deprecate inference with scalar date

Author: jbrockmendel

pandas/core/dtypes/cast.py

@@ -843,28 +843,25 @@ def infer_dtype_from_scalar(val) -> tuple[DtypeObj, Any]:
                 pa_dtype = pa.time64("us")
                 dtype = ArrowDtype(pa_dtype)
 
-    elif isinstance(val, dt.time):
-        if val.tzinfo is None:
-            # pyarrow doesn't have a dtype for timetz.
-            opt = get_option("future.infer_time")
-            if opt is None:
-                warnings.warn(
-                    "Pandas type inference with a `datetime.time` "
-                    "object is deprecated. In a future version, this will give "
-                    "time32[pyarrow] dtype, which will require pyarrow to be "
-                    "installed. To opt in to the new behavior immediately set "
-                    "`pd.set_option('future.infer_time', True)`. To keep the "
-                    "old behavior pass `dtype=object`.",
-                    FutureWarning,
-                    stacklevel=find_stack_level(),
-                )
-            elif opt is True:
-                import pyarrow as pa
+    elif isinstance(val, dt.date):
+        opt = get_option("future.infer_date")
+        if opt is None:
+            warnings.warn(
+                "Pandas type inference with a `datetime.date` "
+                "object is deprecated. In a future version, this will give "
+                "date32[pyarrow] dtype, which will require pyarrow to be "
+                "installed. To opt in to the new behavior immediately set "
+                "`pd.set_option('future.infer_date', True)`. To keep the "
+                "old behavior pass `dtype=object`.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+        elif opt is True:
+            import pyarrow as pa
 
-                pa_dtype = pa.time64("us")
-                from pandas.core.arrays.arrow import ArrowDtype
+            pa_dtype = pa.date32()
 
-                dtype = ArrowDtype(pa_dtype)
+            dtype = ArrowDtype(pa_dtype)
 
     elif is_bool(val):
         dtype = np.dtype(np.bool_)

pandas/tests/dtypes/cast/test_infer_dtype.py

@@ -23,6 +23,7 @@
     Timestamp,
     date_range,
 )
+import pandas._testing as tm
 
 
 def test_infer_dtype_from_int_scalar(any_int_numpy_dtype):
@@ -102,7 +103,9 @@ def test_infer_dtype_from_period(freq):
 
 def test_infer_dtype_misc():
     dt = date(2000, 1, 1)
-    dtype, val = infer_dtype_from_scalar(dt)
+    msg = "type inference with a `datetime.date` object"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        dtype, val = infer_dtype_from_scalar(dt)
     assert dtype == np.object_
 
     ts = Timestamp(1, tz="US/Eastern")

pandas/tests/dtypes/cast/test_promote.py

@@ -16,6 +16,7 @@
 from pandas.core.dtypes.missing import isna
 
 import pandas as pd
+import pandas._testing as tm
 
 
 def _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar=None):
@@ -354,12 +355,21 @@ def test_maybe_promote_any_with_datetime64(any_numpy_dtype, fill_value):
         expected_dtype = np.dtype(object)
         exp_val_for_scalar = fill_value
 
+    msg = "type inference with a `datetime.date` object"
+    warn = None
+    if type(fill_value) is datetime.date and any_numpy_dtype in [
+        "datetime64[ns]",
+        "timedelta64[ns]",
+    ]:
+        warn = FutureWarning
+
     if type(fill_value) is datetime.date and dtype.kind == "M":
         # Casting date to dt64 is deprecated, in 2.0 enforced to cast to object
         expected_dtype = np.dtype(object)
         exp_val_for_scalar = fill_value
 
-    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+    with tm.assert_produces_warning(warn, match=msg):
+        _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
 @pytest.mark.parametrize(

pandas/tests/indexing/test_loc.py

@@ -1443,7 +1443,10 @@ def test_loc_setitem_datetime_coercion(self):
         df.loc[0:1, "c"] = np.datetime64("2008-08-08")
         assert Timestamp("2008-08-08") == df.loc[0, "c"]
         assert Timestamp("2008-08-08") == df.loc[1, "c"]
-        df.loc[2, "c"] = date(2005, 5, 5)
+
+        warn_msg = "type inference with a `datetime.date` object"
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            df.loc[2, "c"] = date(2005, 5, 5)
         assert Timestamp("2005-05-05").date() == df.loc[2, "c"]
 
     @pytest.mark.parametrize("idxer", ["var", ["var"]])

pandas/tests/io/pytables/test_errors.py

@@ -56,13 +56,15 @@ def test_table_index_incompatible_dtypes(setup_path):
 
 
 def test_unimplemented_dtypes_table_columns(setup_path):
+    warn_msg = "type inference with a `datetime.date` object"
     with ensure_clean_store(setup_path) as store:
         dtypes = [("date", datetime.date(2001, 1, 2))]
 
         # currently not supported dtypes ####
         for n, f in dtypes:
             df = tm.makeDataFrame()
-            df[n] = f
+            with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+                df[n] = f
             msg = re.escape(f"[{n}] is not implemented as a table column")
             with pytest.raises(TypeError, match=msg):
                 store.append(f"df1_{n}", df)
@@ -71,7 +73,8 @@ def test_unimplemented_dtypes_table_columns(setup_path):
     df = tm.makeDataFrame()
     df["obj1"] = "foo"
     df["obj2"] = "bar"
-    df["datetime1"] = datetime.date(2001, 1, 2)
+    with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+        df["datetime1"] = datetime.date(2001, 1, 2)
     df = df._consolidate()
 
     with ensure_clean_store(setup_path) as store: