NumPy 1.15.4 release

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I am pleased to announce the release of NumPy
1.15.4. This is a bugfix release for bugs and regressions reported
following the 1.15.3 release.  The most noticeable fix is probably having a
boolean type fill value for masked arrays after the use of `==` and `!=`.
The Python versions supported by this release are 2.7, 3.4-3.7. Wheels for
this release can be downloaded from PyPI
, source archives are available
from Github .

Compatibility Note
==

The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit
binaries.  That will also be the case in future releases. See `#11625 <
https://github.com/numpy/numpy/issues/11625>`__ for the related
discussion.  Those needing 32-bit support should look elsewhere or build
from source.

Contributors


A total of 4 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

   - Charles Harris
   - Matti Picus
   - Sebastian Berg
   - ba +

Pull requests merged


A total of 4 pull requests were merged for this release.

   - `#12296 `__: BUG: Dealloc
   cached buffer info
   - `#12297 `__: BUG: Fix fill
   value in masked array '==' and '!=' ops.
   - `#12307 `__: DOC: Correct
   the default value of `optimize` in `numpy.einsum`
   - `#12320 `__: REL: Prepare
   for the NumPy 1.15.4 release

Cheers,

Charles Harris
-- 
https://mail.python.org/mailman/listinfo/python-announce-list

Support the Python Software Foundation:
http://www.python.org/psf/donations/

ANN: Python Meeting Düsseldorf - 07.11.2018

[eGenix Team: M.-A. Lemburg]

[This announcement is in German since it targets a local user group
 meeting in Düsseldorf, Germany]



ANKÜNDIGUNG

  Python Meeting Düsseldorf

   https://pyddf.de/

Ein Treffen von Python Enthusiasten und Interessierten
 in ungezwungener Atmosphäre.

   Mittwoch, 07.11.2018, 18:00 Uhr
   Raum 1, 2.OG im Bürgerhaus Stadtteilzentrum Bilk
 Düsseldorfer Arcaden, Bachstr. 145, 40217 Düsseldorf

Diese Nachricht ist auch online verfügbar:
https://www.egenix.com/company/news/Python-Meeting-Duesseldorf-2018-11-07


NEUIGKEITEN

 * Bereits angemeldete Vorträge:

   Charlie Clark
 "Context Managers and Generators"

   Marc-André Lemburg
 "The History of Unicode in Python"

   Jens Diemer
 "Django for Runners"

   Ilya Kamenschchikov
 "Object Detection Using TensorFlow"

   Sowie einige Buchrezensionen.

   Weitere Vorträge können gerne noch angemeldet werden: i...@pyddf.de

 * Startzeit und Ort:

   Wir treffen uns um 18:00 Uhr im Bürgerhaus in den Düsseldorfer
   Arcaden.

   Das Bürgerhaus teilt sich den Eingang mit dem Schwimmbad und
   befindet sich an der Seite der Tiefgarageneinfahrt der Düsseldorfer
   Arcaden.

   Über dem Eingang steht ein großes "Schwimm' in Bilk" Logo. Hinter
   der Tür direkt links zu den zwei Aufzügen, dann in den 2. Stock
   hochfahren. Der Eingang zum Raum 1 liegt direkt links, wenn man aus
   dem Aufzug kommt.

   Google Street View: http://bit.ly/11sCfiw



EINLEITUNG

Das Python Meeting Düsseldorf ist eine regelmäßige Veranstaltung in
Düsseldorf, die sich an Python Begeisterte aus der Region wendet:

 * https://pyddf.de/

Einen guten Überblick über die Vorträge bietet unser YouTube-Kanal,
auf dem wir die Vorträge nach den Meetings veröffentlichen:

 * https://www.youtube.com/pyddf/

Veranstaltet wird das Meeting von der eGenix.com GmbH, Langenfeld,
in Zusammenarbeit mit Clark Consulting & Research, Düsseldorf:

 * https://www.egenix.com/
 * http://www.clark-consulting.eu/



PROGRAMM

Das Python Meeting Düsseldorf nutzt eine Mischung aus (Lightning)
Talks und offener Diskussion.

Vorträge können vorher angemeldet werden, oder auch spontan während
des Treffens eingebracht werden. Ein Beamer mit XGA Auflösung
steht zur Verfügung.

(Lightning) Talk Anmeldung bitte formlos per EMail an i...@pyddf.de



KOSTENBETEILIGUNG

Das Python Meeting Düsseldorf wird von Python Nutzern für Python
Nutzer veranstaltet. Um die Kosten zumindest teilweise zu
refinanzieren, bitten wir die Teilnehmer um einen Beitrag in Höhe von
EUR 10,00 inkl. 19% Mwst, Schüler und Studenten zahlen EUR 5,00
inkl. 19% Mwst.

Wir möchten alle Teilnehmer bitten, den Betrag in bar mitzubringen.



ANMELDUNG

Da wir nur für ca. 20 Personen Sitzplätze haben, möchten wir
bitten, sich per EMail anzumelden. Damit wird keine Verpflichtung
eingegangen. Es erleichtert uns allerdings die Planung.

