import pandas as pd
import numpy as np
rango de fechas
- Se puede especificar el tiempo de inicio y el período
- H: hora
- D: día
- M: mes
rng = pd.date_range('2016-07-01', periods = 10, freq = '3D')
rng
DatetimeIndex ([ '01/07/2016', '04/07/2016', '07/07/2016', '07/10/2016',
'07/13/2016', '07/16/2016' , '07/19/2016', '07/22/2016',
'07/25/2016', '07/28/2016'],
dtype = 'datetime64 [ns]', frec = '3D')
time=pd.Series(np.random.randn(20),
index=pd.date_range(dt.datetime(2016,1,1),periods=20))
print(time)
-0.129379 01/01/2016
01/02/2016 0,164480
01/03/2016 -0.639117
01/04/2016 -0.427224
01/05/2016 2,055133
01/06/2016 1,116075
01/07/2016 0,357426
01/08/2016 0.274249
01/09/2016 0,834405
01/10/2016 -0.005444
01/11/2016 -0.134409
01/12/2016 0,249318
01/13/2016 -0.297842
01/14/2016 -0.128514
01/15/2016 0.063690
2016-01 -2.246031 -16
17.01.2016 0,359552
01/18/2016 0,383030
01/19/2016 0,402717
01/20/2016 -0.694068
Frec: D, dtype: float64
filtro truncado
time.truncate(before='2016-1-10')
-0.005444 01/10/2016
01/11/2016 -0.134409
01/12/2016 0,249318
01/13/2016 -0.297842
01/14/2016 -0.128514
01/15/2016 0,063690
01/16/2016 -2.246031
2016-01 0.359552 -17
18.01.2016 0,383030
01/19/2016 0,402717
01/20/2016 -0.694068
Frec: D, dtype: float64
time.truncate(after='2016-1-10')
-0.129379 01/01/2016
01/02/2016 0,164480
01/03/2016 -0.639117
01/04/2016 -0.427224
01/05/2016 2,055133
01/06/2016 1,116075
01/07/2016 0,357426
01/08/2016 0.274249
01/09/2016 0,834405
01/10/2016 -0.005444
Frec: D, dtype: float64
#时间戳
pd.Timestamp('2016-07-10')
# TIME OFFSETS
pd.Timedelta('1 day')
pd.Period('2016-01-01 10:10') + pd.Timedelta('1 day')
Período ( '02/01/2016 10:10', 'T')
