41 lines
1.6 KiB
Python
41 lines
1.6 KiB
Python
import pandas as pd
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# 文件路径
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difficulty_path = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---4d1d9fe3-1e36-4df1-baf6-d826fcf7a05e.xlsx" # 难度_成本单词系数1.0表
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lower_path = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---59ff96e7-d862-476b-be16-3162afcd818f.xlsx" # 最新的下册单词表
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output_path = "/root/.openclaw/workspace-xiaoban/最终版_LV1下册词汇匹配系数结果.xlsx"
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# 读取表格
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df_diff = pd.read_excel(difficulty_path)
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df_lower = pd.read_excel(lower_path)
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print(f"下册单词表总条数：{len(df_lower)}")
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# 创建映射字典，所有单词统一转为字符串匹配，包含数字
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word_map = {}
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for _, row in df_diff.iterrows():
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word_key = str(row['单词']).strip()
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word_map[word_key] = {
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'难度（D）': row['难度（D）'],
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'实现成本(T)': row['实现成本(T)'],
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'单词系数': row['单词系数']
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}
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# 匹配字段
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def match_field(word, field):
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key = str(word).strip()
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return word_map.get(key, {}).get(field, None)
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df_lower['难度（D）'] = df_lower['单词'].apply(lambda x: match_field(x, '难度（D）'))
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df_lower['实现成本(T)'] = df_lower['单词'].apply(lambda x: match_field(x, '实现成本(T)'))
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df_lower['单词系数'] = df_lower['单词'].apply(lambda x: match_field(x, '单词系数'))
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# 保存结果
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df_lower.to_excel(output_path, index=False)
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# 统计
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success_count = df_lower['难度（D）'].notna().sum()
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print(f"\n匹配完成！结果已保存到：{output_path}")
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print(f"成功匹配条数：{success_count}")
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print(f"未匹配条数：{len(df_lower) - success_count}")
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