149 lines
5.5 KiB
Python
149 lines
5.5 KiB
Python
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import os
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import csv
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import glob
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import pandas as pd
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import json
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def merge_csv_files(output_file='merged_results.csv'):
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"""合并当前目录下的所有 temp_results_*.csv 和 query_results.csv 文件"""
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print("🚀 开始合并 CSV 文件...")
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# 查找所有相关 CSV 文件
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csv_files = glob.glob('temp_results_*.csv')
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if os.path.exists('query_results.csv'):
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csv_files.append('query_results.csv')
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if not csv_files:
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print("⚠️ 未找到任何结果文件")
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return
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print(f"📋 找到 {len(csv_files)} 个文件: {csv_files}")
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all_data = []
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seen_vins = set()
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for file in csv_files:
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try:
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# 尝试使用 pandas 读取，处理编码问题
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try:
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df = pd.read_csv(file, encoding='utf-8-sig')
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except:
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df = pd.read_csv(file, encoding='gbk')
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# 标准化列名（处理之前版本可能存在的列名差异）
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# 假设现在的列是 ['VIN', '结果', '查询时间', '状态']
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# 旧的可能是 ['VIN', '单车在线时长', ...]
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for index, row in df.iterrows():
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vin = str(row.get('VIN', '')).strip().upper()
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if not vin or vin in seen_vins:
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continue
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# 获取结果数据
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result = row.get('结果', row.get('单车在线时长', ''))
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# 尝试解析 JSON 结果
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parsed_data = {}
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is_json = False
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try:
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if result and result.strip().startswith('{'):
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parsed_data = json.loads(result)
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is_json = True
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except:
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pass
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# 基础信息
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entry = {
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'VIN': vin,
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'查询时间': row.get('查询时间', ''),
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'状态': row.get('状态', ''),
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'原始结果': result
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}
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# 如果是 JSON 数据，提取详细字段
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if is_json:
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# 提取 intervalList 和 daysList
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intervals = parsed_data.get('intervalList', [])
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days = parsed_data.get('daysList', [])
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if len(intervals) == len(days):
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for i, interval in enumerate(intervals):
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entry[f'时长区间_{interval}'] = days[i]
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# 也可以提取 startTime 和 endTime
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# entry['startTime'] = parsed_data.get('startTime', '')
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# entry['endTime'] = parsed_data.get('endTime', '')
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all_data.append(entry)
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seen_vins.add(vin)
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except Exception as e:
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print(f"❌ 处理文件 {file} 出错: {e}")
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if not all_data:
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print("⚠️ 没有有效数据")
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return
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# 转换为 DataFrame
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result_df = pd.DataFrame(all_data)
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# 计算估算在线总时长
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# 映射规则：取区间下限作为估算值（'≤0.5'按0.5算，'>6'按6算，区间按平均值或下限算）
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# 用户需求是“至少在线时长”，所以我们统一使用区间下限
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# 区间列表: ['≤0.5', '(0.5,1]', '(1,2]', '(2,3]', '(3,4]', '(4,6]', '>6']
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# 对应权重: [0, 0.5, 1, 2, 3, 4, 6] (≤0.5 算作 0，确保是“至少”)
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weight_map = {
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'时长区间_≤0.5': 0,
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'时长区间_(0.5,1]': 0.5,
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'时长区间_(1,2]': 1,
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'时长区间_(2,3]': 2,
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'时长区间_(3,4]': 3,
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'时长区间_(4,6]': 4,
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'时长区间_>6': 6
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}
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def calculate_min_duration(row):
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total_hours = 0
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for col, weight in weight_map.items():
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if col in row:
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try:
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days = float(row[col])
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if pd.notnull(days):
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total_hours += days * weight
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except:
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pass
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return total_hours
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# 只有当包含相关列时才计算
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if any(col in result_df.columns for col in weight_map.keys()):
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result_df['估算至少在线总时长(小时)'] = result_df.apply(calculate_min_duration, axis=1)
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print("✅ 已计算‘至少在线总时长’")
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# 调整列顺序
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# 优先显示的列
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first_cols = ['VIN', '估算至少在线总时长(小时)', '状态', '查询时间']
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# 动态生成的区间列
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interval_cols = [c for c in result_df.columns if c.startswith('时长区间_')]
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# 其他列
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other_cols = [c for c in result_df.columns if c not in first_cols and c not in interval_cols]
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final_cols = first_cols + interval_cols + other_cols
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result_df = result_df.reindex(columns=final_cols)
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# 导出
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result_df.to_csv(output_file, index=False, encoding='utf-8-sig')
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print(f"\n✅ 合并完成！结果已保存至: {output_file}")
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print(f"📊 总计合并数据: {len(result_df)} 条")
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if __name__ == '__main__':
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# 检查 pandas 是否安装
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try:
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import pandas
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merge_csv_files()
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except ImportError:
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print("❌ 需要安装 pandas 库: pip install pandas")
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# 降级处理：使用标准库简单的合并（不解析JSON）
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print("⚠️ 降级模式：仅简单合并文件")
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# ... (简易合并逻辑可以省略，通常用户环境已有 pandas)
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