{"id":1026300,"date":"2024-12-31T10:41:22","date_gmt":"2024-12-31T02:41:22","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1026300.html"},"modified":"2024-12-31T10:41:25","modified_gmt":"2024-12-31T02:41:25","slug":"python%e5%a6%82%e4%bd%95%e5%af%b9%e4%b8%ad%e6%96%87%e6%96%87%e6%9c%ac%e8%af%8d%e9%a2%91%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1026300.html","title":{"rendered":"python\u5982\u4f55\u5bf9\u4e2d\u6587\u6587\u672c\u8bcd\u9891\u5206\u6790"},"content":{"rendered":"

\"python\u5982\u4f55\u5bf9\u4e2d\u6587\u6587\u672c\u8bcd\u9891\u5206\u6790\"<\/p>\n

Python\u5bf9\u4e2d\u6587\u6587\u672c\u8bcd\u9891\u5206\u6790\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528jieba\u8fdb\u884c\u5206\u8bcd\u3001\u4f7f\u7528collections\u5e93\u7684Counter\u7c7b\u8fdb\u884c\u8bcd\u9891\u7edf\u8ba1\u3001\u4f7f\u7528pandas\u5bf9\u7ed3\u679c\u8fdb\u884c\u6574\u7406\u548c\u5206\u6790\u3002<\/strong> \u5176\u4e2d\uff0cjieba\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u4e2d\u6587\u5206\u8bcd\u5e93\uff0c\u80fd\u591f\u5bf9\u4e2d\u6587\u6587\u672c\u8fdb\u884c\u9ad8\u6548\u5206\u8bcd\u5e76\u63d0\u53d6\u5173\u952e\u8bcd\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528jieba\u5bf9\u6587\u672c\u8fdb\u884c\u5206\u8bcd\uff0c\u7136\u540e\u4f7f\u7528Counter\u7c7b\u5bf9\u5206\u8bcd\u7ed3\u679c\u8fdb\u884c\u8bcd\u9891\u7edf\u8ba1\uff0c\u6700\u540e\u901a\u8fc7pandas\u5e93\u5bf9\u7edf\u8ba1\u7ed3\u679c\u8fdb\u884c\u6574\u7406\u548c\u53ef\u89c6\u5316\u5206\u6790\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Jieba\u8fdb\u884c\u5206\u8bcd<\/strong><\/h2>\n

Jieba\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u4e2d\u6587\u5206\u8bcd\u5de5\u5177\u5305\uff0c\u652f\u6301\u4e09\u79cd\u5206\u8bcd\u6a21\u5f0f\uff1a\u7cbe\u786e\u6a21\u5f0f\u3001\u5168\u6a21\u5f0f\u548c\u641c\u7d22\u5f15\u64ce\u6a21\u5f0f\u3002\u7cbe\u786e\u6a21\u5f0f\u662f\u6700\u5e38\u7528\u7684\u6a21\u5f0f\uff0c\u5b83\u80fd\u7cbe\u786e\u5730\u5c06\u53e5\u5b50\u5207\u5206\u6210\u6700\u77ed\u7684\u8bcd\u8bed\u3002\u5168\u6a21\u5f0f\u4f1a\u5c06\u53e5\u5b50\u4e2d\u6240\u6709\u53ef\u80fd\u7684\u8bcd\u8bed\u90fd\u626b\u63cf\u51fa\u6765\uff0c\u4f46\u4e0d\u5982\u7cbe\u786e\u6a21\u5f0f\u9ad8\u6548\u3002\u641c\u7d22\u5f15\u64ce\u6a21\u5f0f\u5728\u7cbe\u786e\u6a21\u5f0f\u7684\u57fa\u7840\u4e0a\uff0c\u5bf9\u957f\u8bcd\u518d\u8fdb\u884c\u4e00\u6b21\u5207\u5206\uff0c\u63d0\u9ad8\u53ec\u56de\u7387\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Jieba\u8fdb\u884c\u5206\u8bcd\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

import jieba<\/p>\n
def chinese_tokenizer(text):<\/p>\n
    # \u4f7f\u7528\u7cbe\u786e\u6a21\u5f0f\u8fdb\u884c\u5206\u8bcd<\/p>\n
    seg_list = jieba.cut(text, cut_all=False)<\/p>\n
    return list(seg_list)<\/p>\n
text = "\u6211\u7231\u5317\u4eac\u5929\u5b89\u95e8\uff0c\u5929\u5b89\u95e8\u4e0a\u592a\u9633\u5347\u3002"<\/p>\n
