Hi, today I checked out how to implement custom aggregation functions in Python. Works pretty neat and scales well when you have few columns which you want to aggregate, however I have many :) Imagine that the following aggregate functions has variants which perform a similar iteration but call a different function (not sum). The call to numpy.unique() and the iteration would have to be performed for each aggregated column which is bad in terms of performance. Background: I have records in a key / value like structure so there's a key column and a value column. I want to group by primary key and then create a column for each key. It's like pivot but without aggregation. Any ideas how to do this with SQL or custom functions in MonetDB? Kind regards, Daniel CREATE AGGREGATE python_aggregate(val INTEGER) RETURNS INTEGER LANGUAGE PYTHON { try: unique = numpy.unique(aggr_group) x = numpy.zeros(shape=(unique.size)) for i in range(0, unique.size): x[i] = numpy.sum(val[aggr_group==unique[i]]) except NameError: # aggr_group doesn't exist. no groups, aggregate on all data x = numpy.sum(val) return(x) };