I mentioned in my last post (and a couple others) how invaluable my wife has been in my journey to learn Python. That was turned up to 11 last week and I (we) started working on the scoring calculations.
I started to write the scoring calculations exactly as you would expect a newbie coder to – step by step. With the changes to the data model Kelly recommended, I still have a hard time wrapping my head around how we’ve abstracted the pick information.
NFLPool has been live for almost two weeks – and hasn’t crashed (yet!) After the rush to get the site up and allow a user to make their picks before I left on vacation, there is one more large chunk of work to get to 1.0 release: calculate the score for all players every week of the NFL season.
I spent all of last week in the middle of Minnesota at a friend’s cabin.
I took the week off from work to see how much progress I could make on NFLPool this week (and to get some stuff done around the house, but really, for NFLPool). I told myself I’d blog my progress every day and here it is Wednesday already.
In my last blog post, I noted how I was going to have to move back to SQLite for the database. Over the weekend I ripped out most of the MongoDB code and started laying the groundwork for SQLite.
I’ve had to throw in the towel on MongoDB and move back to SQLite for the datastore. For now.
I was successful in being able to call an API, take the JSON object from the API, and store that JSON in MongoDB as a collection. But I what really wanted was to store that JSON object as an EmbeddedDocument within a collection.
My original goal was to stick JSON objects into MongoDB and then query against that.
Last weekend I discovered how to pretty print the five JSON files I get from MySportsFeeds. This was helpful to understand just how much data is nested within each file. I also spent a good chunk of the weekend writing in a notebook. I mostly did some data modeling on what each table in the database should store and what their primary keys would be. I also captured things I need to research and started breaking the project into chunks.