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BUG: groupby.rank for dt64tz, period dtypes #38187

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merged 6 commits into from
Dec 2, 2020
Merged

BUG: groupby.rank for dt64tz, period dtypes #38187

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0137eaa
REF: implement disallow_invalid_ops
jbrockmendel Nov 29, 2020
6e2b30e
REF: implement _ea_wrap_cython_operation
jbrockmendel Nov 29, 2020
99cb342
BUG: groupby.rank with EA dtypes
jbrockmendel Nov 30, 2020
b6604e6
Merge branch 'master' of https://github.com/pandas-dev/pandas into bu…
jbrockmendel Nov 30, 2020
ceab1ec
Merge branch 'master' of https://github.com/pandas-dev/pandas into bu…
jbrockmendel Dec 2, 2020
86c9ce8
whatsnew
jbrockmendel Dec 2, 2020
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.2.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -766,6 +766,7 @@ Groupby/resample/rolling
- Bug in :meth:`DataFrame.groupby` dropped ``nan`` groups from result with ``dropna=False`` when grouping over a single column (:issue:`35646`, :issue:`35542`)
- Bug in :meth:`.DataFrameGroupBy.head`, :meth:`.DataFrameGroupBy.tail`, :meth:`SeriesGroupBy.head`, and :meth:`SeriesGroupBy.tail` would raise when used with ``axis=1`` (:issue:`9772`)
- Bug in :meth:`.DataFrameGroupBy.transform` would raise when used with ``axis=1`` and a transformation kernel (e.g. "shift") (:issue:`36308`)
- Bug in :meth:`DataFrameGroupBy.rank` with ``datetime64tz`` or period dtype incorrectly casting results to those dtypes instead of returning ``float64`` dtype (:issue:`38187`)

Reshaping
^^^^^^^^^
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2 changes: 1 addition & 1 deletion pandas/core/groupby/groupby.py
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Expand Up @@ -995,7 +995,7 @@ def _cython_transform(

try:
result, _ = self.grouper._cython_operation(
"transform", obj.values, how, axis, **kwargs
"transform", obj._values, how, axis, **kwargs
)
except NotImplementedError:
continue
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5 changes: 4 additions & 1 deletion pandas/core/groupby/ops.py
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Expand Up @@ -491,8 +491,11 @@ def _ea_wrap_cython_operation(
res_values, names = self._cython_operation(
kind, values, how, axis, min_count, **kwargs
)
if how in ["rank"]:
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hmm, this is prob not the best location for this longer term (eg.. we should have mappings from ops -> whether they should coerce back or not).

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yah, we have something like that in the calling class. im in the process of moving all of this wrapping to the lowest level possible (which luckily coincides with it being in the fewest places possible), xref #37494 (comment)

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Could this also be handled in maybe_cast_result_dtype? (which basically is somewhat implementing a mapping from ops -> expected dtype)

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potentially, yah. this calls simple_new instead of from_sequence, which makes it a bit of an outlier.

there's also groupby.base.cython_cast_blocklist that i think is intended for something along these lines

# preserve float64 dtype
return res_values, names

res_values = res_values.astype("i8", copy=False)
# FIXME: this is wrong for rank, but not tested.
result = type(orig_values)._simple_new(res_values, dtype=orig_values.dtype)
return result, names

