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