Serious performance regression in DataFrame construction with monthly DatetimeIndex
Hi,

After upgrading from v0.12.0 to v0.13.1, I noticed about a 100% slowdown on a pandas-heavy project. I've just started looking, but I've come up with a test case that shows a time-complexity change from O(1) to O(n) (~240x slowdown for my inputs).

Here's the comparison for v0.12.0 (y-axis is milliseconds):

![perf_12](https://f.cloud.github.com/assets/440095/2263404/746ae6ce-9e65-11e3-92c2-5d7d3d2c6cf9.png)

And the comparison for v0.13.1:

![perf_131](https://f.cloud.github.com/assets/440095/2263407/791db6a6-9e65-11e3-940e-cb97ffc53809.png)

The test code (I'll convert this to vbench later):

```
rows = 1000
columns = 10
data = DataFrame(np.random.random((rows, columns)), index=DatetimeIndex(start='1/1/1900', periods=rows, freq='M'))

d = {}

for col in data:
    d[col] = data[col]

%timeit DataFrame(d)
```

Daily indices don't appear to be affected, though I suspect other frequencies are impacted. I'm seeing similar regressions in v0.13.0.