Meeting Anmeldung bitte formlos per EMail an i...@pyddf.de



WEITERE INFORMATIONEN

Weitere Informationen finden Sie auf der Webseite des Meetings:

https://pyddf.de/

Mit freundlichen Grüßen,
-- 
Marc-Andre Lemburg
eGenix.com

Professional Python Services directly from the Experts (#1, Nov 03 2018)
>>> Python Projects, Coaching and Consulting ...  http://www.egenix.com/
>>> Python Database Interfaces ...   http://products.egenix.com/
>>> Plone/Zope Database Interfaces ...   http://zope.egenix.com/


::: We implement business ideas - efficiently in both time and costs :::

   eGenix.com Software, Skills and Services GmbH  Pastor-Loeh-Str.48
D-40764 Langenfeld, Germany. CEO Dipl.-Math. Marc-Andre Lemburg
   Registered at Amtsgericht Duesseldorf: HRB 46611
   http://www.egenix.com/company/contact/
  http://www.malemburg.com/


-- 
https://mail.python.org/mailman/listinfo/python-announce-list

Support the Python Software Foundation:
http://www.python.org/psf/donations/

pytest 3.10

[Bruno Oliveira]

The pytest team is proud to announce the 3.10.0 release!

pytest is a mature Python testing tool with more than a 2000 tests
against itself, passing on many different interpreters and platforms.

This release contains a number of bugs fixes and improvements, so users are
encouraged
to take a look at the CHANGELOG:

https://docs.pytest.org/en/latest/changelog.html

For complete documentation, please visit:

https://docs.pytest.org/en/latest/

As usual, you can upgrade from pypi via:

pip install -U pytest

Thanks to all who contributed to this release, among them:

* Anders Hovmöller
* Andreu Vallbona Plazas
* Ankit Goel
* Anthony Sottile
* Bernardo Gomes
* Brianna Laugher
* Bruno Oliveira
* Daniel Hahler
* David Szotten
* Mick Koch
* Niclas Olofsson
* Palash Chatterjee
* Ronny Pfannschmidt
* Sven-Hendrik Haase
* Ville Skyttä
* William Jamir Silva


Happy testing,
The Pytest Development Team
-- 
https://mail.python.org/mailman/listinfo/python-announce-list

Support the Python Software Foundation:
http://www.python.org/psf/donations/

ANN: TkGridGUI 0.0.20 released

[Charlie Taylor]

To All,

I'm announcing the first release of TkGridGUI.
I consider it to be Alpha code, but still very usable.

TkGridGUI is a graphic user interface for creating python Tkinter
applications.

The user creates a fully "wired" python Tkinter GUI application by placing
widgets into a hierarchical grid structure.

TkGridGUI generates python source code for all the basic Tkinter widgets,
Button, Label and Entry, as well structures such as menus, notebooks and
statusbars. Within the generated python source code, user application code
is placed in designated areas.

Common widget bindings, keyboard shortcuts and StringVar objects are
created by default.

The workflow moves between placing widgets, editing widget properties,
adding application logic to the generated python source code and testing
the application after each change.

TkGridGUI runs on Linux and Windows with python 2 and 3.

See the Code at: https://github.com/sonofeft/TkGridGUI

See the Docs at: https://sonofefttkgridgui.readthedocs.io/en/latest/

See PyPI page at:https://pypi.python.org/pypi/tkgridgui
-- 
https://mail.python.org/mailman/listinfo/python-announce-list

Support the Python Software Foundation:
http://www.python.org/psf/donations/

Introducing NimbusML - experimental Python bindings for ML.NET

[Gani Nazirov via Python-announce-list]

We are excited to announce that yesterday we released and open sourced 
NimbusML
 ! This project provides experimental Python bindings for 
ML.NET
 (an open source and cross-platform machine learning framework for .NET)


NimbusML allows you to build ML.NET pipelines in Python and also integrate them 
into Scikit-Learn pipelines.



Highlights



· Cross-platform: NimbusML is supported on Mac, Linux, and Windows.

· Efficient interop with Scikit-learn/Pandas: NimbusML can accept 
Pandas dataframes as input and its components can also be used within 
Scikit-learn pipelines.

· Majority of ML.NET components are available: Most ML.NET components 
can be used through NimbusML.

· Performance parity with ML.NET: When using only NimbusML components 
(loaders, transforms, scorers, and evaluators), NimbusML performance matches 
ML.NET performance.

· Familiar APIs for Scikit-learn users: NimbusML adheres to existing 
Scikit-learn conventions but also introduces some new concepts such as how to 
work with multiple columns in the pipelines.

· Open-source: NimbusML will be built in the open and we encourage any 
non-confidential issues/questions to be added on GitHub. Please let us know if 
you are interested in contributing.

· Interop with ML.NET models: models trained in NimbusML can be 
deployed in .NET applications using ML.NET (see 
here
 for an example).



Click here to view the NimbusML 
repo.

Click here to view the NimbusML 
samples.

Click here to view the NimbusML 
docs.



Installation



NimbusML can be installed using pip:

pip install nimbusml




You can run a quick test with:

python -m nimbusml.examples.FastLinearClassifier




NimbusML has been tested on Windows 10, MacOS 10.13, Ubuntu 14.04, Ubuntu 
16.04, Ubuntu 18.04, CentOS 7, and RHEL 7.



NimbusML requires Python 2.7, 3.5, or 3.6 (64 bit). Python 3.7 is not supported 
yet.



Getting Started



Documentation can be found 
here.
 Sample notebooks can be found 
here.
 A few examples:



· Twitter Sentiment 
Analysis

· Ranking w