What is a good heuristic to detect if a column in a pandas.DataFrame is categorical?
I think the real question here is whether you'd like to bother the user once in a while or silently fail once in a while.
If you don't mind bothering the user,  maybe detecting ambiguity and raising an error is the way to go.
If you don't mind failing silently, then your heuristics are ok. I don't think you'll find anything that's significantly better. I guess you could make this into a learning problem if you really want to. Download a bunch of datasets, assume they are collectively a decent representation of all data sets in the world, and train based on features over each data set / column to predict categorical vs. continuous.
But of course in the end nothing can be perfect. E.g. is the column [1, 8, 22, 8, 9, 8] referring to hours of the day or to dog breeds?
I've been developing a tool that automatically preprocesses data in pandas.DataFrame format. During this preprocessing step, I want to treat continuous and categorical data differently. In particular, I want to be able to apply, e.g., a OneHotEncoder to only the categorical data.
Now, let's assume that we're provided a pandas.DataFrame and have no other information about the data in the DataFrame. What is a good heuristic to use to determine whether a column in the pandas.DataFrame is categorical?
My initial thoughts are:
1) If there are strings in the column (e.g., the column data type is object), then the column very likely contains categorical data
2) If some percentage of the values in the column is unique (e.g., >=20%), then the column very likely contains continuous data
I've found 1) to work fine, but 2) hasn't panned out very well. I need better heuristics. How would you solve this problem?
Edit: Someone requested that I explain why 2) didn't work well. There were some tests cases where we still had continuous values in a column but there weren't many unique values in the column. The heuristic in 2) obviously failed in that case. There were also issues where we had a categorical column that had many, many unique values, e.g., passenger names in the Titanic data set. Same column type misclassification problem there.