infer

pandas/_libs/lib.pyx

@@ -107,6 +107,8 @@ from pandas._libs.tslibs.period cimport is_period_object
 from pandas._libs.tslibs.timedeltas cimport convert_to_timedelta64
 from pandas._libs.tslibs.timezones cimport tz_compare
 
+from pandas.core.dtypes.base import _registry
+
 # constants that will be compared to potentially arbitrarily large
 # python int
 cdef:
@@ -1693,6 +1695,11 @@ def infer_dtype(value: object, skipna: bool = True) -> str:
         if is_interval_array(values):
             return "interval"
 
+    print("infer_dtype")
+    reg_dtype = _registry.match_scalar(val)
+    if reg_dtype:
+        return str(reg_dtype)
+
     cnp.PyArray_ITER_RESET(it)
     for i in range(n):
         val = PyArray_GETITEM(values, PyArray_ITER_DATA(it))

pandas/core/construction.py

@@ -370,6 +370,10 @@ def array(
 
         elif data.dtype.kind == "b":
             return BooleanArray._from_sequence(data, dtype="boolean", copy=copy)
+        # elif inferred_dtype != "mixed":
+        #     dtype = pandas_dtype(inferred_dtype)
+        #     cls = dtype.construct_array_type()
+        #     return cls._from_sequence(data, dtype=dtype, copy=copy)
         else:
             # e.g. complex
             return NumpyExtensionArray._from_sequence(data, dtype=data.dtype, copy=copy)

pandas/core/dtypes/base.py

@@ -444,6 +444,13 @@ def _can_fast_transpose(self) -> bool:
         """
         return False
 
+    def is_unambiguous_scalar(self):
+        return False
+
+    @classmethod
+    def construct_from_scalar(cls, scalar):
+        return cls()
+
 
 class StorageExtensionDtype(ExtensionDtype):
     """ExtensionDtype that may be backed by more than one implementation."""
@@ -582,5 +589,13 @@ def find(
 
         return None
 
+    def match_scalar(
+        self, scalar: Any
+    ) -> type_t[ExtensionDtype] | ExtensionDtype | None:
+        for dtype in self.dtypes:
+            if dtype.is_unambiguous_scalar(scalar):
+                return dtype.construct_from_scalar(scalar)
+        return None
+
 
 _registry = Registry()

pandas/core/dtypes/cast.py

@@ -44,6 +44,7 @@
     LossySetitemError,
 )
 
+from pandas.core.dtypes.base import _registry
 from pandas.core.dtypes.common import (
     ensure_int8,
     ensure_int16,
@@ -857,6 +858,10 @@ def infer_dtype_from_scalar(val) -> tuple[DtypeObj, Any]:
         subtype = infer_dtype_from_scalar(val.left)[0]
         dtype = IntervalDtype(subtype=subtype, closed=val.closed)
 
+    reg_dtype = _registry.match_scalar(val)
+    if reg_dtype:
+        dtype = reg_dtype
+
     return dtype, val
 
 
@@ -913,6 +918,10 @@ def infer_dtype_from_array(arr) -> tuple[DtypeObj, ArrayLike]:
     inferred = lib.infer_dtype(arr, skipna=False)
     if inferred in ["string", "bytes", "mixed", "mixed-integer"]:
         return (np.dtype(np.object_), arr)
+    else:
+        arr_dtype = pandas_dtype_func(inferred)
+        if isinstance(arr_dtype, ExtensionDtype):
+            return arr_dtype, arr
 
     arr = np.asarray(arr)
     return arr.dtype, arr

pandas/core/series.py

@@ -501,6 +501,8 @@ def __init__(
             elif copy:
                 data = data.copy()
         else:
+            if dtype is None:
+                dtype = infer_dtype_from(data)[0]
             data = sanitize_array(data, index, dtype, copy)
             data = SingleBlockManager.from_array(data, index, refs=refs)