diff --git a/7.3.3~7.3.5.py b/7.3.3~7.3.5.py
new file mode 100644
index 0000000..ed29f42
--- /dev/null
+++ b/7.3.3~7.3.5.py
@@ -0,0 +1,77 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Oct 23 20:53:57 2018
+
+@author: Administrator
+"""
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+
+# 读取数据
+data = pd.read_excel('Data.xlsx')
+
+# 检查数据类型
+# print(data.dtypes)
+
+# 确保 'ts_code' 列是字符串类型
+data['ts_code'] = data['ts_code'].astype(str)
+
+# 保留 'ts_code' 列，以便后续使用
+ts_code = data['ts_code'].copy()
+
+# 过滤掉非数值列
+numeric_columns = data.select_dtypes(include=[np.number]).columns
+data_numeric = data[numeric_columns]
+
+# 过滤掉非正数
+data_numeric = data_numeric[data_numeric > 0]
+
+# 删除缺失值
+data_numeric = data_numeric.dropna()
+
+# 确保 'ts_code' 列与数据同步
+ts_code = ts_code[data_numeric.index]
+
+# 标准化数据
+X = data_numeric.iloc[:, 1:]  # 排除第一列 ts_code
+scaler = StandardScaler()
+scaler.fit(X)
+X_scaled = scaler.transform(X)
+
+# 主成分分析
+pca = PCA(n_components=0.95)  # 累计贡献率为95%
+Y = pca.fit_transform(X_scaled)  # 满足累计贡献率为95%的主成分数据
+gxl = pca.explained_variance_ratio_  # 贡献率
+
+# 计算综合得分
+F = np.zeros((len(Y)))
+for i in range(len(gxl)):
+    f = Y[:, i] * gxl[i]
+    F += f
+
+# 创建 Series 并排序
+fs1 = pd.Series(F, index=ts_code.values)
+Fscore1 = fs1.sort_values(ascending=False)  # 降序，True 为升序
+
+# 读取股票代码数据
+stk = pd.read_excel('stkcode.xlsx')
+stk = pd.Series(stk['name'].values, index=stk['ts_code'].values)
+
+# 过滤掉不在 stk 中的 ts_code
+ts_code_filtered = ts_code[ts_code.isin(stk.index)]
+F_filtered = F[ts_code.isin(stk.index)]
+
+# 获取对应的股票名称
+stk1 = stk[ts_code_filtered.values]
+
+# 创建包含股票名称的 Series 并排序
+fs2 = pd.Series(F_filtered, index=stk1.values)
+Fscore2 = fs2.sort_values(ascending=False)  # 降序，True 为升序
+
+# 输出结果
+print("按股票代码排序的综合得分：")
+print(Fscore1)
+print("\n按股票名称排序的综合得分：")
+print(Fscore2)