issues: 479434052

Now that we have preliminary support for sparse arrays in xarray, one really cool feature we could explore is creating sparse arrays from MultiIndexed pandas DataFrames.

Right now, xarray's methods for creating objects from pandas always create dense arrays, but the size of these dense arrays can get big really quickly if the MultiIndex is sparsely populated, e.g., python import pandas as pd import numpy as np import xarray df = pd.DataFrame({ 'w': range(10), 'x': list('abcdefghij'), 'y': np.arange(0, 100, 10), 'z': np.ones(10), }).set_index(['w', 'x', 'y']) print(xarray.Dataset.from_dataframe(df)) This length 10 DataFrame turned into a dense array with 1000 elements (only 10 of which are not NaN): <xarray.Dataset> Dimensions: (w: 10, x: 10, y: 10) Coordinates: * w (w) int64 0 1 2 3 4 5 6 7 8 9 * x (x) object 'a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' 'i' 'j' * y (y) int64 0 10 20 30 40 50 60 70 80 90 Data variables: z (w, x, y) float64 1.0 nan nan nan nan nan ... nan nan nan nan 1.0

We can imagine xarray.Dataset.from_dataframe(df, sparse=True) would make the same Dataset, but with sparse array (with a NaN fill value) instead of dense arrays.

Once sparse arrays work pretty well, this could actually obviate most of the use cases for MultiIndex in arrays. Arguably the model is quite a bit cleaner.