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Deprecate passing default args as positional in DataFrame.set_index #41495

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Merged
merged 6 commits into from
May 26, 2021
Merged

Deprecate passing default args as positional in DataFrame.set_index #41495

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f90896e
deprecate default args as positional in set_index
MarcoGorelli May 15, 2021
f85d72c
Merge remote-tracking branch 'upstream/master' into deprecate-nonkeyw…
MarcoGorelli May 19, 2021
f4da184
assert result in test
MarcoGorelli May 19, 2021
87a6f59
Merge remote-tracking branch 'upstream/master' into deprecate-nonkeyw…
MarcoGorelli May 23, 2021
792e391
Merge branch 'master' into deprecate-nonkeyword-args-replace
jreback May 26, 2021
9546ae8
Merge branch 'master' into deprecate-nonkeyword-args-replace
simonjayhawkins May 26, 2021
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.3.0.rst
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Expand Up @@ -647,6 +647,7 @@ Deprecations
- Deprecated setting :attr:`Categorical._codes`, create a new :class:`Categorical` with the desired codes instead (:issue:`40606`)
- Deprecated behavior of :meth:`DatetimeIndex.union` with mixed timezones; in a future version both will be cast to UTC instead of object dtype (:issue:`39328`)
- Deprecated using ``usecols`` with out of bounds indices for ``read_csv`` with ``engine="c"`` (:issue:`25623`)
- Deprecated passing arguments (apart from ``keys``) as positional in :meth:`DataFrame.set_index` (:issue:`41485`)

.. ---------------------------------------------------------------------------

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2 changes: 2 additions & 0 deletions pandas/core/frame.py
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Expand Up @@ -77,6 +77,7 @@
Appender,
Substitution,
deprecate_kwarg,
deprecate_nonkeyword_arguments,
doc,
rewrite_axis_style_signature,
)
Expand Down Expand Up @@ -5301,6 +5302,7 @@ def shift(
periods=periods, freq=freq, axis=axis, fill_value=fill_value
)

@deprecate_nonkeyword_arguments(version="2.0", allowed_args=["self", "keys"])
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do we set the version in other deprecations? I don't think we generally want to, for example what if we delayed this to 3.0 then we'd have to update all of these messages.

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(I think in general it is useful information to mention the version when it is going to be removed)

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what our deprecation policy does say

Deprecations will only be enforced in major releases. For example, if a behavior is deprecated in pandas 1.2.0, it will continue to work, with a warning, for all releases in the 1.x series. The behavior will change and the deprecation removed in the next major release (2.0.0).

out deprecation policy also says

pandas will introduce deprecations in minor releases.

what our deprecation policy does NOT say is what happens when the deprecation is introduced late in the series and there are no further releases in the series.

so if our next release after 1.3 (where these deprecations are introduced) is 2.0, do we deprecate the behaviour? "Deprecations will only be enforced in major releases." so the next major release is 3.0.

if the next release is 1.4, then OK to deprecate in 2.0?

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I would've thought it would be OK to deprecate in 2.0 regardless of whether there's a 1.4 release

If the deprecation is introduced in 1.3.0 (minor release) and then enforced in 2.0 (the next major release), then isn't that inline with the policy?

I don't understand how you got to "so the next major release is 3.0"

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this is yet another reason we don't want to actually use the version string in the message

in any event we can choose or not to release 1.4 (no depreciations removed ) or just do 2.0 (where all depreciations are removed)

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I recall a comment from Joris that we should not expect all users to upgrade to every version and that users could skip versions.

so if we introduce in one version, should we enforce in the next?

def set_index(
self,
keys,
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10 changes: 10 additions & 0 deletions pandas/tests/frame/methods/test_set_index.py
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Expand Up @@ -705,3 +705,13 @@ def test_set_index_periodindex(self):
tm.assert_index_equal(df.index, idx1)
df = df.set_index(idx2)
tm.assert_index_equal(df.index, idx2)

def test_drop_pos_args_deprecation(self):
# https://github.com/pandas-dev/pandas/issues/41485
df = DataFrame({"a": [1, 2, 3]})
msg = (
r"Starting with Pandas version 2\.0 all arguments of set_index except for "
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and the comment on the other PRs about the message.

r"the arguments 'self' and 'keys' will be keyword-only"
)
with tm.assert_produces_warning(FutureWarning, match=msg):
df.set_index("a", True)