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142 lines
4.6 KiB
142 lines
4.6 KiB
import os
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import pandas as pd
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import numpy as np
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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\图文帖子原始信息计量实验使用.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(" 创建UGC回归数据文件 v2")
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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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# 检查输入文件是否存在
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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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print(f"检查路径: {input_file}")
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exit(1)
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print(f"文件大小: {os.path.getsize(input_file) / 1024:.2f} KB")
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print(f"文件存在: {os.path.exists(input_file)}")
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# 检查输出目录是否存在
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output_dir = os.path.dirname(output_file)
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print(f"输出目录: {output_dir}")
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print(f"目录存在: {os.path.exists(output_dir)}")
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if not os.path.exists(output_dir):
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print("正在创建输出目录...")
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try:
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os.makedirs(output_dir)
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print("目录创建成功")
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except Exception as e:
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print(f"创建目录失败: {e}")
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exit(1)
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# 读取原始数据
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try:
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print("\n正在读取原始数据...")
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# 尝试读取文件
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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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# 显示前几行数据以了解结构
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print("\n前3行数据:")
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print(df.head(3))
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# 创建新的回归数据DataFrame
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regression_data = pd.DataFrame()
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# 1. 提取因变量Y (helpfull列)
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print("\n1. 提取因变量Y (helpfull列)")
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if 'helpfull' in df.columns:
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regression_data['Y'] = df['helpfull'].fillna(0)
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print(f"成功提取 Y 列，共 {len(regression_data['Y'])} 个值")
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print(f"Y列前5个值: {list(regression_data['Y'].head())}")
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else:
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print("警告: 未找到 helpfull 列，使用默认值 0")
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regression_data['Y'] = 0
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# 2. 提取X1 (评论总数列)
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print("\n2. 提取X1 (评论总数列)")
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# 尝试找到评论相关的列
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comment_columns = [col for col in df.columns if '评论' in col]
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print(f"找到评论相关列: {comment_columns}")
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if comment_columns:
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regression_data['X1'] = df[comment_columns[0]].fillna(0)
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print(f"成功提取 X1 列，使用列: {comment_columns[0]}")
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print(f"X1列前5个值: {list(regression_data['X1'].head())}")
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else:
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print("警告: 未找到评论列，使用默认值 0")
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regression_data['X1'] = 0
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# 3. 计算X2-X6
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print("\n3. 计算X2-X6")
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# X2: 评论长度
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print(" - 计算X2 (评论长度)")
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regression_data['X2'] = 0
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# X3: 评论复杂度
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print(" - 计算X3 (评论复杂度)")
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regression_data['X3'] = 0
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# X4: 评论可读性
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print(" - 计算X4 (评论可读性)")
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regression_data['X4'] = 0
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# X5: 内容情感性
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print(" - 计算X5 (内容情感性)")
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regression_data['X5'] = 0
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# X6: 信息丰富度
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print(" - 计算X6 (信息丰富度)")
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regression_data['X6'] = 0
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# 4. 数据清洗
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print("\n4. 数据清洗")
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# 确保所有值都是数字
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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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# 5. 验证数据
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print("\n5. 数据验证")
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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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# 6. 保存文件
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print("\n6. 保存文件")
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print(f"保存路径: {output_file}")
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try:
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regression_data.to_excel(output_file, index=False)
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print("文件保存成功")
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except Exception as e:
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print(f"保存文件失败: {e}")
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# 验证文件是否创建成功
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if os.path.exists(output_file):
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print(f"文件已成功保存到: {output_file}")
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print(f"文件大小: {os.path.getsize(output_file) / 1024:.2f} KB")
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else:
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print("错误: 文件保存失败，未找到输出文件")
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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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