The Django framework is one option to implement RESTful API's using the Python programming language. In combination with its integrated ORM it is particular well suited to provide CRUD operations to relational data via REST. However, web sites rendered with Django templates offer only limited interactivity and responsiveness compared ro modern responsive and interactive web site.
Flutter allows to implement mobile application in a platform independent manner using a single code base written in the Dart language.
In this Meetup the Python and Flutter community of Graz team up and implement a small example application showing how Django and Flutter can work together and view and edit data stored in an SQL database.
Django applications that use PostgreSQL as database can utilize its full text search capabilities. The following issues and educational pull requests gradually introduce new features. Look at the changed files to learn what was needed to implement it.
While the Python logging module makes it simple to add flexible logging to your application, wording log messages and choosing the appropriate level to maximize their helpfulness is a topic hardly covered in the documentation. This talk give guidelines on when to choose a certain log level, what information to include and which wording templates to use.
Django applications running on an Apache web server can be monitored based on log messages transferred by Python log handlers and filebeat, collected and parsed by Logstash, stored in Elasticsearch and analyzed with Kibana and Grafana.
This talk shows how to setup and configure Django, Python logging and Apache to get the most out of your logging data.
The Python logging module or print statements are widely used to describe the actions a software performs with the intention to simplify error analysis after the fact, integrate application monitoring to quickly react on specific events and perform application archeology to learn about user behavior and usage patterns.
While the technical aspects of using loggers, formatters and handlers are well described in the respective documentation and various blogs, less thought is given on how to actually word log messages, which information to include/exclude and in which situations to apply certain log levels. In this talk we are going to take a closer look at these topics in order to make your software easier to debug and support.
This is a talk given at the Linuxtage Graz on 2019-04-25, provided as Jupyter notebook to play around with yourself.
It covers the following topics:
Java has a long history with logging libraries that help developers and operations. Logging capabilities help to analyze errors after they happened, integrate application monitoring to quickly react on specific events and perform application archeology to learn about user behavior and usage patterns. Libraries like Log4j or the JDK's own java.util.logging have been available for a long time and are widely used in many projects
While the technical aspects of using loggers, formatters and handlers are well described in the respective documentation and various blogs, less thought is given on how to actually word log messages, which information to include/exclude and in which situations to apply certain log levels. In this talk we are going to take a closer look at these topics in order to make your software easier to debug and support.
This talk from the Java User Group Graz meetup provides recommendations on how to address these issues.
Pygments is a Python packages to syntax highlight more than 300 programming languages and text formats.
This PyGRAZ talk explains the basics of Pygments and then shows to to utilize its API to quickly implement simple source code converters. It also describes how to implements your own lexer.
At the Grazer Linuxtage 2018 I gave a talk about using Python instead of shell scripts, especially when it comes to complex shell scripts that quickly become fragile and difficult to maintain.
Sentiment detection is the process of collecting opinions from text written in natural language and storing them in a structured way. It has been around for a while is described in depth by many articles and books. Also, there are several tools and libraries available.
However, most of them focus on the English or Chinese language.
This talk from the PyDays Vienna 2018 explains the basics of sentiment detection and how to utilize spaCy in order to extract sentiments from restaurant feedbacks. Related code samples are provided in an accompanying Jupyter notebook.