{"id":1058334,"date":"2024-12-31T15:15:31","date_gmt":"2024-12-31T07:15:31","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1058334.html"},"modified":"2024-12-31T15:15:33","modified_gmt":"2024-12-31T07:15:33","slug":"python%e5%a6%82%e4%bd%95%e5%a4%84%e7%90%86%e4%ba%a4%e9%80%9a%e6%95%b0%e6%8d%ae%e5%ba%93","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1058334.html","title":{"rendered":"python\u5982\u4f55\u5904\u7406\u4ea4\u901a\u6570\u636e\u5e93"},"content":{"rendered":"

\"python\u5982\u4f55\u5904\u7406\u4ea4\u901a\u6570\u636e\u5e93\"<\/p>\n

Python\u5904\u7406\u4ea4\u901a\u6570\u636e\u5e93\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528SQL\u6570\u636e\u5e93\u8fde\u63a5\u5e93\u3001\u6570\u636e\u5904\u7406\u5e93\u548c\u6570\u636e\u53ef\u89c6\u5316\u5e93\u3002<\/strong><\/p>\n<\/p>\n

\u6570\u636e\u67e5\u8be2\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u53ef\u89c6\u5316<\/strong>\u662f\u5176\u4e2d\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u6211\u4eec\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u5229\u7528Python\u5728\u8fd9\u4e9b\u6b65\u9aa4\u4e2d\u5904\u7406\u4ea4\u901a\u6570\u636e\u5e93\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u67e5\u8be2<\/h3>\n<\/p>\n

\u4f7f\u7528Python\u5904\u7406\u4ea4\u901a\u6570\u636e\u5e93\u7684\u7b2c\u4e00\u6b65\u662f\u67e5\u8be2\u6570\u636e\u3002\u8fd9\u901a\u5e38\u6d89\u53ca\u8fde\u63a5\u5230SQL\u6570\u636e\u5e93\u5e76\u63d0\u53d6\u6240\u9700\u7684\u6570\u636e\u3002Python\u7684sqlite3<\/code>\u5e93\u548cSQLAlchemy<\/code>\u5e93\u662f\u5e38\u7528\u7684\u5de5\u5177\u3002<\/p>\n<\/p>\n

1. \u4f7f\u7528sqlite3<\/h4>\n<\/p>\n

sqlite3<\/code>\u662fPython\u6807\u51c6\u5e93\u4e2d\u7684\u4e00\u90e8\u5206\uff0c\u7528\u4e8e\u4e0eSQLite\u6570\u636e\u5e93\u4ea4\u4e92\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff0c\u5c55\u793a\u5982\u4f55\u8fde\u63a5\u5230SQLite\u6570\u636e\u5e93\u5e76\u67e5\u8be2\u6570\u636e\uff1a<\/p>\n<\/p>\n

