BUG: permit str dtype -> IntegerDtype conversions

Resolves pandas-dev#25472, resolves pandas-dev#25288.

pandas/core/arrays/integer.py

@@ -32,6 +32,7 @@
     is_integer_dtype,
     is_list_like,
     is_object_dtype,
+    is_string_dtype,
     pandas_dtype,
 )
 from pandas.core.dtypes.missing import isna
@@ -124,12 +125,15 @@ def safe_cast(values, dtype, copy: bool):
     Safely cast the values to the dtype if they
     are equivalent, meaning floats must be equivalent to the
     ints.
-
     """
+    if is_string_dtype(values):
+        # casts from str are always safe since they raise
+        # a ValueError if the str cannot be parsed into an int
+        return values.astype(dtype, copy=copy)
+
     try:
         return values.astype(dtype, casting="safe", copy=copy)
     except TypeError as err:
-
         casted = values.astype(dtype, copy=copy)
         if (casted == values).all():
             return casted
@@ -143,7 +147,7 @@ def coerce_to_array(
     values, dtype, mask=None, copy: bool = False
 ) -> tuple[np.ndarray, np.ndarray]:
     """
-    Coerce the input values array to numpy arrays with a mask
+    Coerce the input values array to numpy arrays with a mask.
 
     Parameters
     ----------
@@ -204,7 +208,7 @@ def coerce_to_array(
     elif is_bool_dtype(values) and is_integer_dtype(dtype):
         values = np.array(values, dtype=int, copy=copy)
 
-    elif not (is_integer_dtype(values) or is_float_dtype(values)):
+    elif not (is_integer_dtype(values) or is_float_dtype(values) or is_string_dtype(values)):
         raise TypeError(f"{values.dtype} cannot be converted to an IntegerDtype")
 
     if mask is None:
@@ -230,9 +234,7 @@ def coerce_to_array(
     if mask.any():
         values = values.copy()
         values[mask] = 1
-        values = safe_cast(values, dtype, copy=False)
-    else:
-        values = safe_cast(values, dtype, copy=False)
+    values = safe_cast(values, dtype, copy=False)
 
     return values, mask
 

pandas/core/dtypes/common.py

@@ -143,14 +143,14 @@ def ensure_python_int(value: int | np.integer) -> int:
 
 
 def classes(*klasses) -> Callable:
-    """evaluate if the tipo is a subclass of the klasses"""
+    """Evaluate if the tipo is a subclass of the klasses."""
     return lambda tipo: issubclass(tipo, klasses)
 
 
 def classes_and_not_datetimelike(*klasses) -> Callable:
     """
-    evaluate if the tipo is a subclass of the klasses
-    and not a datetimelike
+    Evaluate if the tipo is a subclass of the klasses
+    and not a datetimelike.
     """
     return lambda tipo: (
         issubclass(tipo, klasses)
@@ -674,7 +674,7 @@ def is_integer_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of an integer dtype.
 
-    Unlike in `in_any_int_dtype`, timedelta64 instances will return False.
+    Unlike in `is_any_int_dtype`, timedelta64 instances will return False.
 
     The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered
     as integer by this function.
@@ -726,7 +726,7 @@ def is_signed_integer_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of a signed integer dtype.
 
-    Unlike in `in_any_int_dtype`, timedelta64 instances will return False.
+    Unlike in `is_any_int_dtype`, timedelta64 instances will return False.
 
     The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered
     as integer by this function.
@@ -1521,7 +1521,7 @@ def is_complex_dtype(arr_or_dtype) -> bool:
 
 def _is_dtype(arr_or_dtype, condition) -> bool:
     """
-    Return a boolean if the condition is satisfied for the arr_or_dtype.
+    Return true if the condition is satisfied for the arr_or_dtype.
 
     Parameters
     ----------
@@ -1580,7 +1580,7 @@ def get_dtype(arr_or_dtype) -> DtypeObj:
 
 def _is_dtype_type(arr_or_dtype, condition) -> bool:
     """
-    Return a boolean if the condition is satisfied for the arr_or_dtype.
+    Return true if the condition is satisfied for the arr_or_dtype.
 
     Parameters
     ----------