44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
import pandas as pd
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# 文件路径
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table1_path = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---4d1d9fe3-1e36-4df1-baf6-d826fcf7a05e.xlsx"
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table3_path = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---e503b23c-829e-4367-b819-762856bd50b5.xlsx"
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output_path = "/root/.openclaw/workspace-xiaoban/匹配完成的LV1词汇表.xlsx"
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# 读取两个表格
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df1 = pd.read_excel(table1_path)
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df3 = pd.read_excel(table3_path)
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print(f"表一总条数：{len(df1)}")
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print(f"表三总条数：{len(df3)}")
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print(f"表一列名：{list(df1.columns)}")
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print(f"表三列名：{list(df3.columns)}")
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# 创建映射：统一将单词转为字符串作为key，匹配三个字段
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word_map = {}
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for _, row in df1.iterrows():
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word = str(row['单词']).strip()
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word_map[word] = {
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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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# 给表三添加三列
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def get_value(word, col):
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key = str(word).strip()
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return word_map.get(key, {}).get(col, None)
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df3['难度（D）'] = df3['单词'].apply(lambda x: get_value(x, '难度（D）'))
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df3['实现成本(T)'] = df3['单词'].apply(lambda x: get_value(x, '实现成本(T)'))
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df3['单词系数'] = df3['单词'].apply(lambda x: get_value(x, '单词系数'))
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# 保存结果
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df3.to_excel(output_path, index=False)
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# 统计匹配情况
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match_count = df3['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：{match_count}")
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print(f"未匹配条数：{len(df3) - match_count}")
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