CLN: avoid infer_dtype

pandas/core/arrays/masked.py

@@ -75,7 +75,11 @@
 from pandas.core.array_algos.quantile import quantile_with_mask
 from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays.base import ExtensionArray
-from pandas.core.construction import ensure_wrapped_if_datetimelike
+from pandas.core.construction import (
+    array as pd_array,
+    ensure_wrapped_if_datetimelike,
+    extract_array,
+)
 from pandas.core.indexers import check_array_indexer
 from pandas.core.ops import invalid_comparison
 
@@ -645,20 +649,8 @@ def _arith_method(self, other, op):
             and len(other) == len(self)
         ):
             # Try inferring masked dtype instead of casting to object
-            inferred_dtype = lib.infer_dtype(other, skipna=True)
-            if inferred_dtype == "integer":
-                from pandas.core.arrays import IntegerArray
-
-                other = IntegerArray._from_sequence(other)
-            elif inferred_dtype in ["floating", "mixed-integer-float"]:
-                from pandas.core.arrays import FloatingArray
-
-                other = FloatingArray._from_sequence(other)
-
-            elif inferred_dtype in ["boolean"]:
-                from pandas.core.arrays import BooleanArray
-
-                other = BooleanArray._from_sequence(other)
+            other = pd_array(other)
+            other = extract_array(other, extract_numpy=True)
 
         if isinstance(other, BaseMaskedArray):
             other, omask = other._data, other._mask

pandas/core/dtypes/common.py

@@ -1654,9 +1654,8 @@ def is_all_strings(value: ArrayLike) -> bool:
     dtype = value.dtype
 
     if isinstance(dtype, np.dtype):
-        return (
-            dtype == np.dtype("object")
-            and lib.infer_dtype(value, skipna=False) == "string"
+        return dtype == np.dtype("object") and lib.is_string_array(
+            np.asarray(value), skipna=False
         )
     elif isinstance(dtype, CategoricalDtype):
         return dtype.categories.inferred_type == "string"

pandas/core/internals/construction.py

@@ -27,8 +27,6 @@
 )
 from pandas.core.dtypes.common import (
     is_1d_only_ea_dtype,
-    is_bool_dtype,
-    is_float_dtype,
     is_integer_dtype,
     is_list_like,
     is_named_tuple,
@@ -44,14 +42,10 @@
     algorithms,
     common as com,
 )
-from pandas.core.arrays import (
-    BooleanArray,
-    ExtensionArray,
-    FloatingArray,
-    IntegerArray,
-)
+from pandas.core.arrays import ExtensionArray
 from pandas.core.arrays.string_ import StringDtype
 from pandas.core.construction import (
+    array as pd_array,
     ensure_wrapped_if_datetimelike,
     extract_array,
     range_to_ndarray,
@@ -1027,12 +1021,8 @@ def convert(arr):
                     if dtype_backend != "numpy" and arr.dtype == np.dtype("O"):
                         arr = StringDtype().construct_array_type()._from_sequence(arr)
                 elif dtype_backend != "numpy" and isinstance(arr, np.ndarray):
-                    if is_integer_dtype(arr.dtype):
-                        arr = IntegerArray(arr, np.zeros(arr.shape, dtype=np.bool_))
-                    elif is_bool_dtype(arr.dtype):
-                        arr = BooleanArray(arr, np.zeros(arr.shape, dtype=np.bool_))
-                    elif is_float_dtype(arr.dtype):
-                        arr = FloatingArray(arr, np.isnan(arr))
+                    if arr.dtype.kind in "iufb":
+                        arr = pd_array(arr, copy=False)
 
             elif isinstance(dtype, ExtensionDtype):
                 # TODO: test(s) that get here

pandas/io/parsers/base_parser.py

@@ -757,8 +757,7 @@ def _infer_types(
                 result = BooleanArray(result, bool_mask)
             elif result.dtype == np.object_ and non_default_dtype_backend:
                 # read_excel sends array of datetime objects
-                inferred_type = lib.infer_dtype(result)
-                if inferred_type != "datetime":
+                if not lib.is_datetime_array(result, skipna=True):
                     result = StringDtype().construct_array_type()._from_sequence(values)
 
         if dtype_backend == "pyarrow":