A total of 13 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.
```
### 1.5.1
```
compared to `1.5.0`. In particular, an issue where DLL loading
can fail for SciPy wheels on Windows with Python `3.6` has been
fixed.
Authors
=======
* Peter Bell
* Loïc Estève
* Philipp Thölke +
* Tyler Reddy
* Paul van Mulbregt
* Pauli Virtanen
* Warren Weckesser
A total of 7 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.
```
Links
- PyPI: https://pypi.org/project/scipy
- Changelog: https://pyup.io/changelogs/scipy/
- Repo: https://github.com/scipy/scipy/releases
- Homepage: https://www.scipy.org
Changelog
### 3.3.4
```
This is the fourth bugfix release of the 3.3.x series.
This release contains several critical bug-fixes:
* Fix WebAgg initialization.
* Fix parsing `QT_API` setting with mixed case.
* Fix build with link-time optimization disabled in environment.
* Fix test compatibility with NumPy 1.20.
* Fix test compatibility with pytest 6.2.
```
### 3.3.3
```
This is the third bugfix release of the 3.3.x series.
This release contains several critical bug-fixes:
* Fix calls to `Axis.grid` with argument `visible=True`.
* Fix fully masked `imshow`.
* Fix inconsistent color mapping in scatter for 3D plots.
* Fix notebook/nbAgg figures when used with ipywidgets in the same cell.
* Fix notebook/nbAgg/WebAgg on older (e.g., Firefox ESR) browsers.
* Fix pcolormesh with `datetime` coordinates.
* Fix performance regression with `datetime`s.
* Fix singular ticks with small log ranges.
* Fix timers/animations on wx and notebook backends.
* Remove certifi as a hard runtime dependency.
```
### 3.3.2
```
This is the second bugfix release of the 3.3.x series.
This release contains several critical bug-fixes:
* fix `Axis` scale on twinned `Axes`
* fix auto-close of `Figure`s in nbagg
* fix automatic title placement if `Axes` is off the `Figure`
* fix autoranging of log scales with barstacked histogram
* fix extra redraws when using `Button` or `TextBox` widgets
* fix imshow with `LogNorm` and large vmin/vmax ranges
* fix plotting Pandas `DataFrame` with string `MultiIndex`
* fix `scatter` with `marker=''`
* fix `scatter3d` color/linewidth re-projection
* fix state of mode buttons in TkAgg backends
* include license files in built distribution
* reduce Visual C++ runtime requirements on Windows
```
### 3.3.1
```
This is the first bugfix release of the 3.3.x series.
This release contains several critical bug-fixes:
* fix docstring import issues when running Python with optimization
* fix `hist` with categorical data, such as with Pandas
* fix install on BSD systems
* fix nbagg compatibility with Chrome 84+
* fix ordering of scatter marker size in 3D plots
* fix performance regression when plotting `Path`s
* fix reading from URL in `imread`
* fix several regressions with new date epoch handling
* fix some bad constrained and tight layout interactions with colorbars
* fix use of customized toolbars in TkAgg and WXAgg backends
```
### 3.3.0
```
Highlights of this release include:
- Provisional API for composing semantic axes layouts from text or nested lists
- New Axes.sharex, Axes.sharey methods
- Turbo colormap
- colors.BoundaryNorm supports extend keyword argument
- Text color for legend labels
- Pcolor and Pcolormesh now accept shading='nearest' and 'auto'
- Allow tick formatters to be set with str or function inputs
- New Axes.axline method
- Dates use a modern epoch
- Improved font weight detection
- Axes3D no longer distorts the 3D plot to match the 2D aspect ratio
- More consistent toolbar behavior across backends
- Toolbar icons are now styled for dark themes
- Cursor text now uses a number of significant digits matching pointing precision
- Functions to compute a Path's size
- savefig() gained a backend keyword argument
- Saving SVG now supports adding metadata
- Saving PDF metadata via PGF now consistent with PDF backend
- NbAgg and WebAgg no longer use jQuery & jQuery UI
For the full details please see the [What's New](https://matplotlib.org/3.3.0/users/whats_new.html) and [API changes](https://matplotlib.org/3.3.0/api/api_changes.html) in the documentation.
```
Links
- PyPI: https://pypi.org/project/matplotlib
- Changelog: https://pyup.io/changelogs/matplotlib/
- Homepage: https://matplotlib.org
Changelog
### 0.11.1
```
This a bug fix release and is a recommended upgrade for all users on v0.11.0.
