Backport PR #48229 on branch 1.5.x (TST: Test Nullable int floordiv by 0) (#48413)

Backport PR #48229: TST: Test Nullable int floordiv by 0 Co-authored-by: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>

Author: meeseeksmachine

doc/source/whatsnew/v1.5.0.rst

@@ -1011,6 +1011,8 @@ Time Zones
 Numeric
 ^^^^^^^
 - Bug in operations with array-likes with ``dtype="boolean"`` and :attr:`NA` incorrectly altering the array in-place (:issue:`45421`)
+- Bug in arithmetic operations with nullable types without :attr:`NA` values not matching the same operation with non-nullable types (:issue:`48223`)
+- Bug in ``floordiv`` when dividing by ``IntegerDtype`` ``0`` would return ``0`` instead of ``inf`` (:issue:`48223`)
 - Bug in division, ``pow`` and ``mod`` operations on array-likes with ``dtype="boolean"`` not being like their ``np.bool_`` counterparts (:issue:`46063`)
 - Bug in multiplying a :class:`Series` with ``IntegerDtype`` or ``FloatingDtype`` by an array-like with ``timedelta64[ns]`` dtype incorrectly raising (:issue:`45622`)
 - Bug in :meth:`mean` where the optional dependency ``bottleneck`` causes precision loss linear in the length of the array. ``bottleneck`` has been disabled for :meth:`mean` improving the loss to log-linear but may result in a performance decrease. (:issue:`42878`)

pandas/tests/arrays/integer/test_arithmetic.py

@@ -75,6 +75,21 @@ def test_floordiv(dtype):
  tm.assert_extension_array_equal(result, expected)
 
 
+def test_floordiv_by_int_zero_no_mask(any_int_ea_dtype):
+ # GH 48223: Aligns with non-masked floordiv
+ # but differs from numpy
+ # https://github.com/pandas-dev/pandas/issues/30188#issuecomment-564452740
+ ser = pd.Series([0, 1], dtype=any_int_ea_dtype)
+ result = 1 // ser
+ expected = pd.Series([np.inf, 1.0], dtype="Float64")
+ tm.assert_series_equal(result, expected)
+
+ ser_non_nullable = ser.astype(ser.dtype.numpy_dtype)
+ result = 1 // ser_non_nullable
+ expected = expected.astype(np.float64)
+ tm.assert_series_equal(result, expected)
+
+
 def test_mod(dtype):
  a = pd.array([1, 2, 3, None, 5], dtype=dtype)
  b = pd.array([0, 1, None, 3, 4], dtype=dtype)