tokens = chinese_tokenizer(text)<\/p>\n
print(tokens)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f7f\u7528Jieba\u5bf9\u6587\u672c\u8fdb\u884c\u5206\u8bcd\uff0c\u5e76\u8fd4\u56de\u5206\u8bcd\u7ed3\u679c\u3002\u8f93\u51fa\u7684\u7ed3\u679c\u662f\u4e00\u4e2a\u5305\u542b\u6240\u6709\u5206\u8bcd\u7684\u5217\u8868\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Counter\u8fdb\u884c\u8bcd\u9891\u7edf\u8ba1<\/strong><\/h2>\n
Python\u7684collections\u5e93\u4e2d\u6709\u4e00\u4e2a\u975e\u5e38\u6709\u7528\u7684\u7c7bCounter\uff0c\u5b83\u662f\u4e00\u4e2a\u54c8\u5e0c\u8868\u5bf9\u8c61\uff0c\u7528\u4e8e\u5bf9\u53ef\u54c8\u5e0c\u5bf9\u8c61\u8fdb\u884c\u8ba1\u6570\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Counter\u7c7b\u5bf9\u5206\u8bcd\u7ed3\u679c\u8fdb\u884c\u8bcd\u9891\u7edf\u8ba1\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Counter\u8fdb\u884c\u8bcd\u9891\u7edf\u8ba1\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from collections import Counter<\/p>\n
def word_freq(tokens):<\/p>\n
    return Counter(tokens)<\/p>\n
word_frequencies = word_freq(tokens)<\/p>\n
print(word_frequencies)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f7f\u7528Counter\u7c7b\u5bf9\u5206\u8bcd\u7ed3\u679c\u8fdb\u884c\u8bcd\u9891\u7edf\u8ba1\uff0c\u5e76\u8fd4\u56de\u4e00\u4e2aCounter\u5bf9\u8c61\uff0c\u5176\u4e2d\u5305\u542b\u8bcd\u9891\u7edf\u8ba1\u7ed3\u679c\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Pandas\u5bf9\u7ed3\u679c\u8fdb\u884c\u6574\u7406\u548c\u5206\u6790<\/strong><\/h2>\n
Pandas\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u65b9\u4fbf\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u5bf9\u8bcd\u9891\u7edf\u8ba1\u7ed3\u679c\u8fdb\u884c\u6574\u7406\u548c\u5206\u6790\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Pandas\u5bf9\u8bcd\u9891\u7edf\u8ba1\u7ed3\u679c\u8fdb\u884c\u6574\u7406\u548c\u5206\u6790\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
def freq_analysis(word_frequencies):<\/p>\n
    # \u5c06\u8bcd\u9891\u7edf\u8ba1\u7ed3\u679c\u8f6c\u6362\u4e3aDataFrame<\/p>\n
    df = pd.DataFrame.from_dict(word_frequencies, orient='index', columns=['frequency'])<\/p>\n
    # \u6309\u9891\u7387\u964d\u5e8f\u6392\u5217<\/p>\n
    df = df.sort_values(by='frequency', ascending=False)<\/p>\n
    return df<\/p>\n
word_freq_df = freq_analysis(word_frequencies)<\/p>\n
print(word_freq_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u5c06\u8bcd\u9891\u7edf\u8ba1\u7ed3\u679c\u8f6c\u6362\u4e3a\u4e00\u4e2aPandas DataFrame\u5bf9\u8c61\uff0c\u5e76\u6309\u8bcd\u9891\u964d\u5e8f\u6392\u5217\u3002\u8f93\u51fa\u7684\u7ed3\u679c\u662f\u4e00\u4e2a\u5305\u542b\u8bcd\u9891\u7edf\u8ba1\u7ed3\u679c\u7684DataFrame\u5bf9\u8c61\u3002<\/p>\n<\/p>\n
