{"id":1030258,"date":"2024-12-31T11:15:35","date_gmt":"2024-12-31T03:15:35","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1030258.html"},"modified":"2024-12-31T11:15:38","modified_gmt":"2024-12-31T03:15:38","slug":"python%e5%a6%82%e4%bd%95%e6%8f%90%e5%8f%96%e9%9d%9e%e7%bb%93%e6%9e%84%e5%8c%96%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1030258.html","title":{"rendered":"python\u5982\u4f55\u63d0\u53d6\u975e\u7ed3\u6784\u5316\u6570\u636e"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-docs.pingcode.com\/wp-content\/uploads\/2024\/12\/184093de-cd1e-45af-a39f-5b251c4bc37d.webp?x-oss-process=image\/auto-orient,1\/format,webp\" alt=\"python\u5982\u4f55\u63d0\u53d6\u975e\u7ed3\u6784\u5316\u6570\u636e\" \/><\/p>\n<p><p> <strong>Python \u63d0\u53d6\u975e\u7ed3\u6784\u5316\u6570\u636e\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406 (NLP) \u6280\u672f\u3001Web Scraping \u5de5\u5177\u3001\u6587\u672c\u5206\u6790\u5e93\u7b49\u3002<\/strong> \u5176\u4e2d\uff0c\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u662f\u6700\u57fa\u7840\u4e14\u5e38\u7528\u7684\u65b9\u6cd5\u4e4b\u4e00\u3002\u901a\u8fc7\u6b63\u5219\u8868\u8fbe\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u6307\u5b9a\u6a21\u5f0f\u6765\u5339\u914d\u548c\u63d0\u53d6\u6570\u636e\uff0c\u4ece\u800c\u5904\u7406\u975e\u7ed3\u6784\u5316\u7684\u6570\u636e\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u4e00\u4e9b\u5b9e\u7528\u7684\u793a\u4f8b\u4ee3\u7801\u6765\u5e2e\u52a9\u7406\u89e3\u3002<\/p>\n<\/p>\n<hr>\n<p><h3>\u4e00\u3001\u6b63\u5219\u8868\u8fbe\u5f0f<\/h3>\n<\/p>\n<p><p>\u6b63\u5219\u8868\u8fbe\u5f0f\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u7528\u4e8e\u5339\u914d\u5b57\u7b26\u4e32\u4e2d\u7684\u6a21\u5f0f\u3002Python \u63d0\u4f9b\u4e86 <code>re<\/code> \u6a21\u5757\u6765\u5904\u7406\u6b63\u5219\u8868\u8fbe\u5f0f\u3002\u6b63\u5219\u8868\u8fbe\u5f0f\u53ef\u4ee5\u7528\u4e8e\u89e3\u6790\u548c\u63d0\u53d6\u975e\u7ed3\u6784\u5316\u6570\u636e\uff0c\u4f8b\u5982\u4ece\u6587\u672c\u4e2d\u63d0\u53d6\u7535\u5b50\u90ae\u4ef6\u5730\u5740\u3001\u7535\u8bdd\u53f7\u7801\u7b49\u3002<\/p>\n<\/p>\n<p><h4>\u793a\u4f8b\u4ee3\u7801\uff1a<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">import re<\/p>\n<p>text = &quot;Contact us at info@example.com or support@example.org&quot;<\/p>\n<p>pattern = r&#39;\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b&#39;<\/p>\n<p>em<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>ls = re.findall(pattern, text)<\/p>\n<p>print(emails)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e0a\u8ff0\u4ee3\u7801\u5c06\u4ece\u7ed9\u5b9a\u7684\u6587\u672c\u4e2d\u63d0\u53d6\u6240\u6709\u7684\u7535\u5b50\u90ae\u4ef6\u5730\u5740\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406 (NLP)<\/h3>\n<\/p>\n<p><p>\u81ea\u7136\u8bed\u8a00\u5904\u7406 (NLP) \u662f\u5904\u7406\u548c\u5206\u6790\u81ea\u7136\u8bed\u8a00\u6570\u636e\u7684\u4e00\u7ec4\u6280\u672f\u3002Python \u63d0\u4f9b\u4e86\u591a\u79cd NLP \u5e93\uff0c\u5982 NLTK\u3001spaCy\u3001Gensim \u7b49\uff0c\u6765\u5904\u7406\u6587\u672c\u6570\u636e\u3002\u901a\u8fc7 NLP \u6280\u672f\uff0c\u6211\u4eec\u53ef\u4ee5\u5b9e\u73b0\u6587\u672c\u5206\u7c7b\u3001\u60c5\u611f\u5206\u6790\u3001\u5b9e\u4f53\u8bc6\u522b\u7b49\u4efb\u52a1\u3002<\/p>\n<\/p>\n<p><h4>\u4f7f\u7528 NLTK \u8fdb\u884c\u6587\u672c\u5904\u7406\uff1a<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">import nltk<\/p>\n<p>from nltk.tokenize import word_tokenize<\/p>\n<p>from nltk.corpus import stopwords<\/p>\n<p>text = &quot;Natural language processing with Python is very powerful.