data/maker/__init__.py

@@ -432,51 +432,7 @@ class Generator (Learner):
             return _date.strftime(FORMAT)
 
         pass
-    def _format(self,_df,_schema):
-        """
-        :_df    data-frame being processed
-        :_schema    table schema with types
-        """
     
-        
-        _columns = [_item['name'] for _item in _schema]
-        _map = {'INT64':np.int64,'FLOAT64':np.float64,'DATE':np.datetime64,'TIMESTAMP':(lambda field: pd.to_datetime(field).dt.tz_localize(None))}
-        
-        # pd.to_datetime(_df.measurement_datetime).dt.tz_localize(None)
-        
-        for _item in _schema :
-            _name = _item['name']
-            if _item['type'] not in _map :
-                continue
-            _pointer = _map[_item['type']]
-            try:
-                if type(_pointer).__name__ == 'type':
-                    if _item['type'] in ['INT64','FLOAT64'] :
-                        
-                        novalue = np.int64(0) if _item['type'] == 'INT64' else np.float64(0)
-                    elif _item['type'] == 'STRING' :
-                        novalue = ''
-                    
-                    if _item['type'] in ['INT64','FLOAT64','STRING'] :
-                        
-                        _df[_name] = _df[_name].fillna(novalue)
-                        
-                    #
-                    # This is not guaranteed to work but may help ...     
-                    _df[_name] = _df[_name].values.astype(_pointer)
-
-                    
-                    
-                else:
-                    _df[_name] = _pointer(_df[_name])
-                    pass
-            except Exception as e:
-                
-                pass
-        # bqw =  transport.factory.instance(**_store['target'])
-        # bqw.write(_df,schema=_schema)
-        
-        return _df[_columns]
     def post(self,_candidates):
         
         if 'target'  in self.store :
@@ -499,22 +455,26 @@ class Generator (Learner):
             if self.columns and _haslist is False:
                 _df[self.columns] = _iodf[self.columns]
             else:
-                #
-                # In here we have the case of all attributes have undergone random permutations
-                # 
                 _df = _iodf
             
               
             N += _df.shape[0]
             if self._states and 'post' in self._states:
                 _df = State.apply(_df,self._states['post'])
+            # #
+            # #@TODO:
+            # # Improve formatting with better post-processing pipeline
+            # if 'approximate' in self.info :                
+            #     _df = self.approximate(_df)
+            # if 'make_date' in self.info :
+            #     for name in self.info['make_date'] :
+            #         # iname = self.info['make_date']['init_field']
+            #         iname = self.info['make_date'][name]
 
-            #
-            # Let us format the data frame so as to be able to minimize write errors
-            #
-            if _schema :
-                _df = self._format(_df,_schema) 
-                
+            #         years = _df[iname]
+            #         _dates = [self.make_date(year=_year,field=name) for _year in years]
+            #         if _dates :                         
+            #             _df[name] = _dates
                         
                         
            
@@ -529,16 +489,15 @@ class Generator (Learner):
                 _log = {'action':'write','input':{'table':self.info['from'],'schema':[],'rows':_df.shape[0]}}
                
                 writer = transport.factory.instance(**_store)                
-
-                if _store['provider'] == 'bigquery' and _schema:
+                if _store['provider'] == 'bigquery':
                     try:
                         _log['schema'] = _schema
-                        writer.write(_df,schema=_schema)
+                        writer.write(_df,schema=_schema,table=self.info['from'])
                     except Exception as e:
-                        print (e)
-                        writer.write(_df)
+                        _log['schema'] = []
+                        writer.write(_df,table=self.info['from'])
                 else:
-                    writer.write(_df)
+                    writer.write(_df,table=self.info['from'])
                 self.log(**_log)
             else:
                 self.cache.append(_df)

data/maker/version.py

@@ -1 +1 @@
-__version__='1.7.6'
+__version__='1.7.5'