Fix to check if values are integer or float and convert accordingly. (#…

…21277)

This code will prevent the loss of data if the value is a float it will convert to float if it is not then int.  It will use pd.Float64Dtype() for floats instead of using the the pd.Int64Dtype(). Since there could be floating-point values in the array this will fix the exception for safely casting the array to data type.
fixes error when using mysql_to_s3 (TypeError: cannot safely cast non-equivalent object to int64) #16919

airflow/providers/amazon/aws/transfers/sql_to_s3.py

@@ -127,10 +127,14 @@ def _fix_int_dtypes(df: pd.DataFrame) -> None:
             if "float" in df[col].dtype.name and df[col].hasnans:
                 # inspect values to determine if dtype of non-null values is int or float
                 notna_series = df[col].dropna().values
-                if np.isclose(notna_series, notna_series.astype(int)).all():
+                if np.equal(notna_series, notna_series.astype(int)).all():
                     # set to dtype that retains integers and supports NaNs
                     df[col] = np.where(df[col].isnull(), None, df[col])
                     df[col] = df[col].astype(pd.Int64Dtype())
+                elif np.isclose(notna_series, notna_series.astype(int)).all():
+                    # set to float dtype that retains floats and supports NaNs
+                    df[col] = np.where(df[col].isnull(), None, df[col])
+                    df[col] = df[col].astype(pd.Float64Dtype())
 
     def execute(self, context: 'Context') -> None:
         sql_hook = self._get_hook()