将 Pandas TimeDelta 转换为整数
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Convert Pandas TimeDelta to integer
提问by Will
Suppose I have a Pandas Series that contains TimeDelta data. In fact it has been generated by taking the difference of a DateTimeIndex with a shifted version of itself, hence giving the delta between consecutive timestamps.
假设我有一个包含 TimeDelta 数据的 Pandas 系列。事实上，它是通过将 DateTimeIndex 与它自身的移位版本的差值而生成的，因此给出了连续时间戳之间的增量。
It looks something like
它看起来像
timestamp
2015-02-01 00:00:04 00:00:04
2015-02-01 00:00:08 00:00:04
2015-02-01 00:00:12 00:00:04
....
Name: ?timestamp, dtype: timedelta64[ns]
The values are obviously numpy.timedelta64 but I need to get them into seconds. There have been similar questions asked relating to this but no answers I have seen yet that deals with Pandas 0.16.1.
这些值显然是 numpy.timedelta64 但我需要把它们变成几秒钟。已经提出了与此相关的类似问题，但我还没有看到涉及 Pandas 0.16.1 的答案。
What I've tried is:
我试过的是：
ts.apply(lambda x: x.seconds)
Which gives an error of
这给出了一个错误
AttributeError: 'numpy.timedelta64' object has no attribute 'seconds'
AttributeError: 'numpy.timedelta64' 对象没有属性 'seconds'
Then tried
然后试过
numpy.int64(ts)
But that gives me an array. Now I know I can convert that back into a Series but is there not another way to do this in one Pandas call or mapping function?
但这给了我一个数组。现在我知道我可以将它转换回系列，但是在 Pandas 调用或映射函数中没有另一种方法可以做到这一点吗？
采纳答案by EdChum
The following worked for me:
以下对我有用：
In [24]:
t="""index,timestamp
2015-02-01 00:00:04,00:00:04
2015-02-01 00:00:08,00:00:04
2015-02-01 00:00:12,00:00:04"""
s = pd.read_csv(io.StringIO(t),parse_dates=[0,1], squeeze=True, index_col=[0])
In [26]:
s.dt.second
Out[26]:
index
2015-02-01 00:00:04 4
2015-02-01 00:00:08 4
2015-02-01 00:00:12 4
dtype: int64
datetime dtype values have a dtaccessor where you can access the seconds attribute.
datetime dtype 值有一个dt访问器，您可以在其中访问 seconds 属性。