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77 changes: 73 additions & 4 deletions pandas/tests/groupby/test_rank.py
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Expand Up @@ -42,15 +42,40 @@ def test_rank_apply():
@pytest.mark.parametrize(
"vals",
[
[2, 2, 8, 2, 6],
np.array([2, 2, 8, 2, 6], dtype=dtype)
for dtype in ["i8", "i4", "i2", "i1", "u8", "u4", "u2", "u1", "f8", "f4", "f2"]
]
+ [
[
pd.Timestamp("2018-01-02"),
pd.Timestamp("2018-01-02"),
pd.Timestamp("2018-01-08"),
pd.Timestamp("2018-01-02"),
pd.Timestamp("2018-01-06"),
],
[
pd.Timestamp("2018-01-02", tz="US/Pacific"),
pd.Timestamp("2018-01-02", tz="US/Pacific"),
pd.Timestamp("2018-01-08", tz="US/Pacific"),
pd.Timestamp("2018-01-02", tz="US/Pacific"),
pd.Timestamp("2018-01-06", tz="US/Pacific"),
],
[
pd.Timestamp("2018-01-02") - pd.Timestamp(0),
pd.Timestamp("2018-01-02") - pd.Timestamp(0),
pd.Timestamp("2018-01-08") - pd.Timestamp(0),
pd.Timestamp("2018-01-02") - pd.Timestamp(0),
pd.Timestamp("2018-01-06") - pd.Timestamp(0),
],
[
pd.Timestamp("2018-01-02").to_period("D"),
pd.Timestamp("2018-01-02").to_period("D"),
pd.Timestamp("2018-01-08").to_period("D"),
pd.Timestamp("2018-01-02").to_period("D"),
pd.Timestamp("2018-01-06").to_period("D"),
],
],
ids=lambda x: type(x[0]),
)
@pytest.mark.parametrize(
"ties_method,ascending,pct,exp",
Expand Down Expand Up @@ -79,7 +104,12 @@ def test_rank_apply():
)
def test_rank_args(grps, vals, ties_method, ascending, pct, exp):
key = np.repeat(grps, len(vals))
vals = vals * len(grps)

orig_vals = vals
vals = list(vals) * len(grps)
if isinstance(orig_vals, np.ndarray):
vals = np.array(vals, dtype=orig_vals.dtype)

df = DataFrame({"key": key, "val": vals})
result = df.groupby("key").rank(method=ties_method, ascending=ascending, pct=pct)

Expand Down Expand Up @@ -142,7 +172,10 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
@pytest.mark.parametrize(
"vals",
[
[2, 2, np.nan, 8, 2, 6, np.nan, np.nan],
np.array([2, 2, np.nan, 8, 2, 6, np.nan, np.nan], dtype=dtype)
for dtype in ["f8", "f4", "f2"]
]
+ [
[
pd.Timestamp("2018-01-02"),
pd.Timestamp("2018-01-02"),
Expand All @@ -153,7 +186,38 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
np.nan,
np.nan,
],
[
pd.Timestamp("2018-01-02", tz="US/Pacific"),
pd.Timestamp("2018-01-02", tz="US/Pacific"),
np.nan,
pd.Timestamp("2018-01-08", tz="US/Pacific"),
pd.Timestamp("2018-01-02", tz="US/Pacific"),
pd.Timestamp("2018-01-06", tz="US/Pacific"),
np.nan,
np.nan,
],
[
pd.Timestamp("2018-01-02") - pd.Timestamp(0),
pd.Timestamp("2018-01-02") - pd.Timestamp(0),
np.nan,
pd.Timestamp("2018-01-08") - pd.Timestamp(0),
pd.Timestamp("2018-01-02") - pd.Timestamp(0),
pd.Timestamp("2018-01-06") - pd.Timestamp(0),
np.nan,
np.nan,
],
[
pd.Timestamp("2018-01-02").to_period("D"),
pd.Timestamp("2018-01-02").to_period("D"),
np.nan,
pd.Timestamp("2018-01-08").to_period("D"),
pd.Timestamp("2018-01-02").to_period("D"),
pd.Timestamp("2018-01-06").to_period("D"),
np.nan,
np.nan,
],
],
ids=lambda x: type(x[0]),
)
@pytest.mark.parametrize(
"ties_method,ascending,na_option,pct,exp",
Expand Down Expand Up @@ -346,7 +410,12 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
)
def test_rank_args_missing(grps, vals, ties_method, ascending, na_option, pct, exp):
key = np.repeat(grps, len(vals))
vals = vals * len(grps)

orig_vals = vals
vals = list(vals) * len(grps)
if isinstance(orig_vals, np.ndarray):
vals = np.array(vals, dtype=orig_vals.dtype)

df = DataFrame({"key": key, "val": vals})
result = df.groupby("key").rank(
method=ties_method, ascending=ascending, na_option=na_option, pct=pct
Expand Down