Complete release notes are available on the [seaborn website](http://seaborn.pydata.org/whatsnew.htmlv0-11-1-december-2020).
```
### 0.11.0
```
This is a major release with important new features, enhancements to existing functions, and changes to the library. Highlights include an overhaul and modernization of the distributions plotting functions, more flexible data specification, new colormaps, and better narrative documentation.
Complete release notes are available on the [seaborn website](https://seaborn.pydata.org/whatsnew.html).
```
### 0.11.0.rc0
```
This is the first release candidate for v0.11.0, a major release with several important new features and changes to the library.
Highlights of the new version include:
- A modernization of the distributions module, with the introduction of [`displot`](https://github.com/mwaskom/seaborn/pull/2157), [`histplot`](https://github.com/mwaskom/seaborn/pull/2125), and [`ecdfplot`](https://github.com/mwaskom/seaborn/pull/2141), a complete rewrite of [`kdeplot`](https://github.com/mwaskom/seaborn/pull/2104), and substantial enhancements to [`jointplot`](https://github.com/mwaskom/seaborn/pull/2210) and [`pairplot`](https://github.com/mwaskom/seaborn/pull/2234)
- Centralized [input data processing](https://github.com/mwaskom/seaborn/pull/2071) allowing for both standardization and increased flexibility of long- and wide-form variable specification
- Substantial [internal refactoring](https://github.com/mwaskom/seaborn/pull/2090) to support the new distributions functions and planned improvements in other modules
- Warning-based enforcement of [keyword-only parameters](https://github.com/mwaskom/seaborn/pull/2081) in most functions
- New [perceptually uniform colormaps](https://github.com/mwaskom/seaborn/pull/2237) and better tools for creating custom numeric color palettes
- Improvements to the narrative documentation, including user guide chapters on function types and data structures, along with better cross-linking within the seaborn docs and between the seaborn and matplotlib docs
- Numerous other [targeted enhancements and bug fixes](https://github.com/mwaskom/seaborn/blob/v0.11.0.rc0/doc/releases/v0.11.0.txt)
Please test the release by installing from here or with `python -m pip install --upgrade --pre seaborn`
```
Links
- PyPI: https://pypi.org/project/seaborn
- Changelog: https://pyup.io/changelogs/seaborn/
- Repo: https://github.com/mwaskom/seaborn/
- Homepage: https://seaborn.pydata.org
Changelog
### 0.24.1
```
We're happy to announce the 0.24.1 release with several bugfixes:
You can see the changelog here: https://scikit-learn.org/stable/whats_new/v0.24.html#version-0-24-1
You can upgrade with pip as usual:
pip install -U scikit-learn
The conda-forge builds will be available shortly, which you can then install using:
conda install -c conda-forge scikit-learn
```
### 0.24.0
```
We're happy to announce the 0.24 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_24_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v0.24.html#version-0-24-0
This version supports Python versions 3.6 to 3.9.
```
### 0.23.2
```
We're happy to announce the 0.23.2 release with several bugfixes:
You can see the changelog here: https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-2
You can upgrade with pip as usual:
pip install -U scikit-learn
The conda-forge builds will be available shortly, which you can then install using:
conda install -c conda-forge scikit-learn
```
Links
- PyPI: https://pypi.org/project/scikit-learn
- Changelog: https://pyup.io/changelogs/scikit-learn/
- Homepage: http://scikit-learn.org
Changelog
### 0.2.4
```
-------------
Released on September 12th, 2020.
- Bumped previously incorrect dependency bound on scipy to 0.19.
- Bug fix for 'params' argument usage in GMMHMM.
- Warn when an explicitly set attribute would be overridden by
``init_params_``.
```
Links
- PyPI: https://pypi.org/project/hmmlearn
- Changelog: https://pyup.io/changelogs/hmmlearn/
- Repo: https://github.com/hmmlearn/hmmlearn
pyup-bot
commented
3 years ago
Update cython from 0.29.20 to 0.29.21.
