GroupBy

Coral.GroupBy.group_by(frame, key_name) groups rows by one integer, float, or string column. Groups appear in the order their keys first occur. Bool keys fail when grouping runs.

Call agg_sum, agg_mean, agg_min, or agg_max with a grouped frame and a float or integer value-column name. Masked integer values are skipped. agg_count(grouped) counts rows, including rows whose value columns have missing entries. value_counts(frame, key_name) returns the key and a count column.

Use agg(grouped, specs) for several results in the order listed. A spec pairs a value-column name with AggSum, AggMean, AggCount, AggMin, or AggMax. Each value column may appear only once, and AggCount in this form also requires a float or integer value column. For a plain row count, use agg_count.

module Coral.BookGroupBy
import Coral.Frame (FloatCol, StringCol, from_pairs, nrows, int_col_of_list)
import Coral.GroupBy (group_by, agg, AggSum, AggMean)
export (main)
def main() -> i64 = {
  frame = from_pairs([("city", StringCol(["london", "paris", "london"])), ("qty", int_col_of_list([5i64, 6i64, 7i64])), ("price", FloatCol(to_tensor([10.0f32, 20.0f32, 30.0f32])))])
  grouped = group_by(frame, "city")
  totals = agg(grouped, [("qty", AggSum), ("price", AggMean)])
  nrows(totals)
}

main returns 2, one row per city. The result columns are city, qty_sum, and price_mean.