xarray

The pandas pivot tables documentation seems to recomend dealing with more than two dimensions of data by using multiindexing: In [1]: import pandas as pd In [2]: import numpy as np In [3]: import pandas.util.testing as tm; tm.N = 3 In [4]: def unpivot(frame): ...: N, K = frame.shape ...: data = {'value' : frame.values.ravel('F'), ...: 'variable' : np.asarray(frame.columns).repeat(N), ...: 'date' : np.tile(np.asarray(frame.index), K)} ...: return pd.DataFrame(data, columns=['date', 'variable', 'value']) ...: In [5]: df = unpivot(tm.makeTimeDataFrame()) In [6]: df Out[6]: date variable value

When I do running / rolling mean with weights in numpy, I e.g. do something like this: data = np.random.random(100) # Example data... weights = np.array([1, 2, 1]) data_m = np.convolve(data, weights/float(np.sum(weights)), "same") And then replace data_m[0] and data_m[-1] with e.g. nans, depending on application. Something alike can be done with xarray. What I do (in this case) is xr.DataArray(data).rolling(dim_0=3, center=True).mean(dim="dim_0") But this corresponds to the weights weights = np.array([1, 1, 1]) in the numpy example. How would I apply other weights, when using xarray? The