invalid-data-cleaning
用于大规模Excel数据的预处理，通过统计总行数判断是否转换为Parquet格式以提升读写效率，并使用正则表达式清洗指定文本列（如仅保留中文字符），最后导出清洗后的文件并提供下载链接。
npx skills add OpenSenseNova/SenseNova-Skills --skill invalid-data-cleaning --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.
# Invalid_Data_Cleaning > This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md. ## Skill Steps Step1 根据总行数判断是否数据量过大，若满足条件，则将 Excel 文件转换为 Parquet 格式提升读写效率，再读取数据进行后续分析。 ```python import pandas as pd file_path = "input_data.xlsx" parquet_path = "temp_data.parquet" # 读取 Excel 文件并转换为 Parquet 格式 xls = pd.ExcelFile(file_path) dfs = [] for sheet in xls.sheet_names: df_sheet = pd.read_excel(xls, sheet_name=sheet) dfs.append(df_sheet) # 合并所有 sheet 数据并写入 Parquet 文件 if dfs: df_all = pd.concat(dfs, ignore_index=True) df_all.to_parquet(parquet_path, engine='pyarrow', index=False) # 读取 Parquet 文件用于后续处理 df = pd.read_parquet(parquet_path) ``` Step2 对目标文本字段中的特殊字符（如 #、-、数字）进行清洗，使用正则表达式仅保留中文字符。 ```python import pandas as pd import re target_col = 'target_column' # 替换为实际需要清洗的列名 # 定义清洗函数 def clean_chinese_text(text): if pd.isna(text): return text s = str(text) # 提取所有中文字符（Unicode 范围：[一-鿿]） chinese_chars = re.findall(r'[一-鿿]', s) cleaned = ''.join(chinese_chars) return cleaned if cleaned else '' # 应用清洗函数 if target_col in df.columns: df[target_col] = df[target_col].apply(clean_chinese_text) ``` Step3 将清洗后的数据保存为表格文件（.xlsx），并在
- Skill Steps
What does the invalid-data-cleaning skill do?
用于大规模Excel数据的预处理，通过统计总行数判断是否转换为Parquet格式以提升读写效率，并使用正则表达式清洗指定文本列（如仅保留中文字符），最后导出清洗后的文件并提供下载链接。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill invalid-data-cleaning --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.