{"id":1045207,"date":"2024-12-31T13:21:07","date_gmt":"2024-12-31T05:21:07","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1045207.html"},"modified":"2024-12-31T13:21:13","modified_gmt":"2024-12-31T05:21:13","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e5%81%9a%e5%85%a8%e7%bd%91%e6%90%9c%e7%b4%a2%e5%99%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1045207.html","title":{"rendered":"\u5982\u4f55\u7528python\u505a\u5168\u7f51\u641c\u7d22\u5668"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u505a\u5168\u7f51\u641c\u7d22\u5668\"<\/p>\n

\u7528Python\u5236\u4f5c\u5168\u7f51\u641c\u7d22\u5668\u6d89\u53ca\u591a\u4e2a\u6b65\u9aa4\u548c\u6280\u672f\uff0c\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\uff1a\u7f51\u9875\u6293\u53d6\u3001\u6570\u636e\u5904\u7406\u3001\u6570\u636e\u5e93\u7ba1\u7406\u548c\u7528\u6237\u754c\u9762\u8bbe\u8ba1\u3002\u5176\u4e2d\u7684\u5173\u952e\u6b65\u9aa4\u5305\u62ec\uff1a\u9009\u62e9\u5408\u9002\u7684\u7f51\u9875\u6293\u53d6\u5e93\u3001\u5904\u7406\u548c\u5b58\u50a8\u6570\u636e\u3001\u5b9e\u73b0\u641c\u7d22\u7b97\u6cd5\u3001\u8bbe\u8ba1\u7528\u6237\u754c\u9762<\/strong>\u3002\u5176\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u7f51\u9875\u6293\u53d6\u5e93\u5c24\u4e3a\u91cd\u8981\uff0c\u56e0\u4e3a\u8fd9\u662f\u6574\u4e2a\u641c\u7d22\u5668\u7684\u57fa\u7840\u3002<\/p>\n<\/p>\n

\u4f7f\u7528Python\u521b\u5efa\u5168\u7f51\u641c\u7d22\u5668\u9700\u8981\u591a\u65b9\u9762\u7684\u77e5\u8bc6\u548c\u6280\u80fd\u3002\u9996\u5148\uff0c\u9009\u62e9\u5408\u9002\u7684\u7f51\u9875\u6293\u53d6\u5e93\uff0c\u6bd4\u5982BeautifulSoup\u548cScrapy\u3002BeautifulSoup\u9002\u5408\u5c0f\u89c4\u6a21\u7684\u6570\u636e\u6293\u53d6\uff0c\u6613\u4e8e\u4e0a\u624b\u548c\u4f7f\u7528\uff1bScrapy\u5219\u9002\u5408\u5927\u89c4\u6a21\u3001\u590d\u6742\u7684\u7f51\u9875\u6293\u53d6\u4efb\u52a1\uff0c\u5177\u6709\u9ad8\u6548\u3001\u53ef\u6269\u5c55\u7684\u7279\u70b9\u3002\u5bf9\u4e8e\u5927\u591a\u6570\u521d\u5b66\u8005\u6765\u8bf4\uff0cBeautifulSoup\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u8d77\u70b9\uff0c\u56e0\u4e3a\u5b83\u7684\u5b66\u4e60\u66f2\u7ebf\u8f83\u4f4e\uff0c\u800c\u4e14\u53ef\u4ee5\u6ee1\u8db3\u57fa\u672c\u7684\u7f51\u9875\u6293\u53d6\u9700\u6c42\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u9009\u62e9\u5408\u9002\u7684\u7f51\u9875\u6293\u53d6\u5e93<\/h3>\n<\/p>\n

1\u3001BeautifulSoup<\/h4>\n<\/p>\n

BeautifulSoup\u662f\u4e00\u4e2a\u7528\u4e8e\u89e3\u6790HTML\u548cXML\u6587\u6863\u7684\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u4ece\u7f51\u9875\u4e2d\u63d0\u53d6\u6570\u636e\u3002\u4ee5\u4e0b\u662fBeautifulSoup\u7684\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n

from bs4 import BeautifulSoup<\/p>\n
import requests<\/p>\n
url = 'http:\/\/example.com'<\/p>\n
response = requests.get(url)<\/p>\n
soup = BeautifulSoup(response.text, 'html.parser')<\/p>\n
\u627e\u5230\u6240\u6709\u7684\u94fe\u63a5<\/strong><\/h2>\n
for link in soup.find_all('a'):<\/p>\n
    print(link.get('href'))<\/p>\n
<\/code><\/pre>\n<\/p>\n
