为什么numpy数组不能隐式地从datetime转换为np.datetime64？ [英] Why cannot numpy arrays convert from datetime to np.datetime64 implicitly?

问题描述

说，我有一个 datetime ：

given_time = datetime(2013, 10, 8, 0, 0, 33, 945109,
                      tzinfo=psycopg2.tz.FixedOffsetTimezone(offset=60, 
                                                             name=None))

我想将其转换为 np.datetime64 ：

np.datetime64(given_time)
> numpy.datetime64('2013-10-08T00:00:33.945109+0100')

。但是，如果我有一个数组 given_time ：

It works well. However, if I have an array of given_time:

given_times = np.array([given_time]*3) # dtype is object

两个 given_times .stype（'datetime64'）和 given_times = np.array（[given_time] * 3，dtype = np.datetime64）将触发 TypeError：根据规则'same_kind'不能将datetime.datetime对象从元数据[us]转换为[D]

所以，我必须指定单位：

So, I have to specify the unit:

given_times.astype('datetime64[us]')
# or
given_times = np.array([given_time]*3, dtype='datetime64[us]')

$ b $我的问题是，为什么我必须在这里指定单位？它不需要 np.datatime64 构造函数中的单元。

推荐答案

我知道这是一个古老的问题，但如果有人遇到这个问题，我会尝试回答。

I know it's an old question, but I'd try to answer in case anyone else comes across this.

1. 截至1.11， numpy 不尝试自动将date / datetime对象的迭代转换为datetime64数组，这从此摘录

1. As of 1.11, numpy doesn't try to automatically convert iterables of date/datetime objects to datetime64 arrays, this is pretty clear from this excerpt in the test suite:

# at the moment, we don't automatically convert these to datetime64

dt = datetime.date(1970, 1, 1)
arr = np.array([dt])
assert_equal(arr.dtype, np.dtype('O'))

dt = datetime.datetime(1970, 1, 1, 12, 30, 40)     
arr = np.array([dt])
assert_equal(arr.dtype, np.dtype('O'))

理想情况下， numpy 将会使用正确单位的 datetime64 请参阅此问题。

Ideally, numpy would figure that datetime64 with correct units could be used; see this issue.

1. 从标量构建 datetime64 时，设置为 M8 [D] 对于日期对象，对于日期时间对象（ M8 [us] blob / 1429c606643d1ad305e710c4a31cb6f398d04c53 / numpy / core / tests / test_datetime.py＃L392rel =nofollow>相关测试）。

1. When constructing datetime64 from a scalar, the unit it set to M8[D] for date objects and to M8[us] for datetime objects (a relevant test).

您指定 dtype ='datetime64'，或者类似地， dtype ='M8'，单位设置为通用，稍后解析为 M8 [D] （尽管将其解析为 M8 [D] ，请参见此问题）：

When you specify dtype='datetime64', or, similarly, dtype='M8', the units are set to "generic", which later resolves to M8[D] (although it would be logical to have it resolve to M8[D], see this issue):

>>> np.datetime_data(np.dtype('datetime64'))
('generic', 1)
>>> np.datetime_data(np.dtype('M8'))
('generic', 1)
>>> np.datetime_data(np.dtype('M8[D]'))
('D', 1)
>>> np.datetime_data(np.dtype('M8[us]'))
('us', 1)

1. given_times.astype（'datetime64'）不再引发异常在1.11中修正了。

1. given_times.astype('datetime64') no longer raises an exception -- this was fixed in 1.11.

从1.11开始，datetime64对象是时区天真的，所以传递一个datetime对象与 tzinfo 设置像提供的示例将触发一个废弃警告。

Starting from 1.11, datetime64 objects are timezone-naive, so passing a datetime object with tzinfo set like in the provided example will trigger a deprecation warning.

这篇关于为什么numpy数组不能隐式地从datetime转换为np.datetime64？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！