def make_timeseries(start, end, dtypes, freq, partition_freq, seed=None):
 divisions = list(pd.DatetimeIndex(start=start, end=end, freq=partition_freq))
 state_data = random_state_data((len(divisions) - 1), seed)
 name = ('make-timeseries-' + tokenize(start, end, dtypes, freq, partition_freq))
 dsk = {(name, i): (make_timeseries_part, divisions[i], divisions[(i + 1)], dtypes, freq, state_data[i]) for i in range((len(divisions) - 1))}
 head = make_timeseries_part('2000', '2000', dtypes, '1H', state_data[0])
 return DataFrame(dsk, name, head, divisions)

def make_timeseries ( start , end , dtypes , freq , partition_freq , seed = None ) : divisions = list ( pd . DatetimeIndex ( start = start , end = end , freq = partition_freq ) ) state_data = random_state_data ( ( len ( divisions ) - 1 ) , seed ) name = ( 'make-timeseries-' + tokenize ( start , end , dtypes , freq , partition_freq ) ) dsk = { ( name , i ) : ( make_timeseries_part , divisions [ i ] , divisions [ ( i + 1 ) ] , dtypes , freq , state_data [ i ] ) for i in range ( ( len ( divisions ) - 1 ) ) } head = make_timeseries_part ( '2000' , '2000' , dtypes , '1H' , state_data [ 0 ] ) return DataFrame ( dsk , name , head , divisions )
create timeseries dataframe with random data parameters start: datetime start of time series end: datetime end of time series dtypes: dict mapping of column names to types .