Clean numeric columns in Excel, filter records above a chosen threshold, and count matching rows. The skill also covers checking row totals across sheets and marking qualifying cells with formatting for export.
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。
Review the instructions the agent will receive before you install.
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
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