blocks|key|1585318|text|一种选择是使用str，然后使用to_datetime+(或类似的)：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1585319|In+[11]:+str(dt64)
Out[11]:+'2012-05-01T01:00:00.000000%2B0100'

In+[12]:+pd.to_datetime(str(dt64))
Out[12]:+datetime.datetime(2012,+5,+1,+1,+0,+tzinfo=tzoffset(None,+3600))|code-block|syntax|javascript|1585320|注意:它不等于dt，因为它变成了|1585321|In+[13]:+pd.to_datetime(str(dt64)).replace(tzinfo=None)
Out[13]:+datetime.datetime(2012,+5,+1,+1,+0)|1585322|这似乎很不雅观。|1585323|。|1585324|更新:这可以处理“令人讨厌的例子”：|1585325|In+[21]:+dt64+=+numpy.datetime64('2002-06-28T01:00:00.000000000%2B0100')

In+[22]:+pd.to_datetime(str(dt64)).replace(tzinfo=None)
Out[22]:+datetime.datetime(2002,+6,+28,+1,+0)|1585326|entityMap^0|7|3|F|B|0|0|7|2|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]|$9|11|A|12|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|13|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|14|8|@$9|15|A|16|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|17|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|18|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|19|8|@]|D|@]|E|$]]|$1|S|3|T|5|6|7|1A|8|@]|D|@]|E|$]]|$1|U|3|V|5|H|7|1B|8|@]|D|@]|E|$I|J]]|$1|W|3|-4|5|6|7|1C|8|@]|D|@]|E|$]]]|X|$]]

One option is to use <code>str</code>, and then <code>to_datetime</code> (or similar):

<pre><code>In [11]: str(dt64)
Out[11]: '2012-05-01T01:00:00.000000+0100'

In [12]: pd.to_datetime(str(dt64))
Out[12]: datetime.datetime(2012, 5, 1, 1, 0, tzinfo=tzoffset(None, 3600))
</code></pre>

Note: it is not equal to <code>dt</code> because it's become <a href="https://stackoverflow.com/questions/796008/python-cant-subtract-offset-naive-and-offset-aware-datetimes">"offset-aware"</a>:

<pre><code>In [13]: pd.to_datetime(str(dt64)).replace(tzinfo=None)
Out[13]: datetime.datetime(2012, 5, 1, 1, 0)
</code></pre>

This seems inelegant.

.

Update: this can deal with the "nasty example":

<pre><code>In [21]: dt64 = numpy.datetime64('2002-06-28T01:00:00.000000000+0100')

In [22]: pd.to_datetime(str(dt64)).replace(tzinfo=None)
Out[22]: datetime.datetime(2002, 6, 28, 1, 0)
</code></pre>

blocks|key|1585462|text|>>>+dt64.tolist()
datetime.datetime(2012,+5,+1,+0,+0)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|1585463|对于DatetimeIndex，tolist返回datetime对象的列表。对于单个datetime64对象，它返回单个datetime对象。|unstyled|offset|length|style|CODE|1585464|entityMap^0|0|2|D|G|6|O|8|16|A|1O|8|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|N|8|@$G|O|H|P|I|J]|$G|Q|H|R|I|J]|$G|S|H|T|I|J]|$G|U|H|V|I|J]|$G|W|H|X|I|J]]|9|@]|A|$]]|$1|K|3|-4|5|F|7|Y|8|@]|9|@]|A|$]]]|L|$]]

<pre><code>&gt;&gt;&gt; dt64.tolist()
datetime.datetime(2012, 5, 1, 0, 0)
</code></pre>

For <code>DatetimeIndex</code>, the <code>tolist</code> returns a list of <code>datetime</code> objects. For a single <code>datetime64</code> object it returns a single <code>datetime</code> object.

