describe casts all values to str for several types

[bdice]

The describe implementations, especially for datetime and timedelta types, appear to be casting all the values to str (aside from the numeric implementation). This does not align with Pandas behavior:

>>> s = pd.Series([
...   np.datetime64("2000-01-01"),
...   np.datetime64("2010-01-01"),
...   np.datetime64("2010-01-01"),
... ])
>>> print(type(s.describe()["top"]))
<class 'pandas._libs.tslibs.timestamps.Timestamp'>

I recognize there is an issue here with different types, namely that count and freq are not of the same type as mean, min, percentiles, or max. This also affects numerical columns which will upcast integer values like count to floating types.

Some options to resolve this (and their downsides):

1. Current implementation: return all values as str (results are on GPU ...but data is not usable as str type).
2. Return a pd.DataFrame or dict that can have multiple types (not a GPU DataFrame).

I propose changing behavior to adopt option (2), and return a pd.DataFrame. The summary doesn't really need to be a GPU DataFrame since it contains so few values. (Do we have precedent for this kind of behavior returning a CPU (Pandas) DataFrame?)

Originally posted by @bdice in #9867 (comment)

[bdice]

Quoting @vyasr from #9867 (comment):

I agree that a copy to host that preserves more type information is probably preferable. I'm not aware of any API that returns a pandas DataFrame, but we do return pandas objects in some places. One example is DataFrame.columns, which returns a pandas Index, probably based on the same consideration of not wanting to put the data on device. Personally from an API design perspective I would prefer not to return pandas objects and would rather return something like a dict, but I can see that causing more friction for users switching between pandas and cudf.

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