ExtensionArray.map

Both Categorical and SparseArray found implementing a .map method useful. This allows them to efficiently apply a function / mapping to the categories / sp_values, rather than every element of an array. We dispatch to it internally in https://github.com/pandas-dev/pandas/blob/master/pandas/core/series.py#L3379-L3380

So, we need to either

1. Add it to the interface
2. hard-code checks for categorical or sparse dtype there.

Do people have a preference? Right now I’m leaning toward 2. Or are there other array types that would have a similar efficiency gain to Categorical or Sparse?

[jbrockmendel]

any UDF can be remain in the same dtype (which I think is true for categorical and sparse)

Not exactly. For sparse you can make the return dtype always be sparse, but we can come up with UDFs that must have different sparse dtype. For Categorical you could pass your result to type(self)._from_sequence(result, dtype=self.dtype) and that will usually work, but thats bc it will just set any non-fitting element to nan.

[rhshadrach]

Ran into this in #39941, where map is used for categorical and sparse in apply. Here, it results in different dtype behavior than other EAs. But it seems to me that map only makes sense when any UDF can be remain in the same dtype (which I think is true for categorical and sparse). But how would one implement map for e.g. Int64 where the mapper is lambda x: 3.2 or lambda x: "a"?

Edit: I just found datetime64 also implements map which does not have the property I mentioned.