merge_machine/abstract_data_project.py
def from_ES(self, columns=None, chunksize=None, thresh=None):
        """Load or generate pandas DataFrame from the ES associated to the 
        project.
        
        Parameters
        ----------
        columns: list of str
            The columns to load
        chunksize:
            Same as pandas `chunksize` argument in `read_csv`
        thresh: None or float
             If thresh is not None, filter out all data from the referential if
             the __CONFIDENCE is not above the threshold        
        """
        num_rows = ic.stats(self.index_name)['_all']['total']['docs']['count']
    
        if chunksize is None:
            res = self.fetch_by_id(size=num_rows, from_=0)
            return self._ES_res_to_pandas(res, columns, thresh)
        
        else:
            # Return a generator. Code has to be separate to allow returning pandas.DataFrame
            return self._from_ES_gen(num_rows, columns, chunksize, thresh)
Load or generate pandas DataFrame from the ES associated to the 
        project.
        
        Parameters
        ----------
        columns: list of str
            The columns to load
        chunksize:
            Same as pandas `chunksize` argument in `read_csv`
        thresh: None or float
             If thresh is not None, filter out all data from the referential if
             the __CONFIDENCE is not above the threshold        
        
Load or generate pandas DataFrame from the ES associated to the
project.

Parameters

list of str
The columns to load
chunksize:
Same as pandas `chunksize` argument in `read_csv`
thresh: None or float
If thresh is not None, filter out all data from the referential if
the __CONFIDENCE is not above the threshold
Load or generate pandas DataFrame from the ES associated to the project . Parameters list of str The columns to load chunksize : Same as pandas ` chunksize ` argument in ` read_csv ` thresh : None or float If thresh is not None filter out all data from the referential if the __CONFIDENCE is not above the threshold
def from_ES(self, columns=None, chunksize=None, thresh=None):
        num_rows = ic.stats(self.index_name)['_all']['total']['docs']['count']
        if chunksize is None:
            res = self.fetch_by_id(size=num_rows, from_=0)
            return self._ES_res_to_pandas(res, columns, thresh)
        else:
            return self._from_ES_gen(num_rows, columns, chunksize, thresh)
def from_ES ( self , columns = None , chunksize = None , thresh = None ) : num_rows = ic . stats ( self . index_name ) [ '_all' ] [ 'total' ] [ 'docs' ] [ 'count' ] if chunksize is None : res = self . fetch_by_id ( size = num_rows , from_ = 0 ) return self . _ES_res_to_pandas ( res , columns , thresh ) else : return self . _from_ES_gen ( num_rows , columns , chunksize , thresh )
Load or generate pandas DataFrame from the ES associated to the
project.
Load or generate pandas DataFrame from the ES associated to the project .
"""Load or generate pandas DataFrame from the ES associated to the 
        project.
        
        Parameters
        ----------
        columns: list of str
            The columns to load
        chunksize:
            Same as pandas `chunksize` argument in `read_csv`
        thresh: None or float
             If thresh is not None, filter out all data from the referential if
             the __CONFIDENCE is not above the threshold        
        """ # Return a generator. Code has to be separate to allow returning pandas.DataFrame
self
columns
chunksize
thresh