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BUG: GroupBy.quantile fails with pd.NA #43150

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Merged
merged 9 commits into from
Sep 4, 2021
Merged

BUG: GroupBy.quantile fails with pd.NA #43150

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b94ee25
BUG: GroupBy.quantile fails with pd.NA
debnathshoham Aug 21, 2021
5712bf8
suggested change
debnathshoham Aug 21, 2021
105abf4
added fixture; colocated near dtype tests
debnathshoham Aug 21, 2021
485402c
added tests for int_EA & allNA
debnathshoham Aug 21, 2021
e64964f
Merge branch 'master' into gh42849
debnathshoham Aug 30, 2021
8a57188
added suggested tests
debnathshoham Sep 1, 2021
2443008
Merge branch 'gh42849' of https://github.com/debnathshoham/pandas int…
debnathshoham Sep 1, 2021
75aa693
Merge branch 'master' into gh42849
debnathshoham Sep 3, 2021
7c15bb2
Merge branch 'master' into gh42849
jreback Sep 4, 2021
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.3.3.rst
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Expand Up @@ -17,6 +17,7 @@ Fixed regressions
- Fixed regression in :class:`DataFrame` constructor failing to broadcast for defined :class:`Index` and len one list of :class:`Timestamp` (:issue:`42810`)
- Performance regression in :meth:`core.window.ewm.ExponentialMovingWindow.mean` (:issue:`42333`)
- Fixed regression in :meth:`.GroupBy.agg` incorrectly raising in some cases (:issue:`42390`)
- Fixed regression in :meth:`.GroupBy.quantile` which was failing with ``pandas.NA`` (:issue:`42849`)
- Fixed regression in :meth:`RangeIndex.where` and :meth:`RangeIndex.putmask` raising ``AssertionError`` when result did not represent a :class:`RangeIndex` (:issue:`43240`)

.. ---------------------------------------------------------------------------
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4 changes: 4 additions & 0 deletions pandas/core/groupby/groupby.py
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Expand Up @@ -63,6 +63,7 @@ class providing the base-class of operations.
from pandas.core.dtypes.common import (
is_bool_dtype,
is_datetime64_dtype,
is_float_dtype,
is_integer_dtype,
is_numeric_dtype,
is_object_dtype,
Expand Down Expand Up @@ -2453,6 +2454,9 @@ def pre_processor(vals: ArrayLike) -> tuple[np.ndarray, np.dtype | None]:
elif is_timedelta64_dtype(vals.dtype):
inference = np.dtype("timedelta64[ns]")
out = np.asarray(vals).astype(float)
elif isinstance(vals, ExtensionArray) and is_float_dtype(vals):
inference = np.dtype(np.float64)
out = vals.to_numpy(dtype=float, na_value=np.nan)
else:
out = np.asarray(vals)

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27 changes: 27 additions & 0 deletions pandas/tests/groupby/test_quantile.py
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Expand Up @@ -248,6 +248,33 @@ def test_groupby_quantile_skips_invalid_dtype(q):
tm.assert_frame_equal(result, expected)


def test_groupby_quantile_NA_float(any_float_dtype):
# GH#42849
df = DataFrame({"x": [1, 1], "y": [0.2, np.nan]}, dtype=any_float_dtype)
result = df.groupby("x")["y"].quantile(0.5)
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can you do a case with a listlike qs e.g. [0.5, 0.75]

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added below

expected = pd.Series([0.2], dtype=float, index=[1.0], name="y")
expected.index.name = "x"
tm.assert_series_equal(expected, result)


def test_groupby_quantile_NA_int(any_int_ea_dtype):
# GH#42849
df = DataFrame({"x": [1, 1], "y": [2, 5]}, dtype=any_int_ea_dtype)
result = df.groupby("x")["y"].quantile(0.5)
expected = pd.Series([3.5], dtype=float, index=[1], name="y")
expected.index.name = "x"
tm.assert_series_equal(expected, result)


def test_groupby_quantile_allNA_column():
# GH#42849
df = DataFrame({"x": [1, 1], "y": [pd.NA] * 2}, dtype="Float64")
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any reason for this to be Float64 instead of any_foo_dtype?

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not sure what any_foo_dtype means.
In case it means why the explicit dtype? then without explicitly defining with nullable dtypes, the columns becomes object, and quantile fails (maybe I am wrong, but I believe this is the expected behaviour). Below on master.

In [3]: import pandas as pd

In [4]: pd.__version__
Out[4]: '1.4.0.dev0+540.ga826be1f61'

In [5]: DataFrame({"x": [1, 1], "y": [pd.NA] * 2}).dtypes
Out[5]: 
x     int64
y    object
dtype: object

In [6]: DataFrame({"x": [1, 1], "y": [pd.NA] * 2}, dtype=float).dtypes
<ipython-input-6-4731f064b6c6>:1: FutureWarning: Could not cast to float64, falling back to object. This behavior is deprecated. In a future version, when a dtype is passed to 'DataFrame', either all columns will be cast to that dtype, or a TypeError will be raised.
  DataFrame({"x": [1, 1], "y": [pd.NA] * 2}, dtype=float).dtypes
Out[6]: 
x    float64
y     object
dtype: object

result = df.groupby("x")["y"].quantile(0.5)
expected = pd.Series([np.nan], dtype=float, index=[1.0], name="y")
expected.index.name = "x"
tm.assert_series_equal(expected, result)


def test_groupby_timedelta_quantile():
# GH: 29485
df = DataFrame(
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