{"id":1147300,"date":"2025-01-13T16:23:19","date_gmt":"2025-01-13T08:23:19","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1147300.html"},"modified":"2025-01-13T16:23:22","modified_gmt":"2025-01-13T08:23:22","slug":"%e5%a6%82%e4%bd%95%e5%88%a9%e7%94%a8python%e5%88%86%e6%9e%90%e7%bd%91%e7%ab%99","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1147300.html","title":{"rendered":"\u5982\u4f55\u5229\u7528python\u5206\u6790\u7f51\u7ad9"},"content":{"rendered":"

\"\u5982\u4f55\u5229\u7528python\u5206\u6790\u7f51\u7ad9\"<\/p>\n

\u5229\u7528Python\u5206\u6790\u7f51\u7ad9\u53ef\u4ee5\u901a\u8fc7\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u53ef\u89c6\u5316<\/strong>\u8fd9\u51e0\u4e2a\u6b65\u9aa4\u6765\u5b9e\u73b0\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u901a\u8fc7\u6bcf\u4e2a\u6b65\u9aa4\u6765\u5b8c\u6210\u7f51\u7ad9\u6570\u636e\u7684\u5206\u6790\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u6536\u96c6<\/h3>\n<\/p>\n

1.1 \u4f7f\u7528Requests\u5e93\u8fdb\u884c\u6570\u636e\u6536\u96c6<\/h4>\n<\/p>\n

Requests\u5e93\u662fPython\u4e2d\u6700\u5e38\u7528\u7684HTTP\u8bf7\u6c42\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5411\u7f51\u7ad9\u53d1\u9001\u8bf7\u6c42\u5e76\u83b7\u53d6\u54cd\u5e94\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n