import sqlite3<\/p>\n
\u8fde\u63a5\u5230SQLite\u6570\u636e\u5e93<\/strong><\/h2>\n
conn = sqlite3.connect('traffic_data.db')<\/p>\n
cursor = conn.cursor()<\/p>\n
\u6267\u884cSQL\u67e5\u8be2<\/strong><\/h2>\n
cursor.execute('SELECT * FROM traffic_incidents')<\/p>\n
rows = cursor.fetchall()<\/p>\n
\u5173\u95ed\u8fde\u63a5<\/strong><\/h2>\n
conn.close()<\/p>\n
\u6253\u5370\u67e5\u8be2\u7ed3\u679c<\/strong><\/h2>\n
for row in rows:<\/p>\n
    print(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528SQLAlchemy<\/h4>\n<\/p>\n
SQLAlchemy<\/code>\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u5e93\uff0c\u7528\u4e8ePython\u4e2d\u7684SQL\u6570\u636e\u5e93\u64cd\u4f5c\u3002\u5b83\u63d0\u4f9b\u4e86ORM\uff08\u5bf9\u8c61\u5173\u7cfb\u6620\u5c04\uff09\u548cSQL\u8868\u8fbe\u5f0f\u8bed\u8a00\u529f\u80fd\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528SQLAlchemy\u8fde\u63a5\u5230MySQL\u6570\u636e\u5e93\u5e76\u67e5\u8be2\u6570\u636e\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
from sqlalchemy import create_engine, MetaData, Table<\/p>\n
from sqlalchemy.orm import sessionmaker<\/p>\n
\u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce<\/strong><\/h2>\n
engine = create_engine('mysql+pymysql:\/\/username:password@localhost\/traffic_db')<\/p>\n
\u521b\u5efa\u4f1a\u8bdd<\/strong><\/h2>\n
Session = sessionmaker(bind=engine)<\/p>\n
session = Session()<\/p>\n
\u53cd\u5c04\u6570\u636e\u5e93\u4e2d\u7684\u8868<\/strong><\/h2>\n
metadata = MetaData()<\/p>\n
traffic_table = Table('traffic_incidents', metadata, autoload=True, autoload_with=engine)<\/p>\n
\u67e5\u8be2\u6570\u636e<\/strong><\/h2>\n
results = session.query(traffic_table).all()<\/p>\n
\u6253\u5370\u67e5\u8be2\u7ed3\u679c<\/strong><\/h2>\n
for row in results:<\/p>\n
    print(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u6e05\u6d17<\/h3>\n<\/p>\n
\u5728\u83b7\u53d6\u6570\u636e\u540e\uff0c\u901a\u5e38\u9700\u8981\u8fdb\u884c\u6e05\u6d17\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u4e00\u81f4\u6027\u548c\u5b8c\u6574\u6027\u3002Python\u7684pandas<\/code>\u5e93\u662f\u5904\u7406\u6570\u636e\u6e05\u6d17\u7684\u5f3a\u5927\u5de5\u5177\u3002<\/p>\n<\/p>\n
1. \u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u7f3a\u5931\u503c\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e38\u89c1\u95ee\u9898\u3002pandas<\/code>\u63d0\u4f9b\u4e86\u591a\u79cd\u5904\u7406\u7f3a\u5931\u503c\u7684\u65b9\u6cd5\uff0c\u4f8b\u5982\u5220\u9664\u7f3a\u5931\u503c\u3001\u586b\u5145\u7f3a\u5931\u503c\u7b49\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
df = pd.read_sql('SELECT * FROM traffic_incidents', conn)<\/p>\n
\u5220\u9664\u7f3a\u5931\u503c<\/strong><\/h2>\n
df.dropna(inplace=True)<\/p>\n
\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
df.fillna(method='ffill', inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/h4>\n<\/p>\n
\u786e\u4fdd\u6570\u636e\u7c7b\u578b\u4e00\u81f4\u662f\u6570\u636e\u6e05\u6d17\u7684\u53e6\u4e00\u4e2a\u91cd\u8981\u65b9\u9762\u3002\u4f8b\u5982\uff0c\u5c06\u65e5\u671f\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u5bf9\u8c61\uff1a<\/p>\n<\/p>\n
