Creating a column with a set replicates the set n times
#### Code Sample

If we try to define a dataframe using a dictionary containing a set, we get:

```python
pd.DataFrame({'a':{1,2,3}})

       a
0  {1, 2, 3}
1  {1, 2, 3}
2  {1, 2, 3}
```
#### Problem description

The set is being replicated `n` times, `n` being the length of the actual set. 
While defining a column with a set directly might not make a lot of sense given that they are by definition unordered collections, the behaviour  in any case seems clearly unexpected. 

#### Expected Output

In the case of a list, in order to obtain a single row containing a list, we would have to define a nested list, such as `pd.DataFrame({'a':[[1,2,3]]})`.
So similarly, with sets I would expect the same behaviour by defining the row with `pd.DataFrame({'a':[{1,2,3}]})`. 

In the case of a single set, even if the order is not guaranteed to be preserved, I'd see more reasonable the same output that we would obtain with:

```python

pd.DataFrame({'a':[1,2,3]})

   a
0  1
1  2
2  3
```
So:

```python
pd.DataFrame({'a':{1,2,3}})

   a
0  1
1  2
2  3
```

Where:
```python
pd.__version__
# '1.0.0'
```
