def avg(data, column):
global __is_aggregate
__is_aggregate = True
vals = [row[column] for row in data]
data = parallel.run(parallel.map(
lambda chunk: [(sum([int(line) for line in chunk]), len(chunk))]),
vals,
'avg()'
)
dividend = parallel.run(parallel.reduce(lambda data: sum([d[0] for d in data], 0.0)), data)
divisor = parallel.run(parallel.reduce(lambda data: sum([d[1] for d in data])), data)
return sum(dividend)/sum(divisor)
def min(data, column):
global __is_aggregate
__is_aggregate = True
min = __builtins__['min']
vals = [row[column] for row in data]
return min(parallel.run(parallel.reduce(lambda chunk: min([int(i) for i in chunk])), vals))