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.
中文简介正在准备中。当前显示英语简介。
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。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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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Compare counts and shares for two categories in Excel data. The skill cleans the selected columns, calculates differences and percentages, and produces a summary spreadsheet plus bar and pie charts.
中文简介正在准备中。当前显示英语简介。
Analyze Excel data using custom categories, count distributions, and compare groups with charts. Score text using its length, technical terms, numbers, and selected complex concepts, then visualize difficulty levels and the relationship between text length and scores.
中文简介正在准备中。当前显示英语简介。
Count categories in a chosen data column after removing missing values and placeholder labels. Calculate each category’s share and create high-resolution charts combining counts, percentages, pie charts, and horizontal bars for distribution reports.
中文简介正在准备中。当前显示英语简介。
Clean category and numeric fields in structured data, compare groups with counts and cross-tabulations, and produce charts, Excel results, and a Markdown report. Export visuals as high-resolution PNG and SVG files for use in reports.
中文简介正在准备中。当前显示英语简介。
Filter Excel data using candidate column names, RGB color values, text prefixes, and regular expressions, which match text patterns. Calculate totals and averages, then export results to Excel with highlighted rows and a download link.
中文简介正在准备中。当前显示英语简介。
Filter rows from Excel or previously converted Parquet files, a format designed for efficient data storage. Summarize matching records by group with counts, percentages, and totals, then export the summary to Excel.
中文简介正在准备中。当前显示英语简介。