import os
import pandas as pd
import re

# 文件路径
input_file = r'D:\计量经济学\计量实验资料及作业要求\计量实验资料及作业要求\图文帖子实验数据（新）.xlsx'
output_file = r'D:\计量经济学\计量实验资料及作业要求\计量实验资料及作业要求\UGC回归数据.xlsx'

print("========================================")
print("  使用pandas处理所有数据")
print("========================================")
print(f"输入文件: {input_file}")
print(f"输出文件: {output_file}")
print()

# 检查文件是否存在
if not os.path.exists(input_file):
    print("错误: 输入文件不存在！")
    exit(1)

print(f"输入文件大小: {os.path.getsize(input_file) / 1024:.2f} KB")

# 读取原始数据
try:
    print("正在读取原始数据...")
    df = pd.read_excel(input_file)
    print(f"成功读取 {len(df)} 行数据")
    print(f"列名: {list(df.columns)}")
    
    # 识别列
    print("\n识别列...")
    helpfull_col = None
    comment_count_col = None
    comment_cols = []
    
    for col in df.columns:
        col_str = str(col).lower()
        if 'helpfull' in col_str or 'helpful' in col_str:
            helpfull_col = col
            print(f"找到 Y 列 (helpfull): {col}")
        elif '评论总数' in str(col) or '帖子评论总数' in str(col):
            comment_count_col = col
            print(f"找到 X1 列 (评论总数): {col}")
        elif '评论' in str(col) and any(str(i) in str(col) for i in range(1, 6)):
            comment_cols.append(col)
            print(f"找到评论列 {len(comment_cols)}: {col}")
    
    print(f"\n共找到 {len(comment_cols)} 个评论列")
    
    # 创建回归数据
    print("\n创建回归数据...")
    regression_data = pd.DataFrame()
    
    # Y (UGC有用性)
    print("1. 计算 Y (UGC有用性)")
    if helpfull_col:
        regression_data['Y'] = pd.to_numeric(df[helpfull_col], errors='coerce').fillna(0)
    else:
        regression_data['Y'] = 0
    
    # X1 (评论数量)
    print("2. 计算 X1 (评论数量)")
    if comment_count_col:
        regression_data['X1'] = pd.to_numeric(df[comment_count_col], errors='coerce').fillna(0)
    else:
        regression_data['X1'] = 0
    
    # 定义函数计算评论指标
    def calculate_comment_metrics(row):
        lengths = []
        complexities = []
        sentiments = []
        richness = []
        
        for col in comment_cols:
            content = str(row.get(col, ''))
            if content and content != 'None' and content != 'nan':
                # 评论长度
                lengths.append(len(content.replace(' ', '')))
                # 评论复杂度
                complexities.append(len(content.split()))
                # 情感分析
                positive_words = ['好', '棒', '优秀', '喜欢', '满意', '赞', 'positive', 'good', 'great', 'excellent']
                negative_words = ['差', '糟糕', '不好', '失望', '不满', 'negative', 'bad', 'terrible', 'poor']
                
                sentiment = 0
                lower_content = content.lower()
                if any(word in lower_content for word in positive_words):
                    sentiment = 1
                elif any(word in lower_content for word in negative_words):
                    sentiment = -1
                sentiments.append(sentiment)
                # 信息丰富度
                r = 0
                if re.search(r'\d', content):
                    r += 1
                if re.search(r'http[s]?://', content):
                    r += 1
                if re.search(r'[\u2600-\u27BF]|[:;][-]?[)D]', content):
                    r += 1
                richness.append(r)
        
        return lengths, complexities, sentiments, richness
    
    # 计算评论相关指标
    print("3. 计算评论相关指标...")
    comment_metrics = df.apply(calculate_comment_metrics, axis=1)
    
    # X2: 评论长度平均值
    print("4. 计算 X2 (评论长度)")
    regression_data['X2'] = comment_metrics.apply(lambda x: sum(x[0]) / len(x[0]) if x[0] else 0)
    
    # X3: 评论复杂度平均值
    print("5. 计算 X3 (评论复杂度)")
    regression_data['X3'] = comment_metrics.apply(lambda x: sum(x[1]) / len(x[1]) if x[1] else 0)
    
    # X4: 评论可读性
    print("6. 计算 X4 (评论可读性)")
    regression_data['X4'] = regression_data.apply(lambda row: row['X2'] / row['X3'] if row['X3'] > 0 else 0, axis=1)
    
    # X5: 内容情感性平均值
    print("7. 计算 X5 (内容情感性)")
    regression_data['X5'] = comment_metrics.apply(lambda x: sum(x[2]) / len(x[2]) if x[2] else 0)
    
    # X6: 信息丰富度平均值
    print("8. 计算 X6 (信息丰富度)")
    regression_data['X6'] = comment_metrics.apply(lambda x: sum(x[3]) / len(x[3]) if x[3] else 0)
    
    # 数据清洗
    print("\n9. 数据清洗...")
    for col in regression_data.columns:
        regression_data[col] = pd.to_numeric(regression_data[col], errors='coerce').fillna(0)
    
    # 验证数据
    print("\n10. 验证数据...")
    print(f"行数: {len(regression_data)}")
    print(f"列数: {len(regression_data.columns)}")
    print(f"列名: {list(regression_data.columns)}")
    print(f"数据类型:")
    print(regression_data.dtypes)
    print(f"\n前5行数据:")
    print(regression_data.head())
    
    # 保存文件
    print("\n11. 保存文件...")
    regression_data.to_excel(output_file, index=False)
    
    # 验证文件
    print("\n12. 验证文件...")
    if os.path.exists(output_file):
        print(f"文件已成功保存: {output_file}")
        print(f"文件大小: {os.path.getsize(output_file) / 1024:.2f} KB")
        # 重新读取检查
        df_check = pd.read_excel(output_file)
        print(f"输出文件行数: {len(df_check)}")
        print(f"输出文件列数: {len(df_check.columns)}")
    else:
        print("文件保存失败！")
    
    print()
    print("========================================")
    print("  任务完成")
    print("========================================")
    
except Exception as e:
    print(f"处理文件时出错: {str(e)}")
    import traceback
    traceback.print_exc()