As part of managing the PB Python newsletter, I wanted to develop a simple way to write emails once using plain text and turn them into responsive HTML emails for the newsletter. In addition, I needed to maintain a static archive page on the blog that links to the content of each newsletter. This article shows how to use python tools to transform a markdown file into a responsive HTML email suitable for a newsletter as well as a standalone page integrated into a pelican blog.
I am pleased to have another guest post from Duarte O.Carmo. He wrote series of posts in July on report generation with Papermill that were very well received. In this article, he will explore how to use Voilà and Plotly Express to convert a Jupyter notebook into a standalone interactive web site. In addition, this article will show examples of collecting data through an API endpoint, performing sentiment analysis on that data and show multiple approaches to deploying the dashboard.
This article is inspired by a tweet from Peter Baumgartner. In the tweet he mentioned the Fisher-Jenks algorithm and showed a simple example of ranking data into natural breaks using the algorithm. Since I had never heard about it before, I did some research.
After learning more about it, I realized that it is very complimentary to my previous article on Binning Data and it is intuitive and easy to use in standard pandas analysis. It is definitely an approach I would have used in the past if I had known it existed.
I suspect many people are like me and have never heard of the concept of natural breaks before but have probably done something similar on their own data. I hope this article will expose this simple and useful approach to others so that they can add it to their python toolbox.
The rest of this article will discuss what the Jenks optimization method (or Fisher-Jenks algorithm) is and how it can be used as a simple tool to cluster data using “natural breaks”.
I prefer to use miniconda for installing a lightweight python environment on Windows. I also like to create and customize Windows shortcuts for launching different conda environments in specific working directories. This is an especially useful tip for new users that are not as familiar with the command line on Windows.
After spending way too much time trying to get the shortcuts setup properly on multiple Windows machines, I spent some time automating the link creation process. This article will discuss how to use python to create custom Windows shortcuts to launch conda environments.
This article will discuss several tips and shortcuts for using
iloc to work with a
data set that has a large number of columns. Even if you have some experience with using
iloc you should
learn a couple of helpful tricks to speed up your own analysis and avoid typing lots of column names in your code.