Mailman 3 November 2018 - Python-announce-list

pytest 3.10
by Bruno Oliveira 04 Nov '18

04 Nov '18

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

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ANN: Python Meeting Düsseldorf - 07.11.2018
by eGenix Team: M.-A. Lemburg 03 Nov '18

03 Nov '18

[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: info(a)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 info(a)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 info(a)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/

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Introducing NimbusML - experimental Python bindings for ML.NET
by Gani Nazirov 02 Nov '18

02 Nov '18

We are excited to announce that yesterday we released and open sourced NimbusML<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com…> ! This project provides experimental Python bindings for ML.NET<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com…> (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<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.micro…> for an example). Click here to view the NimbusML repo.<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com…> Click here to view the NimbusML samples.<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com…> Click here to view the NimbusML docs.<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.micro…> 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<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.micro…>. Sample notebooks can be found here<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com…>. A few examples: · Twitter Sentiment Analysis<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.micro…> · Ranking with LightGBM<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.micro…> · Image clustering using a TensorFlow model<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.micro…> · Binary classification with Logistic Regression<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.micro…> · Save and load models (and use NimbusML models in ML.NET)<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.micro…> Sentiment analysis example with NimbusML components: from nimbusml import Pipeline, FileDataStream from nimbusml.datasets import get_dataset from nimbusml.ensemble import FastTreesBinaryClassifier from nimbusml.feature_extraction.text import NGramFeaturizer train_file = get_dataset('gen_twittertrain').as_filepath() test_file = get_dataset('gen_twittertest').as_filepath() train_data = FileDataStream.read_csv(train_file, sep='\t') test_data = FileDataStream.read_csv(test_file, sep='\t') pipeline = Pipeline([ # nimbusml pipeline NGramFeaturizer(columns={'Features': ['Text']}), FastTreesBinaryClassifier(feature=['Features'], label='Label') ]) # fit and predict pipeline.fit(train_data) results = pipeline.predict(test_data) A complete notebook for this example can be found here<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com…>. Sentiment analysis example with NimbusML + Scikit-Learn components: from nimbusml.datasets import get_dataset from nimbusml.ensemble import FastTreesBinaryClassifier from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer import pandas as pd train_file = get_dataset('gen_twittertrain').as_filepath() test_file = get_dataset('gen_twittertest').as_filepath() train_data = pd.read_csv(train_file, sep='\t') test_data = pd.read_csv(test_file, sep='\t') pipeline = Pipeline([ # sklearn pipeline ('tfidf', TfidfVectorizer()), # sklearn transform ('clf', FastTreesBinaryClassifier()) # nimbusml learner ]) # fit and predict pipeline.fit(train_data["Text"], train_data["Label"]) results = pipeline.predict(test_data["Text"]) A complete notebook for this example can be found here<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com…>. Thank you! -ML.NET Team

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ANN: TkGridGUI 0.0.20 released
by Charlie Taylor 02 Nov '18

02 Nov '18

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

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[ANN] Josh Bartlett — pygame Artist in Residence
by René Dudfield 01 Nov '18

01 Nov '18

The pygame Artist in Residence grant *celebrates the Python arts community* and lends a tiny bit of support to someones art practice. An artist residency usually works something like; a person spends some time in either a gallery making something to present or in a music club doing a weekly spot. The pygame artist in residence will do it in their own space, but be present on the top of the pygame website in the form of a thumbnail and a link to their patreon/blog/artist statement/website/whatever. Josh Bartlett was the first recipient, and has been featured on the pygame website in October. Please see the pygame artist in residence profile page <https://www.pygame.org/artist-in-residence/1> for more info. Also, Josh is blogging <https://sqizit.bartletts.id.au/>, so you can read about him and some updates on his work there. ps. Thank you to everyone who applied, and those who helped with selection. Hopefully the next one will go more smoothly as the process is improved.

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