![\"python\u5982\u4f55\u5c06\u62db\u8058\u4fe1\u606f\u53ef\u89c6\u5316\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25084633\/d6c7297a-4baa-4bc9-b82d-05aba9656f85.webp\")
<\/p>\n<\/p>\n
Python\u5982\u4f55\u5c06\u62db\u8058\u4fe1\u606f\u53ef\u89c6\u5316<\/strong>\uff1a\u4e3a\u4e86\u5c06\u62db\u8058\u4fe1\u606f\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u901a\u8fc7\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u53ef\u89c6\u5316<\/strong>\u7b49\u51e0\u4e2a\u5173\u952e\u6b65\u9aa4\u5b9e\u73b0\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u51e0\u4e2a\u6b65\u9aa4\u4e2d\u7684\u6bcf\u4e00\u4e2a\uff0c\u5e76\u63d0\u4f9b\u4e00\u4e9b\u5b9e\u9645\u64cd\u4f5c\u7684\u5efa\u8bae\u548c\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n<\/p>\n \u6570\u636e\u6536\u96c6\u662f\u62db\u8058\u4fe1\u606f\u53ef\u89c6\u5316\u7684\u7b2c\u4e00\u6b65\u3002\u5e38\u89c1\u7684\u6570\u636e\u6536\u96c6\u65b9\u6cd5\u5305\u62ec\uff1a\u7f51\u9875\u722c\u866b\u3001API\u63a5\u53e3\u3001\u624b\u52a8\u6536\u96c6<\/strong>\u3002<\/p>\n<\/p>\n \u4f7f\u7528Python\u7684 from bs4 import BeautifulSoup<\/p>\n url = "https:\/\/www.example-job-site.com\/jobs?q=python+developer"<\/p>\n response = requests.get(url)<\/p>\n soup = BeautifulSoup(response.text, 'html.parser')<\/p>\n job_listings = []<\/p>\n for job in soup.find_all('div', class_='job'):<\/p>\n title = job.find('h2').text<\/p>\n company = job.find('span', class_='company').text<\/p>\n location = job.find('span', class_='location').text<\/p>\n job_listings.append({'title': title, 'company': company, 'location': location})<\/p>\n <\/code><\/pre>\n<\/p>\n \u4e00\u4e9b\u62db\u8058\u7f51\u7ad9\u63d0\u4f9bAPI\u63a5\u53e3\uff0c\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7API\u83b7\u53d6\u62db\u8058\u4fe1\u606f\u3002\u4f8b\u5982\uff0c\u4f7f\u7528GitHub Jobs API\uff1a<\/p>\n<\/p>\n url = "https:\/\/jobs.github.com\/positions.json?description=python&location=remote"<\/p>\n response = requests.get(url)<\/p>\n jobs = response.json()<\/p>\n for job in jobs:<\/p>\n print(job['title'], job['company'], job['location'])<\/p>\n <\/code><\/pre>\n<\/p>\n \u5bf9\u4e8e\u4e00\u4e9b\u65e0\u6cd5\u81ea\u52a8\u5316\u83b7\u53d6\u7684\u6570\u636e\uff0c\u624b\u52a8\u6536\u96c6\u4f9d\u7136\u662f\u6709\u6548\u7684\u65b9\u6cd5\u3002\u53ef\u4ee5\u5c06\u6570\u636e\u5b58\u50a8\u5728Excel\u6216CSV\u6587\u4ef6\u4e2d\uff0c\u7136\u540e\u4f7f\u7528Python\u8bfb\u53d6\u3002<\/p>\n<\/p>\n \u5728\u6536\u96c6\u5230\u6570\u636e\u540e\uff0c\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u4e00\u81f4\u6027\u3002\u5e38\u89c1\u7684\u6570\u636e\u6e05\u6d17\u4efb\u52a1\u5305\u62ec\uff1a\u53bb\u91cd\u3001\u5904\u7406\u7f3a\u5931\u503c\u3001\u6807\u51c6\u5316\u5b57\u6bb5<\/strong>\u3002<\/p>\n<\/p>\n \u4f7f\u7528Pandas\u5e93\u53bb\u9664\u91cd\u590d\u7684\u62db\u8058\u4fe1\u606f\uff1a<\/p>\n<\/p>\n df = pd.DataFrame(job_listings)<\/p>\n df.drop_duplicates(inplace=True)<\/p>\n <\/code><\/pre>\n<\/p>\n \u5bf9\u4e8e\u7f3a\u5931\u503c\uff0c\u53ef\u4ee5\u9009\u62e9\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff0c\u6216\u7528\u9ed8\u8ba4\u503c\u586b\u5145\uff1a<\/p>\n<\/p>\n df.fillna('N\/A', inplace=True) # \u7528'N\/A'\u586b\u5145\u7f3a\u5931\u503c<\/p>\n <\/code><\/pre>\n<\/p>\n \u786e\u4fdd\u6240\u6709\u5b57\u6bb5\u7684\u6570\u636e\u683c\u5f0f\u4e00\u81f4\uff0c\u4f8b\u5982\uff0c\u5c06\u85aa\u8d44\u5b57\u6bb5\u7edf\u4e00\u4e3a\u5e74\u85aa\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u6570\u636e\u6e05\u6d17\u4e4b\u540e\uff0c\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u4ee5\u63d0\u53d6\u6709\u7528\u7684\u4fe1\u606f\u548c\u8d8b\u52bf\u3002\u5e38\u89c1\u7684\u6570\u636e\u5206\u6790\u4efb\u52a1\u5305\u62ec\uff1a\u7edf\u8ba1\u5206\u6790\u3001\u6587\u672c\u5206\u6790\u3001\u805a\u7c7b\u5206\u6790<\/strong>\u3002<\/p>\n<\/p>\n \u4f7f\u7528Pandas\u548cNumPy\u5e93\u8fdb\u884c\u57fa\u672c\u7684\u7edf\u8ba1\u5206\u6790\uff0c\u5982\u8ba1\u7b97\u5e73\u5747\u85aa\u8d44\u3001\u804c\u4f4d\u5206\u5e03\u7b49\uff1a<\/p>\n<\/p>\n job_counts = df['title'].value_counts()<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528 nlp = spacy.load('en_core_web_sm')<\/p>\n skills = []<\/p>\n for description in df['description']:<\/p>\n doc = nlp(description)<\/p>\n for ent in doc.ents:<\/p>\n if ent.label_ == 'SKILL':<\/p>\n skills.append(ent.text)<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528 kmeans = KMeans(n_clusters=5)<\/p>\n df['cluster'] = kmeans.fit_predict(df[['salary', 'location_encoded']])<\/p>\n <\/code><\/pre>\n<\/p>\n \u6700\u540e\u4e00\u6b65\u662f\u5c06\u5206\u6790\u7ed3\u679c\u8fdb\u884c\u53ef\u89c6\u5316\u3002\u5e38\u89c1\u7684\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\u5305\u62ec\uff1aMatplotlib\u3001Seaborn\u3001Plotly<\/strong>\u3002<\/p>\n<\/p>\n \u4f7f\u7528Matplotlib\u8fdb\u884c\u57fa\u672c\u7684\u56fe\u8868\u7ed8\u5236\uff0c\u5982\u67f1\u72b6\u56fe\u3001\u6298\u7ebf\u56fe\u7b49\uff1a<\/p>\n<\/p>\n plt.figure(figsize=(10, 6))<\/p>\n plt.bar(job_counts.index, job_counts.values)<\/p>\n plt.xlabel('Job Title')<\/p>\n plt.ylabel('Number of Openings')<\/p>\n plt.title('Job Title Distribution')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528Seaborn\u8fdb\u884c\u9ad8\u7ea7\u56fe\u8868\u7ed8\u5236\uff0c\u5982\u70ed\u529b\u56fe\u3001\u5206\u5e03\u56fe\u7b49\uff1a<\/p>\n<\/p>\n plt.figure(figsize=(10, 6))<\/p>\n sns.heatmap(df.corr(), annot=True)<\/p>\n plt.title('Correlation Matrix')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528Plotly\u8fdb\u884c\u4ea4\u4e92\u5f0f\u56fe\u8868\u7ed8\u5236\uff0c\u5982\u5730\u56fe\u3001\u6563\u70b9\u56fe\u7b49\uff1a<\/p>\n<\/p>\n