利用python进行tf-idf算法绘制高频词汇的条形图

好的，下面是使用Python进行tf-idf算法绘制高频词汇的条形图的代码：

import jieba
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import TfidfVectorizer

# 读取文件
with open('your_file.txt', 'r', encoding='utf-8') as f:
    text = f.read()

# 分词
words = jieba.cut(text)

# 统计词频
word_counts = {}
for word in words:
    if len(word) > 1:
        word_counts[word] = word_counts.get(word, 0) + 1

# 转换成DataFrame
df = pd.DataFrame.from_dict(word_counts, orient='index', columns=['count'])

# 计算TF-IDF值
tfidf = TfidfVectorizer()
tfidf_matrix = tfidf.fit_transform([text])
weights = np.asarray(tfidf_matrix.mean(axis=0)).ravel().tolist()
weights_df = pd.DataFrame({'term': tfidf.get_feature_names(), 'weight': weights})
weights_df = weights_df.sort_values('weight', ascending=False)

# 绘制条形图
top_n = 10  # 取前10个高频词
top_words = df.sort_values('count', ascending=False).head(top_n)
top_words.plot(kind='bar', x=top_words.index, y='count', color='blue', legend=False)
plt.xlabel('Words')
plt.ylabel('Frequency')
plt.title('Top {} Words'.format(top_n))
plt.show()

请将`your_file.txt`替换为您的文本文件路径。这段代码将会读取文件、分词、统计词频、计算TF-IDF值和绘制高频词汇的条形图，您可以通过修改`top_n`的值来控制绘制的高频词汇数量。