cross section coercion with output iterating
I'm am trying to call the to_dict function on the following DataFrame:

```
import pandas as pd

data = {"a": [1,2,3,4,5], "b": [90,80,40,60,30]}

df = pd.DataFrame(data)
```

```
df
   a   b
0  1  90
1  2  80
2  3  40
3  4  60
4  5  30
```

```
df.reset_index().to_dict("r")
[{'a': 1, 'b': 90, 'index': 0},
 {'a': 2, 'b': 80, 'index': 1},
 {'a': 3, 'b': 40, 'index': 2},
 {'a': 4, 'b': 60, 'index': 3},
 {'a': 5, 'b': 30, 'index': 4}]

```

However my problem occurs if I perform a float operation on the dataframe, which mutates the index into a float:

```
(df*1.0).reset_index().to_dict("r")
[{'a': 1.0, 'b': 90.0, 'index': 0.0},  
{'a': 2.0, 'b': 80.0, 'index': 1.0},  
{'a': 3.0, 'b': 40.0, 'index': 2.0},  
{'a': 4.0, 'b': 60.0, 'index': 3.0},  
{'a': 5.0, 'b': 30.0, 'index': 4.0}]
```

Can anyone explain the above behaviour or recommend a workaround, or verify whether or not this could be a pandas bug? None of the other outtypes in the to_dict method mutates the index as shown above.

I've replicated this on both pandas 0.14 and 0.18 (latest)

Many thanks!

link to stackoverflow: http://stackoverflow.com/questions/36548151/pandas-to-dict-changes-index-type-with-outtype-records
