Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

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

Merged
merged 9 commits into from
Sep 4, 2021
Merged

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

Show file tree
Hide file tree
Changes from 5 commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
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
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.3.3.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
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`)

.. ---------------------------------------------------------------------------
Expand Down
4 changes: 4 additions & 0 deletions pandas/core/groupby/groupby.py
View file
Open in desktop
Original file line number Diff line number Diff line change
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)
jbrockmendel marked this conversation as resolved.
Show resolved Hide resolved
out = vals.to_numpy(dtype=float, na_value=np.nan)
jbrockmendel marked this conversation as resolved.
Show resolved Hide resolved
else:
out = np.asarray(vals)

Expand Down
27 changes: 27 additions & 0 deletions pandas/tests/groupby/test_quantile.py
View file
Open in desktop
Original file line number Diff line number Diff line change
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)
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

can you do a case with a listlike qs e.g. [0.5, 0.75]

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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)
jbrockmendel marked this conversation as resolved.
Show resolved Hide resolved
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")
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

any reason for this to be Float64 instead of any_foo_dtype?

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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(
Expand Down