BeautifulSoup\u7b80\u5355\u6613\u7528\uff0c\u975e\u5e38\u9002\u5408\u65b0\u624b\u548c\u5c0f\u89c4\u6a21\u7684\u6570\u636e\u6293\u53d6\u4efb\u52a1\u3002<\/p>\n<\/p>\n
2\u3001Scrapy<\/h4>\n<\/p>\n
Scrapy\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u7f51\u9875\u6293\u53d6\u6846\u67b6\uff0c\u9002\u5408\u5927\u89c4\u6a21\u548c\u590d\u6742\u7684\u6293\u53d6\u4efb\u52a1\u3002\u4ee5\u4e0b\u662fScrapy\u7684\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
\u9996\u5148\uff0c\u5b89\u88c5Scrapy\uff1a<\/p>\n<\/p>\n
pip install scrapy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u521b\u5efa\u4e00\u4e2a\u65b0\u7684Scrapy\u9879\u76ee\uff1a<\/p>\n<\/p>\n
scrapy startproject myproject<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u722c\u866b\uff1a<\/p>\n<\/p>\n
cd myproject<\/p>\n
scrapy genspider myspider example.com<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7f16\u8f91\u751f\u6210\u7684\u722c\u866b\u6587\u4ef6myspider.py<\/code>\uff1a<\/p>\n<\/p>\n
import scrapy<\/p>\n
class MySpider(scrapy.Spider):<\/p>\n
    name = 'myspider'<\/p>\n
    start_urls = ['http:\/\/example.com']<\/p>\n
    def parse(self, response):<\/p>\n
        for link in response.css('a::attr(href)').getall():<\/p>\n
            yield {'link': link}<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6700\u540e\uff0c\u8fd0\u884c\u722c\u866b\uff1a<\/p>\n<\/p>\n
scrapy crawl myspider<\/p>\n
<\/code><\/pre>\n<\/p>\n
Scrapy\u5177\u6709\u9ad8\u5ea6\u7684\u53ef\u6269\u5c55\u6027\u548c\u7075\u6d3b\u6027\uff0c\u9002\u5408\u9700\u8981\u9ad8\u6548\u7387\u548c\u590d\u6742\u6293\u53d6\u4efb\u52a1\u7684\u7528\u6237\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u5904\u7406\u4e0e\u5b58\u50a8<\/h3>\n<\/p>\n
1\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406<\/h4>\n<\/p>\n
\u5728\u6293\u53d6\u5230\u7f51\u9875\u6570\u636e\u540e\uff0c\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u9884\u5904\u7406\u3002\u6570\u636e\u6e05\u6d17\u5305\u62ec\u53bb\u9664\u65e0\u7528\u4fe1\u606f\u3001\u5904\u7406\u7f3a\u5931\u503c\u548c\u6807\u51c6\u5316\u6570\u636e\u683c\u5f0f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u636e\u6e05\u6d17\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u5047\u8bbe\u6293\u53d6\u7684\u6570\u636e\u5b58\u50a8\u5728\u4e00\u4e2aDataFrame\u4e2d<\/strong><\/h2>\n
data = pd.DataFrame({'link': ['http:\/\/example.com', 'http:\/\/example2.com', None]})<\/p>\n
\u53bb\u9664\u7f3a\u5931\u503c<\/strong><\/h2>\n
data = data.dropna()<\/p>\n
\u53bb\u9664\u91cd\u590d\u503c<\/strong><\/h2>\n
data = data.drop_duplicates()<\/p>\n
\u6e05\u6d17\u94fe\u63a5<\/strong><\/h2>\n
data['link'] = data['link'].str.strip()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u5e93\u7ba1\u7406<\/h4>\n<\/p>\n
\u4e3a\u4e86\u9ad8\u6548\u5730\u5b58\u50a8\u548c\u67e5\u8be2\u5927\u91cf\u7684\u6570\u636e\uff0c\u5efa\u8bae\u4f7f\u7528\u6570\u636e\u5e93\u3002\u5e38\u7528\u7684\u6570\u636e\u5e93\u5305\u62ecSQLite\u3001MySQL\u548cMongoDB\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528SQLite\u5b58\u50a8\u6570\u636e\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import sqlite3<\/p>\n
