Return from to_timedelta is forced to dtype timedelta64[ns]. 
This is a revised change, fixing the issue I experienced (issue #9011). The proper change seems to be setting the dtype to `'timedelta64[ns]'`, as was already done for the other cases in the `to_timedelta` routine. 

I have also added more tests, and moved them to the `test_timedeltas` function (next to the other tests of the functionality of the constructor method). I have not looked too much at #8886, it is difficult for me to understand the issue.

Could you please add a note about the bug fix to what's new for 0.15.2? Depending on timing I think we could still get this into 0.15.2.
 I indicated in a comment that the change needs to happen here: https://github.com/pydata/pandas/blob/master/pandas/tseries/tdi.py#L184
 @jreback It is not obvious to me how to best do the change in [tdi.py](https://github.com/pydata/pandas/blob/master/pandas/tseries/tdi.py#L184) as you mentioned. This is both my first look at pandas code and first pull request, so apologies if I am fumbling.

Anyways, the [docstring](https://github.com/ahjulstad/pandas/blob/e93867063b5731d4e0ead68bd767b6ff0b063a18/pandas/tseries/timedeltas.py#L30) in `to_timedelta` states

```
Returns
-------
ret : timedelta64/arrays of timedelta64 if parsing succeeded
```

But it currently returns with a dtype of `'<m8[ns]'`

```
In [6]: to_timedelta(range(3), unit='s')
Out[6]:
<class 'pandas.tseries.tdi.TimedeltaIndex'>
['00:00:00', ..., '00:00:02']
Length: 3, Freq: None

In [7]: a = to_timedelta(range(3), unit='s')

In [8]: a.dtype
Out[8]: dtype('<m8[ns]')
```
 @ahjulstad Thanks for taking a look at this. `to_timedelta` should return a numpy `timedelta64[ns]` array (or a `TimedeltaIndex`, if `box=True`) -- does that make things clearer?

Can you reproduce this only using `to_timedelta` with `box=False`? (if so, then it's not a `TimedeltaIndex` issue directly)
 In 0.15.1: 

```
In [18]: from pandas.tseries.timedeltas import to_timedelta

In [19]: a = to_timedelta(range(3), unit='s', box=False)

In [20]: print(a)
[         0 1000000000 2000000000]

In [21]: print(a.dtype)
int64
```

After my change:

```

In [7]: from pandas.tseries.timedeltas import to_timedelta

In [8]: a = to_timedelta(range(3), unit='s', box=False)

In [9]: print(a)
[         0 1000000000 2000000000]

In [10]: print(a.dtype)
timedelta64[ns]
```
 OK, this _does_ look like the right fix to me. I don't see how it fits in `TimedeltaIndex`. @jreback thoughts?

It would be great if you add this `box=False` example as another test case (slightly more direct than testing `TimedeltaIndex`).
 see the change here: https://github.com/jreback/pandas/commit/e38e94f438fe836fdbd5c71ad8ae60b4f0bdf041

The constructor code is buggy here, not `to_timedelta`.  The constructor internally always tries to convert to ns based. (m8[ns]). While to_timedelta is user facing.

I always try to avoid using astype because if could be hiding subtle bugs. (In this case it DOES work though).
 This last test fails, though. Floating point arithmetic won't give accurate results, but I was surprised it was so much off:

```
>>> to_timedelta(1.0/3, unit='h')
Timedelta('0 days 00:19:59.998800')
```

The test is:

```
# Tests with fractional hours as input:
expected = np.array([0, 20*60*1000000000, 30*60*1000000000, 40*60*1000000000], dtype='timedelta64[ns]')
result = to_timedelta([0., 1.0/3, 0.5, 2.0/3], unit='h', box=False)
tm.assert_almost_equal(expected, result)
```
 @ahjulstad 

I would create another issue for the floating point. Their is some internal rounding that is necessary to avoid precision issues. It may be that for say h,min its too strict.
 Floating point issue raised in issue #9048. 

As for the astype, I have no strong opinions, of course; I just thought that since all the other main if-branches in `_convert_listlike` included an astype conversion, it would make sense to include it for this specific case as well. 

What is really the difference between `'m8[ns]'` and `'timedelta64[ns]'`?
 those dtypes are exactly the same (m8 is just a short name)
 ok this is fine. pls add a release note. and squash. ping me when green.
 OK. Please clarify what you mean with squash (git is somewhat new to me). Shall I rebase from pydata/pandas/master, and squash all my commits to a single commit with the interactive rebase functionality? Thanks for helping.

From: jreback [mailto:notifications@github.com]
Sent: 9. desember 2014 13:25
To: pydata/pandas
Cc: Åsmund Hjulstad
Subject: Re: [pandas] Return from to_timedelta is forced to dtype timedelta64[ns]. (#9040)

ok this is fine. pls add a release note. and squash. ping me when green.

—
Reply to this email directly or view it on GitHubhttps://github.com/pydata/pandas/pull/9040#issuecomment-66274955.

---

The information contained in this message may be CONFIDENTIAL and is
intended for the addressee only. Any unauthorised use, dissemination of the
information or copying of this message is prohibited. If you are not the
addressee, please notify the sender immediately by return e-mail and delete
this message.
Thank you
 @ahjulstad [That's correct](https://github.com/pydata/pandas/wiki/Using-Git#fetch-and-then-rebase-interactively-to-squash-reword-fix-otherwise-change-some-commits). 
 @TomAugspurger but since origin (ahjulstad/pandas) has the old rework-fix-9011 branch, how can I make it accept my squashed version (that I have created locally?) I did the rebase from upstream directly, without pulling 'through' origin. 

```
> git push origin 

To https://github.com/ahjulstad/pandas.git
 ! [rejected]        rework-fix-9011 -> rework-fix-9011 (non-fast-forward)
error: failed to push some refs to 'https://github.com/ahjulstad/pandas.git'
hint: Updates were rejected because the tip of your current branch is behind
hint: its remote counterpart. Integrate the remote changes (e.g.
hint: 'git pull ...') before pushing again.
hint: See the 'Note about fast-forwards' in 'git push --help' for details.
```
 `git push origin thisbranchname -f`

forces a replacement of the commits to the new ones 
 @jreback Done. 
 @ahjulstad thanks!
