Threshold filtering

Skill OpenSenseNova/SenseNova-Skills/skills/sn-da-excel-workflow/capability/excel-data-filtering/threshold-filtering

Modular SenseNova skills for building AI-powered office assistants and productivity workflows

Install

npx -y skills add OpenSenseNova/SenseNova-Skills --skill threshold-filtering

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

根据 Excel 数据量级自动判断处理策略，执行数值列清洗、条件过滤，并使用 openpyxl 对符合条件的单元格进行样式标记与导出。

SKILL.md

1.9 KB, as published. Nobody here has run it

Excel Threshold Analysis and Styling

Note: This sub-skill covers one step of the Excel analysis workflow. For the full pipeline (file reading, row counting, large-file optimization, export), see the parent workflow SKILL.md.

Step1 读取 Excel 文件中所有工作表的行数并汇总，用于评估数据规模。

import pandas as pd

file_path = 'input_file.xlsx'

# 读取所有 sheet 名称并统计总行数
xls = pd.ExcelFile(file_path)
sheet_names = xls.sheet_names
total_rows = 0

for sheet in sheet_names:
    # header=None 用于快速统计包含表头的总行数
    df_tmp = pd.read_excel(file_path, sheet_name=sheet, header=None)
    rows = len(df_tmp)
    total_rows += rows
    print(f"Sheet '{sheet}': {rows} 行")

print(f"\n总行数汇总: {total_rows}")

Step2 对目标数据表进行清洗，将指定列的非数值内容转换为缺失值并剔除，确保数据类型为数值型。

target_sheet = 'Sheet1'
target_col = '数量' # 待处理的目标列名
header_idx = 1     # 表头所在行索引（0开始计数）

df = pd.read_excel(file_path, sheet_name=target_sheet, header=header_idx)

# 强制转换数值类型，无法转换的内容变为 NaN 并删除
df[target_col] = pd.to_numeric(df[target_col], errors='coerce')
df_cleaned = df.dropna(subset=[target_col])

print(f"清洗完成，有效数据行数: {len(df_cleaned)}")

Step3 筛选符合特定数值条件的记录并进行统计。

filter_threshold = 10 
df_filtered = df_cleaned[df_cleaned[target_col] > filter_threshold]

print(f"{target_col} 大于 {filter_threshold} 的记录共有 {len(df_filtered)} 条")

Step4 使用 openpyxl 对原始文件中

What its author says it does

SKILL.md

Excel Threshold Analysis and Styling

Keep looking