DEPR: Series([np.nan], dtype='i8') silently ignoring dtype

doc/source/whatsnew/v1.4.0.rst

@@ -656,8 +656,10 @@ Other Deprecations
 - Deprecated ``numeric_only=None`` in :meth:`DataFrame.rank`; in a future version ``numeric_only`` must be either ``True`` or ``False`` (the default) (:issue:`45036`)
 - Deprecated the behavior of :meth:`Timestamp.utcfromtimestamp`, in the future it will return a timezone-aware UTC :class:`Timestamp` (:issue:`22451`)
 - Deprecated :meth:`NaT.freq` (:issue:`45071`)
+- Deprecated behavior of :class:`Series` and :class:`DataFrame` construction when passed float-dtype data containing ``NaN`` and an integer dtype ignoring the dtype argument; in a future version this will raise (:issue:`40110`)
 -
 
+
 .. ---------------------------------------------------------------------------
 
 .. _whatsnew_140.performance:

pandas/core/construction.py

@@ -535,6 +535,15 @@ def sanitize_array(
             try:
                 subarr = _try_cast(data, dtype, copy, True)
             except IntCastingNaNError:
+                warnings.warn(
+                    "In a future version, passing float-dtype values containing NaN "
+                    "and an integer dtype will raise IntCastingNaNError "
+                    "(subclass of ValueError) instead of silently ignoring the "
+                    "passed dtype. To retain the old behavior, call Series(arr) or "
+                    "DataFrame(arr) without passing a dtype.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
                 subarr = np.array(data, copy=copy)
             except ValueError:
                 if not raise_cast_failure:

pandas/tests/frame/test_constructors.py

@@ -97,12 +97,14 @@ def test_constructor_dict_with_tzaware_scalar(self):
     def test_construct_ndarray_with_nas_and_int_dtype(self):
         # GH#26919 match Series by not casting np.nan to meaningless int
         arr = np.array([[1, np.nan], [2, 3]])
-        df = DataFrame(arr, dtype="i8")
+        with tm.assert_produces_warning(FutureWarning):
+            df = DataFrame(arr, dtype="i8")
         assert df.values.dtype == arr.dtype
         assert isna(df.iloc[0, 1])
 
         # check this matches Series behavior
-        ser = Series(arr[0], dtype="i8", name=0)
+        with tm.assert_produces_warning(FutureWarning):
+            ser = Series(arr[0], dtype="i8", name=0)
         expected = df.iloc[0]
         tm.assert_series_equal(ser, expected)
 
@@ -937,7 +939,11 @@ def _check_basic_constructor(self, empty):
         assert len(frame.index) == 3
         assert len(frame.columns) == 1
 
-        frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.int64)
+        warn = None if empty is np.ones else FutureWarning
+        with tm.assert_produces_warning(warn):
+            frame = DataFrame(
+                mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.int64
+            )
         if empty is np.ones:
             # passing dtype casts
             assert frame.values.dtype == np.int64
@@ -1766,7 +1772,9 @@ def test_constructor_mix_series_nonseries(self, float_frame):
             DataFrame({"A": float_frame["A"], "B": list(float_frame["B"])[:-2]})
 
     def test_constructor_miscast_na_int_dtype(self):
-        df = DataFrame([[np.nan, 1], [1, 0]], dtype=np.int64)
+        msg = "float-dtype values containing NaN and an integer dtype"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            df = DataFrame([[np.nan, 1], [1, 0]], dtype=np.int64)
         expected = DataFrame([[np.nan, 1], [1, 0]])
         tm.assert_frame_equal(df, expected)
 
@@ -2713,10 +2721,19 @@ def test_floating_values_integer_dtype(self):
             # if they can be cast losslessly, no warning
             DataFrame(arr.round(), dtype="i8")
 
-        # with NaNs, we already have the correct behavior, so no warning
+        # with NaNs, we go through a different path with a different warning
         arr[0, 0] = np.nan
-        with tm.assert_produces_warning(None):
+        msg = "passing float-dtype values containing NaN"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
             DataFrame(arr, dtype="i8")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            Series(arr[0], dtype="i8")
+        # The future (raising) behavior matches what we would get via astype:
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame(arr).astype("i8")
+        with pytest.raises(ValueError, match=msg):
+            Series(arr[0]).astype("i8")
 
 
 class TestDataFrameConstructorWithDatetimeTZ:

pandas/tests/frame/test_stack_unstack.py

@@ -1046,8 +1046,8 @@ def _test_stack_with_multiindex(multiindex):
                 names=[None, "Lower"],
             ),
             columns=Index(["B", "C"], name="Upper"),
-            dtype=df.dtypes[0],
         )
+        expected["B"] = expected["B"].astype(df.dtypes[0])
         tm.assert_frame_equal(result, expected)
 
     @pytest.mark.parametrize("ordered", [False, True])

pandas/tests/series/test_constructors.py

@@ -652,8 +652,10 @@ def test_constructor_sanitize(self):
         s = Series(np.array([1.0, 1.0, 8.0]), dtype="i8")
         assert s.dtype == np.dtype("i8")
 
-        s = Series(np.array([1.0, 1.0, np.nan]), copy=True, dtype="i8")
-        assert s.dtype == np.dtype("f8")
+        msg = "float-dtype values containing NaN and an integer dtype"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            ser = Series(np.array([1.0, 1.0, np.nan]), copy=True, dtype="i8")
+        assert ser.dtype == np.dtype("f8")
 
     def test_constructor_copy(self):
         # GH15125