# \u5c06\u65e5\u671f\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u5bf9\u8c61<\/p>\n
df['date'] = pd.to_datetime(df['date'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u5206\u6790<\/h3>\n<\/p>\n
\u6570\u636e\u6e05\u6d17\u540e\uff0c\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002Python\u7684pandas<\/code>\u548cnumpy<\/code>\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5206\u6790\u529f\u80fd\u3002<\/p>\n<\/p>\n
1. \u6570\u636e\u7edf\u8ba1<\/h4>\n<\/p>\n
\u901a\u8fc7\u57fa\u672c\u7edf\u8ba1\u5206\u6790\uff0c\u53ef\u4ee5\u83b7\u53d6\u6570\u636e\u7684\u603b\u4f53\u7279\u5f81\u3002\u4f8b\u5982\uff0c\u8ba1\u7b97\u4e8b\u6545\u7684\u5e73\u5747\u6570\u91cf\u3001\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u57fa\u672c\u7edf\u8ba1\u91cf<\/p>\n
mean_incidents = df['number_of_incidents'].mean()<\/p>\n
max_incidents = df['number_of_incidents'].max()<\/p>\n
min_incidents = df['number_of_incidents'].min()<\/p>\n
print(f"\u5e73\u5747\u4e8b\u6545\u6570\u91cf: {mean_incidents}, \u6700\u5927\u4e8b\u6545\u6570\u91cf: {max_incidents}, \u6700\u5c0f\u4e8b\u6545\u6570\u91cf: {min_incidents}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u5206\u7ec4\u5206\u6790<\/h4>\n<\/p>\n
\u5206\u7ec4\u5206\u6790\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u7279\u5f81\u3002\u4f8b\u5982\uff0c\u6309\u57ce\u5e02\u5206\u7ec4\uff0c\u8ba1\u7b97\u6bcf\u4e2a\u57ce\u5e02\u7684\u5e73\u5747\u4e8b\u6545\u6570\u91cf\uff1a<\/p>\n<\/p>\n
# \u6309\u57ce\u5e02\u5206\u7ec4\uff0c\u8ba1\u7b97\u5e73\u5747\u4e8b\u6545\u6570\u91cf<\/p>\n
city_group = df.groupby('city')['number_of_incidents'].mean()<\/p>\n
print(city_group)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff0c\u5e2e\u52a9\u6211\u4eec\u76f4\u89c2\u5730\u7406\u89e3\u6570\u636e\u3002Python\u7684matplotlib<\/code>\u548cseaborn<\/code>\u5e93\u662f\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5de5\u5177\u3002<\/p>\n<\/p>\n
1. \u7ed8\u5236\u67f1\u72b6\u56fe<\/h4>\n<\/p>\n
\u67f1\u72b6\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u5206\u7c7b\u6570\u636e\uff0c\u4f8b\u5982\u4e0d\u540c\u57ce\u5e02\u7684\u4e8b\u6545\u6570\u91cf\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
city_group.plot(kind='bar')<\/p>\n
plt.xlabel('\u57ce\u5e02')<\/p>\n
plt.ylabel('\u5e73\u5747\u4e8b\u6545\u6570\u91cf')<\/p>\n
plt.title('\u4e0d\u540c\u57ce\u5e02\u7684\u5e73\u5747\u4ea4\u901a\u4e8b\u6545\u6570\u91cf')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u56fe<\/h4>\n<\/p>\n
\u65f6\u95f4\u5e8f\u5217\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u65f6\u95f4\u53d8\u5316\u6570\u636e\uff0c\u4f8b\u5982\u6bcf\u4e2a\u6708\u7684\u4e8b\u6545\u6570\u91cf\uff1a<\/p>\n<\/p>\n
# \u6309\u6708\u5206\u7ec4\uff0c\u8ba1\u7b97\u6bcf\u4e2a\u6708\u7684\u4e8b\u6545\u6570\u91cf<\/p>\n
df['month'] = df['date'].dt.to_period('M')<\/p>\n
monthly_group = df.groupby('month')['number_of_incidents'].sum()<\/p>\n
\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u56fe<\/strong><\/h2>\n
monthly_group.plot(kind='line')<\/p>\n
plt.xlabel('\u6708\u4efd')<\/p>\n
plt.ylabel('\u4e8b\u6545\u6570\u91cf')<\/p>\n
plt.title('\u6bcf\u4e2a\u6708\u7684\u4ea4\u901a\u4e8b\u6545\u6570\u91cf')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u8fdb\u9636\u5206\u6790<\/h3>\n<\/p>\n
