问题描述
我正在处理一些使用 itertools.imap
生成的迭代器,我想如果有一种方法可以访问 for-loop 我用来遍历元素。
我可以肯定的是,迭代器不会生成无限量的数据。
另外,因为循环的信息是从查询到数据库的,所以我可以从那里得到信息的长度,但是我使用的函数必须返回一个迭代器。
我想到了一些选择:
def iterator_function(some,arguments,that,I,need):
query_result = query()
return_iterator = itertools.imap(
mapping_function,
query_result
)
return return_iterator
因为我不行改变返回的迭代器,我想到了一些(真的很丑),如:
query_result = query()
query_size = len(query_result)
$ b return_iterator = itertools .imap(
lambda item:(mapping_function(item),query_size),
query_result
)
return return_iterator
但是我不太喜欢这个选项,我在想Python是否有办法从循环中获取迭代器大小在迭代器中,如下所示:
$ b
for iterator()中的项目:
print item.iterator_length()
#做其他的东西
甚至类似于:
打印iterator.length()#或iterator() 。长度()???谢谢!
假设你有一个好的数据库,在那里进行计数。怀疑python中的任何解决方案都会更快更清洁。
I'm working with some iterator generated using itertools.imap
, and I was thinking if there is a way to access the iterator length inside the for-loop
that I use to loop over the elements.
What I can say for sure is that the iterator doesn't generate an infinite amount of data.
Also, because the information I'm looping are from a query to a database, I can get the length of the information from there, but the function I'm using has to return an iterator.
I thought of some options:
def iterator_function(some, arguments, that, I, need):
query_result = query()
return_iterator = itertools.imap(
mapping_function,
query_result
)
return return_iterator
Because I cannot change the returned iterator, I thought of something (really ugly) like:
query_result = query()
query_size = len(query_result)
return_iterator = itertools.imap(
lambda item: (mapping_function(item), query_size),
query_result
)
return return_iterator
But I don't really like this option, and I was thinking if there is a way, in Python, to get the iterator size from inside the loop over the iterator, something like:
for item in iterator():
print item.iterator_length()
# do other stuff
Or even something like:
for item in iterator():
print iterator.length() # or iterator().length()???
Thanks!
Assuming you have a good database, do the count there. Doubt any solution in python will be faster/cleaner.
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