Import Python: ImportPython Issue 79

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One of the cornerstones of Django is that there’s a solid core that others can extend and share their extensions. So it’s good to celebrate the best of those. Here’s my list derived from this query on Github for projects started this year. Not sure about top but useful projects that one can use in personal or work projects/products. Have a look.

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These design patterns also solidified a mentality within our team that there is an internal standard of handling models and views. We can extend our base classes to tackle any business requirement. Our codebase is constantly evolving, both in new features and how we model the world. Django has proven to be a valuable tool that lets us aim big and iterate often. Some might call this an anti-pattern nevertheless a novel approach.

In this tutorial, we are going to have an introduction to basic concepts of Celery with RabbitMQ and then set up Celery for a small demo project. At the end of this tutorial, you will be able to setup a Celery web console monitoring your tasks.

That's it :) ... Go read the review.

The Python 2.7.x series has a new bugfix release, Python 2.7.12, available for download.

Code coverage reports help you see which parts of your code are still untested. Yes, it doesn’t say anything about the quality of your tests, but at the least it tells you which parts of your code have absolute the worst kind of tests: those that are absent :-)

Anna Makarudze lives in Harare, Zimbabwe, and was born and raised in Masvingo. She is an an ICT consultant as well as a Python/Django developer.

Anymail integrates several transactional email service providers (ESPs) into Django, with a consistent API that lets you use ESP-added features without locking your code to a particular ESP. It currently fully supports Mailgun, Postmark, SendGrid, and SparkPost, and has limited support for Mandrill.

Note these articles were published a year back, currently trending on social media. Curating it because it's pretty interesting and fun.

After reading a couple of posts by Michael Lopez about the NFL draft, I decided to recreate some of his analysis using Python (instead of R). Post being - https://statsbylopez.com/2016/05/02/the-nfl-draft-where-we-stand-in-2016/

Text analysis and patterns are very interesting topics for me. I think every author has their own unique, distinctive pattern. The way they build sentences and phrases, not only in terms of tone, but the subconscious choices of word pairings and sequences. How their sentences begin and end, and what they put in the middle; they leave their cerebral fingerprints on paper, blog posts or even FaceBook status updates. I think it is a very achievable, having enough base data, to identify the author Note - It's a trivial example but a good starting point for someone looking to learn/explore Markov chains.

A web-based dashboard built on graphs and their metadata.

Change your wallpaper to most upvoted image of the day on /r/wallpapers or from any other subreddit (on startup).

Better handling of Python pip's requirements.txt files

A simple DynamoDB ORM for python, based on boto2

Create scheduled tasks at runtime easily (Django, Flask, Bottle etc.)

Git log viewer

Python module for Markov Chain algorithm