Changelog
### 0.29.21 ``` ==================== Bugs fixed ---------- * Fix a regression in 0.29.20 where ``__div__`` failed to be found in extension types. (Github issue 3688) * Fix a regression in 0.29.20 where a call inside of a finally clause could fail to compile. Patch by David Woods. (Github issue 3712) * Zero-sized buffers could fail to validate as C/Fortran-contiguous. Patch by Clemens Hofreither. (Github issue 2093) * ``exec()`` did not allow recent Python syntax features in Py3.8+ due to https://bugs.python.org/issue35975. (Github issue 3695) * Binding staticmethods of Cython functions were not behaving like Python methods in Py3. Patch by Jeroen Demeyer and Michał Górny. (Github issue 3106) * Pythran calls to NumPy methods no longer generate useless method lookup code. * The ``PyUnicode_GET_LENGTH()`` macro was missing from the ``cpython.*`` declarations. Patch by Thomas Caswell. (Github issue 3692) * The deprecated ``PyUnicode_*()`` C-API functions are no longer used, except for Unicode strings that contain lone surrogates. Unicode strings that contain non-BMP characters or surrogate pairs now generate different C code on 16-bit Python 2.x Unicode deployments (such as MS-Windows). Generating the C code on Python 3.x is recommended in this case. Original patches by Inada Naoki and Victor Stinner. (Github issues 3677, 3721, 3697) * Some template parameters were missing from the C++ ``std::unordered_map`` declaration. Patch by will. (Github issue 3685) * Several internal code generation issues regarding temporary variables were resolved. (Github issue 3708) ```Links
- PyPI: https://pypi.org/project/cython - Changelog: https://pyup.io/changelogs/cython/ - Homepage: http://cython.org/Update numpy from 1.19.0 to 1.20.0.
The bot wasn't able to find a changelog for this release. Got an idea?
Links
- PyPI: https://pypi.org/project/numpy - Homepage: https://www.numpy.orgUpdate dask[array] from 2.19.0 to 2021.1.1.
The bot wasn't able to find a changelog for this release. Got an idea?
Links
- PyPI: https://pypi.org/project/dask - Repo: https://github.com/dask/dask/Update scipy from 1.5.0 to 1.6.0.
Changelog
### 1.6.0 ``` many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with ``python -Wd`` and check for ``DeprecationWarning`` s). Our development attention will now shift to bug-fix releases on the 1.6.x branch, and on adding new features on the master branch. This release requires Python `3.7`+ and NumPy `1.16.5` or greater. For running on PyPy, PyPy3 `6.0`+ is required. Highlights of this release ---------------------------- - `scipy.ndimage` improvements: Fixes and ehancements to boundary extension modes for interpolation functions. Support for complex-valued inputs in many filtering and interpolation functions. New ``grid_mode`` option for `scipy.ndimage.zoom` to enable results consistent with scikit-image's ``rescale``. - `scipy.optimize.linprog` has fast, new methods for large, sparse problems from the ``HiGHS`` library. - `scipy.stats` improvements including new distributions, a new test, and enhancements to existing distributions and tests New features ============ `scipy.special` improvements ----------------------------- `scipy.special` now has improved support for 64-bit ``LAPACK`` backend `scipy.odr` improvements ------------------------- `scipy.odr` now has support for 64-bit integer ``BLAS`` `scipy.odr.ODR` has gained an optional ``overwrite`` argument so that existing files may be overwritten. `scipy.integrate` improvements ------------------------------- Some renames of functions with poor names were done, with the old names retained without being in the reference guide for backwards compatibility reasons: - ``integrate.simps`` was renamed to ``integrate.simpson`` - ``integrate.trapz`` was renamed to ``integrate.trapezoid`` - ``integrate.cumtrapz`` was renamed to ``integrate.cumulative_trapezoid`` `scipy.cluster` improvements ------------------------------- `scipy.cluster.hierarchy.DisjointSet` has been added for incremental connectivity queries. `scipy.cluster.hierarchy.dendrogram` return value now also includes leaf color information in `leaves_color_list`. `scipy.interpolate` improvements --------------------------------- `scipy.interpolate.interp1d` has a new method ``nearest-up``, similar to the existing method ``nearest`` but rounds half-integers up instead of down. `scipy.io` improvements ------------------------ Support has been added for reading arbitrary bit depth integer PCM WAV files from 1- to 32-bit, including the commonly-requested 24-bit depth. `scipy.linalg` improvements ---------------------------- The new function `scipy.linalg.matmul_toeplitz` uses the FFT to compute the product of a Toeplitz matrix