blocks|key|1800421|text|在numpy-1.8上将numpy.datetime64转换为表示时间的datetime对象|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1800422|>>>+from+datetime+import+datetime
>>>+import+numpy+as+np
>>>+dt+=+datetime.utcnow()
>>>+dt
datetime.datetime(2012,+12,+4,+19,+51,+25,+362455)
>>>+dt64+=+np.datetime64(dt)
>>>+ts+=+(dt64+-+np.datetime64('1970-01-01T00:00:00Z'))+/+np.timedelta64(1,+'s')
>>>+ts
1354650685.3624549
>>>+datetime.utcfromtimestamp(ts)
datetime.datetime(2012,+12,+4,+19,+51,+25,+362455)
>>>+np.__version__
'1.8.0.dev-7b75899'|code-block|syntax|javascript|1800423|上面的示例假设np.datetime64将一个朴素的datetime对象解释为使用UTC表示的时间。|1800424|1800425|要在datetime与np.datetime64之间来回转换(numpy-1.6)，请执行以下操作：|1800426|>>>+np.datetime64(datetime.utcnow()).astype(datetime)
datetime.datetime(2012,+12,+4,+13,+34,+52,+827542)|1800427|它既适用于单个np.datetime64对象，也适用于np.datetime64的numpy数组。|1800428|像对待np.int8、np.int16等一样思考np.datetime64，并应用相同的方法在np.int8、datetime和相应的numpy对象之间进行转换。|1800429|你的“讨厌的例子”工作正常：|1800430|>>>+from+datetime+import+datetime
>>>+import+numpy+
>>>+numpy.datetime64('2002-06-28T01:00:00.000000000%2B0100').astype(datetime)
datetime.datetime(2002,+6,+28,+0,+0)
>>>+numpy.__version__
'1.6.2'+#+current+version+available+via+pip+install+numpy|1800431|我可以在安装为以下代码的numpy-1.8.0上重现long值：|1800432|pip+install+git%2Bhttps://github.com/numpy/numpy.git#egg=numpy-dev|1800433|同样的例子：|1800434|>>>+from+datetime+import+datetime
>>>+import+numpy
>>>+numpy.datetime64('2002-06-28T01:00:00.000000000%2B0100').astype(datetime)
1025222400000000000L
>>>+numpy.__version__
'1.8.0.dev-7b75899'|1800435|它返回long，因为对于numpy.datetime64类型，.astype(datetime)等同于在numpy-1.8上返回Python整数(long)的.astype(object)。|1800436|要获取datetime对象，您可以：|1800437|>>>+dt64.dtype
dtype('<M8[ns]')
>>>+ns+=+1e-9+#+number+of+seconds+in+a+nanosecond
>>>+datetime.utcfromtimestamp(dt64.astype(int)+*+ns)
datetime.datetime(2002,+6,+28,+0,+0)|1800438|要获取直接使用秒的datetime64，请执行以下操作：|1800439|>>>+dt64+=+numpy.datetime64('2002-06-28T01:00:00.000000000%2B0100',+'s')
>>>+dt64.dtype
dtype('<M8[s]')
>>>+datetime.utcfromtimestamp(dt64.astype(int))
datetime.datetime(2002,+6,+28,+0,+0)|1800440|numpy+docs表示，datetime+API是实验性的，在未来的numpy版本中可能会发生变化。|1800441|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.scipy.org/doc/numpy-dev/reference/arrays.datetime.html^0|1|9|C|G|0|0|7|D|0|0|V|9|0|0|0|0|0|0|C|B|Q|4|0|0|0|0|3|4|C|G|V|H|1G|9|21|4|27|F|0|0|0|0|0|0|A|0|0^^$0|@$1|2|3|4|5|6|7|1R|8|@$9|1S|A|1T|B|C]|$9|1U|A|1V|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1W|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1X|8|@$9|1Y|A|1Z|B|C]]|D|@]|E|$]]|$1|M|3|-4|5|6|7|20|8|@]|D|@]|E|$]]|$1|N|3|O|5|6|7|21|8|@$9|22|A|23|B|C]]|D|@]|E|$]]|$1|P|3|Q|5|H|7|24|8|@]|D|@]|E|$I|J]]|$1|R|3|S|5|6|7|25|8|@]|D|@]|E|$]]|$1|T|3|U|5|6|7|26|8|@]|D|@]|E|$]]|$1|V|3|W|5|6|7|27|8|@]|D|@]|E|$]]|$1|X|3|Y|5|H|7|28|8|@]|D|@]|E|$I|J]]|$1|Z|3|10|5|6|7|29|8|@$9|2A|A|2B|B|C]|$9|2C|A|2D|B|C]]|D|@]|E|$]]|$1|11|3|12|5|H|7|2E|8|@]|D|@]|E|$I|J]]|$1|13|3|14|5|6|7|2F|8|@]|D|@]|E|$]]|$1|15|3|16|5|H|7|2G|8|@]|D|@]|E|$I|J]]|$1|17|3|18|5|6|7|2H|8|@$9|2I|A|2J|B|C]|$9|2K|A|2L|B|C]|$9|2M|A|2N|B|C]|$9|2O|A|2P|B|C]|$9|2Q|A|2R|B|C]|$9|2S|A|2T|B|C]]|D|@]|E|$]]|$1|19|3|1A|5|6|7|2U|8|@]|D|@]|E|$]]|$1|1B|3|1C|5|H|7|2V|8|@]|D|@]|E|$I|J]]|$1|1D|3|1E|5|6|7|2W|8|@]|D|@]|E|$]]|$1|1F|3|1G|5|H|7|2X|8|@]|D|@]|E|$I|J]]|$1|1H|3|1I|5|6|7|2Y|8|@]|D|@$9|2Z|A|30|1|31]]|E|$]]|$1|1J|3|-4|5|6|7|32|8|@]|D|@]|E|$]]]|1K|$1L|$5|1M|1N|1O|E|$1P|1Q]]]]

