2025-06-12 22:50:19 +08:00
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import os
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2025-06-02 18:10:22 +08:00
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import pandas as pd
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2025-06-12 22:50:19 +08:00
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def parse_excel_file(file_path):
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"""
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通用表格解析函数，支持 Excel (.xlsx/.xls) 和 CSV 文件
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返回统一格式的数据块列表
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"""
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blocks = []
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# 根据文件扩展名选择读取方式
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file_ext = os.path.splitext(file_path)[1].lower()
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try:
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if file_ext in (".xlsx", ".xls"):
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# 处理Excel文件（多sheet）
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all_sheets = pd.read_excel(file_path, sheet_name=None)
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for sheet_name, df in all_sheets.items():
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blocks.extend(_process_dataframe(df, sheet_name))
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elif file_ext == ".csv":
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# 处理CSV文件（单sheet）
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df = pd.read_csv(file_path)
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blocks.extend(_process_dataframe(df, "CSV"))
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else:
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raise ValueError(f"Unsupported file format: {file_ext}")
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except Exception as e:
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raise ValueError(f"Failed to parse file {file_path}: {str(e)}")
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2025-06-09 19:06:09 +08:00
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2025-06-12 22:50:19 +08:00
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return blocks
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def _process_dataframe(df, sheet_name):
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"""处理单个DataFrame，生成统一格式的数据块"""
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df = df.ffill()
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headers = df.columns.tolist()
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2025-06-09 19:06:09 +08:00
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blocks = []
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2025-06-12 22:50:19 +08:00
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for _, row in df.iterrows():
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html_table = "<html><body><table><tr>{}</tr><tr>{}</tr></table></body></html>".format("".join(f"<td>{col}</td>" for col in headers), "".join(f"<td>{row[col]}</td>" for col in headers))
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2025-06-09 19:06:09 +08:00
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2025-06-12 22:50:19 +08:00
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block = {"type": "table", "img_path": "", "table_caption": "", "table_footnote": [], "table_body": html_table, "page_idx": 0}
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blocks.append(block)
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2025-06-09 19:06:09 +08:00
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2025-06-02 18:10:22 +08:00
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return blocks
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if __name__ == "__main__":
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2025-06-12 22:50:19 +08:00
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# 测试示例
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excel_path = "test.xlsx"
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csv_path = "test.csv"
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try:
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# 测试Excel解析
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excel_blocks = parse_excel_file(excel_path)
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print(f"Excel解析结果（共{len(excel_blocks)}条）:")
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print(excel_blocks[:1]) # 打印第一条示例
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# 测试CSV解析
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csv_blocks = parse_excel_file(csv_path)
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print(f"\nCSV解析结果（共{len(csv_blocks)}条）:")
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print(csv_blocks[:1])
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except Exception as e:
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print(f"错误: {str(e)}")
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