20110615 20000 129360
20110615 30000 115111
20110615 40000 108915
20110615 50000 123313
20110615 60000 148282
20110615 70000 159998
20110615 80000 185772
20110615 90000 203086
Ich lese sie ein
Code: Alles auswählen
climate_Moxa = 'data/climateMOX_long_therm.txt'
converters = {'hhmmss': lambda x: str(x).zfill(6)}
data_climateMOX = pd.io.parsers.read_csv(climate_Moxa,
delim_whitespace=True,decimal='.',
converters=converters,header=None,
parse_dates={'DateTime': [0,1]},
names=(['YYYYMMDD','hhmmss','out_temp_C']),
index_col='DateTime')
# drop duplices
data_climateMOX["index"] = data_climateMOX.index
data_climateMOX.drop_duplicates(cols='index', take_last=True,
inplace=True)
del data_climateMOX["index"]
data_climateMOX_res = data_climateMOX.sort()
data_climateMOX_res = data_climateMOX_res/10000Code: Alles auswählen
from pandas.tseries.offsets import QuarterBegin
ts = pd.Timestamp('2011-6-15')
offset = QuarterBegin(2, startingMonth=6)
offset.onOffset(ts)
True
data_climateMOX_new = data_climateMOX_res.resample('2Q-JUN').shift(-1, freq='2QS-JUN')