{"id":1059438,"date":"2024-12-31T15:25:11","date_gmt":"2024-12-31T07:25:11","guid":{"rendered":""},"modified":"2024-12-31T15:25:14","modified_gmt":"2024-12-31T07:25:14","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e9%87%87%e9%9b%86%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1059438.html","title":{"rendered":"\u5982\u4f55\u7528python\u91c7\u96c6\u6570\u636e\u5904\u7406"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u91c7\u96c6\u6570\u636e\u5904\u7406\"<\/p>\n

\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u91c7\u96c6\u4e0e\u5904\u7406<\/strong><\/p>\n<\/p>\n

\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u91c7\u96c6\u4e0e\u5904\u7406\u7684\u6838\u5fc3\u5728\u4e8e\u6570\u636e\u6293\u53d6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u5b58\u50a8<\/strong>\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u901a\u8fc7Python\u8fdb\u884c\u6570\u636e\u91c7\u96c6\u548c\u5904\u7406\u7684\u5404\u4e2a\u6b65\u9aa4\uff0c\u5e76\u63d0\u4f9b\u4e00\u4e9b\u6709\u7528\u7684\u4ee3\u7801\u793a\u4f8b\u548c\u5de5\u5177\u63a8\u8350\u3002\u6211\u4eec\u5c06\u91cd\u70b9\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u5e93\u8fdb\u884c\u7f51\u9875\u6570\u636e\u6293\u53d6\uff0c\u5e76\u5bf9\u6293\u53d6\u7684\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u6293\u53d6<\/h3>\n<\/p>\n

\u6570\u636e\u6293\u53d6\u662f\u6570\u636e\u91c7\u96c6\u7684\u7b2c\u4e00\u6b65\uff0c\u4e3b\u8981\u901a\u8fc7\u7f51\u9875\u722c\u866b\u6280\u672f\u5b9e\u73b0\u3002Python\u6709\u591a\u4e2a\u5f3a\u5927\u7684\u5e93\u53ef\u4ee5\u7528\u6765\u6293\u53d6\u7f51\u9875\u6570\u636e\uff0c\u4f8b\u5982\uff1aRequests\u3001BeautifulSoup\u3001Scrapy\u3001Selenium<\/strong>\u3002<\/p>\n<\/p>\n

1. \u4f7f\u7528Requests\u5e93\u8fdb\u884c\u7b80\u5355\u6570\u636e\u6293\u53d6<\/h4>\n<\/p>\n

Requests\u5e93\u662f\u4e00\u4e2a\u7b80\u5355\u6613\u7528\u7684HTTP\u5e93\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u53d1\u9001HTTP\u8bf7\u6c42\uff0c\u4ece\u7f51\u9875\u83b7\u53d6\u6570\u636e\u3002<\/p>\n<\/p>\n