with another matrix. `scipy.linalg.sqrtm` and `scipy.linalg.logm` have performance improvements thanks to additional Cython code. Python ``LAPACK`` wrappers have been added for ``pptrf``, ``pptrs``, ``ppsv``, ``pptri``, and ``ppcon``. `scipy.linalg.norm` and the ``svd`` family of functions will now use 64-bit integer backends when available. `scipy.ndimage` improvements ----------------------------- `scipy.ndimage.convolve`, `scipy.ndimage.correlate` and their 1d counterparts now accept both complex-valued images and/or complex-valued filter kernels. All convolution-based filters also now accept complex-valued inputs (e.g. ``gaussian_filter``, ``uniform_filter``, etc.). Multiple fixes and enhancements to boundary handling were introduced to `scipy.ndimage` interpolation functions (i.e. ``affine_transform``, ``geometric_transform``, ``map_coordinates``, ``rotate``, ``shift``, ``zoom``). A new boundary mode, ``grid-wrap`` was added which wraps images periodically, using a period equal to the shape of the input image grid. This is in contrast to the existing ``wrap`` mode which uses a period that is one sample smaller than the original signal extent along each dimension. A long-standing bug in the ``reflect`` boundary condition has been fixed and the mode ``grid-mirror`` was introduced as a synonym for ``reflect``. A new boundary mode, ``grid-constant`` is now available. This is similar to the existing ndimage ``constant`` mode, but interpolation will still performed at coordinate values outside of the original image extent. This ``grid-constant`` mode is consistent with OpenCV's ``BORDER_CONSTANT`` mode and scikit-image's ``constant`` mode. Spline pre-filtering (used internally by ``ndimage`` interpolation functions when ``order >= 2``), now supports all boundary modes rather than always defaulting to mirror boundary conditions. The standalone functions ``spline_filter`` and ``spline_filter1d`` have analytical boundary conditions that match modes ``mirror``, ``grid-wrap`` and ``reflect``. `scipy.ndimage` interpolation functions now accept complex-valued inputs. In this case, the interpolation is applied independently to the real and imaginary components. The ``ndimage`` tutorials (https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been updated with new figures to better clarify the exact behavior of all of the interpolation boundary modes. `scipy.ndimage.zoom` now has a ``grid_mode`` option that changes the coordinate of the center of the first pixel along an axis from 0 to 0.5. This allows resizing in a manner that is consistent with the behavior of scikit-image's ``resize`` and ``rescale`` functions (and OpenCV's ``cv2.resize``). `scipy.optimize` improvements ------------------------------ `scipy.optimize.linprog` has fast, new methods for large, sparse problems from the ``HiGHS`` C++ library. ``method='highs-ds'`` uses a high performance dual revised simplex implementation (HSOL), ``method='highs-ipm'`` uses an interior-point method with crossover, and ``method='highs'`` chooses between the two automatically. These methods are typically much faster and often exceed the accuracy of other ``linprog`` methods, so we recommend explicitly specifying one of these three method values when using ``linprog``. `scipy.optimize.quadratic_assignment` has been added for approximate solution of the quadratic assignment problem. `scipy.optimize.linear_sum_assignment` now has a substantially reduced overhead for small cost matrix sizes `scipy.optimize.least_squares` has improved performance when the user provides the jacobian as a sparse jacobian already in ``csr_matrix`` format `scipy.optimize.linprog` now has an ``rr_method`` argument for specification of the method used for redundancy handling, and a new method for this purpose is available based on the interpolative decomposition approach. `scipy.signal` improvements ---------------------------- `scipy.signal.gammatone` has been added to design FIR or IIR filters that model the human auditory system. `scipy.signal.iircomb` has been added to design IIR peaking/notching comb filters that can boost/attenuate a frequency from a signal. `scipy.signal.sosfilt` performance has been improved to avoid some previously- observed slowdowns `scipy.signal.windows.taylor` has been added--the Taylor window function is commonly used in radar digital signal processing `scipy.signal.gauss_spline` now supports ``list`` type input for consistency with other related SciPy functions `scipy.signal.correlation_lags` has been added to allow calculation of the lag/ displacement indices array for 1D cross-correlation. `scipy.sparse` improvements ---------------------------- A solver for the minimum weight full matching problem