详细分析python3的reduce函数

reduce(...)
reduce(function, sequence[, initial]) -> value

Apply a function of two arguments cumulatively to the items of a sequence,
from left to right, so as to reduce the sequence to a single value.
For example, reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) calculates
((((1+2)+3)+4)+5). If initial is present, it is placed before the items
of the sequence in the calculation, and serves as a default when the
sequence is empty.

从左到右对一个序列的项累计地应用有两个参数的函数，以此合并序列到一个单一值。

例如，reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) 计算的就是((((1+2)+3)+4)+5)。

如果提供了 initial 参数，计算时它将被放在序列的所有项前面，如果序列是空的，它也就是计算的默认结果值了

>>> def add(x, y):
...  return x+y
...
>>> from functools import reduce
>>> reduce(add, [1,2,3,4])
>>>

>>> sum([1,2,3,4])
10
>>>

>>> from functools import reduce
>>> reduce(lambda x, y: x * 10 + y, [1 , 2, 3, 4, 5])
12345
>>>

from functools import reduce
scientists =({'name':'Alan Turing', 'age':105},
    {'name':'Dennis Ritchie', 'age':76},
    {'name':'John von Neumann', 'age':114},
    {'name':'Guido van Rossum', 'age':61})
def reducer(accumulator , value):
 sum = accumulator['age'] + value['age']
 return sum
total_age = reduce(reducer, scientists)
print(total_age)

from functools import reduce
scientists =({'name':'Alan Turing', 'age':105, 'gender':'male'},
    {'name':'Dennis Ritchie', 'age':76, 'gender':'male'},
    {'name':'Ada Lovelace', 'age':202, 'gender':'female'},
    {'name':'Frances E. Allen', 'age':84, 'gender':'female'})
def reducer(accumulator , value):
 sum = accumulator + value['age']
 return sum
total_age = reduce(reducer, scientists, 0)
print(total_age)

sum([x['age'] for x in scientists ])

from functools import reduce
scientists =({'name':'Alan Turing', 'age':105, 'gender':'male'},
    {'name':'Dennis Ritchie', 'age':76, 'gender':'male'},
    {'name':'Ada Lovelace', 'age':202, 'gender':'female'},
    {'name':'Frances E. Allen', 'age':84, 'gender':'female'})
def group_by_gender(accumulator , value):
 accumulator[value['gender']].append(value['name'])
 return accumulator
grouped = reduce(group_by_gender, scientists, {'male':[], 'female':[]})
print(grouped)

{'male': ['Alan Turing', 'Dennis Ritchie'], 'female': ['Ada Lovelace', 'Frances E. Allen']}

grouped = reduce(group_by_gender, scientists, collections.defaultdict(list))

import itertools
scientists =({'name':'Alan Turing', 'age':105, 'gender':'male'},
    {'name':'Dennis Ritchie', 'age':76, 'gender':'male'},
    {'name':'Ada Lovelace', 'age':202, 'gender':'female'},
    {'name':'Frances E. Allen', 'age':84, 'gender':'female'})
grouped = {item[0]:list(item[1])
   for item in itertools.groupby(scientists, lambda x: x['gender'])}
print(grouped)

from functools import reduce
scientists =({'name':'Alan Turing', 'age':105, 'gender':'male'},
    {'name':'Dennis Ritchie', 'age':76, 'gender':'male'},
    {'name':'Ada Lovelace', 'age':202, 'gender':'female'},
    {'name':'Frances E. Allen', 'age':84, 'gender':'female'})
grouped = reduce(lambda acc, val: {**acc, **{val['gender']: acc[val['gender']]+ [val['name']]}}, scientists, {'male':[], 'female':[]})
print(grouped)