parallel for
# Parallel processing with Pool.apply_async()
import multiprocessing as mp
pool = mp.Pool(mp.cpu_count())
results = []
# Step 1: Redefine, to accept `i`, the iteration number
def howmany_within_range2(i, row, minimum, maximum):
"""Returns how many numbers lie within `maximum` and `minimum` in a given `row`"""
count = 0
for n in row:
if minimum <= n <= maximum:
count = count + 1
return (i, count)
# Step 2: Define callback function to collect the output in `results`
def collect_result(result):
global results
results.append(result)
# Step 3: Use loop to parallelize
for i, row in enumerate(data):
pool.apply_async(howmany_within_range2, args=(i, row, 4, 8), callback=collect_result)
# Step 4: Close Pool and let all the processes complete
pool.close()
pool.join() # postpones the execution of next line of code until all processes in the queue are done.
# Step 5: Sort results [OPTIONAL]
results.sort(key=lambda x: x[0])
results_final = [r for i, r in results]
print(results_final[:10])
#> [3, 1, 4, 4, 4, 2, 1, 1, 3, 3]