BUG: require arraylike in infer_dtype_from_array (#38473)

pandas/core/dtypes/cast.py

@@ -673,7 +673,7 @@ def infer_dtype_from(val, pandas_dtype: bool = False) -> Tuple[DtypeObj, Any]:
         If False, scalar/array belongs to pandas extension types is inferred as
         object
     """
-    if is_scalar(val):
+    if not is_list_like(val):
         return infer_dtype_from_scalar(val, pandas_dtype=pandas_dtype)
     return infer_dtype_from_array(val, pandas_dtype=pandas_dtype)
 
@@ -814,7 +814,7 @@ def infer_dtype_from_array(
         return arr.dtype, arr
 
     if not is_list_like(arr):
-        arr = [arr]
+        raise TypeError("'arr' must be list-like")
 
     if pandas_dtype and is_extension_array_dtype(arr):
         return arr.dtype, arr

pandas/core/missing.py

@@ -10,7 +10,7 @@
 from pandas._typing import ArrayLike, Axis, DtypeObj
 from pandas.compat._optional import import_optional_dependency
 
-from pandas.core.dtypes.cast import infer_dtype_from_array
+from pandas.core.dtypes.cast import infer_dtype_from
 from pandas.core.dtypes.common import (
     ensure_float64,
     is_integer_dtype,
@@ -40,7 +40,7 @@ def mask_missing(arr: ArrayLike, values_to_mask) -> np.ndarray:
     # When called from Block.replace/replace_list, values_to_mask is a scalar
     #  known to be holdable by arr.
     # When called from Series._single_replace, values_to_mask is tuple or list
-    dtype, values_to_mask = infer_dtype_from_array(values_to_mask)
+    dtype, values_to_mask = infer_dtype_from(values_to_mask)
     values_to_mask = np.array(values_to_mask, dtype=dtype)
 
     na_mask = isna(values_to_mask)

pandas/tests/dtypes/cast/test_infer_dtype.py

@@ -141,12 +141,29 @@ def test_infer_dtype_from_scalar_errors():
 
 
 @pytest.mark.parametrize(
-    "arr, expected, pandas_dtype",
+    "value, expected, pandas_dtype",
     [
         ("foo", np.object_, False),
         (b"foo", np.object_, False),
-        (1, np.int_, False),
+        (1, np.int64, False),
         (1.5, np.float_, False),
+        (np.datetime64("2016-01-01"), np.dtype("M8[ns]"), False),
+        (Timestamp("20160101"), np.dtype("M8[ns]"), False),
+        (Timestamp("20160101", tz="UTC"), np.object_, False),
+        (Timestamp("20160101", tz="UTC"), "datetime64[ns, UTC]", True),
+    ],
+)
+def test_infer_dtype_from_scalar(value, expected, pandas_dtype):
+    dtype, _ = infer_dtype_from_scalar(value, pandas_dtype=pandas_dtype)
+    assert is_dtype_equal(dtype, expected)
+
+    with pytest.raises(TypeError, match="must be list-like"):
+        infer_dtype_from_array(value, pandas_dtype=pandas_dtype)
+
+
+@pytest.mark.parametrize(
+    "arr, expected, pandas_dtype",
+    [
         ([1], np.int_, False),
         (np.array([1], dtype=np.int64), np.int64, False),
         ([np.nan, 1, ""], np.object_, False),
@@ -155,8 +172,6 @@ def test_infer_dtype_from_scalar_errors():
         (Categorical([1, 2, 3]), np.int64, False),
         (Categorical(list("aabc")), "category", True),
         (Categorical([1, 2, 3]), "category", True),
-        (Timestamp("20160101"), np.object_, False),
-        (np.datetime64("2016-01-01"), np.dtype("=M8[D]"), False),
         (date_range("20160101", periods=3), np.dtype("=M8[ns]"), False),
         (
             date_range("20160101", periods=3, tz="US/Eastern"),