[Pandas-dev] Version Policy following 1.0

[Tom Augspurger]

On Wed, Jul 17, 2019 at 4:42 PM Brock Mendel <jbrockmendel at gmail.com> wrote:

> Do we anticipate the rate of deprecations decreasing significantly?  i.e.
> if right now we deprecated everything on which there is a consensus in GH,
> would we be done for a while?
>
> If not, then I think we're better off sticking with zero-dot-*, or else
> we'll be bumping major versions really frequently.
>

I think this is why I prefer sticking with rolling. If every release bumps
the major version number, then no release is a major release.

So my preference would be

1. Formally adopt and document that we using a rolling deprecation cycle
2. State that deprecations will be around for `N` major releases (3?)
3. Require that every new deprecation includes the version it'll be
enforced in. e.g.

```
DataFrame.get_dtype_counts is deprecated, and will be removed in pandas
1.3.0.
Use DataFrame.dtypes.value_counts() instead.
```

Tom

On Wed, Jul 17, 2019 at 1:49 PM Tom Augspurger <tom.augspurger88 at gmail.com>
> wrote:
>
>> Split from
>> https://mail.python.org/pipermail/pandas-dev/2019-July/001030.html
>>
>> Following 1.0, I think we stop outright breaking APIs. I think that
>> stability will be welcome to users.
>>
>> We still have to decide how we deprecate APIs. The two options are
>>
>> 1. Rolling deprecations: Essentially what we do today: An API is
>> deprecated in release 1.1.0 and can be removed in (say) 1.4.0.
>> 2. SemVer: An API may be deprecated in 1.x.0. It can be removed in 2.0.0
>>
>> Do people have preferences between the two? The (dis?)advantage of Semver
>> is that all the API-breaking changes are restricted to a single releases.
>> With rolling deprecations, the upgrades from any 1.x to 1.y should be
>> smoother than 1.x to 2.x.
>>
>> Once we choose a strategy, we may want to formalize release schedules
>> around it.
>>
>> Tom
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>
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