import requests<\/p>\n
url = 'http:\/\/example.com'<\/p>\n
response = requests.get(url)<\/p>\n
print(response.text)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u4f7f\u7528BeautifulSoup\u5e93\u89e3\u6790HTML<\/h4>\n<\/p>\n
BeautifulSoup\u662f\u4e00\u4e2a\u7528\u4e8e\u89e3\u6790HTML\u548cXML\u6587\u6863\u7684Python\u5e93\uff0c\u80fd\u591f\u65b9\u4fbf\u5730\u4ece\u7f51\u9875\u4e2d\u63d0\u53d6\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f7f\u7528\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
from bs4 import BeautifulSoup<\/p>\n
html_content = '<html><head><title>Example<\/title><\/head><body><h1>Hello, world!<\/h1><\/body><\/html>'<\/p>\n
soup = BeautifulSoup(html_content, 'html.parser')<\/p>\n
print(soup.title.text)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 \u4f7f\u7528Selenium\u5e93\u8fdb\u884c\u52a8\u6001\u7f51\u9875\u6570\u636e\u6536\u96c6<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u4e00\u4e9b\u9700\u8981\u8fdb\u884cJavaScript\u4ea4\u4e92\u7684\u7f51\u7ad9\uff0c\u53ef\u4ee5\u4f7f\u7528Selenium\u5e93\u6765\u6a21\u62df\u6d4f\u89c8\u5668\u64cd\u4f5c\u5e76\u83b7\u53d6\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
from selenium import webdriver<\/p>\n
driver = webdriver.Chrome()<\/p>\n
driver.get('http:\/\/example.com')<\/p>\n
print(driver.page_source)<\/p>\n
driver.quit()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u6e05\u6d17<\/h3>\n<\/p>\n
2.1 \u6570\u636e\u6e05\u6d17\u7684\u91cd\u8981\u6027<\/h4>\n<\/p>\n
\u5728\u8fdb\u884c\u6570\u636e\u5206\u6790\u4e4b\u524d\uff0c\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u662f\u975e\u5e38\u91cd\u8981\u7684\u4e00\u6b65\u3002\u6570\u636e\u6e05\u6d17\u5305\u62ec\u53bb\u9664\u65e0\u7528\u6570\u636e\u3001\u5904\u7406\u7f3a\u5931\u503c\u3001\u89c4\u8303\u5316\u6570\u636e\u683c\u5f0f\u7b49\u3002<\/p>\n<\/p>\n
2.2 \u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
Pandas\u5e93\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u6570\u636e\u8fdb\u884c\u5904\u7406\u548c\u6e05\u6d17\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'Name': ['John', 'Anna', 'Peter'], 'Age': [28, None, 34]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
df['Age'].fillna(df['Age'].mean(), inplace=True)<\/p>\n
\u5220\u9664\u65e0\u7528\u5217<\/strong><\/h2>\n
df.drop(columns=['Name'], inplace=True)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u5206\u6790<\/h3>\n<\/p>\n
3.1 \u4f7f\u7528Pandas\u8fdb\u884c\u57fa\u672c\u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
Pandas\u5e93\u4e0d\u4ec5\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u6e05\u6d17\uff0c\u8fd8\u53ef\u4ee5\u8fdb\u884c\u57fa\u672c\u7684\u6570\u636e\u5206\u6790\u64cd\u4f5c\uff0c\u5982\u63cf\u8ff0\u7edf\u8ba1\u3001\u6570\u636e\u5206\u7ec4\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'Name': ['John', 'Anna', 'Peter'], 'Age': [28, 22, 34], 'Salary': [50000, 60000, 70000]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u63cf\u8ff0\u7edf\u8ba1<\/strong><\/h2>\n
print(df.describe())<\/p>\n
\u6309\u5e74\u9f84\u5206\u7ec4\u5e76\u8ba1\u7b97\u5e73\u5747\u5de5\u8d44<\/strong><\/h2>\n
print(df.groupby('Age')['Salary'].mean())<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u4f7f\u7528NumPy\u8fdb\u884c\u9ad8\u7ea7\u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
NumPy\u5e93\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u53ef\u4ee5\u8fdb\u884c\u9ad8\u7ea7\u7684\u6570\u636e\u5206\u6790\u64cd\u4f5c\uff0c\u5982\u77e9\u9635\u8fd0\u7b97\u3001\u7edf\u8ba1\u5206\u6790\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.array([[28, 50000], [22, 60000], [34, 70000]])<\/p>\n
\u8ba1\u7b97\u5e73\u5747\u5e74\u9f84\u548c\u5e73\u5747\u5de5\u8d44<\/strong><\/h2>\n
print(np.mean(data, axis=0))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
4.1 \u4f7f\u7528Matplotlib\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ed8\u5236\u5404\u79cd\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
data = {'Name': ['John', 'Anna', 'Peter'], 'Age': [28, 22, 34], 'Salary': [50000, 60000, 70000]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(df['Name'], df['Salary'])<\/p>\n
plt.xlabel('Name')<\/p>\n
plt.ylabel('Salary')<\/p>\n
plt.title('Salary by Name')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u4f7f\u7528Seaborn\u8fdb\u884c\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u53ef\u4ee5\u66f4\u65b9\u4fbf\u5730\u7ed8\u5236\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
data = {'Name': ['John', 'Anna', 'Peter'], 'Age': [28, 22, 34], 'Salary': [50000, 60000, 70000]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
sns.barplot(x='Name', y='Salary', data=df)<\/p>\n
plt.title('Salary by Name')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5b9e\u6218\u6848\u4f8b<\/h3>\n<\/p>\n
5.1 \u722c\u53d6IMDB\u7535\u5f71\u6570\u636e\u5e76\u8fdb\u884c\u5206\u6790<\/h4>\n<\/p>\n
