This is the fifth post on my internship on the Outreachy Program with Project Jupyter. The previous posts are available and should be read in order if you want to understand the big picture: Outreachy I Outreachy II Outreachy III Outreachy IV Increasing documentation Native Authenticator was pretty advanced, but we still needed more information available on the documentation. And this got me thinking: what is relevant to make a good documentation?
I’ve been working with Project Jupyter since December of last year and it has been a wonderful experience. The last couple of days I struggled with the SQLAlchemy library that JupyterHub works on its internals. Since I studied this library and had to scratch some Stack Overflow questions to find some answers, I created this post to help digesting some of my doubts and findings.
On November I discovered that I was selected for the Outreachy internship program for the batch of December 2018 to March 2019.
When you start entering the data science world, things can become really messy. There are thousands of concepts and meanings, most of them thrown at you at the same time.
On this post you could have a small idea how SQLAlchemy works. However, all my study on SQLAlchemy basics was due to a problem I was having that took me a lot of time to figure it out. Since the problem was more complex and didn’t actually fit on the last post, I decided to create a new one dedicated to it, so here it is :)