Pandas API: Series[td64].astype("timedelta64[unsupported]")

ser = pd.Series([1e15, 2e15, 3e15], dtype="m8[ns]")

first = ser.astype("m8[s]")
second = ser.astype("m8[D]")

ATM both first and second come back as float64. In 2.0 with non-nanosecond (specifically s, us, ms) support, first will come back as the requested "m8[s]" (xref #48963). The question here is: do we want to change the behavior for second? I lean towards raising. Could do a deprecation cycle.

UpdateRelated: Series(.., dtype="m8[D]") currently just swaps out "m8[D]" for "m8[ns]" (and in 2.0 is planned to swap it out for "m8[s]"). Ideally the behavior should be consistent with astype. i.e. I'd want to raise.

Comment From: mroeschke

+1 to raising in the future. IMO ideally done with a deprecation cycle.

Comment From: jbrockmendel

Current thought is that we should move to raising without a deprecation cycle. Users who are passing non-supported dt64/td64 dtypes are going to have to update their code in 2.0, might as well have it be all-at-once.