Compute the histogram of a csv file with a python parallel program

phisto.py

import sys
from multiprocessing import Pool

# Compute the histogram of a csv file with a python parallel program
# Input file format:
#   name,count\n
# Output the frequency of "count".

# Stolen from http://mikecvet.wordpress.com/2010/07/02/parallel-mapreduce-in-python/
# Adapted to histogram computation instead of word count
# And ported to python3

"""
Given a list of tokens, return a list of tuples of
titlecased (or proper noun) tokens and a count of '1'.
Also remove any leading or trailing punctuation from
each token.
"""
def Map(L):
  results = []
  for w in L:
    results.append ((w, 1))
  return results


"""
Group the sublists of (token, 1) pairs into a term-frequency-list
map, so that the Reduce operation later can work on sorted
term counts. The returned result is a dictionary with the structure
{token : [(token, 1), ...] .. }
"""
def Partition(L):
  tf = {}
  for sublist in L:
    for p in sublist:
      # Append the tuple to the list in the map
      try:
        tf[p[0]].append (p)
      except KeyError:
        tf[p[0]] = [p]
  return tf


"""
Given a (token, [(token, 1) ...]) tuple, collapse all the
count tuples from the Map operation into a single term frequency
number for this token, and return a final tuple (token, frequency).
"""
def Reduce(Mapping):
  return (Mapping[0], sum(pair[1] for pair in Mapping[1]))



"""
Load the contents the file at the given
path into a big list and return it.
"""
def load(path):
  stars = []
  with open(path, "r") as f:
    for line in f:
      stars.append(int(line.split()[1]))

  # Efficiently concatenate Python string objects
  # return (''.join(stars)).split ()
  return stars


"""
A generator function for chopping up a given list into chunks of
length n.
"""
def chunks(l, n):
  for i in range(0, len(l), n):
    yield l[i:i+n]


if __name__ == '__main__':

  if (len(sys.argv) != 3):
    print("Usage: phisto file nprocs")
    sys.exit(1)

  nprocs=int(sys.argv[2])

  print("Load file, stuff it into a string",file=sys.stderr)
  stars = load (sys.argv[1])

  print("Build a pool of 8 processes",file=sys.stderr)
  pool = Pool(processes=nprocs)

  print("Fragment the string data into 8 chunks",file=sys.stderr)
  partitioned_stars = list(chunks(stars, len(stars) // nprocs))

  print("Generate count tuples for title-cased tokens",file=sys.stderr)
  single_count_tuples = pool.map(Map, partitioned_stars)

  print("Organize the count tuples; lists of tuples by token key",file=sys.stderr)
  token_to_tuples = Partition(single_count_tuples)

  print("Collapse the lists of tuples into total term frequencies",file=sys.stderr)
  term_frequencies = pool.map(Reduce, token_to_tuples.items())

  print("Sort the term frequencies in increasing order",file=sys.stderr)
  # term_frequencies.sort(key=lambda x: x[1]) # nb of projects
  term_frequencies.sort(key=lambda x: x[0])   # nb of stars

  for pair in term_frequencies[:20]:
    print( "%i occurs %i times" % pair )