Combine data from multiple Excel sheets, clean inconsistent labels and merged cells, and summarize counts by category. Create Excel summary tables with percentages and totals, plus labeled bar charts for comparing categories.
中文简介正在准备中。当前显示英语简介。
读取多工作表Excel文件，自动处理合并单元格与数据清洗，进行交叉分组统计并生成带总计行的结果表，最后绘制支持中英文字体的美化柱状图，适用于多维度数据汇总与可视化分析。
安装前可以查看交给 AI 代理的指令内容。
This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.
combined_df = pd.concat(data_frames, ignore_index=True)
# 数据清洗：使用正则表达式统一命名
if '题型' in combined_df.columns:
combined_df['题型'] = combined_df['题型'].astype(str).str.replace('判', '判断题', regex=False)
# 处理合并单元格技巧1：前向填充
if '流程描述' in combined_df.columns:
combined_df['流程描述'] = combined_df['流程描述'].fillna(method='ffill')
# 处理合并单元格技巧2：通过逻辑判断与手动映射还原完整名称
group_col = '项目阶段'
target_col = '控制要点'
if group_col in combined_df.columns and target_col in combined_df.columns:
project_stages, control_points = [], []
current_stage = None
for _, row in combined_df.iterrows():
stage = row[group_col]
point = row[target_col]
if pd.notna(point) and point != target_col:
if pd.notna(stage):
current_stage = stage
project_stages.append(current_stage)
control_points.append(point)
combined_df = pd.DataFrame({
group_col: project_stages,
target_col: control_points
})
# 分类映射函数骨架
if group_col in combined_df.columns:
stage_mapping = {
'碎片值1': '标准分类A',
'碎片值2': '标准分类A',
'碎片值3': '标准分类B',
'异常值': '其他'
}
combined_df[f'{group_col}_合并'] = combined_df[group_col].map(stage_mapping).fillna('其他')
grouped_stats = combined_df.groupby(f'{group_col}_合并')[target_col].count().sort_values(ascending=False)
elif '题目分类' in combined_df.columns and '题型' in combined_df.columns:
# 交叉分析 crosstab/pivot
grouped_stats = combined_df.groupby(['题目分类', '题型']).size().unstack(fill_value=0)
else:
grouped_stats = combined_df.groupby(combined_df.columns[0]).size()
import tempfile
import os
output_path = os.path.join(tempfile.gettempdir(), "统计结果.xlsx")
# 计算占比并生成包含总计行的Excel文件
if isinstance(grouped_stats, pd.Series):
result_df = pd.DataFrame({
'分类': grouped_stats.index,
'数量': grouped_stats.values,
'占比(%)': (grouped_stats.values / grouped_stats.sum() * 100).round(2)
})
total_row = pd.DataFrame({
'分类': ['总计'],
'数量': [grouped_stats.sum()],
'占比(%)': [100.00]
})
result_df = pd.concat([result_df, total_row], ignore_index=True)
else:
result_df = grouped_stats.reset_index()
result_df.to_excel(output_path, index=False)
# 生成临时可访问的下载链接
download_url = invoke_skill("file_service.get_download_url", {"file_path": output_path})
print(f"下载链接: {download_url}")
import matplotlib.pyplot as plt
import matplotlib
# 技巧：配置中英文字体以确保在不同系统中正常显示
matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
matplotlib.rcParams['axes.unicode_minus'] = False
stage_mapping_en = {
'标准分类A': 'Standard Category A',
'标准分类B': 'Standard Category B',
'其他': 'Others'
}
if isinstance(grouped_stats, pd.Series):
stage_counts_sorted = grouped_stats.sort_values(ascending=True)
stage_counts_en = stage_counts_sorted.rename(index=stage_mapping_en)
# 图表美化（dpi、颜色方案、标签位置）
fig, ax = plt.subplots(figsize=(12, 8), dpi=120)
colors = plt.cm.Set3(range(len(stage_counts_en)))
bars = ax.barh(stage_counts_en.index, stage_counts_en.values, color=colors, edgecolor='black', linewidth=0.5)
for bar, value in zip(bars, stage_counts_en.values):
ax.text(bar.get_width() + (stage_counts_en.max() * 0.01),
bar.get_y() + bar.get_height()/2,
str(value), va='center', ha='left', fontsize=11, fontweight='bold')
ax.set_xlabel('Count', fontsize=12, fontweight='bold')
ax.set_ylabel('Category', fontsize=12, fontweight='bold')
plt.tight_layout()
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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。