\u9664\u4e86\u57fa\u672c\u7684\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\uff0cPython\u8fd8\u53ef\u4ee5\u7528\u4e8e\u66f4\u9ad8\u7ea7\u7684\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60<\/a>\u3002\u4f8b\u5982\uff0c\u4f7f\u7528scikit-learn<\/code>\u5e93\u8fdb\u884c\u805a\u7c7b\u5206\u6790\u3001\u56de\u5f52\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n
1. \u805a\u7c7b\u5206\u6790<\/h4>\n<\/p>\n
\u805a\u7c7b\u5206\u6790\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u53d1\u73b0\u6570\u636e\u4e2d\u7684\u6f5c\u5728\u6a21\u5f0f\u3002\u4f8b\u5982\uff0c\u4f7f\u7528K\u5747\u503c\u805a\u7c7b\u5206\u6790\u4ea4\u901a\u4e8b\u6545\u6570\u636e\uff1a<\/p>\n<\/p>\n
from sklearn.cluster import KMeans<\/p>\n
\u63d0\u53d6\u7279\u5f81<\/strong><\/h2>\n
X = df[['latitude', 'longitude']]<\/p>\n
\u6267\u884cK\u5747\u503c\u805a\u7c7b\u5206\u6790<\/strong><\/h2>\n
kmeans = KMeans(n_clusters=3)<\/p>\n
df['cluster'] = kmeans.fit_predict(X)<\/p>\n
\u53ef\u89c6\u5316\u805a\u7c7b\u7ed3\u679c<\/strong><\/h2>\n
plt.scatter(df['latitude'], df['longitude'], c=df['cluster'], cmap='viridis')<\/p>\n
plt.xlabel('\u7eac\u5ea6')<\/p>\n
plt.ylabel('\u7ecf\u5ea6')<\/p>\n
plt.title('\u4ea4\u901a\u4e8b\u6545\u805a\u7c7b\u5206\u6790')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u56de\u5f52\u5206\u6790<\/h4>\n<\/p>\n
\u56de\u5f52\u5206\u6790\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u9884\u6d4b\u6570\u636e\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u9884\u6d4b\u4ea4\u901a\u4e8b\u6545\u6570\u91cf\uff1a<\/p>\n<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
\u63d0\u53d6\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf<\/strong><\/h2>\n
X = df[['population', 'number_of_vehicles']]<\/p>\n
y = df['number_of_incidents']<\/p>\n
\u6267\u884c\u7ebf\u6027\u56de\u5f52<\/strong><\/h2>\n
model = LinearRegression()<\/p>\n
model.fit(X, y)<\/p>\n
\u9884\u6d4b\u4ea4\u901a\u4e8b\u6545\u6570\u91cf<\/strong><\/h2>\n
predictions = model.predict(X)<\/p>\n
\u53ef\u89c6\u5316\u9884\u6d4b\u7ed3\u679c<\/strong><\/h2>\n
plt.scatter(y, predictions)<\/p>\n
plt.xlabel('\u5b9e\u9645\u4e8b\u6545\u6570\u91cf')<\/p>\n
plt.ylabel('\u9884\u6d4b\u4e8b\u6545\u6570\u91cf')<\/p>\n
plt.title('\u4ea4\u901a\u4e8b\u6545\u6570\u91cf\u9884\u6d4b')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u9ad8\u6548\u5730\u5904\u7406\u4ea4\u901a\u6570\u636e\u5e93\u6570\u636e\u3002\u6570\u636e\u67e5\u8be2\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u53ef\u89c6\u5316<\/strong>\u662f\u5904\u7406\u4ea4\u901a\u6570\u636e\u5e93\u6570\u636e\u7684\u5173\u952e\u6b65\u9aa4\u3002\u901a\u8fc7\u4f7f\u7528sqlite3<\/code>\u6216SQLAlchemy<\/code>\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fde\u63a5\u5230\u6570\u636e\u5e93\u5e76\u67e5\u8be2\u6570\u636e\uff1b\u901a\u8fc7pandas<\/code>\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u548c\u57fa\u672c\u7684\u6570\u636e\u5206\u6790\uff1b\u901a\u8fc7matplotlib<\/code>\u548cseaborn<\/code>\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff1b\u901a\u8fc7scikit-learn<\/code>\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u884c\u9ad8\u7ea7\u7684\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u3002<\/p>\n<\/p>\n
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