multi-file-excel-parquet-analysis

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 Read the Excel file, iterate through all sheets to count rows, and assess the data scale.

import pandas as pd
import os

file_path = "input_data.xlsx"  # 替换为实际文件路径

if not os.path.exists(file_path):
    print(f"Error: 文件 {file_path} 不存在")
else:
    # 获取所有 sheet 名称
    xl = pd.ExcelFile(file_path)
    sheet_names = xl.sheet_names
    print("Sheet 列表:", sheet_names)

total_rows = 0
for sheet in sheet_names:
    # 仅读取第一列以快速统计行数，避免大文件内存溢出
    df_tmp = pd.read_excel(file_path, sheet_name=sheet, usecols=[0])
    row_count = len(df_tmp)
    total_rows += row_count
    print(f"Sheet: {sheet}, 行数: {row_count}")

print(f"总行数汇总: {total_rows}")

Step2 读取转换后的数据，执行分类统计分析，计算频数与占比。
```python
import pandas as pd

# 读取 Parquet 文件
df_analyzed = pd.read_parquet(output_parquet)

# 定义目标统计列（如 '剪裁结果'、'状态' 等）
target_col = '剪裁结果' 

if target_col in df_analyzed.columns:
    # 统计各分类数量及占比
    counts = df_analyzed[target_col].value_counts()
    percent = df_analyzed[target_col].value_counts(normalize=True) * 100
    
    # 构建统计表格并添加总计行
    summary_df = pd.DataFrame({
        '分类': counts.index,
        '数量': counts.values,
        '占比(%)': percent.values.round(2)
    })
    
    # 添加总计行
    total_row = pd.DataFrame([['总计', summary_df['数量'].sum(), 100.0]], columns=summary_df.columns)
    summary_df = pd.concat([summary_df, total_row], ignore_index=True)
    
    print("统计摘要:\n", summary_df)
else:
    print(f"未找到目标列: {target_col}")

Step3 Generate a visualization pie chart and save the analysis report, providing a download link for the results.

import matplotlib.pyplot as plt

# 配置中文字体（实战技巧：防止图表乱码）
plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False

if target_col in df_analyzed.columns:
    # 绘制饼图
    plt.figure(figsize=(10, 7), dpi=100)
    plot_data = df_analyzed[target_col].value_counts()
    plt.pie(plot_data, labels=plot_data.index, autopct='%1.1f%%', startangle=90, colors=plt.cm.Paired.colors)
    plt.title(f'{target_col} 分布占比')
    
    # 保存图表
    chart_output = "analysis_pie_chart.png"
    plt.savefig(chart_output, bbox_inches='tight')
    
    # 保存统计结果为 Excel
    report_output = "analysis_report.xlsx"
    summary_df.to_excel(report_output, index=False)
    
    print(f"分析图表已保存: {chart_output}")
    print(f"统计表格已保存: {report_output}")
    
    # 生成下载链接（用于报告展示）
    print(f"下载链接: {os.path.abspath(report_output)}")