fig = px.scatter(df, x='salary', y='experience', color='cluster', hover_data=['title', 'company'])<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u4ee5\u4e0a\u51e0\u4e2a\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u5c06\u62db\u8058\u4fe1\u606f\u8fdb\u884c\u6709\u6548\u7684\u53ef\u89c6\u5316\uff0c\u4ece\u800c\u5e2e\u52a9\u7528\u6237\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u62db\u8058\u5e02\u573a\u7684\u52a8\u6001\u3002\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u53ef\u89c6\u5316<\/strong>\u662f\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u6bcf\u4e00\u6b65\u90fd\u9700\u8981\u6839\u636e\u5b9e\u9645\u9700\u6c42\u8fdb\u884c\u8c03\u6574\u548c\u4f18\u5316\u3002<\/p>\n<\/p>\n \u5982\u4f55\u4f7f\u7528Python\u5904\u7406\u62db\u8058\u4fe1\u606f\u6570\u636e\u4ee5\u4fbf\u53ef\u89c6\u5316\uff1f<\/strong> \u6709\u54ea\u4e9bPython\u5e93\u53ef\u4ee5\u7528\u4e8e\u62db\u8058\u4fe1\u606f\u7684\u53ef\u89c6\u5316\uff1f<\/strong> \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u53ef\u89c6\u5316\u56fe\u8868\u7c7b\u578b\u6765\u5c55\u793a\u62db\u8058\u4fe1\u606f\uff1f<\/strong>\u4e00\u3001\u6570\u636e\u6536\u96c6<\/h2>\n<\/p>\n
1. \u7f51\u9875\u722c\u866b<\/h3>\n<\/p>\n
BeautifulSoup<\/code>\u548crequests<\/code>\u5e93\uff0c\u53ef\u4ee5\u4ece\u62db\u8058\u7f51\u7ad9\uff08\u5982Indeed\u3001LinkedIn\u7b49\uff09\u722c\u53d6\u62db\u8058\u4fe1\u606f\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\nimport requests<\/p>\n2. API\u63a5\u53e3<\/h3>\n<\/p>\n
import requests<\/p>\n3. \u624b\u52a8\u6536\u96c6<\/h3>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u6e05\u6d17<\/h2>\n<\/p>\n
1. \u53bb\u91cd<\/h3>\n<\/p>\n
import pandas as pd<\/p>\n2. \u5904\u7406\u7f3a\u5931\u503c<\/h3>\n<\/p>\n
df.dropna(inplace=True) # \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n3. \u6807\u51c6\u5316\u5b57\u6bb5<\/h3>\n<\/p>\n
df['salary'] = df['salary'].apply(lambda x: convert_to_annual_salary(x))<\/p>\n\u4e09\u3001\u6570\u636e\u5206\u6790<\/h2>\n<\/p>\n
1. \u7edf\u8ba1\u5206\u6790<\/h3>\n<\/p>\n
average_salary = df['salary'].mean()<\/p>\n2. \u6587\u672c\u5206\u6790<\/h3>\n<\/p>\n
NLTK<\/code>\u6216spaCy<\/code>\u5e93\u8fdb\u884c\u6587\u672c\u5206\u6790\uff0c\u5982\u63d0\u53d6\u62db\u8058\u4fe1\u606f\u4e2d\u7684\u6280\u80fd\u8981\u6c42\uff1a<\/p>\n<\/p>\nimport spacy<\/p>\n3. \u805a\u7c7b\u5206\u6790<\/h3>\n<\/p>\n
scikit-learn<\/code>\u5e93\u8fdb\u884c\u805a\u7c7b\u5206\u6790\uff0c\u4ee5\u8bc6\u522b\u7c7b\u4f3c\u7684\u804c\u4f4d\u6216\u516c\u53f8\uff1a<\/p>\n<\/p>\nfrom sklearn.cluster import KMeans<\/p>\n\u56db\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h2>\n<\/p>\n
1. Matplotlib<\/h3>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n2. Seaborn<\/h3>\n<\/p>\n
