Simply put, what are the preferred methods for writing large python applications that use pandas dataframes as their primary method of presenting data?
I often try to maintain inconsistency in data frames, sometimes invariants leak into the data, data types are not what you expect, etc.
I am wondering what are the best practices for writing larger, more stable applications in pandas? I want to use the representation of the array in the data for speed, but I also want to make sure that there is a way to further determine the "boundaries" of the data frame, what it should have in it, in a clean way.
- Allegations of receipt of a data frame from the caller.
- Forcing the dataframe parameter to specific types of types.
- Determining the "type" data type based on its columns.
- File System OOP Features
Also, sorry for the vague nature of this. I am starting a project and I want to ask this question before I study too far. I was burned in the past because I didn't apply enough structure when it comes to data frames.
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