\u521b\u5efa\u6570\u636e\u5e93\u8fde\u63a5<\/strong><\/h2>\n
conn = sqlite3.connect('search_engine.db')<\/p>\n
c = conn.cursor()<\/p>\n
\u521b\u5efa\u8868\u683c<\/strong><\/h2>\n
c.execute('''CREATE TABLE IF NOT EXISTS links<\/p>\n
             (id INTEGER PRIMARY KEY AUTOINCREMENT, url TEXT)''')<\/p>\n
\u63d2\u5165\u6570\u636e<\/strong><\/h2>\n
for link in data['link']:<\/p>\n
    c.execute('INSERT INTO links (url) VALUES (?)', (link,))<\/p>\n
\u63d0\u4ea4\u4e8b\u52a1<\/strong><\/h2>\n
conn.commit()<\/p>\n
\u5173\u95ed\u8fde\u63a5<\/strong><\/h2>\n
conn.close()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u5b9e\u73b0\u641c\u7d22\u7b97\u6cd5<\/h3>\n<\/p>\n
1\u3001\u57fa\u672c\u641c\u7d22\u7b97\u6cd5<\/h4>\n<\/p>\n
\u57fa\u672c\u7684\u641c\u7d22\u7b97\u6cd5\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u5173\u952e\u8bcd\u5339\u914d\u6765\u5b9e\u73b0\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
def search(keyword, data):<\/p>\n
    return data[data['link'].str.contAI<\/a>ns(keyword, case=False)]<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6539\u8fdb\u7684\u641c\u7d22\u7b97\u6cd5<\/h4>\n<\/p>\n
\u4e3a\u4e86\u63d0\u9ad8\u641c\u7d22\u7ed3\u679c\u7684\u51c6\u786e\u6027\u548c\u76f8\u5173\u6027\uff0c\u53ef\u4ee5\u91c7\u7528\u66f4\u590d\u6742\u7684\u641c\u7d22\u7b97\u6cd5\uff0c\u5982TF-IDF\uff08Term Frequency-Inverse Document Frequency\uff09\u548cBM25\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528TF-IDF\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\n
\u5047\u8bbe\u6293\u53d6\u7684\u6570\u636e\u5b58\u50a8\u5728\u4e00\u4e2aDataFrame\u4e2d<\/strong><\/h2>\n
data = pd.DataFrame({'link': ['http:\/\/example.com', 'http:\/\/example2.com']})<\/p>\n
\u8ba1\u7b97TF-IDF<\/strong><\/h2>\n
vectorizer = TfidfVectorizer()<\/p>\n
tfidf_matrix = vectorizer.fit_transform(data['link'])<\/p>\n
\u67e5\u8be2<\/strong><\/h2>\n
query = 'example'<\/p>\n
query_vec = vectorizer.transform([query])<\/p>\n
\u8ba1\u7b97\u76f8\u4f3c\u5ea6<\/strong><\/h2>\n
from sklearn.metrics.pairwise import cosine_similarity<\/p>\n
similarity = cosine_similarity(query_vec, tfidf_matrix)<\/p>\n
\u83b7\u53d6\u6700\u76f8\u4f3c\u7684\u94fe\u63a5<\/strong><\/h2>\n
most_similar = similarity.argsort()[0][-1]<\/p>\n
print(data['link'].iloc[most_similar])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u8bbe\u8ba1\u7528\u6237\u754c\u9762<\/h3>\n<\/p>\n
1\u3001\u547d\u4ee4\u884c\u754c\u9762<\/h4>\n<\/p>\n
\u6700\u7b80\u5355\u7684\u7528\u6237\u754c\u9762\u662f\u547d\u4ee4\u884c\u754c\u9762\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
def main():<\/p>\n
    keyword = input('Enter a keyword: ')<\/p>\n
    results = search(keyword, data)<\/p>\n
    for result in results['link']:<\/p>\n
        print(result)<\/p>\n
if __name__ == '__main__':<\/p>\n
    main()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u56fe\u5f62\u7528\u6237\u754c\u9762<\/h4>\n<\/p>\n
