b = " ".join(x for x in lst)
时间: 2023-03-05 15:27:00 浏览: 46
这行代码的作用是将一个列表 lst 中的所有元素用空格连接成一个字符串,并将结果赋值给变量 b。具体来说,它使用了列表推导式,遍历列表 lst 中的每个元素 x,然后使用字符串的 join 方法将它们用空格连接起来。最终的字符串赋值给了变量 b。
相关问题
class Node: def __init__(self, value): self.value = value self.next = None class CircularLinkedList: def __init__(self): self.head = None def add(self, value): node = Node(value) if not self.head: self.head = node node.next = node else: curr = self.head while curr.next != self.head: curr = curr.next curr.next = node node.next = self.head def remove(self, node): if not self.head: return if self.head == node: if self.head.next == self.head: self.head = None else: curr = self.head while curr.next != self.head: curr = curr.next curr.next = self.head.next self.head = self.head.next else: prev = self.head curr = self.head.next while curr != self.head: if curr == node: prev.next = curr.next break prev = curr curr = curr.next def josephus(n, m): lst = CircularLinkedList() for i in range(1, n + 1): lst.add(i) count = 1 curr = lst.head while lst.head and lst.head.next != lst.head: if count == m: print(curr.value, end=' ') next_node = curr.next lst.remove(curr) curr = next_node count = 1 else: curr = curr.next count += 1 for node in lst.head, lst.head.next: print(node.value, end=' ')
这段代码实现了约瑟夫问题(Josephus Problem),其中 CircularLinkedList 是一个循环链表,add() 方法用于向链表中添加元素,remove() 方法用于删除指定的节点,josephus() 方法则用于解决约瑟夫问题,并返回最后留下的元素。
具体来说,假设有 n 个人围成一圈,从第一个人开始报数,报到 m 的人出圈,接着从出圈的下一个人开始重新报数,再次报到 m 的人出圈,直到剩下最后一个人为止。该问题的解法就是遍历循环链表,每次找到报数为 m 的节点,将其从链表中删除,直到链表中只剩下一个或者两个节点。最后输出剩下节点的值即可。
优化代码import os import re import pandas as pd from pandas import DataFrame lst1=[] lst2=[] path1 = r'D:\C-cn' path2 = r'D:\C-en' files1 = os.listdir(path1) files2 = os.listdir(path2) lst1=[] lst2=[] reg1=re.compile(r"[^。?!…]*[。?!……]") reg2=re.compile(r'.*\.[\n ]') df1 = [] df2 = [] for i in range(0,39): domain=os.path.abspath(r'D:\C-cn') file1=os.path.join(domain,files1[i]) fn = open(str(file1),encoding='gbk') f1 = fn.readlines() #print(f1) for line in f1: line=line.rstrip('\n') if "。" not in line: lst1.append(line) else: lis=reg1.findall(line) for j in lis: lst1.append(j) data1=DataFrame({"Chinese":lst1}) df1.append(data1) df1 = pd.concat(df1) df1.to_excel("Chinese.xlsx",index="false") for i in range(0,39): domains=os.path.abspath(r'D:\C-en') file2=os.path.join(domains,files2[i]) ft = open(str(file2),encoding='gbk') f2 = ft.readlines() print(f2) for line in f2: if "." not in line: line=line.rstrip("\n") lst2.append(line) else: liss=line.split(". ") for j in liss: j=j+"." lst2.append(j) data2=DataFrame({"English":lst2}) df2.append(data2) df2 = pd.concat(df2)# 合并所有数据 df2.to_excel("English.xlsx",index="false")
这段代码可以进行一些优化,包括:
1. 将重复的代码封装成函数,避免重复代码。
2. 不需要在每次迭代时都重新初始化 `lst1` 和 `lst2` 列表,可以将其初始化放在循环外面。
3. 在使用 `pandas` 库时,最好使用 `read_csv()` 函数和 `to_csv()` 函数,而不是 `DataFrame()` 和 `to_excel()` 函数。
下面是优化后的代码:
```python
import os
import re
import pandas as pd
def get_sentences(file_path, regex):
with open(file_path, encoding='gbk') as f:
lines = f.readlines()
sentences = []
for line in lines:
line = line.rstrip('\n')
if "。" not in line:
sentences.append(line)
else:
lis = regex.findall(line)
for j in lis:
sentences.append(j)
return sentences
def get_english_sentences(file_path):
with open(file_path, encoding='gbk') as f:
lines = f.readlines()
sentences = []
for line in lines:
if "." not in line:
line = line.rstrip("\n")
sentences.append(line)
else:
liss = line.split(". ")
for j in liss:
j = j + "."
sentences.append(j)
return sentences
path1 = r'D:\C-cn'
path2 = r'D:\C-en'
files1 = os.listdir(path1)
files2 = os.listdir(path2)
reg1 = re.compile(r"[^。?!…]*[。?!……]")
reg2 = re.compile(r'.*\.[\n ]')
lst1 = []
for i in range(0, 39):
file1 = os.path.join(path1, files1[i])
lst1 += get_sentences(file1, reg1)
lst2 = []
for i in range(0, 39):
file2 = os.path.join(path2, files2[i])
lst2 += get_english_sentences(file2)
df1 = pd.DataFrame({"Chinese": lst1})
df1.to_csv("Chinese.csv", index=False)
df2 = pd.DataFrame({"English": lst2})
df2.to_csv("English.csv", index=False)
```
这里将中文和英文的句子分别存储到了 `Chinese.csv` 和 `English.csv` 文件中,以便后续的处理和分析。