for bipartite graphs, also known as the linear assignment problem, has been added in `scipy.sparse.csgraph.min_weight_full_bipartite_matching`. In particular, this provides functionality analogous to that of `scipy.optimize.linear_sum_assignment`, but with improved performance for sparse inputs, and the ability to handle inputs whose dense representations would not fit in memory. The time complexity of `scipy.sparse.block_diag` has been improved dramatically from quadratic to linear. `scipy.sparse.linalg` improvements ----------------------------------- The vendored version of ``SuperLU`` has been updated `scipy.fft` improvements ------------------------- The vendored ``pocketfft`` library now supports compiling with ARM neon vector extensions and has improved thread pool behavior. `scipy.spatial` improvements ----------------------------- The python implementation of ``KDTree`` has been dropped and ``KDTree`` is now implemented in terms of ``cKDTree``. You can now expect ``cKDTree``-like performance by default. This also means ``sys.setrecursionlimit`` no longer needs to be increased for querying large trees. ``transform.Rotation`` has been updated with support for Modified Rodrigues Parameters alongside the existing rotation representations (PR gh-12667). `scipy.spatial.transform.Rotation` has been partially cythonized, with some performance improvements observed `scipy.spatial.distance.cdist` has improved performance with the ``minkowski`` metric, especially for p-norm values of 1 or 2. `scipy.stats` improvements --------------------------- New distributions have been added to `scipy.stats`: - The asymmetric Laplace continuous distribution has been added as `scipy.stats.laplace_asymmetric`. - The negative hypergeometric distribution has been added as `scipy.stats.nhypergeom`. - The multivariate t distribution has been added as `scipy.stats.multivariate_t`. - The multivariate hypergeometric distribution has been added as `scipy.stats.multivariate_hypergeom`. The ``fit`` method has been overridden for several distributions (``laplace``, ``pareto``, ``rayleigh``, ``invgauss``, ``logistic``, ``gumbel_l``, ``gumbel_r``); they now use analytical, distribution-specific maximum likelihood estimation results for greater speed and accuracy than the generic (numerical optimization) implementation. The one-sample Cramér-von Mises test has been added as `scipy.stats.cramervonmises`. An option to compute one-sided p-values was added to `scipy.stats.ttest_1samp`, `scipy.stats.ttest_ind_from_stats`, `scipy.stats.ttest_ind` and `scipy.stats.ttest_rel`. The function `scipy.stats.kendalltau` now has an option to compute Kendall's tau-c (also known as Stuart's tau-c), and support has been added for exact p-value calculations for sample sizes ``> 171``. `stats.trapz` was renamed to `stats.trapezoid`, with the former name retained as an alias for backwards compatibility reasons. The function `scipy.stats.linregress` now includes the standard error of the intercept in its return value. The ``_logpdf``, ``_sf``, and ``_isf`` methods have been added to `scipy.stats.nakagami`; ``_sf`` and ``_isf`` methods also added to `scipy.stats.gumbel_r` The ``sf`` method has been added to `scipy.stats.levy` and `scipy.stats.levy_l` for improved precision. `scipy.stats.binned_statistic_dd` performance improvements for the following computed statistics: ``max``, ``min``, ``median``, and ``std``. We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source Software for Science program for supporting many of these improvements to `scipy.stats`. Deprecated features =================== `scipy.spatial` changes ------------------------ Calling ``KDTree.query`` with ``k=None`` to find all neighbours is deprecated. Use ``KDTree.query_ball_point`` instead. ``distance.wminkowski`` was deprecated; use ``distance.minkowski`` and supply weights with the ``w`` keyword instead. Backwards incompatible changes ============================== `scipy` changes ---------------- Using `scipy.fft` as a function aliasing ``numpy.fft.fft`` was removed after being deprecated in SciPy ``1.4.0``. As a result, the `scipy.fft` submodule must be explicitly imported now, in line with other SciPy subpackages. `scipy.signal` changes ----------------------- The output of ``decimate``, ``lfilter_zi``, ``lfiltic``, ``sos2tf``, and ``sosfilt_zi`` have been changed to match ``numpy.result_type`` of their inputs. The window function ``slepian`` was removed. It had been deprecated since SciPy ``1.1``. `scipy.spatial` changes ------------------------ ``cKDTree.query`` now returns 64-bit rather than 32-bit integers on Windows, making behaviour consistent between platforms (PR gh-12673). `scipy.stats` changes ---------------------- The ``frechet_l`` and ``frechet_r`` distributions were removed. They were deprecated since SciPy ``1.0``. Other changes ============= ``setup_requires`` was removed from ``setup.py``. This means that users invoking ``python setup.py install`` without having numpy already installed will now get an error, rather than having numpy installed for them via ``easy_install``. This install method was always fragile and problematic, users are encouraged to use ``pip`` when installing from source. - Fixed a bug in `scipy.optimize.dual_annealing` ``accept_reject`` calculation that caused uphill jumps to be accepted less frequently. - The time required for (un)pickling of `scipy.stats.rv_continuous`, `scipy.stats.rv_discrete`, and `scipy.stats.rv_frozen` has been significantly reduced (gh12550). Inheriting subclasses should note that ``__setstate__`` no longer calls ``__init__`` upon unpickling. Authors ======= * endolith * vkk800 * aditya + * George Bateman + * Christoph Baumgarten * Peter Bell * Tobias Biester + * Keaton J. Burns + * Evgeni Burovski * Rüdiger Busche + * Matthias Bussonnier * Dominic C + * Corallus Caninus + * CJ Carey * Thomas A Caswell * chapochn + * Lucía Cheung * Zach Colbert + * Coloquinte + * Yannick Copin + * Devin Crowley + * Terry Davis + * Michaël Defferrard + * devonwp + * Didier + * divenex + * Thomas Duvernay + * Eoghan O'Connell + * Gökçen Eraslan * Kristian Eschenburg + * Ralf Gommers * Thomas Grainger + * GreatV + * Gregory Gundersen + * h-vetinari + * Matt Haberland * Mark Harfouche + * He He + * Alex Henrie * Chun-Ming Huang + * Martin James McHugh III + * Alex Izvorski + * Joey + * ST John + * Jonas Jonker + * Julius Bier Kirkegaard * Marcin Konowalczyk + * Konrad0 * Sam Van Kooten + * Sergey Koposov + * Peter Mahler Larsen * Eric Larson * Antony Lee * Gregory R. Lee * Loïc Estève * Jean-Luc Margot + * MarkusKoebis + * Nikolay Mayorov * G. D. McBain * Andrew McCluskey + * Nicholas McKibben * Sturla Molden * Denali Molitor + * Eric Moore * Shashaank N + * Prashanth Nadukandi + * nbelakovski + * Andrew Nelson * Nick + * Nikola Forró + * odidev * ofirr + * Sambit Panda * Dima Pasechnik * Tirth Patel + * Paweł Redzyński + * Vladimir Philipenko + * Philipp Thölke + * Ilhan Polat * Eugene Prilepin + * Vladyslav Rachek * Ram Rachum + * Tyler Reddy * Martin Reinecke + * Simon Segerblom Rex + * Lucas Roberts * Benjamin Rowell + * Eli Rykoff + * Atsushi Sakai * Moritz Schulte + * Daniel B. Smith * Steve Smith + * Jan Soedingrekso + * Victor Stinner + * Jose Storopoli + * Diana Sukhoverkhova + * Søren Fuglede Jørgensen * taoky + * Mike Taves + * Ian Thomas + * Will Tirone + * Frank Torres + * Seth Troisi * Ronald van Elburg + * Hugo van Kemenade * Paul van Mulbregt * Saul Ivan Rivas Vega + * Pauli Virtanen * Jan Vleeshouwers * Samuel Wallan * Warren Weckesser * Ben West + * Eric Wieser * WillTirone + * Levi John Wolf + * Zhiqing Xiao * Rory Yorke + * Yun Wang (Maigo) + * Egor Zemlyanoy + * ZhihuiChen0903 + * Jacob Zhong + 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. ``` ### 1.5.4 ``` compared to `1.5.3`. Importantly, wheels are now available for Python `3.9` and a more complete fix has been applied for issues building with XCode `12`. Authors ===== * Peter Bell * CJ Carey * Andrew McCluskey + * Andrew Nelson * Tyler Reddy * Eli Rykoff + * Ian Thomas + A total of 7 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. ``` ### 1.5.3 ``` compared to `1.5.2`. In particular, Linux ARM64 wheels are now available and a compatibility issue with XCode 12 has been fixed. Authors ======= * Peter Bell * CJ Carey * Thomas Duvernay + * Gregory Lee * Eric Moore * odidev * Dima Pasechnik * Tyler Reddy * Simon Segerblom Rex + * Daniel B. Smith * Will Tirone + * Warren Weckesser A total of 12 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. ``` ### 1.5.2 ``` compared to `1.5.1`. Authors ===== * Peter Bell * Tobias Biester + * Evgeni Burovski * Thomas A Caswell * Ralf Gommers * Sturla Molden * Andrew Nelson * ofirr + * Sambit Panda * Ilhan Polat * Tyler Reddy * Atsushi Sakai * Pauli Virtanen A total of 13 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. ``` ### 1.5.1 ``` compared to `1.5.0`. In particular, an issue where DLL loading can fail for SciPy wheels on Windows with Python `3.6` has been fixed. Authors ======= * Peter Bell * Loïc Estève * Philipp Thölke + * Tyler Reddy * Paul van Mulbregt * Pauli Virtanen * Warren Weckesser A total of 7 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. ```Links
- PyPI: https://pypi.org/project/scipy - Changelog: https://pyup.io/changelogs/scipy/ - Repo: https://github.com/scipy/scipy/releases - Homepage: https://www.scipy.orgUpdate matplotlib from 3.2.2 to 3.3.4.
