Tag: Pyhton

Git With Python HowTo GitPython Tutorial And PyGit2 Tutorial

Posted on by Marco Antonio Carcano

Interacting with Git using Python is a very common use case in the DevOps field: very often it is necessary to checkout application’s or scripts along with their configuration or even just checkout versioned configurations. Although more rare, it is sometimes necessary to update the checked out contents and push the committed version back to the “origin” remote repository. In the "Git With Python HowTo GitPython Tutorial And PyGit2 Tutorial" post we play with the two most commonly used Python libraries used to interconnect to Git: gitpython and pygit2.

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Python Serialization as JSON or YAML exploiting YAML TAGS

Posted on by Marco Antonio Carcano

Python enables you to easily serialize objects as either JSON or YAML: very often it is very convenient to leverage on these features exploiting them to enhance your own object. YAML serialization comes almost for free if you derive your classes from the YAML object, whereas automatic instantiation of objects from a YAML document requires a little bit of work and leverages on YAML tags. As for JSON serialization, it is bloody useful and enables you to quickly and easily serialize the contents of your objects into JSON documents that can be exploited for example to interact with or to develop a REST API.

This post, Python Serialization as JSON or YAML exploiting YAML TAGS, shows you how to develop a Python package that provides such kinds of objects. As usual we take particularly care of the code style, using a very clean and portable design and adhering best practices.

The operating environment used in this post is Red Hat Enterprise Linux 9 or Rocky Linux 9 - using a different environment may lead to having to adapt or even change things.

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YAML in a nutshell

Posted on by Marco Antonio Carcano

YAML is a must-have skill for IT professionals, since it is probably becoming the the most commonly used document format for manifest and configuration files - think for example to Kubernetes, Ansible and a lot of other modern Dev-Ops oriented or CI/CD tools such as drone.

Being skilled on YAML does not only mean being able to write YAML documents, but also efficiently query and manipulate YAML files.

This post provides everything it is very likely you must know to exploit YAML in your daily work, explaining its syntax and showing things in action by using yq - a tool we can consider "the jq for YAML", and using Python with PyYAML.

By the way, this post is part of a trilogy of posts dedicated to markup and serialization formats, so be sure not to miss

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Python Setup Tools

Posted on by Marco Antonio Carcano

This post is focused on packaging distribution modules using "setuptools" and publish them onto PyPI. To better understand these concepts we will clarify the concept of module and, since most of the people use the term "package" in place of either "import package" and "distribution package", we will also clarify the  term "package" too to avoid confusion. In addition to that, we will highlight the differences, pros and cons of source, binary and wheel distribution packages. All of this taking care of "styling" things so that they can easily be used within a Continuous Integration environment.

The operating environment used in this post is Red Hat Enterprise Linux 8 or CentOS 8 - using a different environment may lead to having to adapt or even change things.

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Python Full Featured Project

Posted on by Marco Antonio Carcano

This is the first of a set of three posts aimed at showing how to create a full featured Python3 project: since the topic is quite massive, I decided to split it into three different posts. In this post we do not only quickly see how to develop a full featured Python application, since I wanted to do something that shows a lot of things, such as:

  • creating Python objects
  • put the custom Python objects inside a Python package within the scope of our own namespace
  • develop accordingly to encapsulation rules, by implementing getters and setters methods that look like regular attributes by exploiting decorators
  • use the standard Python logging facility, configuring everything with an external settings file
  • altering the __eq__ comparison so to consider two objects as equal when one of their attribute has the same value
  • implementing comparison methods and the __iter__ method, so to be able to use Python standard functions such as sorted() to sort it also in reverse order
  • exploit total_ordering to make an object fully sortable

The next parts of this post will be on the following topics

The operating environment used in this post is Red Hat Enterprise Linux 8 or CentOS 8 - using a different environment may lead to having to adapt or even change things.

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