avoid floating points for integral floor division

pandas/core/arrays/arrow/array.py

@@ -127,12 +127,22 @@ def floordiv_compat(
  left: pa.ChunkedArray | pa.Array | pa.Scalar,
  right: pa.ChunkedArray | pa.Array | pa.Scalar,
  ) -> pa.ChunkedArray:
- # Ensure int // int -> int mirroring Python/Numpy behavior
- # as pc.floor(pc.divide_checked(int, int)) -> float
- converted_left = cast_for_truediv(left, right)
- result = pc.floor(pc.divide(converted_left, right))
- if pa.types.is_integer(left.type) and pa.types.is_integer(right.type):
+ divided = pc.divide(left, right)
+ if pa.types.is_integer(divided.type):
+ has_remainder = pc.not_equal(pc.multiply(divided, right), left)
+ result = pc.if_else(
+ pc.less(divided, 0),
+ pc.if_else(has_remainder, pc.subtract(divided, 1), divided),
+ divided,
+ )
+ # Ensure compatibility with older versions of pandas where
+ # int8 // int64 returned int8 rather than int64.
  result = result.cast(left.type)
+ else:
+ assert pa.types.is_floating(divided.type) or pa.types.is_decimal(
+ divided.type
+ )
+ result = pc.floor(divided)
  return result
 
  ARROW_ARITHMETIC_FUNCS = {

pandas/tests/extension/test_arrow.py

@@ -3246,6 +3246,14 @@ def test_arrow_floordiv_large_values():
  tm.assert_series_equal(result, expected)
 
 
+def test_arrow_floordiv_large_integral_result():
+ # GH XXXXX
+ a = pd.Series([18014398509481983], dtype="int64[pyarrow]")
+ expected = pd.Series([18014398509481983], dtype="int64[pyarrow]")
+ result = a // 1
+ tm.assert_series_equal(result, expected)
+
+
 def test_string_to_datetime_parsing_cast():
  # GH 56266
  string_dates = ["2020-01-01 04:30:00", "2020-01-02 00:00:00", "2020-01-03 00:00:00"]