## Question

Asked By – user7289

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.

**Now we will see solution for issue: extracting days from a numpy.timedelta64 value **

## Answer

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.

This question is answered By – Viktor Kerkez

**This answer is collected from stackoverflow and reviewed by FixPython community admins, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0 **