Changelog
### 3.3.4 ``` This is the fourth bugfix release of the 3.3.x series. This release contains several critical bug-fixes: * Fix WebAgg initialization. * Fix parsing `QT_API` setting with mixed case. * Fix build with link-time optimization disabled in environment. * Fix test compatibility with NumPy 1.20. * Fix test compatibility with pytest 6.2. ``` ### 3.3.3 ``` This is the third bugfix release of the 3.3.x series. This release contains several critical bug-fixes: * Fix calls to `Axis.grid` with argument `visible=True`. * Fix fully masked `imshow`. * Fix inconsistent color mapping in scatter for 3D plots. * Fix notebook/nbAgg figures when used with ipywidgets in the same cell. * Fix notebook/nbAgg/WebAgg on older (e.g., Firefox ESR) browsers. * Fix pcolormesh with `datetime` coordinates. * Fix performance regression with `datetime`s. * Fix singular ticks with small log ranges. * Fix timers/animations on wx and notebook backends. * Remove certifi as a hard runtime dependency. ``` ### 3.3.2 ``` This is the second bugfix release of the 3.3.x series. This release contains several critical bug-fixes: * fix `Axis` scale on twinned `Axes` * fix auto-close of `Figure`s in nbagg * fix automatic title placement if `Axes` is off the `Figure` * fix autoranging of log scales with barstacked histogram * fix extra redraws when using `Button` or `TextBox` widgets * fix imshow with `LogNorm` and large vmin/vmax ranges * fix plotting Pandas `DataFrame` with string `MultiIndex` * fix `scatter` with `marker=''` * fix `scatter3d` color/linewidth re-projection * fix state of mode buttons in TkAgg backends * include license files in built distribution * reduce Visual C++ runtime requirements on Windows ``` ### 3.3.1 ``` This is the first bugfix release of the 3.3.x series. This release contains several critical bug-fixes: * fix docstring import issues when running Python with optimization * fix `hist` with categorical data, such as with Pandas * fix install on BSD systems * fix nbagg compatibility with Chrome 84+ * fix ordering of scatter marker size in 3D plots * fix performance regression when plotting `Path`s * fix reading from URL in `imread` * fix several regressions with new date epoch handling * fix some bad constrained and tight layout interactions with colorbars * fix use of customized toolbars in TkAgg and WXAgg backends ``` ### 3.3.0 ``` Highlights of this release include: - Provisional API for composing semantic axes layouts from text or nested lists - New Axes.sharex, Axes.sharey methods - Turbo colormap - colors.BoundaryNorm supports extend keyword argument - Text color for legend labels - Pcolor and Pcolormesh now accept shading='nearest' and 'auto' - Allow tick formatters to be set with str or function inputs - New Axes.axline method - Dates use a modern epoch - Improved font weight detection - Axes3D no longer distorts the 3D plot to match the 2D aspect ratio - More consistent toolbar behavior across backends - Toolbar icons are now styled for dark themes - Cursor text now uses a number of significant digits matching pointing precision - Functions to compute a Path's size - savefig() gained a backend keyword argument - Saving SVG now supports adding metadata - Saving PDF metadata via PGF now consistent with PDF backend - NbAgg and WebAgg no longer use jQuery & jQuery UI For the full details please see the [What's New](https://matplotlib.org/3.3.0/users/whats_new.html) and [API changes](https://matplotlib.org/3.3.0/api/api_changes.html) in the documentation. ```Links
- PyPI: https://pypi.org/project/matplotlib - Changelog: https://pyup.io/changelogs/matplotlib/ - Homepage: https://matplotlib.orgUpdate seaborn from 0.10.1 to 0.11.1.
