NEP 23 — Backwards compatibility and deprecation policy¶
|Author:||Ralf Gommers <email@example.com>|
|Resolution:||<url> (required for Accepted | Rejected | Withdrawn)|
In this NEP we describe NumPy’s approach to backwards compatibility, its deprecation and removal policy, and the trade-offs and decision processes for individual cases where breaking backwards compatibility is considered.
NumPy has a very large user base. Those users rely on NumPy being stable and the code they write that uses NumPy functionality to keep working. NumPy is also actively maintained and improved – and sometimes improvements require, or are made much easier, by breaking backwards compatibility. Finally, there are trade-offs in stability for existing users vs. avoiding errors or having a better user experience for new users. These competing needs often give rise to heated debates and delays in accepting or rejecting contributions. This NEP tries to address that by providing a policy as well as examples and rationales for when it is or isn’t a good idea to break backwards compatibility.
- Aim not to break users’ code unnecessarily.
- Aim never to change code in ways that can result in users silently getting incorrect results from their previously working code.
- Backwards incompatible changes can be made, provided the benefits outweigh the costs.
- When assessing the costs, keep in mind that most users do not read the mailing list, do not look at deprecation warnings, and sometimes wait more than one or two years before upgrading from their old version. And that NumPy has many hundreds of thousands or even a couple of million users, so “no one will do or use this” is very likely incorrect.
- Benefits include improved functionality, usability and performance (in order of importance), as well as lower maintenance cost and improved future extensibility.
- Bug fixes are exempt from the backwards compatibility policy. However in case of serious impact on users (e.g. a downstream library doesn’t build anymore), even bug fixes may have to be delayed for one or more releases.
- The Python API and the C API will be treated in the same way.
We now discuss a number of concrete examples to illustrate typical issues and trade-offs.
Changing the behavior of a function
np.histogram is probably the most infamous example.
First, a new keyword
new=False was introduced, this was then switched
over to None one release later, and finally it was removed again.
Also, it has a
normed keyword that had behavior that could be considered
either suboptimal or broken (depending on ones opinion on the statistics).
A new keyword
density was introduced to replace it;
normed started giving
DeprecationWarning only in v.1.15.0. Evolution of
def histogram(a, bins=10, range=None, normed=False): # v1.0.0 def histogram(a, bins=10, range=None, normed=False, weights=None, new=False): #v1.1.0 def histogram(a, bins=10, range=None, normed=False, weights=None, new=None): #v1.2.0 def histogram(a, bins=10, range=None, normed=False, weights=None): #v1.5.0 def histogram(a, bins=10, range=None, normed=False, weights=None, density=None): #v1.6.0 def histogram(a, bins=10, range=None, normed=None, weights=None, density=None): #v1.15.0 # v1.15.0 was the first release where `normed` started emitting # DeprecationWarnings
new keyword was planned from the start to be temporary. Such a plan
forces users to change their code more than once, which is almost never the
right thing to do. Instead, a better approach here would have been to
histogram and introduce a new function
hist in its place.
Returning a view rather than a copy
ndarray.diag method used to return a copy. A view would be better for
both performance and design consistency. This change was warned about
FutureWarning) in v.8.0, and in v1.9.0
diag was changed to return
a read-only view. The planned change to a writeable view in v1.10.0 was
postponed due to backwards compatibility concerns, and is still an open issue
What should have happened instead: nothing. This change resulted in a lot of discussions and wasted effort, did not achieve its final goal, and was not that important in the first place. Finishing the change to a writeable view in the future is not desired, because it will result in users silently getting different results if they upgraded multiple versions or simply missed the warnings.
Disallowing indexing with floats
Indexing an array with floats is asking for something ambiguous, and can be a sign of a bug in user code. After some discussion, it was deemed a good idea to deprecate indexing with floats. This was first tried for the v1.8.0 release, however in pre-release testing it became clear that this would break many libraries that depend on NumPy. Therefore it was reverted before release, to give those libraries time to fix their code first. It was finally introduced for v1.11.0 and turned into a hard error for v1.12.0.
This change was disruptive, however it did catch real bugs in, e.g., SciPy and scikit-learn. Overall the change was worth the cost, and introducing it in master first to allow testing, then removing it again before a release, is a useful strategy.
Similar recent deprecations also look like good examples of cleanups/improvements:
- removing deprecated boolean indexing (gh-8312)
- deprecating truth testing on empty arrays (gh-9718)
np.sum(generator)(gh-10670, one issue with this one is that its warning message is wrong - this should error in the future).
Removing the financial functions
The financial functions (e.g.
np.pmt) are badly named, are present in the
main NumPy namespace, and don’t really fit well within NumPy’s scope.