\u56db\u3001\u793a\u4f8b\uff1a\u7efc\u5408\u5e94\u7528<\/strong><\/h2>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u5b83\u4eec\u7efc\u5408\u5e94\u7528\u5230\u4e00\u4e2a\u5b9e\u9645\u7684\u793a\u4f8b\u4e2d\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Python\u5bf9\u4e2d\u6587\u6587\u672c\u8fdb\u884c\u8bcd\u9891\u5206\u6790\uff1a<\/p>\n<\/p>\n
import jieba<\/p>\n
from collections import Counter<\/p>\n
import pandas as pd<\/p>\n
def chinese_tokenizer(text):<\/p>\n
    seg_list = jieba.cut(text, cut_all=False)<\/p>\n
    return list(seg_list)<\/p>\n
def word_freq(tokens):<\/p>\n
    return Counter(tokens)<\/p>\n
def freq_analysis(word_frequencies):<\/p>\n
    df = pd.DataFrame.from_dict(word_frequencies, orient='index', columns=['frequency'])<\/p>\n
    df = df.sort_values(by='frequency', ascending=False)<\/p>\n
    return df<\/p>\n
def mAI<\/a>n(text):<\/p>\n
    tokens = chinese_tokenizer(text)<\/p>\n
    word_frequencies = word_freq(tokens)<\/p>\n
    word_freq_df = freq_analysis(word_frequencies)<\/p>\n
    print(word_freq_df)<\/p>\n
if __name__ == "__main__":<\/p>\n
    text = "\u6211\u7231\u5317\u4eac\u5929\u5b89\u95e8\uff0c\u5929\u5b89\u95e8\u4e0a\u592a\u9633\u5347\u3002"<\/p>\n
    main(text)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bf9\u6587\u672c\u8fdb\u884c\u5206\u8bcd\uff0c\u7136\u540e\u5bf9\u5206\u8bcd\u7ed3\u679c\u8fdb\u884c\u8bcd\u9891\u7edf\u8ba1\uff0c\u6700\u540e\u4f7f\u7528Pandas\u5bf9\u8bcd\u9891\u7edf\u8ba1\u7ed3\u679c\u8fdb\u884c\u6574\u7406\u548c\u5206\u6790\u3002\u6700\u7ec8\u8f93\u51fa\u7684\u7ed3\u679c\u662f\u4e00\u4e2a\u5305\u542b\u8bcd\u9891\u7edf\u8ba1\u7ed3\u679c\u7684DataFrame\u5bf9\u8c61\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u4e2d\u6587\u6587\u672c\u8fdb\u884c\u8bcd\u9891\u5206\u6790\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5904\u7406\u505c\u7528\u8bcd\u548c\u7279\u6b8a\u5b57\u7b26<\/strong><\/h2>\n
\u5728\u8fdb\u884c\u8bcd\u9891\u5206\u6790\u65f6\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5904\u7406\u505c\u7528\u8bcd\u548c\u7279\u6b8a\u5b57\u7b26\u3002\u505c\u7528\u8bcd\u662f\u6307\u5728\u6587\u672c\u4e2d\u51fa\u73b0\u9891\u7387\u8f83\u9ad8\uff0c\u4f46\u5bf9\u6587\u672c\u5185\u5bb9\u8d21\u732e\u8f83\u5c0f\u7684\u8bcd\u8bed\uff0c\u5982\u201c\u7684\u201d\u3001\u201c\u4e86\u201d\u3001\u201c\u5728\u201d\u7b49\u3002\u7279\u6b8a\u5b57\u7b26\u662f\u6307\u6807\u70b9\u7b26\u53f7\u3001\u6570\u5b57\u7b49\u975e\u8bcd\u8bed\u7684\u5b57\u7b26\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u505c\u7528\u8bcd\u8868\u548c\u6b63\u5219\u8868\u8fbe\u5f0f\u6765\u5904\u7406\u8fd9\u4e9b\u8bcd\u8bed\u548c\u5b57\u7b26\u3002\u4ee5\u4e0b\u662f\u5904\u7406\u505c\u7528\u8bcd\u548c\u7279\u6b8a\u5b57\u7b26\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import re<\/p>\n
def remove_stopwords(tokens, stopwords):<\/p>\n
    return [token for token in tokens if token not in stopwords]<\/p>\n
def remove_special_chars(tokens):<\/p>\n
    return [token for token in tokens if re.match(r'^[\\u4e00-\\u9fa5]+$', token)]<\/p>\n
