[英]Convert float32 array to datetime64 in Numpy 1.6.1
What is the proper way of converting integer dates to datetime64 in numpy? 在numpy中将整数日期转换为datetime64的正确方法是什么? I tried: 我试过了:
import numpy
a = numpy.array([20090913, 20101020, 20110125])
numpy.datetime64(a.astype("S8"))
but get an incorrect conversion. 但得到的转换不正确。 How about reading them in correctly as numpy.datetime64 objects using numpy.loadtxt (they are coming from a csv file)? 如何使用numpy.loadtxt(它们来自csv文件)正确读取numpy.datetime64对象?
You problem is that datetime64
expects a string in the format yyyy-mm-dd
, while the type conversion produces strings in the format yyyymmdd
. 您的问题是datetime64
需要格式为yyyy-mm-dd
字符串,而类型转换会生成格式为yyyymmdd
字符串。 I would suggest something like this: 我会建议这样的事情:
conversion = lambda x: "%s-%s-%s" % (x[:4], x[4:6], x[6:])
np_conversion = numpy.frompyfunc(conversion,1,1)
b = np_conversion(a.astype('S10'))
numpy.datetime64(b)
However it's not working for me (I have numpy 1.6.1), it fails with the message "NotImplementedError: Not implemented for this type". 但是它对我不起作用(我有numpy 1.6.1),它失败并显示消息“NotImplementedError:Not implemented for this type”。 Unless that is implemented in 1.7, I can only suggest a pure Python solution: 除非在1.7中实现,否则我只能建议一个纯Python解决方案:
numpy.datetime64(numpy.array([conversion(str(x)) for x in a], dtype="S10"))
...or pre-processing your input, to deliver the dates in the expected format. ...或预处理您的输入,以预期的格式提供日期。
Edit: I can also offer an alternative solution, using vectorize
, but I don't know very well how it works, so I don't know what's going wrong: 编辑:我也可以使用vectorize
提供替代解决方案,但我不太清楚它是如何工作的,所以我不知道出了什么问题:
>>> conversion = vectorize(lambda x: "%s-%s-%s" % (x[:4], x[4:6], x[6:]), otypes=['S10'])
>>> conversion(a.astype('S10'))
array(['2009', '2010', '2011'],
dtype='|S4')
For some reason it's ignoring the otypes
and outputting |S4
instead of |S10
. 由于某种原因,它忽略了otypes
并输出|S4
而不是|S10
。 Sorry I can't help more, but this should provide a starting point for searching other solutions. 对不起,我无能为力,但这应该是搜索其他解决方案的起点。
Update: Thanks to OP feedback, I thought of a new possibility. 更新:感谢OP反馈,我想到了一个新的可能性。 This should work as expected: 这应该按预期工作:
>>> conversion = lambda x: numpy.datetime64(str(x))
>>> np_conversion = numpy.frompyfunc(conversion, 1, 1)
>>> np_conversion(a)
array([2009-09-13 00:00:00, 2010-10-20 00:00:00, 2011-01-25 00:00:00], dtype=object)
# Works too:
>>> conversion = lambda x: numpy.datetime64("%s-%s-%s" % (x/10000, x/100%100, x%100))
Weird how, in this case, datetime64
works fine with or without the dashes... 奇怪的是,在这种情况下, datetime64
在有或没有短划线的情况下工作正常......
Oddly, this works: numpy.datetime64(a.astype("S8").tolist())
, while this does not: numpy.datetime64(a.astype("S8"))
. 奇怪的是,这有效: numpy.datetime64(a.astype("S8").tolist())
,而这不是: numpy.datetime64(a.astype("S8"))
。 The first method is still a bit less convoluted than: numpy.array([numpy.datetime64(str(i)) for i in a])
. 第一种方法仍然比以下方法更复杂: numpy.array([numpy.datetime64(str(i)) for i in a])
。 I asked why in this question . 我问这个问题为什么。
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