import seaborn as sns<\/p>\n3. Plotly<\/h3>\n<\/p>\n
import plotly.express as px<\/p>\n\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
\u5728\u5904\u7406\u62db\u8058\u4fe1\u606f\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8bfb\u53d6\u548c\u6e05\u6d17\u6570\u636e\u3002\u901a\u8fc7\u6570\u636e\u6846\uff08DataFrame\uff09\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5bf9\u62db\u8058\u4fe1\u606f\u8fdb\u884c\u7b5b\u9009\u548c\u6574\u7406\u3002\u4e4b\u540e\uff0c\u4f7f\u7528Matplotlib\u6216Seaborn\u7b49\u53ef\u89c6\u5316\u5e93\uff0c\u80fd\u591f\u5c06\u6570\u636e\u56fe\u5f62\u5316\u5448\u73b0\uff0c\u5e2e\u52a9\u8bc6\u522b\u62db\u8058\u8d8b\u52bf\u548c\u9700\u6c42\u3002<\/p>\n
\u5e38\u7528\u7684Python\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u3001Plotly\u548cBokeh\u7b49\u3002Matplotlib\u9002\u5408\u57fa\u672c\u7684\u56fe\u8868\u7ed8\u5236\uff0c\u800cSeaborn\u5728\u7edf\u8ba1\u56fe\u8868\u65b9\u9762\u8868\u73b0\u4f18\u5f02\u3002Plotly\u548cBokeh\u5219\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u9002\u5408\u9700\u8981\u7528\u6237\u4e0e\u56fe\u8868\u8fdb\u884c\u4ea4\u4e92\u7684\u573a\u666f\u3002<\/p>\n
\u9009\u62e9\u53ef\u89c6\u5316\u56fe\u8868\u7c7b\u578b\u9700\u6839\u636e\u6570\u636e\u7684\u7279\u70b9\u548c\u60f3\u8981\u4f20\u8fbe\u7684\u4fe1\u606f\u3002\u82e5\u8981\u5c55\u793a\u4e0d\u540c\u804c\u4f4d\u7684\u6570\u91cf\uff0c\u53ef\u4ee5\u4f7f\u7528\u67f1\u72b6\u56fe\uff1b\u82e5\u5173\u6ce8\u65f6\u95f4\u8d8b\u52bf\uff0c\u53ef\u4ee5\u9009\u62e9\u6298\u7ebf\u56fe\uff1b\u82e5\u60f3\u6bd4\u8f83\u4e0d\u540c\u5730\u533a\u7684\u62db\u8058\u9700\u6c42\uff0c\u70ed\u529b\u56fe\u6216\u5730\u7406\u5206\u5e03\u56fe\u53ef\u80fd\u66f4\u5408\u9002\u3002\u901a\u8fc7\u9002\u5f53\u7684\u56fe\u8868\uff0c\u80fd\u591f\u66f4\u6e05\u6670\u5730\u4f20\u8fbe\u62db\u8058\u4fe1\u606f\u7684\u5173\u952e\u70b9\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5982\u4f55\u5c06\u62db\u8058\u4fe1\u606f\u53ef\u89c6\u5316\uff1a\u4e3a\u4e86\u5c06\u62db\u8058\u4fe1\u606f\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u901a\u8fc7\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206 […]","protected":false},"author":3,"featured_media":1123006,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1123003"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1123003"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1123003\/revisions"}],"predecessor-version":[{"id":1123007,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1123003\/revisions\/1123007"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1123006"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1123003"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1123003"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1123003"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}