PERF: Switch to try/except in extract() rather than instance check
See #7269 for more. (doesn't completely resolve the speed difference, but it gets a 10-15% improvement).

Timing improvement is there, but it's small.

```
In [20]: %timeit test.str.match(pattern2)
10 loops, best of 3: 170 ms per loop

In [21]: %timeit test.str.extract(pattern2)
10 loops, best of 3: 317 ms per loop

In [22]: %timeit -n try_except_str_extract2(test, pattern2)
10 loops, best of 3: 265 ms per loop
```

@jreback - this is very minor, but if you think this needs a release note,
where should I put it?

This passed locally for me, but if Travis fails I'll fix it up.

is their a vbench for this (I think yes; pls post results )
release notes in v0.15.0 performance section 
 This performs much worse on the vbench. Maybe worth testing for nans apriori - if a lot then use the current method, if not too many, use your methdo.

master

```
In [8]: %timeit many.str.extract(r'(\w*)matchthis(\w*)')
10 loops, best of 3: 62.6 ms per loop
```

0.14.1

```
In [6]: %timeit many.str.extract(r'(\w*)matchthis(\w*)')
10 loops, best of 3: 37.1 ms per loop
```

```
In [1]: import string

In [2]: import itertools as IT

In [3]: def make_series(letters, strlen, size):
   ...:         return Series(
   ...:             np.fromiter(IT.cycle(letters), count=size*strlen, dtype='|S1')
   ...:             .view('|S{}'.format(strlen)))
   ...: 

In [4]: many = make_series('matchthis'+string.uppercase, strlen=19, size=10000) # 31% matches

In [5]: few = make_series('matchthis'+string.uppercase*42, strlen=19, size=10000) # 1% matches

```
 Sure, good call - I didn't check for a vbench. I'll play around with it.

On Sun, Sep 7, 2014 at 6:02 AM, jreback notifications@github.com wrote:

> This performs much worse on the vbench. Maybe worth testing for nans
> apriori - if a lot then use the current method, if not too many, use your
> methdo.
> 
> master
> 
> In [8]: %timeit many.str.extract(r'(\w_)matchthis(\w_)')
> 10 loops, best of 3: 62.6 ms per loop
> 
> 0.14.1
> 
> In [6]: %timeit many.str.extract(r'(\w_)matchthis(\w_)')
> 10 loops, best of 3: 37.1 ms per loop
> 
> In [1]: import string
> 
> In [2]: import itertools as IT
> 
> In [3]: def make_series(letters, strlen, size):
>    ...:         return Series(
>    ...:             np.fromiter(IT.cycle(letters), count=size*strlen, dtype='|S1')
>    ...:             .view('|S{}'.format(strlen)))
>    ...:
> 
> In [4]: many = make_series('matchthis'+string.uppercase, strlen=19, size=10000) # 31% matches
> 
> In [5]: few = make_series('matchthis'+string.uppercase*42, strlen=19, size=10000) # 1% matches
> 
> —
> Reply to this email directly or view it on GitHub
> https://github.com/pydata/pandas/pull/8202#issuecomment-54746027.
 going to close this until I actually find a way to speed this up.
 @jreback I think the difference there has more to do with how many matches were found than with actual speed differences (when I run the two versions with the same data, I get almost imperceptible differences  - not really sure why I found differences previously).

Is there a good way to write a vbench that generates the same random data each time? Can I seed the generator in the setup phase or something?
 sure out in a np.random.seed(...)

(in fact their is an issue to do this for all of the benches.  - but needs some validation)
 put
 yeah I just saw that
