First assignment with logical indexing is slow for Series (but OK for DataFrames)
First assignment with logical indexing is 1000 slower than second, third, etc. for pandas.Series:

```
import pandas.io.data as web
f = web.DataReader("MSFT", 'yahoo')['Adj Close']
%time f[f==0] = np.NaN
%time f[f==0.1] = np.NaN
```

It gives

```
CPU times: user 1.52 s, sys: 1.65 ms, total: 1.52 s
Wall time: 1.52 s
CPU times: user 704 µs, sys: 22 µs, total: 726 µs
Wall time: 720 µs
```

But for DataFrames everything is OK:

```
import pandas.io.data as web
f = web.DataReader(["MSFT","GOOG"], 'yahoo')['Adj Close']
%time f[f==0] = np.NaN
%time f[f==0.1] = np.NaN
```

gives

```
CPU times: user 1.28 ms, sys: 1e+03 ns, total: 1.28 ms
Wall time: 1.28 ms
CPU times: user 1.23 ms, sys: 1 µs, total: 1.23 ms
Wall time: 1.24 ms
```

I used here data from Yahoo!Finance with datetimeindex since I was not able to reproduce this behavior in Series with integer index.

My versions:
pandas: 0.15.2
Cython: 0.21.2
numpy: 1.9.1
dateutil: 2.4.0
pytz: 2014.10
numexpr: 2.4
