Fix _iquery DataFrame conversion for non-tuple results_type

_iquery must always return List[Dict[str, Any]], but when the connection
uses a non-tuple results_type (polars, pandas, numpy, arrow), the
specialized cursor's fetchall() returns a DataFrame/ndarray instead of
tuples. The previous code had two bugs:

1. list() on a DataFrame iterates by columns, producing Series objects
   instead of row dicts.
2. to_dict(orient='records') is pandas-specific and fails on polars.

Dispatch on the raw fetchall() result type before converting to dicts:
- pandas DataFrame: to_dict(orient='records')
- polars DataFrame: to_dicts()
- Arrow Table: to_pydict() with column-to-row transposition
- numpy ndarray: tolist() with cursor.description column names
- tuples/dicts: existing logic preserved

Centralize fix_names camelCase conversion as a single post-processing
step applied uniformly to all result types.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: kesmit13

singlestoredb/connection.py

@@ -1167,17 +1167,58 @@ def _iquery(
             if not re.match(r'^\s*(select|show|call|echo)\s+', oper, flags=re.I):
                 return []
             raw = cur.fetchall()
-            if hasattr(raw, 'to_dict') and callable(raw.to_dict):
-                return raw.to_dict(orient='records')
-            out = list(raw)
-            if not out:
+            if raw is None:
                 return []
-            if isinstance(out[0], (tuple, list)):
+            # pandas DataFrame
+            if hasattr(raw, 'to_dict') and hasattr(raw, 'columns'):
+                out = raw.to_dict(orient='records')
+            # polars DataFrame
+            elif hasattr(raw, 'to_dicts') and callable(raw.to_dicts):
+                out = raw.to_dicts()
+            # arrow Table
+            elif hasattr(raw, 'to_pydict') and callable(raw.to_pydict):
+                d = raw.to_pydict()
+                cols = list(d.keys())
+                n = len(next(iter(d.values()))) if d else 0
+                out = [{c: d[c][i] for c in cols} for i in range(n)]
+            # numpy ndarray
+            elif hasattr(raw, 'tolist') and hasattr(raw, 'ndim'):
+                rows = raw.tolist()
                 if cur.description:
                     names = [x[0] for x in cur.description]
-                    if fix_names:
-                        names = [under2camel(str(x).replace(' ', '')) for x in names]
-                    out = [{k: v for k, v in zip(names, row)} for row in out]
+                    out = [
+                        {k: v for k, v in zip(names, row)}
+                        for row in rows
+                    ]
+                else:
+                    return []
+            # list of tuples/namedtuples/dicts
+            else:
+                out = list(raw)
+                if not out:
+                    return []
+                if isinstance(out[0], dict):
+                    pass  # already dicts
+                elif isinstance(out[0], (tuple, list)):
+                    if cur.description:
+                        names = [x[0] for x in cur.description]
+                        out = [
+                            {k: v for k, v in zip(names, row)}
+                            for row in out
+                        ]
+                    else:
+                        return []
+            if not out:
+                return []
+            # Apply camelCase name conversion if requested
+            if fix_names:
+                out = [
+                    {
+                        under2camel(str(k).replace(' ', '')): v
+                        for k, v in row.items()
+                    }
+                    for row in out
+                ]
             return out
 
     @abc.abstractmethod