quantile function failure 
If I create a dataframe like this:

```
gravity_df = pd.DataFrame(columns=["accel_time_seconds", "gravity_x", "gravity_y", "gravity_z"])
gravity_df['accel_time_seconds'] = list(range(100))
gravity_df.loc[:70, 'gravity_x'] = -0.145438
gravity_df.loc[:70, 'gravity_y'] = -0.355129
gravity_df.loc[:70, 'gravity_z'] = -0.923434
gravity_df.loc[70:, 'gravity_x'] = -0.114892
gravity_df.loc[70:, 'gravity_y'] = -0.428598
gravity_df.loc[70:, 'gravity_z'] = -0.89616
```

quantile function would not work properly for multiple columns:
```
gravity_df[['gravity_x', 'gravity_y']].quantile(0.5)
```

Error message as follows:
```
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-25-17d112cd6897> in <module>
----> 1 gravity_df[['gravity_x']].quantile(0.5)

~/.local/share/virtualenvs/crashes-ETafZQlO/lib/python3.7/site-packages/pandas/core/frame.py in quantile(self, q, axis, numeric_only, interpolation)
   8279 
   8280         result = data._data.quantile(
-> 8281             qs=q, axis=1, interpolation=interpolation, transposed=is_transposed
   8282         )
   8283 

~/.local/share/virtualenvs/crashes-ETafZQlO/lib/python3.7/site-packages/pandas/core/internals/managers.py in quantile(self, axis, consolidate, transposed, interpolation, qs, numeric_only)
    533 
    534         # single block, i.e. ndim == {1}
--> 535         values = _concat._concat_compat([b.values for b in blocks])
    536 
    537         # compute the orderings of our original data

~/.local/share/virtualenvs/crashes-ETafZQlO/lib/python3.7/site-packages/pandas/core/dtypes/concat.py in _concat_compat(to_concat, axis)
    172                 to_concat = [x.astype("object") for x in to_concat]
    173 
--> 174     return np.concatenate(to_concat, axis=axis)
    175 
    176 

ValueError: need at least one array to concatenate
```

