Fix StringDtype vs object dtype mismatch in Data._safecast_df (facebook#4795)

Summary:

Prompted after some failures in exports not related to my changes: https://github.com/facebook/Ax/actions/runs/21225324302/job/61070638075?fbclid=IwY2xjawPeXMFleHRuA2FlbQIxMQBicmlkETFRTkR6WlE4NHVrd3IyQXNlc3J0YwZhcHBfaWQBMAABHjTAiZi71n24w95hvzEewrKNPKOGzJisgR7t4qJ3APRMYlusgFC-gu7RLiSb_aem_Zk3pmTDonCFsJvZCTkpeMA

Pandas 2.0+ changed default string column dtype from `object` to
`StringDtype(na_value=nan)`. The `_safecast_df()` method doesn't properly
handle the comparison between `StringDtype` and `np.dtype("O")` because
they are different types that don't compare equal.

Add explicit check for `pd.StringDtype` to force casting when needed.

Differential Revision: D91185469

Author: mgarrard

ax/core/data.py

@@ -195,9 +195,13 @@ def _safecast_df(cls: type[TData], df: pd.DataFrame) -> pd.DataFrame:
             if col in df.columns.values and coltype is not Any:
                 # Pandas timestamp handlng is weird
                 dtype = "datetime64[ns]" if coltype is pd.Timestamp else coltype
-                if (dtype != dtypes[col]) and not (
-                    coltype is int and df.loc[:, col].isnull().any()
-                ):
+                current_dtype = dtypes[col]
+                # Handle StringDtype -> object conversion (pandas 2.0+ compatibility)
+                needs_cast = (
+                    isinstance(current_dtype, pd.StringDtype)
+                    or (dtype != current_dtype)
+                ) and not (coltype is int and df.loc[:, col].isnull().any())
+                if needs_cast:
                     df[col] = df[col].astype(dtype)
         df.reset_index(inplace=True, drop=True)
         return df