Filter rows from Excel or previously converted Parquet files, a format designed for efficient data storage. Summarize matching records by group with counts, percentages, and totals, then export the summary to Excel.
动态统计Excel总行数，当数据量过大（≥10000行）时自动转换为Parquet格式加速读取，并对指定目标列进行条件筛选、分类汇总与结果导出，适用于超大体积Excel文件的快速读取与统计分析。
Review the instructions the agent will receive before you install.
This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.
Step1 动态读取数据（Parquet加速或常规读取）。
# 若已加载 sn-da-large-file-analysis 技能，将 Excel 文件转换为 Parquet 格式加速读取
if 'da_large_file_analysis' in globals():
# 假设 sn-da-large-file-analysis 转换后生成了 parquet 文件
parquet_path = 'auto_converted_data.parquet'
df = pd.read_parquet(parquet_path)
print("已使用 Parquet 格式加速读取大文件。")
else:
df = pd.read_excel(file_path, sheet_name='Sheet1', header=0)
print("文件较小，使用常规方式读取。")
Step2 对目标列进行条件筛选，并按分组列进行分类汇总（包含占比与总计）。
target_col = '目标列名' # 示例：'危险级别'
group_col = '分组列名' # 示例：'分项工程'
target_value = 'TARGET_VALUE' # 示例：'★★★★'
# 筛选包含特定值的记录
df_filtered = df[df[target_col].astype(str).str.contains(target_value, na=False)].copy()
# 分类汇总
result = df_filtered[group_col].value_counts()
result_df = pd.DataFrame({
group_col: result.index,
'数量': result.values
})
# 计算占比并添加总计行
if not result_df.empty:
result_df['占比'] = (result_df['数量'] / result_df['数量'].sum()).apply(lambda x: f"{x:.2%}")
total_row = pd.DataFrame({
group_col: ['总计'],
'数量': [result_df['数量'].sum()],
'占比': ['100.00%']
})
result_df = pd.concat([result_df, total_row], ignore_index=True)
Step3 导出汇总结果并生成下载链接。
output_path = 'filtered_summary_output.xlsx'
# 将分类汇总结果保存为表格文件
result_df.to_excel(output_path, index=False)
# 输出下载链接供用户获取
print("数据处理与分类汇总完成。")
print(f"下载链接: {output_path}")
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