diff --git a/data/gan.py b/data/gan.py
index 767a24b..985e706 100644
--- a/data/gan.py
+++ b/data/gan.py
@@ -604,7 +604,7 @@ class Predict(GNet):
                                 
                                 # df =   pd.DataFrame(np.round(f)).astype(np.int32)
                                 # candidates.append (np.round(_matrix).astype(np.int64)) 
-                                candidates.append( [np.round(row).astype(int) for row in _matrix])                       
+                                candidates.append(np.array([np.round(row).astype(int) for row in _matrix]))                       
                 # return candidates[0] if len(candidates) == 1 else candidates
                 
                 return candidates 
diff --git a/data/maker/prepare/__init__.py b/data/maker/prepare/__init__.py
index 381dfc0..9fb0fa7 100644
--- a/data/maker/prepare/__init__.py
+++ b/data/maker/prepare/__init__.py
@@ -111,13 +111,13 @@ class Input :
 
         if 'columns' in _args :
             self._columns = _args['columns']
-        else:
-            #
-            # We will look into the count and make a judgment call
-            _df = pd.DataFrame(self.df.apply(lambda col: col.dropna().unique().size )).T
-            MIN_SPACE_SIZE = 2
-            self._columns  = cols if cols else _df.apply(lambda col:None if col[0] == row_count or col[0] < MIN_SPACE_SIZE else col.name).dropna().tolist()
-            self._io = _df.to_dict(orient='records')
+        # else:
+        #
+        # We will look into the count and make a judgment call
+        _df = pd.DataFrame(self.df.apply(lambda col: col.dropna().unique().size )).T
+        MIN_SPACE_SIZE = 2
+        self._columns  = cols if cols else _df.apply(lambda col:None if col[0] == row_count or col[0] < MIN_SPACE_SIZE else col.name).dropna().tolist()
+        self._io = _df.to_dict(orient='records')
             
     def _initdata(self,**_args):
         """