To convert <code>numpy.datetime64</code> to datetime object that represents time in UTC on <code>numpy-1.8</code>:

<pre><code>&gt;&gt;&gt; from datetime import datetime
&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; dt = datetime.utcnow()
&gt;&gt;&gt; dt
datetime.datetime(2012, 12, 4, 19, 51, 25, 362455)
&gt;&gt;&gt; dt64 = np.datetime64(dt)
&gt;&gt;&gt; ts = (dt64 - np.datetime64('1970-01-01T00:00:00Z')) / np.timedelta64(1, 's')
&gt;&gt;&gt; ts
1354650685.3624549
&gt;&gt;&gt; datetime.utcfromtimestamp(ts)
datetime.datetime(2012, 12, 4, 19, 51, 25, 362455)
&gt;&gt;&gt; np.__version__
'1.8.0.dev-7b75899'
</code></pre>

The above example assumes that a naive datetime object is interpreted by <code>np.datetime64</code> as time in UTC.

<hr>

To convert datetime to np.datetime64 and back (<code>numpy-1.6</code>):

<pre><code>&gt;&gt;&gt; np.datetime64(datetime.utcnow()).astype(datetime)
datetime.datetime(2012, 12, 4, 13, 34, 52, 827542)
</code></pre>

It works both on a single np.datetime64 object and a numpy array of np.datetime64.

Think of np.datetime64 the same way you would about np.int8, np.int16, etc and apply the same methods to convert beetween Python objects such as int, datetime and corresponding numpy objects.

Your "nasty example" works correctly:

<pre><code>&gt;&gt;&gt; from datetime import datetime
&gt;&gt;&gt; import numpy 
&gt;&gt;&gt; numpy.datetime64('2002-06-28T01:00:00.000000000+0100').astype(datetime)
datetime.datetime(2002, 6, 28, 0, 0)
&gt;&gt;&gt; numpy.__version__
'1.6.2' # current version available via pip install numpy
</code></pre>

I can reproduce the <code>long</code> value on <code>numpy-1.8.0</code> installed as:

<pre><code>pip install git+https://github.com/numpy/numpy.git#egg=numpy-dev
</code></pre>

The same example:

<pre><code>&gt;&gt;&gt; from datetime import datetime
&gt;&gt;&gt; import numpy
&gt;&gt;&gt; numpy.datetime64('2002-06-28T01:00:00.000000000+0100').astype(datetime)
1025222400000000000L
&gt;&gt;&gt; numpy.__version__
'1.8.0.dev-7b75899'
</code></pre>

It returns <code>long</code> because for <code>numpy.datetime64</code> type <code>.astype(datetime)</code> is equivalent to <code>.astype(object)</code> that returns Python integer (<code>long</code>) on <code>numpy-1.8</code>. 