They were added in 2008 after
on the mailing list where opinion was divided (but a majority in favor).
At the moment these functions don’t cause a lot of overhead, however there are
multiple issues and PRs a year for them which cost maintainer time to deal
with. And they clutter up the
numpy namespace. Discussion in 2013 happened
on removing them again (gh-2880).
This case is borderline, but given that they’re clearly out of scope,
deprecation and removal out of at least the main
numpy namespace can be
proposed. Alternatively, document clearly that new features for financial
functions are unwanted, to keep the maintenance costs to a minimum.
Examples of features not added because of backwards compatibility
TODO: do we have good examples here? Possibly subclassing related?
Removing complete submodules¶
This year there have been suggestions to consider removing some or all of
The motivation was that all these cost maintenance effort, and that they slow
down work on the core of Numpy (ndarrays, dtypes and ufuncs).
The impact on downstream libraries and users would be very large, and maintenance of these modules would still have to happen. Therefore this is simply not a good idea; removing these submodules should not happen even for a new major version of NumPy.
Subclassing of ndarray¶
ndarray is a pain point.
ndarray was not (or at least
not well) designed to be subclassed. Despite that, a lot of subclasses have
been created even within the NumPy code base itself, and some of those (e.g.
astropy.units.Quantity) are quite popular. The main
problems with subclasses are:
- They make it hard to change
ndarrayin ways that would otherwise be backwards compatible.
- Some of them change the behavior of ndarray methods, making it difficult to write code that accepts array duck-types.
ndarray has been officially discouraged for a long time. Of
the most important subclasses,
np.matrix will be deprecated (see gh-10142)
MaskedArray will be kept in NumPy (NEP 17).
MaskedArray will ideally be rewritten in a way such that it uses only
public NumPy APIs. For subclasses outside of NumPy, more work is needed to
provide alternatives (e.g. mixins, see gh-9016 and gh-10446) or better support
for custom dtypes (see gh-2899). Until that is done, subclasses need to be
taken into account when making change to the NumPy code base. A future change
in NumPy to not support subclassing will certainly need a major version
- Code changes that have the potential to silently change the results of a users’ code must never be made (except in the case of clear bugs).
- Code changes that break users’ code (i.e. the user will see a clear exception) can be made, provided the benefit is worth the cost and suitable deprecation warnings have been raised first.
- Deprecation warnings are in all cases warnings that functionality will be removed. If there is no intent to remove functionlity, then deprecation in documentation only or other types of warnings shall be used.
- Deprecations for stylistic reasons (e.g. consistency between functions) are strongly discouraged.
- shall include the version numbers of both when the functionality was deprecated and when it will be removed (either two releases after the warning is introduced, or in the next major version).
- shall include information on alternatives to the deprecated functionality, or a reason for the deprecation if no clear alternative is available.
- shall use
DeprecationWarningfor cases of relevance to end users (as opposed to cases only relevant to libraries building on top of NumPy).
- shall be listed in the release notes of the release where the deprecation happened.
Removal of deprecated functionality:
- shall be done after 2 releases (assuming a 6-monthly release cycle; if that changes, there shall be at least 1 year between deprecation and removal), unless the impact of the removal is such that a major version number increase is warranted.
- shall be listed in the release notes of the release where the removal happened.
- removal of deprecated code can be done in any minor (but not bugfix) release.
- for heavily used functionality (e.g. removal of
np.matrix, of a whole submodule, or significant changes to behavior for subclasses) the major version number shall be increased.
In concrete cases where this policy needs to be applied, decisions are made according to the NumPy governance model.
Functionality with more strict policies:
numpy.randomhas its own backwards compatibility policy, see NEP 19.
- The file format for
.npzfiles must not be changed in a backwards incompatible way.
Being more aggressive with deprecations.
The goal of being more aggressive is to allow NumPy to move forward faster. This would avoid others inventing their own solutions (often in multiple places), as well as be a benefit to users without a legacy code base. We reject this alternative because of the place NumPy has in the scientific Python ecosystem - being fairly conservative is required in order to not increase the extra maintenance for downstream libraries and end users to an unacceptable level.
This would change the versioning scheme for code removals; those could then only be done when the major version number is increased. Rationale for rejection: semantic versioning is relatively common in software engineering, however it is not at all common in the Python world. Also, it would mean that NumPy’s version number simply starts to increase faster, which would be more confusing than helpful. gh-10156 contains more discussion on this alternative.
This section may just be a bullet list including links to any discussions regarding the NEP:
- This includes links to mailing list threads or relevant GitHub issues.