&quot;<\/p>\n<p>tokens = word_tokenize(text)<\/p>\n<p>stop_words = set(stopwords.words(&#39;english&#39;))<\/p>\n<p>filtered_tokens = [word for word in tokens if word.lower() not in stop_words]<\/p>\n<p>print(filtered_tokens)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e0a\u8ff0\u4ee3\u7801\u5c06\u6587\u672c\u5206\u8bcd\uff0c\u5e76\u8fc7\u6ee4\u6389\u5e38\u89c1\u7684\u505c\u7528\u8bcd\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001Web Scraping<\/h3>\n<\/p>\n<p><p>Web Scraping \u662f\u4ece\u7f51\u7ad9\u4e2d\u63d0\u53d6\u6570\u636e\u7684\u4e00\u79cd\u6280\u672f\u3002Python \u63d0\u4f9b\u4e86\u591a\u79cd\u5de5\u5177\u6765\u5b9e\u73b0 Web Scraping\uff0c\u5982 BeautifulSoup\u3001Scrapy\u3001Requests \u7b49\u3002\u901a\u8fc7 Web Scraping\uff0c\u6211\u4eec\u53ef\u4ee5\u4ece\u7f51\u9875\u4e2d\u63d0\u53d6\u6240\u9700\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n<p><h4>\u4f7f\u7528 BeautifulSoup \u8fdb\u884c Web Scraping\uff1a<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">import requests<\/p>\n<p>from bs4 import BeautifulSoup<\/p>\n<p>url = &#39;https:\/\/example.com&#39;<\/p>\n<p>response = requests.get(url)<\/p>\n<p>soup = BeautifulSoup(response.text, &#39;html.parser&#39;)<\/p>\n<p>titles = soup.find_all(&#39;h1&#39;)<\/p>\n<p>for title in titles:<\/p>\n<p>    print(title.get_text())<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e0a\u8ff0\u4ee3\u7801\u5c06\u4ece\u6307\u5b9a\u7684\u7f51\u9875\u4e2d\u63d0\u53d6\u6240\u6709\u7684 <code>&lt;h1&gt;<\/code> \u6807\u7b7e\u5185\u5bb9\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u6587\u672c\u5206\u6790\u5e93<\/h3>\n<\/p>\n<p><p>Python \u63d0\u4f9b\u4e86\u591a\u79cd\u6587\u672c\u5206\u6790\u5e93\uff0c\u5982 Pandas\u3001Scikit-learn \u7b49\uff0c\u7528\u4e8e\u5904\u7406\u548c\u5206\u6790\u975e\u7ed3\u6784\u5316\u6570\u636e\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u5904\u7406\u6587\u672c\u6570\u636e\uff0c\u4f8b\u5982\u6570\u636e\u6e05\u6d17\u3001\u7279\u5f81\u63d0\u53d6\u3001<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u8bad\u7ec3\u7b49\u3002<\/p>\n<\/p>\n<p><h4>\u4f7f\u7528 Pandas \u8fdb\u884c\u6587\u672c\u6570\u636e\u5904\u7406\uff1a<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<p>data = {&#39;text&#39;: [&#39;I love Python&#39;, &#39;Python is great for data analysis&#39;, &#39;Learning Python is fun&#39;]}<\/p>\n<p>df = pd.DataFrame(data)<\/p>\n<p>df[&#39;text_length&#39;] = df[&#39;text&#39;].apply(len)<\/p>\n<p>print(df)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e0a\u8ff0\u4ee3\u7801\u5c06\u8ba1\u7b97\u6bcf\u6761\u6587\u672c\u7684\u957f\u5ea6\uff0c\u5e76\u5c06\u5176\u6dfb\u52a0\u5230\u6570\u636e\u6846\u4e2d\u3002<\/p>\n<\/p>\n<p><h3>\u4e94\u3001\u7ed3\u5408\u591a\u79cd\u6280\u672f<\/h3>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u9700\u8981\u7ed3\u5408\u591a\u79cd\u6280\u672f\u6765\u5904\u7406\u975e\u7ed3\u6784\u5316\u6570\u636e\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u5148\u4f7f\u7528 Web Scraping \u4ece\u7f51\u9875\u4e2d\u63d0\u53d6\u6570\u636e\uff0c\u7136\u540e\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u8fdb\u884c\u6570\u636e\u6e05\u6d17\uff0c\u6700\u540e\u4f7f\u7528 NLP \u6280\u672f\u8fdb\u884c\u6587\u672c\u5206\u6790\u3002<\/p>\n<\/p>\n<p><h4>\u7efc\u5408\u793a\u4f8b\uff1a<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">import requests<\/p>\n<p>from bs4 import BeautifulSoup<\/p>\n<p>import re<\/p>\n<p>from nltk.tokenize import word_tokenize<\/p>\n<p>from nltk.corpus import stopwords<\/p>\n<h2><strong>Step 1: Web Scraping<\/strong><\/h2>\n<p>url = &#39;https:\/\/example.com&#39;<\/p>\n<p>response = requests.get(url)<\/p>\n<p>soup = BeautifulSoup(response.text, &#39;html.parser&#39;)<\/p>\n<p>text = soup.get_text()<\/p>\n<h2><strong>Step 2: Data Cleaning with Regex<\/strong><\/h2>\n<p>pattern = r&#39;\\b[A-Za-z]+\\b&#39;<\/p>\n<p>words = re.findall(pattern, text)<\/p>\n<h2><strong>Step 3: Text Processing with NLP<\/strong><\/h2>\n<p>stop_words = set(stopwords.words(&#39;english&#39;))<\/p>\n<p>filtered_words = [word for word in words if word.lower() not in stop_words]<\/p>\n<p>print(filtered_words)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e0a\u8ff0\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u7ed3\u5408 Web Scraping\u3001\u6b63\u5219\u8868\u8fbe\u5f0f\u548c NLP \u6280\u672f\u6765\u5904\u7406\u975e\u7ed3\u6784\u5316\u6570\u636e\u3002<\/p>\n<\/p>\n<p><h3>\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>\u901a\u8fc7\u672c\u6587\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86 Python \u63d0\u53d6\u975e\u7ed3\u6784\u5316\u6570\u636e\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u6b63\u5219\u8868\u8fbe\u5f0f\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406 (NLP) \u6280\u672f\u3001Web Scraping \u5de5\u5177\u3001\u6587\u672c\u5206\u6790\u5e93\u7b49\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u9002\u7528\u573a\u666f\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u6216\u8005\u7ed3\u5408\u591a\u79cd\u6280\u672f\u6765\u5b9e\u73b0\u5bf9\u975e\u7ed3\u6784\u5316\u6570\u636e\u7684\u63d0\u53d6\u548c\u5904\u7406\u3002\u5e0c\u671b\u672c\u6587\u80fd\u4e3a\u60a8\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u53c2\u8003\u548c\u5e2e\u52a9\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u8bc6\u522b\u975e\u7ed3\u6784\u5316\u6570\u636e\u7684\u7c7b\u578b\uff1f<\/strong><br \/>\u975e\u7ed3\u6784\u5316\u6570\u636e\u901a\u5e38\u5305\u62ec\u6587\u672c\u3001\u56fe\u50cf\u3001\u97f3\u9891\u548c\u89c6\u9891\u7b49\u683c\u5f0f\u3002\u8bc6\u522b\u8fd9\u4e9b\u6570\u636e\u7684\u7c7b\u578b\u53ef\u4ee5\u901a\u8fc7\u5206\u6790\u6570\u636e\u7684\u6765\u6e90\u548c\u5185\u5bb9\u6765\u8fdb\u884c\u3002\u4f8b\u5982\uff0c\u793e\u4ea4\u5a92\u4f53\u8bc4\u8bba\u3001\u7535\u5b50\u90ae\u4ef6\u5185\u5bb9\u548cPDF\u6587\u6863\u90fd\u662f\u975e\u7ed3\u6784\u5316\u6587\u672c\u6570\u636e\uff0c\u800c\u56fe\u7247\u6587\u4ef6\u3001\u97f3\u9891\u5f55\u97f3\u548c\u89c6\u9891\u526a\u8f91\u5219\u5c5e\u4e8e\u975e\u7ed3\u6784\u5316\u591a\u5a92\u4f53\u6570\u636e\u3002\u4e86\u89e3\u6570\u636e\u7684\u6765\u6e90\u548c\u7279\u70b9\u6709\u52a9\u4e8e\u9009\u62e9\u5408\u9002\u7684\u63d0\u53d6\u65b9\u6cd5\u3002<\/p>\n<p><strong>\u5728Python\u4e2d\uff0c\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u5e2e\u52a9\u63d0\u53d6\u975e\u7ed3\u6784\u5316\u6570\u636e\uff1f<\/strong><br \/>Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5f3a\u5927\u7684\u5e93\u6765\u5904\u7406\u548c\u63d0\u53d6\u975e\u7ed3\u6784\u5316\u6570\u636e\u3002\u5bf9\u4e8e\u6587\u672c\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528BeautifulSoup\u8fdb\u884cHTML\u89e3\u6790\uff0cPandas\u5904\u7406CSV\u548cExcel\u6587\u4ef6\uff0cNLTK\u548cspaCy\u8fdb\u884c\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3002\u5bf9\u4e8e\u56fe\u50cf\uff0c\u53ef\u4ee5\u4f7f\u7528Pillow\u548cOpenCV\u8fdb\u884c\u56fe\u50cf\u5904\u7406\uff0c\u800c\u97f3\u9891\u6570\u636e\u53ef\u4ee5\u901a\u8fc7Librosa\u548cPyDub\u8fdb\u884c\u5206\u6790\u548c\u5904\u7406\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u5c06\u5927\u5927\u63d0\u9ad8\u6570\u636e\u63d0\u53d6\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<p><strong>\u5982\u4f55\u5904\u7406\u548c\u6e05\u6d17\u63d0\u53d6\u7684\u975e\u7ed3\u6784\u5316\u6570\u636e\uff1f<\/strong><br 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