To get datetime object you could:

<pre><code>&gt;&gt;&gt; dt64.dtype
dtype('&lt;M8[ns]')
&gt;&gt;&gt; ns = 1e-9 # number of seconds in a nanosecond
&gt;&gt;&gt; datetime.utcfromtimestamp(dt64.astype(int) * ns)
datetime.datetime(2002, 6, 28, 0, 0)
</code></pre>

To get datetime64 that uses seconds directly:

<pre><code>&gt;&gt;&gt; dt64 = numpy.datetime64('2002-06-28T01:00:00.000000000+0100', 's')
&gt;&gt;&gt; dt64.dtype
dtype('&lt;M8[s]')
&gt;&gt;&gt; datetime.utcfromtimestamp(dt64.astype(int))
datetime.datetime(2002, 6, 28, 0, 0)
</code></pre>

The <a href="http://docs.scipy.org/doc/numpy-dev/reference/arrays.datetime.html">numpy docs</a> say that the datetime API is experimental and may change in future numpy versions.

blocks|key|1585626|text|欢迎来到地狱。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1585627|您可以只将datetime64对象传递给pandas.Timestamp|offset|length|style|CODE|1585628|In+[16]:+Timestamp(numpy.datetime64('2012-05-01T01:00:00.000000'))
Out[16]:+<Timestamp:+2012-05-01+01:00:00>|code-block|syntax|javascript|1585629|我注意到这在NumPy+1.6.1中不能正常工作：|1585630|numpy.datetime64('2012-05-01T01:00:00.000000%2B0100')|1585631|此外，还可以使用pandas.to_datetime+(这不是开发版本，还没有检查v0.9.1)：|1585632|In+[24]:+pandas.to_datetime('2012-05-01T01:00:00.000000%2B0100')
Out[24]:+datetime.datetime(2012,+5,+1,+1,+0,+tzinfo=tzoffset(None,+3600))|1585633|entityMap^0|0|K|G|0|0|0|0|8|I|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|X|8|@$D|Y|E|Z|F|G]]|9|@]|A|$]]|$1|H|3|I|5|J|7|10|8|@]|9|@]|A|$K|L]]|$1|M|3|N|5|6|7|11|8|@]|9|@]|A|$]]|$1|O|3|P|5|J|7|12|8|@]|9|@]|A|$K|L]]|$1|Q|3|R|5|6|7|13|8|@$D|14|E|15|F|G]]|9|@]|A|$]]|$1|S|3|T|5|J|7|16|8|@]|9|@]|A|$K|L]]|$1|U|3|-4|5|6|7|17|8|@]|9|@]|A|$]]]|V|$]]

Welcome to hell.

You can just pass a datetime64 object to <code>pandas.Timestamp</code>:

<pre><code>In [16]: Timestamp(numpy.datetime64('2012-05-01T01:00:00.000000'))
Out[16]: &lt;Timestamp: 2012-05-01 01:00:00&gt;
</code></pre>

I noticed that this doesn't work right though in NumPy 1.6.1:

<pre><code>numpy.datetime64('2012-05-01T01:00:00.000000+0100')
</code></pre>

Also, <code>pandas.to_datetime</code> can be used (this is off of the dev version, haven't checked v0.9.1):

<pre><code>In [24]: pandas.to_datetime('2012-05-01T01:00:00.000000+0100')
Out[24]: datetime.datetime(2012, 5, 1, 1, 0, tzinfo=tzoffset(None, 3600))
</code></pre>

blocks|key|1585733|text|您可以只使用pd.Timestamp构造函数。下图可能对解决此问题和相关问题很有用。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1585734|​|1585735|📷|atomic|offset|length|1585736|1585737|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/f6e6f7dcca6438e536f600a7d95e99c7.png|imageAlt^0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|T|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@$G|V|H|W|1|X]]|A|$]]|$1|I|3|C|5|6|7|Y|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q|R|-4]]]]

You can just use the pd.Timestamp constructor. The following diagram may be useful for this and related questions. 

