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What you say you want to compute can be done as :

sage: MD=matrix(D)
# R's cumsum is a swell way to compute this "cumulative sum", a nice alternative to itertools.accumulate...
sage: MD=MD.augment(vector(r.cumsum(MD.column(1).list()).sage()))
sage: sum([MD.column(0)[u]*(MD.column(2)[u]<=0.4) for u in range(MD.nrows())])
5750.00000000000

But I think that's not what your'e aiming at, which seems to be the 0.4th quantile of the income distribution. Which is about 1650 by ophthalmic interpolation, not what you compute.

Which I leave to you for now, as an exercise. Yell for help if necessary.

Hint: R and R libraries have tons of utilities for working with distributions and their cumulatives... and the semantics of R boolean indexing are quite helphul to roll your own.

A Sage alternative :

sage: g = spline(zip(MD.column(2), MD.column(0)))
sage: g(0.4)
1642.4180721758974
sage: g(0.9)
4917.845951606835

Explanations on request...

HTH,

What you say you want to compute can be done as :

sage: MD=matrix(D)
# R's cumsum is a swell way to compute this "cumulative sum", a nice alternative to itertools.accumulate...
sage: MD=MD.augment(vector(r.cumsum(MD.column(1).list()).sage()))
sage: sum([MD.column(0)[u]*(MD.column(2)[u]<=0.4) for u in range(MD.nrows())])
5750.00000000000

But I think that's not what your'e aiming at, which seems to be the 0.4th quantile of the income distribution. Which is about 1650 by ophthalmic interpolation, not what you compute.

Which I leave to you for now, as an exercise. Yell for help if necessary.

Hint: R and R libraries have tons of utilities for working with distributions and their cumulatives... and the semantics of R boolean indexing are quite helphul to roll your own.

A Sage alternative :

sage: g = spline(zip(MD.column(2), MD.column(0)))
sage: g(0.4)
1642.4180721758974
sage: g(0.9)
4917.845951606835

Explanations on request...

HTH,