import requests<\/p>\n
url = 'https:\/\/example.com'<\/p>\n
response = requests.get(url)<\/p>\n
print(response.text)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528BeautifulSoup\u5e93\u89e3\u6790HTML\u5185\u5bb9<\/h4>\n<\/p>\n
BeautifulSoup\u662f\u4e00\u4e2a\u7528\u4e8e\u89e3\u6790HTML\u548cXML\u6587\u4ef6\u7684\u5e93\uff0c\u5b83\u53ef\u4ee5\u65b9\u4fbf\u5730\u4ece\u7f51\u9875\u4e2d\u63d0\u53d6\u6570\u636e\u3002<\/p>\n<\/p>\n
from bs4 import BeautifulSoup<\/p>\n
soup = BeautifulSoup(response.text, 'html.parser')<\/p>\n
title = soup.title.string<\/p>\n
print(title)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u4f7f\u7528Scrapy\u8fdb\u884c\u590d\u6742\u6570\u636e\u6293\u53d6<\/h4>\n<\/p>\n
Scrapy\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u722c\u866b\u6846\u67b6\uff0c\u9002\u5408\u6293\u53d6\u5927\u91cf\u6570\u636e\u548c\u590d\u6742\u7f51\u7ad9\u3002<\/p>\n<\/p>\n
import scrapy<\/p>\n
class ExampleSpider(scrapy.Spider):<\/p>\n
    name = 'example'<\/p>\n
    start_urls = ['https:\/\/example.com']<\/p>\n
    def parse(self, response):<\/p>\n
        title = response.xpath('\/\/title\/text()').get()<\/p>\n
        yield {'title': title}<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u4f7f\u7528Selenium\u5904\u7406JavaScript\u52a8\u6001\u52a0\u8f7d\u5185\u5bb9<\/h4>\n<\/p>\n
Selenium\u662f\u4e00\u4e2a\u81ea\u52a8\u5316\u6d4b\u8bd5\u5de5\u5177\uff0c\u53ef\u4ee5\u6a21\u62df\u6d4f\u89c8\u5668\u64cd\u4f5c\uff0c\u9002\u5408\u6293\u53d6\u52a8\u6001\u52a0\u8f7d\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
from selenium import webdriver<\/p>\n
driver = webdriver.Chrome()<\/p>\n
driver.get('https:\/\/example.com')<\/p>\n
content = driver.page_source<\/p>\n
print(content)<\/p>\n
driver.quit()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u6e05\u6d17<\/h3>\n<\/p>\n
\u6570\u636e\u6293\u53d6\u5b8c\u6210\u540e\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\uff0c\u4ee5\u4fbf\u540e\u7eed\u7684\u5206\u6790\u548c\u5904\u7406\u3002\u6570\u636e\u6e05\u6d17\u7684\u4e3b\u8981\u6b65\u9aa4\u5305\u62ec\uff1a\u5904\u7406\u7f3a\u5931\u503c\u3001\u53bb\u9664\u91cd\u590d\u6570\u636e\u3001\u6570\u636e\u683c\u5f0f\u8f6c\u6362\u3001\u5904\u7406\u5f02\u5e38\u503c<\/strong>\u3002<\/p>\n<\/p>\n
1. \u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u7f3a\u5931\u503c\u662f\u6307\u6570\u636e\u96c6\u4e2d\u5b58\u5728\u7a7a\u503c\u6216NaN\u503c\uff0c\u9700\u8981\u8fdb\u884c\u586b\u8865\u6216\u5220\u9664\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = pd.read_csv('data.csv')<\/p>\n
data.fillna(method='ffill', inplace=True)  # \u524d\u5411\u586b\u5145<\/p>\n
data.dropna(inplace=True)  # \u5220\u9664\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u53bb\u9664\u91cd\u590d\u6570\u636e<\/h4>\n<\/p>\n
\u91cd\u590d\u6570\u636e\u4f1a\u5f71\u54cd\u5206\u6790\u7ed3\u679c\uff0c\u9700\u8981\u8fdb\u884c\u53bb\u91cd\u5904\u7406\u3002<\/p>\n<\/p>\n
data.drop_duplicates(inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u6570\u636e\u683c\u5f0f\u8f6c\u6362<\/h4>\n<\/p>\n
\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u4e00\u81f4\u7684\u683c\u5f0f\uff0c\u4ee5\u4fbf\u540e\u7eed\u5904\u7406\u3002<\/p>\n<\/p>\n
data['date'] = pd.to_datetime(data['date'])<\/p>\n
data['price'] = data['price'].astype(float)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u5904\u7406\u5f02\u5e38\u503c<\/h4>\n<\/p>\n
\u5f02\u5e38\u503c\u662f\u6307\u4e0e\u5176\u4ed6\u6570\u636e\u70b9\u660e\u663e\u4e0d\u540c\u7684\u6570\u636e\uff0c\u9700\u8981\u8fdb\u884c\u5904\u7406\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = data[(np.abs(data['price'] - data['price'].mean()) <= (3 * data['price'].std()))]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u5206\u6790<\/h3>\n<\/p>\n
\u6570\u636e\u5206\u6790\u662f\u5bf9\u6e05\u6d17\u540e\u7684\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u548c\u5efa\u6a21\uff0c\u4ee5\u53d1\u73b0\u5176\u4e2d\u7684\u89c4\u5f8b\u548c\u8d8b\u52bf\u3002Python\u6709\u591a\u4e2a\u5f3a\u5927\u7684\u5e93\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u4f8b\u5982\uff1aPandas\u3001NumPy\u3001Matplotlib\u3001Seaborn\u3001Scikit-learn<\/strong>\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u64cd\u4f5c<\/h4>\n<\/p>\n
Pandas\u662f\u4e00\u4e2a\u6570\u636e\u5206\u6790\u548c\u64cd\u4f5c\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u6570\u636e\u8868\u3002<\/p>\n<\/p>\n
summary = data.describe()<\/p>\n
print(summary)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528NumPy\u8fdb\u884c\u6570\u503c\u8ba1\u7b97<\/h4>\n<\/p>\n
NumPy\u662f\u4e00\u4e2a\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u53ef\u4ee5\u8fdb\u884c\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
mean_price = np.mean(data['price'])<\/p>\n
print(mean_price)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u4f7f\u7528Matplotlib\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
Matplotlib\u662f\u4e00\u4e2a\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u521b\u5efa\u5404\u79cd\u56fe\u8868\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
