[Pandas-dev] Future Deprecation Policy

[Tom Augspurger]

Hi all,

At the sprint, we touched on this, but I don't recall there being a whole
lot of
discussion. I wanted to confirm that we're on the same page.

Briefly, I see two options:

1. SemVer
   Deprecations are introduced as needed. Enforcing a deprecation is a
   backwards-incompatible change, and so is restricted to major releases
only.

2. Rolling deprecations
   Deprecations are introduced as needed. Deprecations are enforced N
releases
   after they were introduce (N typically being 2-3 "major" release in
practice).

Do people have a preference between these two schemes? Does the fact that
NumPy
uses a rolling deprecation policy swing us one way or the other?

I've added this to the agenda for the meeting tomorrow, but wanted to give
people a chance to collect their thoughts first.

Tom
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