有没有办法在python pandas中对投资组合标准差进行矢量化？

import pandas as pd
import numpy as np

# 示例数据
returns = pd.DataFrame({
    'Asset1': [0.01, 0.02, -0.01, 0.03],
    'Asset2': [0.02, 0.01, 0.03, -0.02],
    'Asset3': [-0.01, 0.03, 0.02, 0.01]
})

weights = np.array([0.3, 0.4, 0.3])

# 计算协方差矩阵
cov_matrix = returns.cov()

# 计算投资组合标准差
portfolio_variance = np.dot(weights.T, np.dot(cov_matrix, weights))
portfolio_std_dev = np.sqrt(portfolio_variance)

print(f"投资组合标准差: {portfolio_std_dev}")

returns = returns.dropna()  # 或者使用 returns.fillna(method='ffill') 等方法填充缺失值

weights = weights / np.sum(weights)

from sklearn.covariance import LedoitWolf

lw = LedoitWolf()
cov_matrix = lw.fit(returns).covariance_