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