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Help Pint-Pandas pass tests when HAS_UNCERTAINTIES is enabled #49181

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Help Pint-Pandas pass tests when HAS_UNCERTAINTIES is enabled #49181

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3 changes: 2 additions & 1 deletion pandas/core/indexing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1825,7 +1825,7 @@ def _setitem_with_indexer_split_path(self, indexer, value, name: str):

# we need an iterable, with a ndim of at least 1
# eg. don't pass through np.array(0)
if is_list_like_indexer(value) and getattr(value, "ndim", 1) > 0:
if is_list_like_indexer(value) and np.iterable(value) and getattr(value, "ndim", 1) > 0:

if isinstance(value, ABCDataFrame):
self._setitem_with_indexer_frame_value(indexer, value, name)
Expand Down Expand Up @@ -1977,6 +1977,7 @@ def _setitem_single_column(self, loc: int, value, plane_indexer):
pass
elif (
is_array_like(value)
and getattr(value, "shape", False)
and len(value.shape) > 0
and self.obj.shape[0] == value.shape[0]
and not is_empty_indexer(pi)
Expand Down
26 changes: 23 additions & 3 deletions pandas/tests/extension/base/setitem.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,15 @@
import numpy as np
import pytest

try:
import uncertainties.unumpy as unp
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this is not appropriate for the pandas test suite as this is not in any way tested in ci

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Thanks for the review! Do you have any suggestions as to what test cases I should look at to see how other extension packages deal with base tests that don't work as written? I've seen some examples where the hooks are there to create the correct sort of data (using fixtures) or hook the right functions (so that base test code calls extension functions). But am new to how I should deal with this case. If you guide, I can implement, test, and amend.

from uncertainties import ufloat, UFloat
HAS_UNCERTAINTIES = True
_ufloat_nan = ufloat(np.nan, 0)
except ImportError:
unp = np
HAS_UNCERTAINTIES = False

from pandas.core.dtypes.dtypes import (
DatetimeTZDtype,
IntervalDtype,
Expand Down Expand Up @@ -97,7 +106,11 @@ def test_setitem_scalar(self, data, setter):
assert arr[0] == data[1]

def test_setitem_loc_scalar_mixed(self, data):
df = pd.DataFrame({"A": np.arange(len(data)), "B": data})
if HAS_UNCERTAINTIES:
scalar_data = np.arange(len(data)) + ufloat(0, 0)
else:
scalar_data = np.arange(len(data))
df = pd.DataFrame({"A": scalar_data , "B": data})
df.loc[0, "B"] = data[1]
assert df.loc[0, "B"] == data[1]

Expand All @@ -112,7 +125,11 @@ def test_setitem_loc_scalar_multiple_homogoneous(self, data):
assert df.loc[10, "B"] == data[1]

def test_setitem_iloc_scalar_mixed(self, data):
df = pd.DataFrame({"A": np.arange(len(data)), "B": data})
if HAS_UNCERTAINTIES:
scalar_data = np.arange(len(data)) + ufloat(0, 0)
else:
scalar_data = np.arange(len(data))
df = pd.DataFrame({"A": scalar_data , "B": data})
df.iloc[0, 1] = data[1]
assert df.loc[0, "B"] == data[1]

Expand Down Expand Up @@ -427,7 +444,10 @@ def test_delitem_series(self, data):
# GH#40763
ser = pd.Series(data, name="data")

taker = np.arange(len(ser))
if HAS_UNCERTAINTIES:
taker = np.arange(len(ser)) + ufloat(0, 0)
else:
taker = np.arange(len(ser))
taker = np.delete(taker, 1)

expected = ser[taker]
Expand Down