文章目录

• 1.示例代码
• • • 说明：
• 2.如何打包
• • • 注意事项：

1.示例代码

以下是一个通常用于数据处理、分析和可视化任务的示例脚本：

# 引入第三方库 import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from scipy import stats from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score import statsmodels.api as sm import requests from bs4 import BeautifulSoup import sqlalchemy from sqlalchemy import create_engine import plotly.express as px import tensorflow as tf from keras.models import Sequential from keras.layers import Dense, LSTM  # 数据获取和处理 url = 'https://example.com/data.csv' response = requests.get(url) with open('data.csv', 'wb') as file:     file.write(response.content)  data = pd.read_csv('data.csv')  # 数据探索和可视化 print(data.head()) sns.pairplot(data) plt.show()  # 数据预处理 X = data.drop('target', axis=1) y = data['target'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) scaler = StandardScaler() X_train_scaled = scaler.fit_transform