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。
pythonimport 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。
pythontarget_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。
pythonfilter_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

