bug fix: missing data, adding an additional type: pandas._lib.missing.NAType in addition to np.nan, np.na

data/maker/__init__.py

@@ -52,6 +52,7 @@ class Learner(Process):
         self._encoder = None
         self._map = None
         self._df = _args['data'] if 'data' in _args else None
+        
         self.name   =  self.__class__.__name__
         
         #
@@ -93,9 +94,21 @@ class Learner(Process):
             self._df     = reader.read(**_read_args)
         columns = self.columns if self.columns else self._df.columns
         #
-        # convert the data to binary here ...
+        # Below is a source of inefficiency, unfortunately python's type inference doesn't work well in certain cases
+        # - The code below tries to address the issue (Perhaps better suited for the reading components)
+        for name in columns :
+            _index = np.random.choice(np.arange(self._df[name].size),5,False)
+            no_value = [type(value) in [int,float,np.int64,np.int32,np.float32,np.float64] for value in self._df[name].values[_index]]
+            print ([name,np.sum(no_value)])
+            no_value = 0 if np.sum(no_value) > 0 else ''
+            
+            self._df[name] = self._df[name].fillna(no_value)
+
 
-        _args = {"schema":self.get_schema(),"data":self._df,"columns":columns}
+        #
+        # convert the data to binary here ...
+        _schema = self.get_schema()       
+        _args = {"schema":_schema,"data":self._df,"columns":columns}
         if self._map :
             _args['map'] = self._map
         self._encoder = prepare.Input(**_args)  if self._df.shape[0] > 0 else None