认识
Python 的itertools模块提供了很多节省内存的高效迭代器, 尤其解决了一些关于数据量太大而导致内存溢出(outofmemory)的场景.
我们平时用的循环绝大多数是这样的.
# while 循环: 求1+2+...100
s, i = 0, 1
while i <= 100:
s += i
i += 1
print('while-loop: the some of 1+2+..100 is:', s)
# for 循环
s = 0
for i in range(101):
s += i
print('for-loop: the some of 1+2+..100 is:', s)
while-loop: the some of 1+2+..100 is: 5050
for-loop: the some of 1+2+..100 is: 5050
但如果数据量特别大的话就凉凉了, 所以引入了itertools,迭代器, 类似于懒加载的思想
常用API
- chain()
- groupby()
- accumulate()
- compress()
- takewhile()
- islice()
- repeat()
chain 拼接元素
- 把一组迭代对象串联起来，形成一个更大的迭代器：
# join / split
s = "If you please draw me a sheep?"
s1 = s.split()
s2 = "-".join(s1)
print("split->:", s1)
print("join->:", s2)
split->: ['If', 'you', 'please', 'draw', 'me', 'a', 'sheep?']
join->: If-you-please-draw-me-a-sheep?
import itertools
# chain
s = itertools.chain(['if', 'you'], ['please draw', 'me', 'a'], 'shape')
s
<itertools.chain at 0x1d883602240>
list(s)
['if', 'you', 'please draw', 'me', 'a', 's', 'h', 'a', 'p', 'e']
不难发现, 这就是迭代器嘛, 真的没啥.跟join差不多. 那么它是如何节省内存的呢, 其实就是一个简单的迭代器思想, 一次读取一个元素进内存,这样就高效节约内存了呀
def chain(*iterables):
for iter_ in iterables:
for elem in iter_:
yield elem
groupby 相邻元素
- 把迭代器中相邻的重复元素挑出来放在一
# 只要作用于函数的两个元素返回的值相等，这两个元素就被认为是在一组的，而函数返回值作为组的key
for key, group in itertools.groupby('AAABBBCCAAAdde'):
print(key, list(group))
A ['A', 'A', 'A']
B ['B', 'B', 'B']
C ['C', 'C']
A ['A', 'A', 'A']
d ['d', 'd']
e ['e']
# 忽略大小写
for key, group in itertools.groupby('AaaBBbcCAAa', lambda c: c.upper()):
print(key, list(group))
A ['A', 'a', 'a']
B ['B', 'B', 'b']
C ['c', 'C']
A ['A', 'A', 'a']
accumulate 累积汇总
list(itertools.accumulate([1,2,3,4,5], lambda x,y: x*y))
[1, 2, 6, 24, 120]
# 伪代码
def accumulate(iterable, func=None, *, initial=None):
iter_ = iter(iterable)
ret = initial
# 循环迭代
if initial is None:
try:
ret = next(iter_)
except StopIteration:
return
yield ret
# 遍历每个元素, 调用传入的函数去处理
for elem in iter_:
ret = func(elem)
yield ret
compress 过滤
list(itertools.compress('youge', [1,0,True,3]))
['y', 'u', 'g']
def compress(data, selectors):
for d, s in zip(data, selectors):
if s:
return d
# demo
for data, key in zip([1,2], 'abcd'):
print(data,key)
if key:
print(data)
1 a
1
2 b
2
# Pythonic
def compress(data, selectors):
return (d for d, s in zip(data, selectors) if s)
# tset
ret = compress(['love', 'you', 'forever'], ['love', None, 'dd', 'forever'])
print(ret)
print(list(ret))
<generator object compress.<locals>.<genexpr> at 0x000001D8831498E0>
['love', 'forever']
生成器
- 在类中实现了iter()方法和next()方法的对象即生成器
- 代码上有两种形式: 元组生成器 或者 函数中出现 yield 关键字
zip
- 对应位置进行元素拼接, 当最短的匹配上了, 则停止, 也被称为"拉长函数"
take-while
- takewhile: 依次迭代, 满足条件则返回, 继续迭代, 一旦不满足条件则退出
# takewhile
s1 = list(itertools.takewhile(lambda x:x<=2, [0,3,2,1,-1,3,0]))
print(s1)
s2 = list(itertools.takewhile(lambda x:x<5, [1,4,6,4,1,3]))
print(s2)
# dropwhile
s3 = list(itertools.filterfalse(lambda x:x%2==0, range(10)))
print(s3)
[0]
[1, 4]
[1, 3, 5, 7, 9]
def take_while(condition, iter_obj):
for elem in iter_obj:
if conditon(elem):
yield elem
else:
break
dropwhile: 不满足条件的则返回
islice 切片
# 普通的切片,也是要先全部读入内存
# 注意是深拷贝的哦
l = [1,2,3,4,5]
print(l[::--1])
# generator 方式
# 默认的 start, stop, step, 只能传0或正数, 但可以自己改写的呀
list(itertools.islice(l, 0,3,1))
s = slice(3,4,5) # 只接收3个参数
s.start
s.stop
[1, 2, 3, 4, 5]
[1, 2, 3]
3
4
import sys
def slice(iter_obj, *args):
s = slice(*args)
start = s.start or 0
stop = s.stop or sys.maxsize # 很大的常量
step = s.step or 1
# 构成可迭代的对象(下标)
iter_ = iter(range(start, stop, step))
try:
next_i = next(iter_)
except StopIteration:
# for i, elem n zip(range(start), iter_obj):
pass
return
try:
i, elem in enumerate(iter_obj):
if i == next_i:
yield elem
next_i = next(elem)
except StopIteration:
pass
[1, 2, 3, 4, 5]
repeat
list(itertools.repeat(['youge'], 3))
[['youge'], ['youge'], ['youge']]
def repeat(obj, times=None):
if times is None:
while True: # 一直返回
yield obj
else:
for i in range(times):
yield obj