\u4e3a\u4e86\u63d0\u4f9b\u66f4\u53cb\u597d\u7684\u7528\u6237\u4f53\u9a8c\uff0c\u53ef\u4ee5\u4f7f\u7528\u56fe\u5f62\u7528\u6237\u754c\u9762\uff08GUI\uff09\u3002\u5e38\u7528\u7684GUI\u5e93\u5305\u62ecTkinter\u548cPyQt\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Tkinter\u521b\u5efa\u7b80\u5355GUI\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import tkinter as tk<\/p>\n
from tkinter import simpledialog<\/p>\n
def search_and_display(keyword, data):<\/p>\n
    results = search(keyword, data)<\/p>\n
    for result in results['link']:<\/p>\n
        listbox.insert(tk.END, result)<\/p>\n
def on_search():<\/p>\n
    keyword = simpledialog.askstring("Input", "Enter a keyword:")<\/p>\n
    if keyword:<\/p>\n
        search_and_display(keyword, data)<\/p>\n
root = tk.Tk()<\/p>\n
root.title("Search Engine")<\/p>\n
listbox = tk.Listbox(root)<\/p>\n
listbox.pack(fill=tk.BOTH, expand=True)<\/p>\n
search_button = tk.Button(root, text="Search", command=on_search)<\/p>\n
search_button.pack()<\/p>\n
root.mainloop()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5168\u7f51\u641c\u7d22\u5668\u9700\u8981\u5168\u9762\u638c\u63e1\u7f51\u9875\u6293\u53d6\u3001\u6570\u636e\u5904\u7406\u4e0e\u5b58\u50a8\u3001\u641c\u7d22\u7b97\u6cd5\u548c\u7528\u6237\u754c\u9762\u8bbe\u8ba1\u7b49\u591a\u4e2a\u65b9\u9762\u7684\u77e5\u8bc6\u3002\u901a\u8fc7\u9009\u62e9\u5408\u9002\u7684\u7f51\u9875\u6293\u53d6\u5e93\uff0c\u6bd4\u5982BeautifulSoup\u548cScrapy\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u6293\u53d6\u7f51\u9875\u6570\u636e\u3002\u6570\u636e\u5904\u7406\u548c\u5b58\u50a8\u662f\u786e\u4fdd\u6570\u636e\u8d28\u91cf\u548c\u67e5\u8be2\u6548\u7387\u7684\u5173\u952e\u3002\u5b9e\u73b0\u641c\u7d22\u7b97\u6cd5\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u5173\u952e\u8bcd\u5339\u914d\u5f00\u59cb\uff0c\u7136\u540e\u9010\u6b65\u5f15\u5165\u66f4\u590d\u6742\u7684\u7b97\u6cd5\u5982TF-IDF\u3002\u6700\u540e\uff0c\u8bbe\u8ba1\u7528\u6237\u754c\u9762\u80fd\u591f\u63d0\u4f9b\u53cb\u597d\u7684\u7528\u6237\u4f53\u9a8c\u3002\u901a\u8fc7\u7cfb\u7edf\u5730\u5b66\u4e60\u548c\u5e94\u7528\u8fd9\u4e9b\u77e5\u8bc6\u548c\u6280\u80fd\uff0c\u4f60\u5c06\u80fd\u591f\u6210\u529f\u5730\u521b\u5efa\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u5168\u7f51\u641c\u7d22\u5668\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u521b\u5efa\u4e00\u4e2a\u5168\u7f51\u641c\u7d22\u5668\uff1f<\/strong>
\u8981\u521b\u5efa\u4e00\u4e2a\u5168\u7f51\u641c\u7d22\u5668\uff0c\u60a8\u9700\u8981\u4f7f\u7528\u4e00\u4e9bPython\u5e93\uff0c\u4f8b\u5982Requests\u548cBeautifulSoup\u6765\u6293\u53d6\u7f51\u9875\u5185\u5bb9\uff0c\u4f7f\u7528Scrapy\u6846\u67b6\u6765\u7ba1\u7406\u722c\u866b\uff0c\u5e76\u8003\u8651\u4f7f\u7528Elasticsearch\u6216SQLite\u6765\u5b58\u50a8\u548c\u7d22\u5f15\u6570\u636e\u3002\u8fd9\u4e9b\u5de5\u5177\u7ed3\u5408\u5728\u4e00\u8d77\uff0c\u53ef\u4ee5\u5e2e\u52a9\u60a8\u9ad8\u6548\u5730\u4ece\u4e0d\u540c\u7f51\u7ad9\u6536\u96c6\u548c\u641c\u7d22\u4fe1\u606f\u3002<\/p>\n
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