excel-multi-sheet-threshold-analysis
统计多Sheet Excel总行数并根据规模选择处理策略，提取特定维度信息进行去重统计，并生成摘要与明细报表。
npx skills add OpenSenseNova/SenseNova-Skills --skill duplicate-removal --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Excel_Multi_Sheet_Deduplication > This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md. Step1 加载目标数据表，并进行初步的数据预览与结构检查。 ```python import pandas as pd file_path = 'input_file.xlsx' target_sheet = 'Sheet1' # 根据实际情况指定 sheet 名称 # 读取数据，header=None 用于处理无表头或非标准表头文件 df = pd.read_excel(file_path, sheet_name=target_sheet, header=None) print(f"数据形状: {df.shape}") print("前 5 行预览：") print(df.head()) ``` Step2 遍历数据行，基于关键词提取目标信息，并执行数据清洗（去除空格、空值过滤）。 ```python import pandas as pd # 设定目标列索引及过滤关键词 target_col_idx = 1 keywords = ["关键词A", "关键词B"] # 示例：如"综合楼"、"控制中心" extracted_data = [] for idx, row in df.iterrows(): cell_val = str(row[target_col_idx]) if pd.notna(row[target_col_idx]) else "" # 数据清洗：去除首尾空格并匹配关键词 clean_val = cell_val.strip() if any(k in clean_val for k in keywords): if clean_val and clean_val.lower() not in ["nan", "null", ""]: extracted_data.append(clean_val) print(f"提取到相关记录共 {len(extracted_data)} 条") ``` Step3 对提取的信息进行分类去重，统计各维度的唯一项数量。 ```python # 使用 set 进行高效去重 category_a_items = set() category_b_items = set() for item in extracted_data: if "关键词A" in item: category_a_items.add(item) elif "关键词B" in item: ca
What does the excel-multi-sheet-threshold-analysis skill do?
统计多Sheet Excel总行数并根据规模选择处理策略，提取特定维度信息进行去重统计，并生成摘要与明细报表。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill duplicate-removal --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From OpenSenseNova/SenseNova-Skills, a repository with 4,855 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.