``` from collections import Counter```from collections import Counter word_count = Counter(words)

`collections.Counter` 是 Python 内置的一个容器映射类型，它用于统计序列（如列表、元组或字符串）中各个元素（在这里是 `words` 列表中的单词）出现的次数。通过给定一个可迭代对象，如你提到的 `words`，`Counter` 对象会创建一个字典，其中键是可计数的项目，值则是该项目的计数值。

例如，如果你有一个包含多个单词的列表：

words = ['apple', 'banana', 'apple', 'orange', 'banana', 'banana']
word_count = Counter(words)

`word_count` 就会是一个字典，内容可能是：

{'apple': 2, 'banana': 3, 'orange': 1}

这表示 'apple' 出现了两次，'banana' 出现了三次，'orange' 只出现了一次。`Counter` 类提供了一些方便的方法，如 `.most_common()` 来获取最常见的元素及其频率，以及 `.update()` 来合并两个计数器等。

相关问题

如何修改代码，使得输出的每一个词的对应词频和密度分成两列显示 import pandas as pd import re from collections import Counter 读取词典txt文件 with open('词典.txt', 'r', encoding='utf-8') as f: list_dict = [line.strip() for line in f] 读取F列数据 df = pd.read_excel('实验体.xlsx') list_f = df['Answer'].tolist() 统计每行文本中的词频 dict_count_list = [] total_density_list = [] for text in list_f: # 匹配文本中的词列表 text = str(text) words = re.findall('|'.join(list_dict), text) # 统计每个词在该行文本中的出现次数和密度 dict_count = Counter(words) dict_count_list.append(dict_count) dict_count = {} for word in words: count = text.count(word) density = count / len(text) dict_count[word] = {'count': count, 'density': density} dict_count_list.append(dict_count) # 计算每行总词数的密度 total_density = sum([v['density'] for v in dict_count.values()]) total_density_list.append(total_density) 将每行文本的词频统计结果合并为一个DataFrame对象 df_count = pd.DataFrame(dict_count_list) df_count.index = df.index 输出为Excel文件 writer = pd.ExcelWriter('数实验体10.xlsx') df_count.to_excel(writer, sheet_name='Sheet1') writer._save()

你可以尝试修改代码如下：

import pandas as pd
import re
from collections import Counter

# 读取词典txt文件
with open('词典.txt', 'r', encoding='utf-8') as f:
    list_dict = [line.strip() for line in f]

# 读取F列数据
df = pd.read_excel('实验体.xlsx')
list_f = df['Answer'].tolist()

# 统计每行文本中的词频和密度
dict_count_list = []
total_density_list = []
for text in list_f:
    # 匹配文本中的词列表
    text = str(text)
    words = re.findall('|'.join(list_dict), text)
    
    # 统计每个词在该行文本中的出现次数和密度
    dict_count = Counter(words)
    dict_count = {k: {'count': v, 'density': v / len(text)} for k, v in dict_count.items()}
    dict_count_list.append(dict_count)
    
    # 计算每行总词数的密度
    total_density = sum([v['density'] for v in dict_count.values()])
    total_density_list.append(total_density)

# 将每行文本的词频统计结果合并为一个DataFrame对象
df_count = pd.concat([pd.DataFrame.from_dict(d, orient='index') for d in dict_count_list], axis=1)
df_count.index = df.index

# 输出为Excel文件并将每个词的词频和密度分成两列显示
with pd.ExcelWriter('数实验体10.xlsx') as writer:
    df_count.to_excel(writer, sheet_name='Sheet1')
    for col in df_count.columns:
        if col.endswith('count'):
            continue
        count_col = col[:-7] + 'count'
        density_col = col[:-7] + 'density'
        df_count[[count_col, density_col]] = df_count[col].apply(pd.Series)
        df_count = df_count.drop(col, axis=1)
    df_count.to_excel(writer, sheet_name='Sheet1', startrow=0, startcol=len(df_count.columns))

修改后的代码会将每个词的词频和密度分成两列显示，并输出到Excel文件的第二列。

把这段代码import math from collections import defaultdict corpus =["二价 二价 二价 四价 预约", "四价 四价 四价 九价 预约", "九价 九价 九价 九价 预约"] words = [] for sentence in corpus: words.append(sentence.strip().split()) # 进行词频统计 def Counter(words): word_count = [] for sentence in words: word_dict = defaultdict(int) for word in sentence: word_dict[word] += 1 word_count.append(word_dict) return word_count word_count = Counter(words) # 计算TF(word代表被计算的单词，word_dict是被计算单词所在句子分词统计词频后的字典) def tf(word, word_dict): return word_dict[word] / sum(word_dict.values()) # 统计含有该单词的句子数 def count_sentence(word, word_count): return sum([1 for i in word_count if i.get(word)]) # i[word] >= 1 # 计算IDF def idf(word, word_count): return math.log((len(word_count) / (count_sentence(word, word_count) + 1)),10) # 计算TF-IDF def tfidf(word, word_dict, word_count): return tf(word, word_dict) * idf(word, word_count) p = 1 for word_dict in word_count: print("part:{}".format(p)) p += 1 for word, cnt in word_dict.items(): print("word: {} ---- TF-IDF:{}".format(word, tfidf(word, word_dict, word_count))) print("word: {} ---- TF:{}".format(word, tf(word, word_dict))) print("word: {} ---- IDF:{}".format(word, idf(word, word_count))) print("word: {} ---- count_sentence:{}".format(word, count_sentence(word, word_count)))中计算idf的def idf(word, word_count)部分改成自定义输入权重[1，2，3]得到最终的改进后的TF-IDF值，请帮我改进一下代码

好的，根据您的要求，我修改了代码，将idf的计算方式修改为自定义输入权重[1,2,3]，代码如下：

import math
from collections import defaultdict

corpus =["二价 二价 二价 四价 预约", "四价 四价 四价 九价 预约", "九价 九价 九价 九价 预约"]
words = []
for sentence in corpus:
    words.append(sentence.strip().split())

# 进行词频统计
def Counter(words):
    word_count = []
    for sentence in words:
        word_dict = defaultdict(int)
        for word in sentence:
            word_dict[word] += 1
        word_count.append(word_dict)
    return word_count

word_count = Counter(words)

# 计算TF(word代表被计算的单词，word_dict是被计算单词所在句子分词统计词频后的字典)
def tf(word, word_dict):
    return word_dict[word] / sum(word_dict.values())

# 统计含有该单词的句子数
def count_sentence(word, word_count):
    return sum([1 for i in word_count if i.get(word)])  # i[word] >= 1

# 计算IDF
def idf(word, word_count, weights):
    return math.log((len(word_count) / (count_sentence(word, word_count) + 1)),10) * weights[word]

# 计算TF-IDF
def tfidf(word, word_dict, word_count, weights):
    return tf(word, word_dict) * idf(word, word_count, weights)

# 自定义权重
weights = {
    "二价": 1,
    "四价": 2,
    "九价": 3,
}

p = 1
for word_dict in word_count:
    print("part:{}".format(p))
    p += 1
    for word, cnt in word_dict.items():
        print("word: {} ---- TF-IDF:{}".format(word, tfidf(word, word_dict, word_count, weights)))
        print("word: {} ---- TF:{}".format(word, tf(word, word_dict)))
        print("word: {} ---- IDF:{}".format(word, idf(word, word_count, weights)))
        print("word: {} ---- count_sentence:{}".format(word, count_sentence(word, word_count)))

在这个代码中，我们增加了一个新的参数weights，用来存储自定义的权重，然后在idf计算时，乘以相应的权重。最后在计算tfidf时，将权重传递给idf函数即可。