ENH: improve performance of df.to_csv GH3054
Needs more testing before merging.

Following [SO question](http://stackoverflow.com/questions/15417574) mentioned in #3054:

``` python
In [7]: def df_to_csv(df,fname):
   ...:     fh=open(fname,'w')
   ...:     fh.write(','.join(df.columns) + '\n')
   ...:     for row in df.itertuples(index=False):
   ...:         slist = [str(x) for x in row]
   ...:         ss = ','.join(slist) + "\n"
   ...:         fh.write(ss)
   ...:     fh.close()
   ...: 
   ...: aa=pd.DataFrame({'A':range(100000)})
   ...: aa['B'] = aa.A + 1.0
   ...: aa['C'] = aa.A + 2.0
   ...: aa['D'] = aa.A + 3.0
   ...: 
   ...: %timeit -r10 aa.to_csv('/tmp/junk1',index=False)   
   ...: %timeit -r10 df_to_csv(aa,'/tmp/junk2') 
   ...: from hashlib import sha1
   ...: print sha1(open("/tmp/junk1").read()).hexdigest()
   ...: print sha1(open("/tmp/junk2").read()).hexdigest()
```

<table>
<tr>
<th>current pandas</th><th>with PR</th><th>example code</th>
</tr>
<tr>
<td>2.3 s</td><td>1.29 s</td><td>1.28 s</td>
</tr>
</table>


wins:
- convert numpy numerics to native types to eliminate expensive numpy specific
  stringify calls.
- if number of columns is < 10000, precompute the `cols` loop range rather
  then creating and walking a generator at each iteration of the inner loop.
- some cargo cult stuff that's probably in the noise.
