DataFrame.from_records() should optionally convert None to NaN
I still run frequently into problems with "NaN" and "None". 
For example:  
I retrieve Data from an SQL DB (postgres in my case) and then I have a list of tuples with "None"  (in SQL NULL) values.
I then convert to a DF by using DataFrame.from_records()
Now, to avoid object Series it would be nice to have None automatically mapped to "NaN"  in the same way as "from_csv" does.
Right now I would have to do something like DF.fillna(np.nan) and then convert the numeric columns again to their original type which is kind of ugly.

```
        time0 = datetime.now()
        result_dataframe = DataFrame.from_records(results, columns=columns)
        time1 = datetime.now()
        for col in result_dataframe:
            if result_dataframe.dtypes[col] == object:
                result_dataframe[col].fillna(np.nan, inplace=True)
        result_dataframe = result_dataframe.from_records(result_dataframe)
        print datetime.now() - time1, time1-time0
```

> > 0:00:07.477906 0:00:01.971754

The fillna takes much longer than the construction of the DF itself. Example with 750k rows.
