BUG: group_shift_indexer checks for null group keys
- [x] closes #13813
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry

Upon further consideration I came to the conclusion that the FIX could also have looked like this:

``` python
...
                # Skip null keys
                if lab == -1:
                    out[ii] = -1
                    continue
...
```

Now, this raises the question of how groups with partially missing keys should be handled:
- either partial matching of group keys is used, in which case the _-1_ in labels should be converted to proper group identifiers (probably, use [get_group_index](https://github.com/pydata/pandas/blob/master/pandas/core/groupby.py#L4062) with `xnull=False` in [_get_compressed_labels](https://github.com/pydata/pandas/blob/master/pandas/core/groupby.py#L1649));
- or the incomplete keys are ignored (like in **nunique()**, **value_counts()**), and specifically in **shift()** should be filled with missing values.

For example, as far as I understand, in SQL **WHERE** clauses **NULL != NULL**, but in GROUP BY clauses **NULL == NULL**. This means that if pandas's **groupby()** were to be compatible with SQL, it should perform partial key matching.

What do you think, @jreback?

For example how should shifted groupings by ['B', 'C'] of this

```
       B    C     F
0    0.0  NaN   0.0
1    1.0  1.0   1.0
2    2.0  2.0   2.0
3    3.0  NaN   3.0
4    4.0  1.0   4.0
5    5.0  2.0   5.0
6    6.0  NaN   6.0
7    7.0  1.0   7.0
8    8.0  2.0   8.0
9    9.0  NaN   9.0
10  10.0  1.0  10.0
11  11.0  2.0  11.0
12   0.0  NaN  12.0
13   1.0  1.0  13.0
14   2.0  2.0  14.0
15   3.0  NaN  15.0
16   4.0  1.0  16.0
```

differ from this

```
       B    C     F
0    0.0  0.0   0.0
1    1.0  1.0   1.0
2    2.0  2.0   2.0
3    3.0  0.0   3.0
4    4.0  1.0   4.0
5    5.0  2.0   5.0
6    6.0  0.0   6.0
7    7.0  1.0   7.0
8    8.0  2.0   8.0
9    9.0  0.0   9.0
10  10.0  1.0  10.0
11  11.0  2.0  11.0
12   0.0  0.0  12.0
13   1.0  1.0  13.0
14   2.0  2.0  14.0
15   3.0  0.0  15.0
16   4.0  1.0  16.0
```

The shifted grouping of the latter gives

```
       F
0   12.0
1   13.0
2   14.0
3   15.0
4   16.0
5    NaN
6    NaN
7    NaN
8    NaN
9    NaN
10   NaN
11   NaN
12   NaN
13   NaN
14   NaN
15   NaN
16   NaN
```

In the case of partial groupby keys the second alternative could yield:

```
       F
0    NaN
1   13.0
2   14.0
3    NaN
4   16.0
5    NaN
6    NaN
7    NaN
8    NaN
9    NaN
10   NaN
11   NaN
12   NaN
13   NaN
14   NaN
15   NaN
16   NaN
```

I used this code

``` python
import pandas as pd
df = pd.DataFrame([(i%12, i%3 if i%3 else float("nan"), i)
                   for i in range(17)], dtype=float,
                  columns=["B", "C", "F"], index=None)

df = pd.DataFrame([(i%12, i%3, i) for i in range(17)], dtype=float,
                  columns=["B", "C", "F"], index=None)

result = df.groupby(['B', 'C']).shift(-1)
```
 ## [Current coverage](https://codecov.io/gh/pydata/pandas/pull/13819?src=pr) is 85.25% (diff: 100%)

> Merging [#13819](https://codecov.io/gh/pydata/pandas/pull/13819?src=pr) into [master](https://codecov.io/gh/pydata/pandas/branch/master?src=pr) will not change coverage

``` diff
@@             master     #13819   diff @@
==========================================
  Files           140        140          
  Lines         50449      50449          
  Methods           0          0          
  Messages          0          0          
  Branches          0          0          
==========================================
  Hits          43008      43008          
  Misses         7441       7441          
  Partials          0          0          
```

> Powered by [Codecov](https://codecov.io?src=pr). Last update [2c55f28...bddf799](https://codecov.io/gh/pydata/pandas/compare/2c55f28fa7e6468d347894f26d6ecddcfada9cd9...bddf799812202357ca94009206654f44f50cd1a3?src=pr)
 pls add some tests
 Ok, I will make a concise test and add it to [test_groupby.py](https://github.com/pydata/pandas/blob/master/pandas/tests/test_groupby.py).
 Note that the file you're editing is from a template, you will need to make the change here:
https://github.com/pydata/pandas/blob/master/pandas/src/algos_groupby_helper.pxi.in

I think your second approach is correct (or at least consistent) - in fact the current impl seems to handle it?

``` python
In [1]: pd.__version__
Out[1]: u'0.18.1'

In [2]: df = pd.DataFrame([(i%12, i%3 if i%3 else float("nan"), i)
   ...:                    for i in range(17)], dtype=float,
   ...:                   columns=["B", "C", "F"], index=None)

In [3]: df.groupby(['B', 'C']).shift(-1)
Out[3]:
       F
0    NaN
1   13.0
2   14.0
3    NaN
4   16.0
5    NaN
6    NaN
7    NaN
8    NaN
9    NaN
10   NaN
11   NaN
12   NaN
13   NaN
14   NaN
15   NaN
16   NaN
```

edit: ok, actually it seem to be dumb luck the current version (which I had written) gives this answer - it has bounds issues, but just sometimes doesn't crash.  But, it is also what `shift()` would have done before this bug was introduced.

``` python
In [7]: pd.__version__
Out[7]: '0.16.1'

In [8]: df = pd.DataFrame([(i%12, i%3 if i%3 else float("nan"), i)
   ...:                    for i in range(17)], dtype=float,
   ...:                   columns=["B", "C", "F"], index=None)

In [9]: df.groupby(['B', 'C']).shift(-1)
Out[9]: 
     F
0  NaN
1   13
2   14
3  NaN
4   16
5  NaN
6  NaN
7  NaN
8  NaN
9  NaN
10 NaN
11 NaN
12 NaN
13 NaN
14 NaN
15 NaN
16 NaN
```
 Can somebody tell me, please, what my actions should be when the Travis CI job [#21159](https://travis-ci.org/pydata/pandas/builds/148239405) of this PR fails, whilst the [build job](https://travis-ci.org/ivannz/pandas/builds/148239391) for the same commit on my forked (and rebased) repository succeeded.
 @ivannz Something went wrong with that build, but probably not your fault. I restarted the build
 Great! Thank you, @jorisvandenbossche .
 thanks @ivannz 
