u

      """

      for k in self._k :

         self._d[k]=np.concatenate(self._d[k])

   def to_arrayDict(self,flatten=False) :

   def to_arrayDict(self,flatten=False,skip=[]) :

      """ dictionary to numpy.array

          if flatten==True applies .flatten_nested_columns() method

      """

      if skip is None :

         _skip=[]

      elif(type(skip)==type('')) :

         _skip=[skip]

      else :

         _skip=skip

      _keys=[k for k in self.keys() if not k in _skip]

      #

      out=OrderedDict()

      if flatten :

         for k in self.keys() :

         for k in _keys :

            out[k]=np.concatenate(self[k])

      else :

         for k in self.keys() :

         for k in _keys :

            out[k]=np.array(self[k])

      return out

   def to_pandas(self,flatten=False,to_array=True) :

   def to_pandas(self,flatten=False,to_array=True,skip=[]) :

      """ dictionary to pandas.DataFrame

          if flatten==True applies .flatten_nested_columns() method

          if to_array==True converts colums to np.array before to perform conversion to pandas.DataFrame,

            if some column contains vectors instead of scalars to_array=False must be used otherways errors may occurs

      """

      if to_array :

         out=self.to_arrayDict(flatten=flatten)

         out=self.to_arrayDict(flatten=flatten,skip=skip)

      else :

         if skip is None :

            _skip=[]

         elif(type(skip)==type('')) :

            _skip=[skip]

         else :

            _skip=skip

         _keys=[k for k in self.keys() if not k in _skip]

         out = OrderedDict()

         for k in self.keys() :

         for k in _keys :

            out[k]=self[k]

      return pandas.DataFrame(out)

   def to_csv_slow(self,csvfile,justBody=False,creator="",index_label=None,sep=',',comment=None,addEnd=False,mode='w') :