DataFrame.to_dict can result in data loss when indexes are not unique
#### Code Sample,

```python
df = pd.DataFrame([["A","B","C"],["A","D","E"],["F","G","K"]])
df.set_index(0, inplace=True)
print(df)
```
---1  2
0      
A  B  C
A  D  E
F  G  K
```python
print(df.to_dict(orient="index"))
```
{'A': {1: 'D', 2: 'E'}, 'F': {1: 'G', 2: 'K'}}

#### Problem description

The to_dict method silently tosses away data when indexes are not unique. At least in my impression the rest of the API doesn’t mind non-unique index’s much, so I don’t like this behavior. I think that it should either preserve the data, or raise an an error / warning saying that to_dict requires unique indexes. 

#### Expected Output
'''
{'A': [{1: 'B', 2: 'C'},{1: 'D', 2: 'E'}], 'F': {1: 'G', 2: 'K'}}

or at least throw an error saying ".to_dict(orient="index") does not support non-unique indexes" 

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


INSTALLED VERSIONS
------------------
commit: None
python: 3.5.5.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.23.4
pytest: None
pip: 10.0.1
setuptools: 40.0.0
Cython: 0.28.5
numpy: 1.15.0
scipy: None
pyarrow: 0.9.0
xarray: None
IPython: 6.5.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
