日期格式是问题所在,
01/01/2015
是有歧义的。如果它符合 ISO 8601 标准,则可以直接使用 numpy 进行解析。在您的情况下,由于您只需要日期,因此拆分和重新排列数据将显着提高速度:
t = np.array([datetime.strptime(d.split(None)[0], "%d/%m/%Y")
for d in t],dtype='datetime64[us]').astype('datetime64[D]')
一些时间,首先在解析后重新排列:
In [36]: %%timeit
from datetime import datetime
t = np.array(['21/02/2014 08:40:00', '11/02/2014 10:50:00 PM',
'07/04/2014 05:50:00 PM', '17/02/2014 10:20:00 PM',
'07/03/2014 06:10:00 AM', '02/03/2014 12:25:00 PM',
'05/02/2014 03:20:00 AM', '31/01/2014 12:30:00 AM',
'28/02/2014 01:25:00 PM']*10000)
t1 = np.array([np.datetime64("{}-{}-{}".format(c[:4], b, a)) for a, b, c in (s.split("/", 2) for s in t)])
....:
10 loops, best of 3: 125 ms per loop
你的代码:
In [37]: %%timeit
from datetime import datetime
t = np.array(['21/02/2014 08:40:00 AM', '11/02/2014 10:50:00 PM',
'07/04/2014 05:50:00 PM', '17/02/2014 10:20:00 PM',
'07/03/2014 06:10:00 AM', '02/03/2014 12:25:00 PM',
'05/02/2014 03:20:00 AM', '31/01/2014 12:30:00 AM',
'28/02/2014 01:25:00 PM']*10000)
t = [datetime.strptime(tt,"%d/%m/%Y %H:%M:%S %p") for tt in t]
t = np.array(t,dtype='datetime64[us]').astype('datetime64[D]')
....:
1 loops, best of 3: 1.56 s per loop
两者结果相同,但存在显著差异:
In [48]: t = np.array(['21/02/2014 08:40:00 AM', '11/02/2014 10:50:00 PM',
'07/04/2014 05:50:00 PM', '17/02/2014 10:20:00 PM',
'07/03/2014 06:10:00 AM', '02/03/2014 12:25:00 PM',
'05/02/2014 03:20:00 AM', '31/01/2014 12:30:00 AM',
'28/02/2014 01:25:00 PM'] * 10000)
In [49]: t1 = [datetime.strptime(tt,"%d/%m/%Y %H:%M:%S %p") for tt in t]
t1 = np.array(t1,dtype='datetime64[us]').astype('datetime64[D]')
....:
In [50]: t2 = np.array([np.datetime64("{}-{}-{}".format(c[:4], b, a)) for a, b, c in (s.split("/", 2) for s in t)])
In [51]: (t1 == t2).all()
Out[51]: True