This is the last post of the trilogy dedicated to how to set up a well structured Python project, developed with professional style, suitable to be used within the context of a Continuous Integration toolchain. This time we focus on how to package all we have done so far as RPM packages, showing how to break down everything into subpackages that also perform post installation tasks.
If you haven't read the previous two posts, you must do it right now since they are requisite to understand this post. In addition to that this post relies onto objects that are being created in the previous posts.

Read them in the following order:

  1. Python Full Featured Project
  2. Python Setup Tools
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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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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When it comes to talk about scripting, we cannot avoid talking about the probably most famous of the shells: the Bourne Again SHell. Thoroughly explaining it would require a whole book, so as usual in this post we explore only the features that it’s theory likely the reader should learn. The post is not intended to be easily understood by new-bies: it is structured as a cheat sheet, so the reader can use it as a quick reference when needed, but this approach has the drawback that there’s not much room to elaborate things enough.

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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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