data['price'].hist(bins=50)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u4f7f\u7528Seaborn\u8fdb\u884c\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u521b\u5efa\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u8868\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
sns.boxplot(x='category', y='price', data=data)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
5. \u4f7f\u7528Scikit-learn\u8fdb\u884c\u673a\u5668\u5b66\u4e60<\/a><\/h4>\n<\/p>\n
Scikit-learn\u662f\u4e00\u4e2a\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u5efa\u6a21\u548c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
X = data[['feature1', 'feature2']]<\/p>\n
y = data['price']<\/p>\n
model = LinearRegression()<\/p>\n
model.fit(X, y)<\/p>\n
predictions = model.predict(X)<\/p>\n
print(predictions)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u5b58\u50a8<\/h3>\n<\/p>\n
\u6570\u636e\u5b58\u50a8\u662f\u5c06\u5904\u7406\u540e\u7684\u6570\u636e\u4fdd\u5b58\u5230\u6587\u4ef6\u6216\u6570\u636e\u5e93\u4e2d\uff0c\u4ee5\u4fbf\u540e\u7eed\u4f7f\u7528\u3002Python\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u6570\u636e\u5b58\u50a8\uff0c\u4f8b\u5982\uff1aPandas\u3001SQLAlchemy\u3001PyMongo<\/strong>\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528Pandas\u4fdd\u5b58\u6570\u636e\u5230CSV\u6587\u4ef6<\/h4>\n<\/p>\n
data.to_csv('cleaned_data.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528SQLAlchemy\u4fdd\u5b58\u6570\u636e\u5230SQL\u6570\u636e\u5e93<\/h4>\n<\/p>\n
SQLAlchemy\u662f\u4e00\u4e2aSQL\u6570\u636e\u5e93\u5de5\u5177\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u4fdd\u5b58\u5230\u5173\u7cfb\u6570\u636e\u5e93\u4e2d\u3002<\/p>\n<\/p>\n
from sqlalchemy import create_engine<\/p>\n
engine = create_engine('sqlite:\/\/\/data.db')<\/p>\n
data.to_sql('table_name', engine, index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u4f7f\u7528PyMongo\u4fdd\u5b58\u6570\u636e\u5230MongoDB\u6570\u636e\u5e93<\/h4>\n<\/p>\n
PyMongo\u662f\u4e00\u4e2aMongoDB\u6570\u636e\u5e93\u5de5\u5177\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u4fdd\u5b58\u5230NoSQL\u6570\u636e\u5e93\u4e2d\u3002<\/p>\n<\/p>\n
from pymongo import MongoClient<\/p>\n
client = MongoClient('localhost', 27017)<\/p>\n
db = client['database_name']<\/p>\n
collection = db['collection_name']<\/p>\n
collection.insert_many(data.to_dict('records'))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7Python\u8fdb\u884c\u6570\u636e\u91c7\u96c6\u4e0e\u5904\u7406\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5e93\u548c\u5de5\u5177\u6765\u5b8c\u6210\u6570\u636e\u6293\u53d6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u548c\u6570\u636e\u5b58\u50a8\u3002Requests\u3001BeautifulSoup\u3001Scrapy\u3001Selenium<\/strong>\u7b49\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u9ad8\u6548\u5730\u6293\u53d6\u7f51\u9875\u6570\u636e\uff0cPandas\u3001NumPy\u3001Matplotlib\u3001Seaborn\u3001Scikit-learn<\/strong>\u7b49\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u3001\u5206\u6790\u548c\u5efa\u6a21\uff0cSQLAlchemy\u3001PyMongo<\/strong>\u7b49\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5c06\u5904\u7406\u540e\u7684\u6570\u636e\u4fdd\u5b58\u5230\u6570\u636e\u5e93\u4e2d\u3002\u901a\u8fc7\u7cfb\u7edf\u5730\u5b66\u4e60\u548c\u5e94\u7528\u8fd9\u4e9b\u5de5\u5177\uff0c\u6211\u4eec\u53ef\u4ee5\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u91c7\u96c6\u548c\u5904\u7406\uff0c\u5e76\u4ece\u4e2d\u53d1\u73b0\u6709\u4ef7\u503c\u7684\u4fe1\u606f\u3002<\/p>\n<\/p>\n
\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u5728\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u91c7\u96c6\u548c\u5904\u7406\u65b9\u9762\u6709\u6240\u5e2e\u52a9\u3002\u5982\u679c\u4f60\u6709\u4efb\u4f55\u95ee\u9898\u6216\u5efa\u8bae\uff0c\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u7559\u8a00\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u91c7\u96c6\u65f6\uff0c\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u5e93\u548c\u5de5\u5177\u63a8\u8350\uff1f<\/strong>
\u5728\u6570\u636e\u91c7\u96c6\u8fc7\u7a0b\u4e2d\uff0cPython\u62e5\u6709\u8bb8\u591a\u5f3a\u5927\u7684\u5e93\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u8f7b\u677e\u83b7\u53d6\u6570\u636e\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecBeautiful Soup\u548cScrapy\uff0c\u8fd9\u4e9b\u5de5\u5177\u80fd\u591f\u5e2e\u52a9\u4f60\u4ece\u7f51\u9875\u4e2d\u63d0\u53d6\u4fe1\u606f\u3002\u6b64\u5916\uff0cRequests\u5e93\u7528\u4e8e\u53d1\u9001\u7f51\u7edc\u8bf7\u6c42\uff0cPandas\u5219\u5728\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u4e2d\u975e\u5e38\u6709\u7528\u3002\u7ed3\u5408\u8fd9\u4e9b\u5de5\u5177\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u8fdb\u884c\u6570\u636e\u91c7\u96c6\u4e0e\u5904\u7406\u3002<\/p>\n
\u8fdb\u884c\u6570\u636e\u5904\u7406\u65f6\uff0cPython\u6709\u54ea\u4e9b\u5e38\u89c1\u7684\u6280\u672f\u548c\u65b9\u6cd5\uff1f<\/strong>
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