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