### Python 财务应用实例 #### 应用场景一：股票价格预测模型 构建一个简单的线性回归模型来预测某只股票的价格变化趋势。此模型利用历史数据训练并尝试对未来股价做出预估。 ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression # 加载数据集 data = pd.read_csv('stock_prices.csv') X = data[['Open', 'High', 'Low', 'Volume']] # 特征变量 y = data['Close'] # 目标变量 # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) model = LinearRegression() model.fit(X_train, y_train) predictions = model.predict(X_test) print(predictions[:5]) # 输出前五个预测值 ``` 该代码片段展示了如何基于开盘价、最高价、最低价以及成交量四个因素建立线性关系去估计收盘价[^1]。 #### 场景二：个人理财管理工具 创建一款简易版的家庭账本程序，帮助用户记录日常收支情况，并提供月度总结报告功能。 ```python class AccountBook: def __init__(self): self.entries = [] def add_entry(self, date, description, amount): entry = {'date': date, 'description': description, 'amount': float(amount)} self.entries.append(entry) def monthly_report(self, month): total_income = sum([e['amount'] for e in self.entries if int(e['date'].split('-')[1]) == month and e['amount']>0]) total_expense = abs(sum([e['amount'] for e in self.entries if int(e['date'].split('-')[1]) == month and e['amount']<0])) print(f"In {month}: Income={total_income:.2f}, Expense={total_expense:.2f}") account_book = AccountBook() # 添加几笔交易作为示范 account_book.add_entry('2023-09-01', "Salary", 8000) account_book.add_entry('2023-09-07', "-Groceries", -300) account_book.monthly_report(9) # 打印九月份的收入支出概况 ``` 上述例子通过类`AccountBook`实现了基本的资金流水登记与统计分析能力[^2]。