\u6211\u4eec\u5c06\u901a\u8fc7\u4e00\u4e2a\u5b9e\u9645\u7684\u6848\u4f8b\u6765\u6f14\u793a\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u7f51\u7ad9\u6570\u636e\u7684\u5206\u6790\u3002\u6211\u4eec\u5c06\u722c\u53d6IMDB\u7f51\u7ad9\u7684\u7535\u5f71\u6570\u636e\uff0c\u5e76\u8fdb\u884c\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
5.2 \u722c\u53d6\u6570\u636e<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u4f7f\u7528Requests\u548cBeautifulSoup\u5e93\u6765\u722c\u53d6IMDB\u7f51\u7ad9\u7684\u7535\u5f71\u6570\u636e\u3002<\/p>\n<\/p>\n
import requests<\/p>\n
from bs4 import BeautifulSoup<\/p>\n
url = 'https:\/\/www.imdb.com\/chart\/top'<\/p>\n
response = requests.get(url)<\/p>\n
soup = BeautifulSoup(response.text, 'html.parser')<\/p>\n
movies = []<\/p>\n
for row in soup.find_all('tr'):<\/p>\n
    title_column = row.find('td', class_='titleColumn')<\/p>\n
    if title_column:<\/p>\n
        title = title_column.a.text<\/p>\n
        year = title_column.span.text.strip('()')<\/p>\n
        rating = row.find('td', class_='imdbRating').strong.text<\/p>\n
        movies.append({'title': title, 'year': int(year), 'rating': float(rating)})<\/p>\n
print(movies)<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.3 \u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u722c\u53d6\u7684\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
df = pd.DataFrame(movies)<\/p>\n
\u8f6c\u6362\u5e74\u4efd\u4e3a\u6574\u6570<\/strong><\/h2>\n
df['year'] = df['year'].astype(int)<\/p>\n
\u53bb\u9664\u91cd\u590d\u6570\u636e<\/strong><\/h2>\n
df.drop_duplicates(inplace=True)<\/p>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.4 \u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u5bf9\u6e05\u6d17\u540e\u7684\u6570\u636e\u8fdb\u884c\u4e00\u4e9b\u57fa\u672c\u7684\u5206\u6790\uff0c\u5982\u8ba1\u7b97\u7535\u5f71\u7684\u5e73\u5747\u8bc4\u5206\u3001\u6309\u5e74\u4efd\u5206\u7ec4\u7edf\u8ba1\u7b49\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u5e73\u5747\u8bc4\u5206<\/p>\n
print(df['rating'].mean())<\/p>\n
\u6309\u5e74\u4efd\u5206\u7ec4\u7edf\u8ba1<\/strong><\/h2>\n
print(df.groupby('year')['rating'].mean())<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.5 \u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cSeaborn\u5e93\u5bf9\u5206\u6790\u7ed3\u679c\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u7535\u5f71\u8bc4\u5206\u5206\u5e03\u56fe<\/strong><\/h2>\n
sns.histplot(df['rating'], bins=10, kde=True)<\/p>\n
plt.xlabel('Rating')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.title('Distribution of Movie Ratings')<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u6309\u5e74\u4efd\u5206\u7ec4\u7684\u5e73\u5747\u8bc4\u5206\u56fe<\/strong><\/h2>\n
average_ratings_by_year = df.groupby('year')['rating'].mean().reset_index()<\/p>\n
sns.lineplot(x='year', y='rating', data=average_ratings_by_year)<\/p>\n
plt.xlabel('Year')<\/p>\n
plt.ylabel('Average Rating')<\/p>\n
plt.title('Average Movie Rating by Year')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u5229\u7528Python\u8fdb\u884c\u7f51\u7ad9\u6570\u636e\u7684\u5206\u6790\u662f\u4e00\u4e2a\u7cfb\u7edf\u7684\u8fc7\u7a0b\uff0c\u5305\u62ec\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u548c\u6570\u636e\u53ef\u89c6\u5316\u7b49\u6b65\u9aa4\u3002\u6bcf\u4e00\u6b65\u90fd\u53ef\u4ee5\u4f7f\u7528\u4e0d\u540c\u7684\u5de5\u5177\u548c\u5e93\u6765\u5b9e\u73b0\uff0c\u5982Requests\u3001BeautifulSoup\u3001Selenium\u3001Pandas\u3001NumPy\u3001Matplotlib\u548cSeaborn\u7b49\u3002\u901a\u8fc7\u8fd9\u4e9b\u5de5\u5177\u548c\u5e93\u7684\u7ed3\u5408\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u4ece\u7f51\u7ad9\u4e2d\u63d0\u53d6\u6570\u636e\uff0c\u5e76\u8fdb\u884c\u6df1\u5165\u7684\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
\u5e0c\u671b\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u638c\u63e1\u5982\u4f55\u5229\u7528Python\u8fdb\u884c\u7f51\u7ad9\u6570\u636e\u7684\u5206\u6790\uff0c\u5e76\u80fd\u591f\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\u5e94\u7528\u8fd9\u4e9b\u6280\u672f\u548c\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u7528Python\u8fdb\u884c\u7f51\u7ad9\u6570\u636e\u6293\u53d6\uff1f<\/strong>
Python\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5e93\u6765\u8fdb\u884c\u7f51\u7ad9\u6570\u636e\u6293\u53d6\uff0c\u6bd4\u5982Beautiful Soup\u548cScrapy\u3002Beautiful Soup\u53ef\u4ee5\u5e2e\u52a9\u89e3\u6790HTML\u6587\u6863\uff0c\u4f7f\u5f97\u63d0\u53d6\u4fe1\u606f\u53d8\u5f97\u7b80\u5355\u3002Scrapy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6846\u67b6\uff0c\u9002\u5408\u9700\u8981\u6293\u53d6\u591a\u4e2a\u9875\u9762\u548c\u7f51\u7ad9\u7684\u590d\u6742\u9879\u76ee\u3002\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u65f6\uff0c\u5efa\u8bae\u9075\u5faa\u7f51\u7ad9\u7684robots.txt\u6587\u4ef6\uff0c\u786e\u4fdd\u5408\u6cd5\u6293\u53d6\u3002<\/p>\n
\u5728\u5206\u6790\u7f51\u7ad9\u6570\u636e\u65f6\uff0cPython\u80fd\u5904\u7406\u54ea\u4e9b\u7c7b\u578b\u7684\u4fe1\u606f\uff1f<\/strong>
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