I thought it would be interesting to load this data in to Pandas and do a bit of data analysis.
While in the Dungeon Brawl repository I started up an ipython shell, then import a couple libraries:
In : import yaml In : import glob In : import pandas
Next I need to find each of my monster’s YaML documents, these files reside in the data directory.
Using the glob library I can easily find all files in the directory with the .yaml extension:
In : files = glob.glob('data/monsters/*.yaml')
I’m now able to iterate over each of my files, open them, parse them as YaML, then store the results in a new list:
In : data =  In : for _file in files: ...: raw = open(_file).read() ...: data.append(yaml.load(raw))
The data list now contains a dictionary for each of my monsters:
In : len(data) Out: 762 In : data['name'] Out: 'Empyrean'
All that is left is to load this data into a Pandas DataFrame :
In : df = pandas.DataFrame(data)
One of the first things I checked was the average hit points and armor class of a monster by challenge rating: I then dug a bit deeper into each of the stats using the Pandas describe method, this gives things like standard deviation, mean, min, and max.
Below are a couple attempts as useful describe tables:
Hit Points by Challenge Rating
Armor Class by Challenge Rating
Challenge Rating by Monster Type
Hit Points by Monster Size
Challenge Rating by Monster Size
Well that is it, hope you found something in this post interesting.