[BUG] don't mangle null-objects in value_counts #42743
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asv shows, what cost mangling of null-like values would have: |
jreback
added
the
Missing-data
jreback
requested changes
Jul 28, 2021
| @@ -133,12 +133,24 @@ class ValueCounts: | |||
| param_names = ["N", "dtype"] | |||
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| def setup(self, N, dtype): | |||
| self.s = Series(np.random.randint(0, N, size=10 * N)).astype(dtype) | |||
| self.s = Series(np.random.randint(0, N, size=10 * N)).astype("object") | |||
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umm, isn't this defeating the purpose, e.g. why did this change
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Thanks, it was an unintentional change...
| @@ -774,8 +774,8 @@ def test_label_indexing_on_nan(self): | |||
| # GH 32431 | |||
| df = Series([1, "{1,2}", 1, None]) | |||
| vc = df.value_counts(dropna=False) | |||
| result1 = vc.loc[np.nan] | |||
| result2 = vc[np.nan] | |||
| result1 = vc.loc[None] | |||
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can you parameterize this test to also include np.nan for indexing
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This pull request is stale because it has been open for thirty days with no activity. Please update or respond to this comment if you're still interested in working on this. |
mroeschke
reviewed
Nov 7, 2021
doc/source/whatsnew/v1.4.0.rst
Outdated
| @@ -504,6 +504,7 @@ Missing | |||
| ^^^^^^^ | |||
| - Bug in :meth:`DataFrame.fillna` with limit and no method ignores axis='columns' or ``axis = 1`` (:issue:`40989`) | |||
| - Bug in :meth:`DataFrame.fillna` not replacing missing values when using a dict-like ``value`` and duplicate column names (:issue:`43476`) | |||
| - :meth:`Series.value_counts` and :meth:`Series.mode` no longer coerce ``None``, ``NaT`` and other null-values to a NaN-value for ``np.object``-dtype. This behavior is now consistent with ``unique``, ``isin`` and others (:issue:`42688`) | |||
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Probably better in the notable bug fix section since the prior behavior was intentional at one point
mroeschke
reviewed
Nov 7, 2021
pandas/tests/libs/test_hashtable.py
Outdated
| # pd.Na and np.nan will have the same representative: np.nan | ||
| # thus we have 2 nans and 1 True | ||
| # GH42688, nans aren't mangled | ||
| values = np.array([True, pd.NA, np.nan, pd.NA, np.nan], dtype=np.object_) |
mroeschke
requested changes
Nov 7, 2021
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@mroeschke some tests are failing, but it looks as if it is not due to this PR. |
mroeschke
approved these changes
Nov 10, 2021
jreback
approved these changes
Nov 12, 2021
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thanks @realead very nice! |
nickleus27
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Nov 28, 2021
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Null-like values are no longer mangled in
value_counts.This was overlooked in #22296, as decision was made not to mangle null-like values (np.nan, None, pd.NaT and so on).
It also has impact on
mode, as it usesvalue_countsunder the hood.