Produce a new DataFrame with rows ['y', 'z', 'w'] and columns ['a', 'c'],
pulling matching values from df; any row or column label not present in the
original becomes NaN.
Sample data:
df:
a b c
x 1 4 7
y 2 5 8
z 3 6 9
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solve(...)