stopwords = set(["\u7684", "\u4e86", "\u5728"])<\/p>\n
tokens = chinese_tokenizer(text)<\/p>\n
tokens = remove_stopwords(tokens, stopwords)<\/p>\n
tokens = remove_special_chars(tokens)<\/p>\n
word_frequencies = word_freq(tokens)<\/p>\n
word_freq_df = freq_analysis(word_frequencies)<\/p>\n
print(word_freq_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528\u505c\u7528\u8bcd\u8868\u53bb\u9664\u505c\u7528\u8bcd\uff0c\u7136\u540e\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u53bb\u9664\u7279\u6b8a\u5b57\u7b26\u3002\u6700\u7ec8\u8f93\u51fa\u7684\u7ed3\u679c\u662f\u4e00\u4e2a\u7ecf\u8fc7\u5904\u7406\u7684\u8bcd\u9891\u7edf\u8ba1\u7ed3\u679c\u3002<\/p>\n<\/p>\n
\u516d\u3001\u6269\u5c55\uff1a\u4f7f\u7528TF-IDF\u8fdb\u884c\u5173\u952e\u8bcd\u63d0\u53d6<\/strong><\/h2>\n
\u9664\u4e86\u8bcd\u9891\u7edf\u8ba1\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528TF-IDF\uff08\u8bcd\u9891-\u9006\u6587\u6863\u9891\u7387\uff09\u65b9\u6cd5\u8fdb\u884c\u5173\u952e\u8bcd\u63d0\u53d6\u3002TF-IDF\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6587\u672c\u6316\u6398\u6280\u672f\uff0c\u7528\u4e8e\u8bc4\u4f30\u4e00\u4e2a\u8bcd\u8bed\u5bf9\u4e8e\u4e00\u4e2a\u6587\u6863\u7684\u91cd\u8981\u7a0b\u5ea6\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528TF-IDF\u8fdb\u884c\u5173\u952e\u8bcd\u63d0\u53d6\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\n
def extract_keywords(text, top_k=10):<\/p>\n
    vectorizer = TfidfVectorizer()<\/p>\n
    tfidf_matrix = vectorizer.fit_transform([text])<\/p>\n
    tfidf_scores = tfidf_matrix.toarray()[0]<\/p>\n
    tfidf_dict = dict(zip(vectorizer.get_feature_names(), tfidf_scores))<\/p>\n
    sorted_tfidf = sorted(tfidf_dict.items(), key=lambda x: x[1], reverse=True)<\/p>\n
    return sorted_tfidf[:top_k]<\/p>\n
keywords = extract_keywords(text)<\/p>\n
print(keywords)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528TfidfVectorizer\u7c7b\u5bf9\u6587\u672c\u8fdb\u884cTF-IDF\u8ba1\u7b97\uff0c\u5e76\u63d0\u53d6\u524dK\u4e2a\u5173\u952e\u8bcd\u3002\u6700\u7ec8\u8f93\u51fa\u7684\u7ed3\u679c\u662f\u4e00\u4e2a\u5305\u542b\u5173\u952e\u8bcd\u53ca\u5176TF-IDF\u5f97\u5206\u7684\u5217\u8868\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u5bf9\u4e2d\u6587\u6587\u672c\u8fdb\u884c\u8bcd\u9891\u5206\u6790\uff0c\u5e76\u63d0\u53d6\u5173\u952e\u8bcd\u3002\u65e0\u8bba\u662f\u4f7f\u7528jieba\u8fdb\u884c\u5206\u8bcd\u3001\u4f7f\u7528Counter\u8fdb\u884c\u8bcd\u9891\u7edf\u8ba1\uff0c\u8fd8\u662f\u4f7f\u7528Pandas\u5bf9\u7ed3\u679c\u8fdb\u884c\u6574\u7406\u548c\u5206\u6790\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u90fd\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u9ad8\u6548\u5730\u5904\u7406\u548c\u5206\u6790\u4e2d\u6587\u6587\u672c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
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