Learning to program is a rewarding pursuit, but is often challenging. One of the roadblocks on the way to proficiency is getting a development environment installed and configured. In order to simplify that process Aivar Annamaa built Thonny, a Python IDE designed for beginning programmers. In this episode he discusses his initial motivations for starting Thonny and how it helps newcomers to Python learn and understand how to write software.
Maintaining a consistent taxonomy for your music library is a challenging and time consuming endeavor. Eventually you end up with a mess of folders and files with inconsistent names and missing metadata. Beets is built to solve this problem by programmatically managing the tags and directory structure for all of your music files and providing a fast lookup when you are trying to find that perfect song to play. Adrian Sampson began the project because he was trying to clean up his own music collection and in this episode he discusses how the project was built, how streaming media is affecting our relationship to digital music, and how he envisions Beets position in the ecosystem in the future.
One of the draws of Python is how dynamic and flexible the language can be. Sometimes, that flexibility can be problematic if the format of variables at various parts of your program is unclear or the descriptions are inaccurate. The growing middle ground is to use type annotations as a way of providing some verification of the format of data as it flows through your application and enforcing gradual typing. To make it simpler to get started with type hinting, Carl Meyer and Matt Page, along with other engineers at Instagram, created MonkeyType to analyze your code as it runs and generate the type annotations. In this episode they explain how that process works, how it has helped them reduce bugs in their code, and how you can start using it today.
Your backups are running every day, right? Are you sure? What about that daily report job? We all have scripts that need to be run on a periodic basis and it is easy to forget about them, assuming that they are working properly. Sometimes they fail and in order to know when that happens you need a tool that will let you know so that you can find and fix the problem. Pēteris Caune wrote Healthchecks to be that tool and made it available both as an open source project and a hosted version. In this episode he discusses his motivation for starting the project, the lessons he has learned while managing the hosting for it, and how you can start using it today.
A majority of projects will eventually need some way of managing periodic or long-running tasks outside of the context of the main application. This is where a distributed task queue becomes useful. For many in the Python community the standard option is Celery, though there are other projects to choose from. This week Bogdan Popa explains why he was dissatisfied with the current landscape of task queues and the features that he decided to focus on while building Dramatiq, a new, opinionated distributed task queue for Python 3. He also describes how it is designed, how you can start using it, and what he has planned for the future.
As we rely more on small, distributed processes for building our applications, being able to take advantage of asynchronous I/O is increasingly important for performance. This week Alex Grönholm explains how the Asphalt Framework was created to make it easier to build these network oriented software stacks and the technical challenges that he faced in the process.
The importance of testing your software is widely talked about and well understood. What is not as often discussed is the different types of testing, and how end-to-end tests can benefit your team to ensure proper functioning of your application when it gets released to production. This week Luciano Puccio shares the work that he has done on Golem, a framework for building and executing an automation suite to exercise the entire system from the perspective of the user. He discusses his reasons for creating the project, how he things about testing, and where he plans on taking Golem in the future. Give it a listen and then take it for a test drive.
The notebook format that has been exemplified by the IPython/Jupyter project has gained in popularity among data scientists. While the existing formats have proven their value, they are still susceptible with difficulties in collaboration and maintainability. Scott Ernst created the Cauldron notebook to be testable, production ready, and friendly to version control. This week we explore the capabilities, use cases, and architecture of Cauldron and how you can start using it today!
HDF5 is a file format that supports fast and space efficient analysis of large datasets. PyTables is a project that wraps and expands on the capabilities of HDF5 to make it easy to integrate with the larger Python data ecosystem. Francesc Alted explains how the project got started, how it works, and how it can be used for creating sharable and archivable data sets.
Python has a wide and growing variety of web frameworks to choose from, but if you want one with super powers then you need Morepath. This week Martijn Faassen shares the story of how Morepath was created, how it differentiates itself from the other available options, and how you can use it to power your next project.