ValueError:未正确调用DataFrame构造函数-在拟合SparsePCA之后

import pandas as pd
from sklearn.decomposition import SparsePCA
import numpy as np

# 示例数据
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# 拟合 SparsePCA
sparse_pca = SparsePCA(n_components=2)
transformed_data = sparse_pca.fit_transform(data)

# 确保 transformed_data 是二维数组
if transformed_data.ndim == 2:
    df = pd.DataFrame(transformed_data, columns=['PC1', 'PC2'])
else:
    raise ValueError("Transformed data is not 2D")

# 检查数据类型
if not np.issubdtype(transformed_data.dtype, np.number):
    raise TypeError("Data contains non-numeric values")

# 检查缺失值
if np.isnan(transformed_data).any():
    transformed_data = np.nan_to_num(transformed_data, nan=0.0)

# 转换为 DataFrame
df = pd.DataFrame(transformed_data, columns=['PC1', 'PC2'])

df = pd.DataFrame.from_records(transformed_data, columns=['PC1', 'PC2'])