如何在python中有效地检查元素是否在列表列表中

Question

我有如下清单。你知道吗

mylist = [[5274919, ["report", "porcelain", "firing", "technic"]], [5274920, ["implantology", "dentistry"]], [52749, ["method", "recognition", "long", "standing", "root", "perforation", "molar"]], [5274923, ["exogenic", "endogenic", "cause", "tooth", "jaw", "anomaly", "method", "method", "standing"]]]

我还有一个概念列表，如下所示。你知道吗

myconcepts = ["method", "standing"]

我想看看myconcepts中的每个概念在mylist记录中出现了多少次。i、 e

"method" = 2 times in records (i.e. in `52749` and `5274923`)
"standing" = 2 times in records

我现在的代码如下。你知道吗

mycounting = 0
for concept in myconcepts:
  for item in mylist:
     if concept in item[1]:
       mycounting = mycounting + 1
print(mycounting)

但是，我现在的mylist非常大，有大约500万条记录。myconcepts这个列表有大约10000个概念。你知道吗

在我当前的代码中，一个概念需要将近1分钟才能得到count，这非常慢。你知道吗

我想知道在python中最有效的方法是什么？你知道吗

出于测试目的，我将数据集的一小部分附加在：https://drive.google.com/file/d/1z6FsBtLyDZClod9hK8nK4syivZToa7ps/view?usp=sharing

如果需要，我很乐意提供更多细节。你知道吗

Answer 1

从https://www.geeksforgeeks.org/python-count-the-sublists-containing-given-element-in-a-list/调整方法3

from itertools import chain 
from collections import Counter 

mylist = [[5274919, ["report", "porcelain", "firing", "technic"]], [5274920, ["implantology", "dentistry"]], [52749, ["method", "recognition", "long", "standing", "root", "perforation", "molar"]], [5274923, ["exogenic", "endogenic", "cause", "tooth", "jaw", "anomaly", "method", "method", "standing"]]]

myconcepts = ["method", "standing"]

def countList(lst, x):
" Counts number of times item x appears in sublists "
    return Counter(chain.from_iterable(set(i[1]) for i in lst))[x] 

# Use dictionary comprehension to apply countList to concept list
result = {x:countList(mylist, x) for x in myconcepts}
print(result) # {'method':2, 'standing':2}

*修改当前方法（只计算一次计数）*

def count_occurences(lst):
    " Number of counts of each item in all sublists "
    return Counter(chain.from_iterable(set(i[1]) for i in lst))

cnts = count_occurences(mylist)
result = {x:cnts[x] for x in myconcepts}
print(result) # {'method':2, 'standing':2}

性能（使用Jupyter笔记本比较发布的方法）

结果表明，该方法与Barmar贴纸法相近（即36对42 us）

对当前方法的改进减少了大约一半的时间（即从36 us减少到19 us）。对于更多的概念（即问题有超过1000个概念），这种改进应该更为重要。你知道吗

然而，原来的方法速度更快，为2.55us/圈。你知道吗

方法当前方法

%timeit { x:countList(mylist, x) for x in myconcepts}
#10000 loops, best of 3: 36.6 µs per loop

Revised current method:

%%timeit
cnts = count_occurences(mylist)
result = {x:cnts[x] for x in myconcepts}
10000 loops, best of 3: 19.4 µs per loop

方法2（来自Barmar post）

%%timeit
r = collections.Counter(flatten(mylist))
{i:r.get(i, 0) for i in myconcepts}
# 10000 loops, best of 3: 42.7 µs per loop

方法3（原始方法）

%%timeit

result = {}
for concept in myconcepts:
  mycounting = 0
  for item in mylist:
     if concept in item[1]:
       mycounting = mycounting + 1
  result[concept] = mycounting
  # 100000 loops, best of 3: 2.55 µs per loop