Member-only story
Building an Economic Data Dashboard with Python & Streamlit
Reviewing my workflow process to design and build a customized dashboard with Python.
Tracking economic indicators, reading a couple of medium articles, and scanning a few news headlines along with a good cup of coffee that’s how I usually start my days. We could say, my morning ritual.
As part of my starting daily routine, the time I spend on this is limited, usually measured by the time I take with my first cup of coffee, so I’m continuously looking for ways to optimize the information I consume during those few minutes.
A couple of days ago, I was thinking about cutting and better-choosing web pages, mainly where I usually track economic data, when I realized that it could be handy to build a custom dashboard and also a nice way to practice my coding skills.
One of the easiest ways I know to build a dashboard in python is through the Streamlit library. It has its pros and cons, but it would allow me to quickly build a dashboard and not burning my neurons when updating it or changing with other indicators.
For this example, I will be reusing some previous code from other articles, especially from the following ones:
(So, make sure you set a side a few minutes in your agenda to read them too…)
As with any project, small or big, the first step I usually start is to brochure what the dashboard will show, identify the data sources to be requested, and the minimum viable product guidelines to follow.
These last guidelines are relevant for any project, as they give me a compass where to go.
