excel-large-file-processing-and-cleaning

npx skills add OpenSenseNova/SenseNova-Skills --skill structured-header-reading --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Skill Steps > This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md. Step1 文本字段清洗，使用正则表达式提取纯中文字符（过滤数字、特殊符号等）。 ```python import re def extract_chinese(text): if pd.isna(text): return text # 仅保留 Unicode 中文字符范围 chinese_chars = re.findall(r'[一-龥]', str(text)) cleaned = ''.join(chinese_chars) return cleaned if cleaned else '' clean_col = '目标清洗列' # 占位示例，如'收货人' if clean_col in df.columns: df[clean_col] = df[clean_col].apply(extract_chinese) ``` Step2 动态模糊匹配列名，并统计该列中特定值的数量。 ```python # 动态查找包含特定关键字的列 keyword = 'type' target_val = 'varchar' target_col = next((col for col in df.columns if keyword in str(col).lower()), None) total_target_count = 0 details = [] if target_col is not None: # 忽略大小写和首尾空格进行匹配 mask = df[target_col].astype(str).str.lower().str.strip() == target_val count = mask.sum() total_target_count += count if count > 0: details.append({ 'sheet': target_sheet, 'target_count': count, 'total_rows': len(df) }) print(f"{'='*50}") print(f"匹配列 '{target_col}' 中值为 '{target_val}' 的总数: {total_target_count}") print(f"{'='*50}") for detail in details: print(f" {detail['sheet']}: {detail['target_count']} 个匹配项 (