python tfidf词频统计
时间: 2023-06-29 16:20:31 浏览: 53
在Python中,可以使用scikit-learn库中的TfidfVectorizer来进行TF-IDF词频统计。下面是一个简单的示例代码:
```python
from sklearn.feature_extraction.text import TfidfVectorizer
# 文本列表
text_list = ['This is the first document.', 'This is the second document.', 'And this is the third one.']
# 创建TfidfVectorizer对象
vectorizer = TfidfVectorizer()
# 转换文本列表为TF-IDF矩阵
tfidf_matrix = vectorizer.fit_transform(text_list)
# 输出TF-IDF矩阵
print(tfidf_matrix.toarray())
```
运行上述代码,将输出一个3行7列的矩阵,其中每一行代表一个文本,在每一列中给出了对应的单词的TF-IDF值。
相关问题
tf-idf算法python词频统计
TF-IDF是一种用于衡量词语在文档中重要程度的统计算法。在Python中,可以从零开始实现TF-IDF算法。TF-IDF的公式如下:
tf-idf(t, d) = tf(t, d) * log(N/(df+1))
其中,tf(t, d)表示词语t在文档d中出现的频率,N表示文档的总数,df表示包含词语t的文档数。在计算tf时,可以使用公式:
tf(t, d) = count of t in d / number of words in d
首先,需要对文档进行分词,并统计每个词语在文档中的出现频率。然后,计算每个词语的tf-idf值。最后,可以根据tf-idf值对词语进行排序,得到词频统计结果。
实现TF-IDF算法的Python代码如下:
```python
import math
def calculate_tf(word, document):
word_count = document.count(word)
total_words = len(document)
tf = word_count / total_words
return tf
def calculate_idf(word, documents):
total_documents = len(documents)
word_documents = sum(1 for document in documents if word in document)
idf = math.log(total_documents / (word_documents + 1))
return idf
def calculate_tfidf(word, document, documents):
tf = calculate_tf(word, document)
idf = calculate_idf(word, documents)
tfidf = tf * idf
return tfidf
def calculate_word_frequency(documents):
word_frequency = {}
for document in documents:
for word in document:
if word not in word_frequency:
word_frequency[word = calculate_tfidf(word, document, documents)
sorted_word_frequency = sorted(word_frequency.items(), key=lambda x: x
利用python进行tf-idf算法绘制词云图_Python文本挖掘: 词频统计,词云图
好的,我了解了您的问题。首先,您需要安装一些Python库,包括`jieba`、`numpy`、`pandas`、`sklearn`和`wordcloud`。您可以通过在终端中输入以下命令来安装这些库:
```
pip install jieba numpy pandas sklearn wordcloud
```
然后,您可以使用以下代码进行tf-idf算法的词频统计和绘制词云图:
```python
import jieba
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from wordcloud import WordCloud
# 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)
# 绘制词云图
wordcloud = WordCloud(background_color='white').generate_from_frequencies(word_counts)
wordcloud.to_file('wordcloud.png')
```
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值和绘制词云图,并将词云图保存为`wordcloud.png`文件。