PERFORMANCE: Very slow vectorized string functions
Using pandas 0.10.
  The docs say pandas has vectorized string functions, but they're running far slower for me than plain python (about 6x slower). Here's two nearly identical versions of the code with wildly different performance numbers. Why is the vectorized version so much slower?
   I thought the difference might be because the vectorized version has to create an extra temporary Series object that gets thrown away. But then I tried to control for that by creating an extra Series object in my fast version, and that didn't change anything. So why is the vectorized version slow? 

``` python
import pandas
strings = [ "hello world" for ii in xrange(10000) ]
p = pandas.DataFrame({ "strings" : strings })

#Use vectorized pandas. 
#Takes ~2 min on my computer
for ii in xrange(1000) :
    p['isHello'] = p['strings'].str.endswith('world')

#Use non-vectorized list comprehension. 
#Takes ~20 seconds on my computer.
for ii in xrange(1000) :
    p['isHello'] = [s.endswith('world') for s in p['strings'].values]

#Non-vectorized version, but also create a temp Series object
#Still takes only ~20 seconds
for ii in xrange(1000) :
    p['isHello'] = pandas.Series([s.endswith('world') for s in p['strings'].values])
```