<img src="https://i.stack.imgur.com/uiXQd.png" alt="Conversions between time representations">

blocks|key|1800610|text|import+numpy+as+np
import+pandas+as+pd+

def+np64toDate(np64):
++++return+pd.to_datetime(str(np64)).replace(tzinfo=None).to_datetime()|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|1800611|使用此函数获取pythons原生datetime对象|unstyled|1800612|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|J|8|@]|9|@]|A|$]]|$1|G|3|-4|5|F|7|K|8|@]|9|@]|A|$]]]|H|$]]

<pre><code>import numpy as np
import pandas as pd 

def np64toDate(np64):
 return pd.to_datetime(str(np64)).replace(tzinfo=None).to_datetime()
</code></pre>

use this function to get pythons native datetime object

blocks|key|1800683|text|我已经数不清这个答案的次数了，所以我决定拼凑一个快速的小类，它将Numpy+datetime64值转换为datetime值。我希望它能帮助其他人。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1800684|from+datetime+import+datetime
import+pandas+as+pd

class+NumpyConverter(object):
++++@classmethod
++++def+to_datetime(cls,+dt64,+tzinfo=None):
++++++++"""
++++++++Converts+a+Numpy+datetime64+to+a+Python+datetime.
++++++++:param+dt64:+A+Numpy+datetime64+variable
++++++++:type+dt64:+numpy.datetime64
++++++++:param+tzinfo:+The+timezone+the+date+/+time+value+is+in
++++++++:type+tzinfo:+pytz.timezone
++++++++:return:+A+Python+datetime+variable
++++++++:rtype:+datetime
++++++++"""
++++++++ts+=+pd.to_datetime(dt64)
++++++++if+tzinfo+is+not+None:
++++++++++++return+datetime(ts.year,+ts.month,+ts.day,+ts.hour,+ts.minute,+ts.second,+tzinfo=tzinfo)
++++++++return+datetime(ts.year,+ts.month,+ts.day,+ts.hour,+ts.minute,+ts.second)|code-block|syntax|javascript|1800685|我要把它放在我的工具包里，有种感觉我又需要它了。|1800686|entityMap^0|12|A|1G|8|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]|$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@]|D|@]|E|$]]|$1|M|3|-4|5|6|7|V|8|@]|D|@]|E|$]]]|N|$]]

I've come back to this answer more times than I can count, so I decided to throw together a quick little class, which converts a Numpy <code>datetime64</code> value to Python <code>datetime</code> value. I hope it helps others out there. 

<pre><code>from datetime import datetime
import pandas as pd

class NumpyConverter(object):
 @classmethod
 def to_datetime(cls, dt64, tzinfo=None):
 """
 Converts a Numpy datetime64 to a Python datetime.
 :param dt64: A Numpy datetime64 variable
 :type dt64: numpy.datetime64
 :param tzinfo: The timezone the date / time value is in
 :type tzinfo: pytz.timezone
 :return: A Python datetime variable
 :rtype: datetime
 """
 ts = pd.to_datetime(dt64)
 if tzinfo is not None:
 return datetime(ts.year, ts.month, ts.day, ts.hour, ts.minute, ts.second, tzinfo=tzinfo)
 return datetime(ts.year, ts.month, ts.day, ts.hour, ts.minute, ts.second)
</code></pre>

I'm gonna keep this in my tool bag, something tells me I'll need it again.

blocks|key|1800783|text|有些解决方案对我来说效果很好，但numpy会弃用一些参数。对我来说，更好的解决方案是将date读取为pandas+datetime，并显式地指定pandas对象的年、月和日。以下代码适用于最常见的情况。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1800784|def+format_dates(dates):
++++dt+=+pd.to_datetime(dates)
++++try:+return+[datetime.date(x.year,+x.month,+x.day)+for+x+in+dt]++++
++++except+TypeError:+return+datetime.date(dt.year,+dt.month,+dt.day)|code-block|syntax|javascript|1800785|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Some solutions work well for me but numpy will deprecate some parameters. 
The solution that work better for me is to read the date as a pandas datetime and excract explicitly the year, month and day of a pandas object.
The following code works for the most common situation.<code>
</code>

