def map_reduce(data, mapper, reducer=None):
'''Simple map/reduce for data analysis.
Each data element is passed to a *mapper* function.
The mapper returns key/value pairs
or None for data elements to be skipped.
Returns a dict with the data grouped into lists.
If an optional *reducer* is specified, it aggregates each list.
>>> def even_odd(elem): # sample mapper
... if 10 <= elem <= 20: # skip elems outside the range
... key = elem % 2 # group into evens and odds
... return key, elem
>>> map_reduce(range(30), even_odd) # group into evens and odds
{0: [10, 12, 14, 16, 18, 20], 1: [11, 13, 15, 17, 19]}
>>> map_reduce(range(30), even_odd, sum) # sum each group
{0: 90, 1: 75}
>>> map_reduce(range(30), even_odd, len) # size of each group
{0: 6, 1: 5}
'''
d = {}
for entry in data:
r = mapper(entry)
if r is not None:
k, v = r
d.setdefault(k, []).append(v)
if reducer is not None:
for k, group in d.items():
d[k] = reducer(group)
return d
if __name__ == '__main__':
from collections import namedtuple
from pprint import pprint
Person = namedtuple('Person', ['name', 'gender', 'age', 'height'])
persons = [
Person('mary', 'fem', 20, 60.2),
Person('suzy', 'fem', 30, 50.1),
Person('jane', 'fem', 20, 58.1),
Person('jill', 'fem', 20, 49.1),
Person('bess', 'fem', 40, 56.6),
Person('john', 'mal', 20, 50.8),
Person('jack', 'mal', 40, 59.1),
Person('jase', 'mal', 50, 60.3),
Person('zack', 'mal', 40, 53.7),
Person('ambr', 'fem', 20, 57.0),
Person('bill', 'mal', 20, 62.1)
]
def height_by_gender_and_agegroup(p):
key = p.gender, p.age //10
val = p.height
return key, val
def avg(s):
return fsum(s) / len(s)
pprint(persons) # input dataset
pprint(map_reduce(persons, lambda p: ((p.gender, p.age), p), None)) # grouped people
pprint(map_reduce(persons, height_by_gender_and_agegroup, None)) # grouped heights
pprint(map_reduce(persons, height_by_gender_and_agegroup, len)) # size of each group
pprint(map_reduce(persons, height_by_gender_and_agegroup, max)) # maximum height by group
pprint(map_reduce(persons, height_by_gender_and_agegroup, avg)) # average height by group
Diff to Previous Revision
--- revision 1 2011-04-25 20:25:55
+++ revision 2 2011-04-25 21:59:10
@@ -1,13 +1,40 @@
def map_reduce(data, mapper, reducer=None):
- 'The mapper returns key/value pairs. Optional reducer aggregates values.'
+ '''Simple map/reduce for data analysis.
+
+ Each data element is passed to a *mapper* function.
+ The mapper returns key/value pairs
+ or None for data elements to be skipped.
+
+ Returns a dict with the data grouped into lists.
+
+ If an optional *reducer* is specified, it aggregates each list.
+
+ >>> def even_odd(elem): # sample mapper
+ ... if 10 <= elem <= 20: # skip elems outside the range
+ ... key = elem % 2 # group into evens and odds
+ ... return key, elem
+
+ >>> map_reduce(range(30), even_odd) # group into evens and odds
+ {0: [10, 12, 14, 16, 18, 20], 1: [11, 13, 15, 17, 19]}
+
+ >>> map_reduce(range(30), even_odd, sum) # sum each group
+ {0: 90, 1: 75}
+
+ >>> map_reduce(range(30), even_odd, len) # size of each group
+ {0: 6, 1: 5}
+
+ '''
d = {}
for entry in data:
- k, v = mapper(entry)
- d.setdefault(k, []).append(v)
+ r = mapper(entry)
+ if r is not None:
+ k, v = r
+ d.setdefault(k, []).append(v)
if reducer is not None:
for k, group in d.items():
d[k] = reducer(group)
return d
+
if __name__ == '__main__':