Changelog
### 0.11.1 ``` This a bug fix release and is a recommended upgrade for all users on v0.11.0. Complete release notes are available on the [seaborn website](http://seaborn.pydata.org/whatsnew.htmlv0-11-1-december-2020). ``` ### 0.11.0 ``` This is a major release with important new features, enhancements to existing functions, and changes to the library. Highlights include an overhaul and modernization of the distributions plotting functions, more flexible data specification, new colormaps, and better narrative documentation. Complete release notes are available on the [seaborn website](https://seaborn.pydata.org/whatsnew.html). ``` ### 0.11.0.rc0 ``` This is the first release candidate for v0.11.0, a major release with several important new features and changes to the library. Highlights of the new version include: - A modernization of the distributions module, with the introduction of [`displot`](https://github.com/mwaskom/seaborn/pull/2157), [`histplot`](https://github.com/mwaskom/seaborn/pull/2125), and [`ecdfplot`](https://github.com/mwaskom/seaborn/pull/2141), a complete rewrite of [`kdeplot`](https://github.com/mwaskom/seaborn/pull/2104), and substantial enhancements to [`jointplot`](https://github.com/mwaskom/seaborn/pull/2210) and [`pairplot`](https://github.com/mwaskom/seaborn/pull/2234) - Centralized [input data processing](https://github.com/mwaskom/seaborn/pull/2071) allowing for both standardization and increased flexibility of long- and wide-form variable specification - Substantial [internal refactoring](https://github.com/mwaskom/seaborn/pull/2090) to support the new distributions functions and planned improvements in other modules - Warning-based enforcement of [keyword-only parameters](https://github.com/mwaskom/seaborn/pull/2081) in most functions - New [perceptually uniform colormaps](https://github.com/mwaskom/seaborn/pull/2237) and better tools for creating custom numeric color palettes - Improvements to the narrative documentation, including user guide chapters on function types and data structures, along with better cross-linking within the seaborn docs and between the seaborn and matplotlib docs - Numerous other [targeted enhancements and bug fixes](https://github.com/mwaskom/seaborn/blob/v0.11.0.rc0/doc/releases/v0.11.0.txt) Please test the release by installing from here or with `python -m pip install --upgrade --pre seaborn` ```Links
- PyPI: https://pypi.org/project/seaborn - Changelog: https://pyup.io/changelogs/seaborn/ - Repo: https://github.com/mwaskom/seaborn/ - Homepage: https://seaborn.pydata.orgUpdate pandas from 1.0.5 to 1.2.1.
The bot wasn't able to find a changelog for this release. Got an idea?
Links
- PyPI: https://pypi.org/project/pandas - Homepage: https://pandas.pydata.orgUpdate scikit-learn from 0.23.1 to 0.24.1.
Changelog
### 0.24.1 ``` We're happy to announce the 0.24.1 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/stable/whats_new/v0.24.html#version-0-24-1 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ``` ### 0.24.0 ``` We're happy to announce the 0.24 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_24_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v0.24.html#version-0-24-0 This version supports Python versions 3.6 to 3.9. ``` ### 0.23.2 ``` We're happy to announce the 0.23.2 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-2 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ```Links
- PyPI: https://pypi.org/project/scikit-learn - Changelog: https://pyup.io/changelogs/scikit-learn/ - Homepage: http://scikit-learn.orgUpdate h5py from 2.10.0 to 3.1.0.
Changelog
### 3.0.0 ``` https://docs.h5py.org/en/latest/whatsnew/3.0.html ```Links
- PyPI: https://pypi.org/project/h5py - Changelog: https://pyup.io/changelogs/h5py/ - Homepage: http://www.h5py.orgUpdate numexpr from 2.7.1 to 2.7.2.
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Links
- PyPI: https://pypi.org/project/numexpr - Changelog: https://pyup.io/changelogs/numexpr/ - Repo: https://github.com/pydata/numexprUpdate zarr from 2.3.2 to 2.6.1.
The bot wasn't able to find a changelog for this release. Got an idea?
Links
- PyPI: https://pypi.org/project/zarr - Repo: https://github.com/zarr-developers/zarr-pythonUpdate hmmlearn from 0.2.3 to 0.2.4.
Changelog
### 0.2.4 ``` ------------- Released on September 12th, 2020. - Bumped previously incorrect dependency bound on scipy to 0.19. - Bug fix for 'params' argument usage in GMMHMM. - Warn when an explicitly set attribute would be overridden by ``init_params_``. ```Links
- PyPI: https://pypi.org/project/hmmlearn - Changelog: https://pyup.io/changelogs/hmmlearn/ - Repo: https://github.com/hmmlearn/hmmlearn