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。
根据数据规模动态选择处理策略。
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 执行多维度数据清洗与条件筛选，包含列名自动识别、RGB 颜色过滤、前缀匹配及正则提取。
# 1. 自动识别同义列名并筛选非空值
target_cols = ['域名', '缩写', 'code', 'domain']
for col in target_cols:
if col in df.columns:
df = df[df[col].notna()]
break
# 2. 基于数值通道的精确筛选（如 RGB 颜色过滤）
# 技巧：多条件组合筛选时使用 & 符号
if all(c in df.columns for c in ['Red', 'Green', 'Blue']):
df = df[(df['Red'] == 0) & (df['Green'] == 0) & (df['Blue'] == 0)]
# 3. 基于字符串前缀筛选并进行数值转换计算
if '编号' in df.columns:
# 筛选特定前缀的项目
df = df[df['编号'].astype(str).str.startswith('TXL3')]
# 技巧：使用 errors='coerce' 处理无法转换的脏数据
df['val_a'] = pd.to_numeric(df['技工'], errors='coerce')
df['val_b'] = pd.to_numeric(df['普工'], errors='coerce')
df['total_val'] = df['val_a'] + df['val_b']
avg_val = df['total_val'].mean()
# 4. 基于特定分类值的筛选与统计
if '钢筋级别' in df.columns:
sub_df = df[df['钢筋级别'] == 'Ⅱ'].copy()
sub_df['target_val'] = pd.to_numeric(sub_df['屈服荷载'], errors='coerce')
avg_target = sub_df['target_val'].mean()
# 5. 正则表达式匹配提取特定字段
if '命令' in df.columns:
pattern = r'--pct-'
matched_df = df[df['命令'].astype(str).str.contains(pattern, na=False)]
# 提取关键列保留追溯性
extracted_data = matched_df[['NO', '命令', '说明']].copy()
Step2 将处理结果保存至 Excel，并对输出文件进行样式美化（如全行标红），最后生成下载链接。
from openpyxl.styles import PatternFill
output_path = "filtered_result.xlsx"
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
if 'total_val' in df.columns:
df.to_excel(writer, sheet_name='统计结果', index=False)
if 'extracted_data' in locals():
extracted_data.to_excel(writer, sheet_name='正则提取', index=False)
# 技巧：使用 openpyxl 进行后期样式加工，突出显示关键结果
wb = openpyxl.load_workbook(output_path)
red_fill = PatternFill(start_color='FFFF0000', end_color='FFFF0000', fill_type='solid')
for sheet_name in wb.sheetnames:
ws = wb[sheet_name]
for row in ws.iter_rows(min_row=2): # 跳过表头
for cell in row:
cell.fill = red_fill
wb.save(output_path)
# 输出标准下载链接格式
print(f"[下载结果文件](sandbox:{output_path})")
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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 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。
Find key figures in tabular data using column names or row keywords, calculate category averages and percentages, and export the results as a structured Excel report and a pie chart.
中文简介正在准备中。当前显示英语简介。