sum by region, in energy.

timed("agg", lambda: df.group_by("region").agg(pl.col("amount").sum()))

timed("agg", lambda: con.execute("SELECT region, sum(amount) FROM t GROUP BY region").fetchall())

timed("agg", lambda: df.groupby("region")["amount"].sum())

timed("agg", lambda: df.groupBy("region").agg(F.sum("amount")).collect())

 194  l.t = csv::parse(raw);
 337  const double f5 = timeIt([&] { Bitmap b = compareDoubles(amt.data(), n, Cmp::Gt, 500, &pool); sink = b.words[0]; }, 7);
 372  const double s4 = timeIt([&] { sortIndexByDouble(amt.data(), n, perm, &pool); sink = perm[0]; }, 3);
 454  const double a5 = G <= 16 ? timeIt([&] { groupSumAnyOrder(amtP, idP, n, G, &pool, pairTotal); sink = static_cast<size_t>(pairTotal[0]); }, 9) : 0;

// groupsum.h — a typed column's sum, count and extremes per group: what the aggregate node's measures need
// once the column is doubles (toDoubles) and every row has a group number.
//
// The results are the ones a loop over the rows in order gives — `if (isFinite(v)) { sum += v; n++;
// if (v < min) min = v; if (v > max) max = v; }` a group, which is what the JavaScript does — to the bit:
//   • counts and extremes are taken a part of the rows at a time on the pool and the parts joined in row
//     order (a strict comparison keeps the earlier of two equal values, as the loop does);
//   • a sum is added in row order on one thread, because a floating-point sum depends on its order —
//     unless the column is whole numbers small enough that no addition can round (wholeNumbers below),
//     where every order gives the same bits and the rows are summed on the pool, two a step.
// bench/bench_agg_node.cpp times each path against the node and checks it; PHD.md §30.

// The sum alone, any order, two rows a step; for tests and the benchmark (groupStats calls it when it may).
// False, and nothing written, when there are more groups than it is built for.
bool groupSumAnyOrder(const double* x, const uint32_t* id, size_t n, size_t groups, Pool* pool, double* sums);

// energy.h — what a process has used, as the operating system's own ledger has it: the energy it was billed,
// its processor time, the wake-ups it caused. The one reader of that ledger, for two questions:
//   what a kernel costs — the Evals page's Energy view (bench_engine_options.cpp reads it around each kernel;
//     tools/energy/energy.cpp checks the counter and reads it for a command it runs;
//     bench/lang/python/yardstick.py reads the same field; PHD.md section 33)
//   what a process costs at rest — the idle studies (scripts/idle_cost.sh and scripts/idle_server.py take
//     `neon_energy --pid <pid>` twice and subtract; PHD.md section 31.1)
//
// macOS on Apple silicon: proc_pid_rusage(RUSAGE_INFO_V6). ri_energy_nj — nanojoules billed to the process's
// threads for the processor cores they ran on, since it started. Not the memory, the disk or the screen, and
// not the machine at rest: what running this code added. (Not ri_billed_energy: against half a second of a
// busy loop that field read 0.08 mJ where ri_energy_nj read 1,887.) Beside it, processor time in nanoseconds
// — `ps` has hundredths of a second, more than a resting window uses in a minute — and the wake-ups. No
// privilege is needed, and any process of the same user can be read, so a tree of processes can be summed.
// Linux: /proc/<pid>/stat and /proc/<pid>/status — clock ticks and context switches. No energy: RAPL is one
// figure for the whole machine and root's to read.
// Elsewhere: nothing is read (ok stays false).
//
// A reading is cumulative; a measurement is the difference of two. The counter moves in steps (a thread's
// share is settled when it leaves a core or enters the kernel), so a window should be a quarter of a second
// or more: `neon_energy --check` prints how far apart the steps are on the machine it runs on.