TST: Isin converted floats unnecessarily to int causing rounding issues

doc/source/whatsnew/v1.2.0.rst

@@ -557,6 +557,7 @@ Reshaping
 - Bug in :meth:`DataFrame.combine_first()` caused wrong alignment with dtype ``string`` and one level of ``MultiIndex`` containing only ``NA`` (:issue:`37591`)
 - Fixed regression in :func:`merge` on merging DatetimeIndex with empty DataFrame (:issue:`36895`)
 - Bug in :meth:`DataFrame.apply` not setting index of return value when ``func`` return type is ``dict`` (:issue:`37544`)
+- Bug in :meth:`Series.isin` cast ``float`` unnecessarily to ``int`` when :class:`Series` to look in was from dtype ``int`` (:issue:`19356`)
 
 Sparse
 ^^^^^^

pandas/core/algorithms.py

@@ -19,6 +19,7 @@
     construct_1d_object_array_from_listlike,
     infer_dtype_from_array,
     maybe_promote,
+    maybe_upcast,
 )
 from pandas.core.dtypes.common import (
     ensure_float64,
@@ -431,6 +432,13 @@ def isin(comps: AnyArrayLike, values: AnyArrayLike) -> np.ndarray:
         return cast("Categorical", comps).isin(values)
 
     comps, dtype = _ensure_data(comps)
+    if is_numeric_dtype(comps):
+        try:
+            # Try finding a dtype which would not change our values
+            values, _ = maybe_upcast(values, dtype=dtype)
+            dtype = values.dtype
+        except (ValueError, TypeError):
+            pass
     values, _ = _ensure_data(values, dtype=dtype)
 
     # faster for larger cases to use np.in1d
@@ -445,7 +453,7 @@ def isin(comps: AnyArrayLike, values: AnyArrayLike) -> np.ndarray:
             f = lambda c, v: np.logical_or(np.in1d(c, v), np.isnan(c))
         else:
             f = np.in1d
-    elif is_integer_dtype(comps):
+    elif is_integer_dtype(comps) and is_integer_dtype(values):
         try:
             values = values.astype("int64", copy=False)
             comps = comps.astype("int64", copy=False)
@@ -454,7 +462,7 @@ def isin(comps: AnyArrayLike, values: AnyArrayLike) -> np.ndarray:
             values = values.astype(object)
             comps = comps.astype(object)
 
-    elif is_float_dtype(comps):
+    elif is_numeric_dtype(comps) or is_numeric_dtype(values):
         try:
             values = values.astype("float64", copy=False)
             comps = comps.astype("float64", copy=False)

pandas/tests/series/methods/test_isin.py

@@ -90,6 +90,14 @@ def test_isin_read_only(self):
         expected = Series([True, True, True])
         tm.assert_series_equal(result, expected)
 
+    @pytest.mark.parametrize("values", [[-9.0, 0.0], [-9, 0]])
+    def test_isin_float_in_int_series(self, values):
+        # GH: 19356
+        ser = Series(values)
+        result = ser.isin([-9, -0.5])
+        expected = Series([True, False])
+        tm.assert_series_equal(result, expected)
+
 
 @pytest.mark.slow
 def test_isin_large_series_mixed_dtypes_and_nan():