Setting values in empty multi-dimensional dataframe with multi-dimensional data?
I am trying to find a clean, concise way of setting the values of a multi-dimensional, multi-indexed dataframe, using data of lower dimensionality. In this case, I am trying to use two-dimensional data to set values in a four-dimensional dataframe.

Unfortunately, the syntax I am using only works with 2D data if the keys I'm using are already in the dataframe's index/column. But empty dataframes do not have any keys (yet) in their indices/columns.

For single points (0D), this is not a problem. Pandas just adds the missing key(s) appropriately and sets the value. For anything else, the key must be already there, it seems, as the below code shows.

#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import numpy as np

# Create an empty 2-level mux (multi-index) for the index.
# The first level is run number ('r'). The second is x-axis values ('x').
mux = pd.MultiIndex(levels=[[]]*2,labels=[[]]*2,names=['r','x'])

# Create an empty 2-level mux for the column
# The first level is parameter value ('p'). The second is y-axis values ('y').
mux2 = pd.MultiIndex(levels=[[]]*2,labels=[[]]*2,names=['p','y'])

# Create the empty multi-indexed and multi-columned dataframe
df = pd.DataFrame(index=mux,columns=mux2)

# run number 0 (r=0), using parameter value 1.024 (p=1.024)...
# ... produces 2D data on an x-y grid.
data = np.array([[1,2,3],[4,5,6]])
ys = np.array([0,1,2])
xs = np.array([0,1])

# Now we want to set values in the 4D dataframe with our 2D data. Throws error.
df.loc[(0,list(xs)),(1.024,list(ys))] = data

Traceback (most recent call last):
    KeyError: 0
```

But single points work fine.

```python
# Single points automatically result in new keys
df.loc[(0,xs[0]),(1.024,ys[0])] = 1
df.loc[(0,xs[0]),(1.024,ys[1])] = 2
df.loc[(0,xs[1]),(1.024,ys[0])] = 3
df.loc[(0,xs[1]),(1.024,ys[1])] = 4

# Keys are now found, and this now works.
df.loc[(0,list(xs[0:2])),(1.024,list(ys[0:2]))] = ((5,6),(7,8))

# But this does not work. '1' is not currently a key.
df.loc[(1,list(xs[0:2])),(1.024,list(ys[0:2]))] = ((1,2),(3,4))

Traceback (most recent call last):
    KeyError: 1L
```
#### Problem description

It seems the default behavior for setting single points (that is, auto-creation of keys) is different than the behavior of setting multiple points (no auto-creation of keys). This seems pretty arbitrary from my outsider perspective; not sure why the behavior shouldn't be identical.

If there is another way of accomplishing this, I would love to hear about it. But perhaps the point behavior should be extended to multiple dimensions.

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 33.1.0.post20170122
Cython: 0.25.2
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.8.0
xarray: 0.9.1
IPython: 5.2.2
sphinx: 1.5.2
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.45.0
pandas_datareader: 0.2.1
</details>
