>>> pd.date_range(start='2018-04-24', end='2018-04-27', periods=3)
DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00',
'2018-04-27 00:00:00'],
dtype='datetime64[ns]', freq=None)
pd.date_range(start, periods=10, freq="2h20min")
Out[240]:
DatetimeIndex(['2011-01-01 00:00:00', '2011-01-01 02:20:00',
'2011-01-01 04:40:00', '2011-01-01 07:00:00',
'2011-01-01 09:20:00', '2011-01-01 11:40:00',
'2011-01-01 14:00:00', '2011-01-01 16:20:00',
'2011-01-01 18:40:00', '2011-01-01 21:00:00'],
dtype='datetime64[ns]', freq='140T')
pd.date_range(start, periods=10, freq="1D10U")
Out[241]:
DatetimeIndex([ '2011-01-01 00:00:00', '2011-01-02 00:00:00.000010',
'2011-01-03 00:00:00.000020', '2011-01-04 00:00:00.000030',
'2011-01-05 00:00:00.000040', '2011-01-06 00:00:00.000050',
'2011-01-07 00:00:00.000060', '2011-01-08 00:00:00.000070',
'2011-01-09 00:00:00.000080', '2011-01-10 00:00:00.000090'],
dtype='datetime64[ns]', freq='86400000010U')
freq aliases
B - business day frequency
C - custom business day frequency
D - calendar day frequency
W - weekly frequency
M - month end frequency
SM - semi-month end frequency (15th and end of month)
BM - business month end frequency
CBM - custom business month end frequency
MS - month start frequency
SMS - semi-month start frequency (1st and 15th)
BMS - business month start frequency
CBMS - custom business month start frequency
Q - quarter end frequency
BQ - business quarter end frequency
QS - quarter start frequency
BQS - business quarter start frequency
A, Y - year end frequency
BA, BY - business year end frequency
AS, YS - year start frequency
BAS, BYS - business year start frequency
BH - business hour frequency
H - hourly frequency
T, min - minutely frequency
S - secondly frequency
L, ms - milliseconds
U, us - microseconds
N - nanoseconds