<pre><code>def format_dates(dates):
 dt = pd.to_datetime(dates)
 try: return [datetime.date(x.year, x.month, x.day) for x in dt] 
 except TypeError: return datetime.date(dt.year, dt.month, dt.day)
</code></pre>

blocks|key|1585928|text|我成功地将pandas+dataframe中包含时间信息的date列转换为numpy数组的唯一方法如下：(dataframe是从csv文件“csvIn.csv”中读取的)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1585929|import+pandas+as+pd
import+numpy+as+np

df+=+pd.read_csv("csvIn.csv")
df["date"]+=+pd.to_datetime(df["date"])
timestamps+=+np.array([np.datetime64(value)+for+dummy,+value+in+df["date"].items()])|code-block|syntax|javascript|1585930|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Only way I managed to convert a column 'date' in pandas dataframe containing time info to numpy array was as following: (dataframe is read from csv file &quot;csvIn.csv&quot;)
<pre><code>import pandas as pd
import numpy as np

df = pd.read_csv(&quot;csvIn.csv&quot;)
df[&quot;date&quot;] = pd.to_datetime(df[&quot;date&quot;])
timestamps = np.array([np.datetime64(value) for dummy, value in df[&quot;date&quot;].items()])
</code></pre>

blocks|key|1585932|text|我确实喜欢这样|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1585933|import+pandas+as+pd

#+Custom+function+to+convert+Pandas+Datetime+to+Timestamp
def+toTimestamp(data):
++++return+data.timestamp()

#+Read+a+csv+file
df+=+pd.read_csv("friends.csv")

#+Replace+the+"birthdate"+column+by:
#+1.+Transform+to+datetime
#+2.+Apply+the+custom+function+to+the+column+just+converted
df["birthdate"]+=+pd.to_datetime(df["birthdate"]).apply(toTimestamp)|code-block|syntax|javascript|1585934|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I did like this
<pre><code>import pandas as pd

# Custom function to convert Pandas Datetime to Timestamp
def toTimestamp(data):
 return data.timestamp()

# Read a csv file
df = pd.read_csv(&quot;friends.csv&quot;)

# Replace the &quot;birthdate&quot; column by:
# 1. Transform to datetime
# 2. Apply the custom function to the column just converted
df[&quot;birthdate&quot;] = pd.to_datetime(df[&quot;birthdate&quot;]).apply(toTimestamp)
</code></pre>

How do I convert a <code>numpy.datetime64</code> object to a <code>datetime.datetime</code> (or <code>Timestamp</code>)?

In the following code, I create a datetime, timestamp and datetime64 objects.

<pre><code>import datetime
import numpy as np
import pandas as pd
dt = datetime.datetime(2012, 5, 1)
# A strange way to extract a Timestamp object, there's surely a better way?
ts = pd.DatetimeIndex([dt])[0]
dt64 = np.datetime64(dt)

In [7]: dt
Out[7]: datetime.datetime(2012, 5, 1, 0, 0)

In [8]: ts
Out[8]: &lt;Timestamp: 2012-05-01 00:00:00&gt;

In [9]: dt64
Out[9]: numpy.datetime64('2012-05-01T01:00:00.000000+0100')
</code></pre>

Note: it's easy to get the datetime from the Timestamp:

<pre><code>In [10]: ts.to_datetime()
Out[10]: datetime.datetime(2012, 5, 1, 0, 0)
</code></pre>

But how do we extract the <code>datetime</code> or <code>Timestamp</code> from a <code>numpy.datetime64</code> (<code>dt64</code>)?

.

Update: a somewhat nasty example in my dataset (perhaps the motivating example) seems to be:

<pre><code>dt64 = numpy.datetime64('2002-06-28T01:00:00.000000000+0100')
</code></pre>

which should be <code>datetime.datetime(2002, 6, 28, 1, 0)</code>, and not a long (!) (<code>1025222400000000000L</code>)...

Converting between datetime, Timestamp and datetime64

如何将numpy.datetime64对象转换为datetime.datetime (或Timestamp)？在下面的代码中，我创建了datetime、timestamp和datetime64对象。import datetimeimport numpy as npimport pandas as pddt = date...

问在datetime、Timestamp和datetime64之间转换
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