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fun.py

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+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+import numpy as np
+
+
+def Fr():
+    # 读取数据
+    data = pd.read_excel('Data.xlsx', index_col=0)  # 确保索引是股票代码
+
+    # 只选择数值列
+    numeric_cols = data.select_dtypes(include=[np.number]).columns
+    data = data[numeric_cols]
+
+    # 处理非正数
+    data = data[data > 0]
+    data = data.dropna()
+
+    # 读取股票代码
+    co = pd.read_excel('stkcode.xlsx', index_col=0)  # 确保索引是股票代码
+    Co = pd.Series(co['name'].values, index=co.index)
+
+    # 打印调试信息
+    print("data.index:", data.index)
+    print("co.index:", co.index)
+
+    # 确保索引匹配
+    common_indices = data.index.intersection(Co.index)
+    if common_indices.empty:
+        raise ValueError("No common indices found between data.index and co.index")
+
+    # 仅使用共同的索引
+    data_common = data.loc[common_indices]
+
+    # 打印调试信息
+    print("common_indices length:", len(common_indices))
+    print("data_common length:", len(data_common))
+
+    # 标准化
+    scaler = StandardScaler()
+    X = scaler.fit_transform(data_common)
+
+    # PCA
+    pca = PCA(n_components=0.95)  # 累计贡献率为95%
+    Y = pca.fit_transform(X)
+    gxl = pca.explained_variance_ratio_
+
+    # 计算综合得分
+    F = np.dot(Y, gxl)
+
+    # 打印调试信息
+    print("F length:", len(F))
+
+    # 创建得分序列
+    fs1 = pd.Series(F, index=data_common.index)
+    Fscore1 = fs1.sort_values(ascending=False)
+
+    Co1 = Co[common_indices]
+
+    # 打印调试信息
+    print("Co1 length:", len(Co1))
+
+    fs2 = pd.Series(F, index=Co1.index)
+    Fscore2 = fs2.sort_values(ascending=False)
+
+    return Fscore1, Fscore2