extracting days from a numpy.timedelta64 value

问题:

I am using pandas/python and I have two date time series s1 and s2, that have been generated using the 'to_datetime' function on a field of the df containing dates/times.

When I subtract s1 from s2

s3 = s2 - s1

I get a series, s3, of type

timedelta64[ns]

0    385 days, 04:10:36 1     57 days, 22:54:00 2    642 days, 21:15:23 3    615 days, 00:55:44 4    160 days, 22:13:35 5    196 days, 23:06:49 6     23 days, 22:57:17 7      2 days, 22:17:31 8    622 days, 01:29:25 9     79 days, 20:15:14 10    23 days, 22:46:51 11   268 days, 19:23:04 12                  NaT 13                  NaT 14   583 days, 03:40:39 

How do I look at 1 element of the series:

s3[10]

I get something like this:

numpy.timedelta64(2069211000000000,'ns')

How do I extract days from s3 and maybe keep them as integers(not so interested in hours/mins etc.)?

Thanks in advance for any help.

回答1:

You can convert it to a timedelta with a day precision. To extract the integer value of days you divide it with a timedelta of one day.

>>> x = np.timedelta64(2069211000000000, 'ns') >>> days = x.astype('timedelta64[D]') >>> days / np.timedelta64(1, 'D') 23 

Or, as @PhillipCloud suggested, just days.astype(int) since the timedelta is just a 64bit integer that is interpreted in various ways depending on the second parameter you passed in ('D', 'ns', ...).

You can find more about it here.

回答2:

Use dt.days to obtain the days attribute as integers.

For eg:

In [14]: s = pd.Series(pd.timedelta_range(start='1 days', end='12 days', freq='3000T'))  In [15]: s Out[15]:  0    1 days 00:00:00 1    3 days 02:00:00 2    5 days 04:00:00 3    7 days 06:00:00 4    9 days 08:00:00 5   11 days 10:00:00 dtype: timedelta64[ns]  In [16]: s.dt.days Out[16]:  0     1 1     3 2     5 3     7 4     9 5    11 dtype: int64 

More generally - You can use the .components property to access a reduced form of timedelta.

In [17]: s.dt.components Out[17]:     days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds 0     1      0        0        0             0             0            0 1     3      2        0        0             0             0            0 2     5      4        0        0             0             0            0 3     7      6        0        0             0             0            0 4     9      8        0        0             0             0            0 5    11     10        0        0             0             0            0 

Now, to get the hours attribute:

In [23]: s.dt.components.hours Out[23]:  0     0 1     2 2     4 3     6 4     8 5    10 Name: hours, dtype: int64 

回答3:

Suppose you have a timedelta series:

import pandas as pd from datetime import datetime z = pd.DataFrame({'a':[datetime.strptime('20150101', '%Y%m%d')],'b':[datetime.strptime('20140601', '%Y%m%d')]})  td_series = (z['a'] - z['b']) 

One way to convert this timedelta column or series is to cast it to a Timedelta object (pandas 0.15.0+) and then extract the days from the object:

td_series.astype(pd.Timedelta).apply(lambda l: l.days) 

Another way is to cast the series as a timedelta64 in days, and then cast it as an int:

td_series.astype('timedelta64[D]').astype(int) 

文章来源: extracting days from a numpy.timedelta64 value