id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
305702311,MDU6SXNzdWUzMDU3MDIzMTE=,1993,DataArray.rolling().mean() is way slower than it should be,1217238,closed,0,,,5,2018-03-15T20:10:22Z,2018-03-18T08:56:27Z,2018-03-18T08:56:27Z,MEMBER,,,,"#### Code Sample, a copy-pastable example if possible

From @RayPalmerTech in https://github.com/kwgoodman/bottleneck/issues/186:
```python
import numpy as np
import pandas as pd
import time
import bottleneck as bn
import xarray
import matplotlib.pyplot as plt

N = 30000200 # Number of datapoints
Fs = 30000 # sample rate
T=1/Fs # sample period
duration = N/Fs # duration in s
t = np.arange(0,duration,T) # time vector
DATA = np.random.randn(N,)+5*np.sin(2*np.pi*0.01*t) # Example noisy sine data and window size
w = 330000

def using_bottleneck_mean(data,width):
  return bn.move_mean(a=data,window=width,min_count = 1)

def using_pandas_rolling_mean(data,width):
  return np.asarray(pd.DataFrame(data).rolling(window=width,center=True,min_periods=1).mean()).ravel()

def using_xarray_mean(data,width):
  return xarray.DataArray(data,dims='x').rolling(x=width,min_periods=1, center=True).mean()

start=time.time()
A = using_bottleneck_mean(DATA,w)
print('Bottleneck: ', time.time()-start, 's')
start=time.time()
B = using_pandas_rolling_mean(DATA,w)
print('Pandas: ',time.time()-start,'s')
start=time.time()
C = using_xarray_mean(DATA,w)
print('Xarray: ',time.time()-start,'s')
```

This results in:
```
Bottleneck:  0.0867006778717041 s
Pandas:  0.563546895980835 s
Xarray:  25.133142709732056 s
```

Somehow xarray is way slower than pandas and bottleneck, even though it's using bottleneck under the hood!

#### Problem description

Profiling shows that the majority of time is spent in `xarray.core.rolling.DataArrayRolling._setup_windows`. Monkey-patching that method with a dummy rectifies the issue:
```
xarray.core.rolling.DataArrayRolling._setup_windows = lambda *args: None
```

Now we obtain:
```
Bottleneck:  0.06775331497192383 s
Pandas:  0.48262882232666016 s
Xarray:  0.1723031997680664 s
```

The solution is to make setting up windows done lazily (in `__iter__`), instead of doing it in the constructor.

#### Output of ``xr.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.96+
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

xarray: 0.10.2
pandas: 0.22.0
numpy: 1.14.2
scipy: 0.19.1
netCDF4: None
h5netcdf: None
h5py: 2.7.1
Nio: None
zarr: None
bottleneck: 1.2.1
cyordereddict: None
dask: None
distributed: None
matplotlib: 2.1.2
cartopy: None
seaborn: 0.7.1
setuptools: 36.2.7
pip: 9.0.1
conda: None
pytest: None
IPython: 5.5.0
sphinx: None
</details>
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