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.
中文简介正在准备中。当前显示英语简介。
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。
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
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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Compare counts and shares for two categories in Excel data. The skill cleans the selected columns, calculates differences and percentages, and produces a summary spreadsheet plus bar and pie charts.
中文简介正在准备中。当前显示英语简介。
Analyze Excel data using custom categories, count distributions, and compare groups with charts. Score text using its length, technical terms, numbers, and selected complex concepts, then visualize difficulty levels and the relationship between text length and scores.
中文简介正在准备中。当前显示英语简介。
Count categories in a chosen data column after removing missing values and placeholder labels. Calculate each category’s share and create high-resolution charts combining counts, percentages, pie charts, and horizontal bars for distribution reports.
中文简介正在准备中。当前显示英语简介。
Clean category and numeric fields in structured data, compare groups with counts and cross-tabulations, and produce charts, Excel results, and a Markdown report. Export visuals as high-resolution PNG and SVG files for use in reports.
中文简介正在准备中。当前显示英语简介。
Filter Excel data using candidate column names, RGB color values, text prefixes, and regular expressions, which match text patterns. Calculate totals and averages, then export results to Excel with highlighted rows and a download link.
中文简介正在准备中。当前显示英语简介。
Find key figures in tabular data using column names or row keywords, calculate category averages and percentages, and export the results as a structured Excel report and a pie chart.
中文简介正在准备中。当前显示英语简介。