Single Sheet Reading And Analysis Agent Skill

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import re
import base64
from IPython.display import HTML

# 设置中英文字体
plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans', 'WenQuanYi Zen Hei']
plt.rcParams['axes.unicode_minus'] = False

def load_and_clean_data(file_path, sheet_name=0):
    # 读取数据
    df = pd.read_excel(file_path, sheet_name=sheet_name)
    
    # 处理合并单元格：向前填充并还原
    # df['group_col'] = df['group_col'].ffill()
    
    # 标准化列名：去除首尾空格及换行符
    df.columns = [str(col).strip().replace('\n', '') for col in df.columns]
    
    # 数据清洗正则表达式示例：提取数值
    if 'target_col' in df.columns:
        df['target_col'] = df['target_col'].astype(str).apply(lambda x: re.sub(r'[^\d.]', '', x))
        df['target_col'] = pd.to_numeric(df['target_col'], errors='coerce')
    
    # 处理全空行缺失值
    df = df.dropna(how='all')
    return df

def categorize_and_score(df, target_col):
    # 分类映射函数骨架
    def map_category(val):
        if pd.isna(val):
            return '未知'
        elif val > 100:  # 占位示例：高阈值
            return 'A类'
        elif val > 50:   # 占位示例：中阈值
            return 'B类'
        else:
            return 'C类'
    
    if target_col in df.columns:
        df['category'] = df[target_col].apply(map_category)
    
    # 多维度评分/分级算法结构
    # df['score'] = df['metric1'] * 0.4 + df['metric2'] * 0.6
    return df

def analyze_data(df, group_col):
    # value_counts + 占比 + 总计行
    counts = df[group_col].value_counts().reset_index()
    counts.columns = [group_col, '数量']
    counts['占比'] = (counts['数量'] / counts['数量'].sum()).map('{:.2%}'.format)
    
    # 添加总计行
    total_row = pd.DataFrame({
        group_col: ['总计'], 
        '数量': [counts['数量'].sum()], 
        '占比': ['100.00%']
    })
    counts = pd.concat([counts, total_row], ignore_index=True)
    
    # 交叉分析 crosstab/pivot
    if 'category' in df.columns:
        cross_tb = pd.crosstab(df[group_col], df['category'], margins=True, margins_name='总计')
    else:
        cross_tb = None
        
    return counts, cross_tb

def visualize_results(df, group_col, target_col, output_path):
    # 设置高分辨率 dpi=300
    fig, ax = plt.subplots(figsize=(10, 6), dpi=300)
    
    # 颜色方案与图表绘制
    valid_data = df.dropna(subset=[group_col, target_col])
    colors = sns.color_palette("husl", len(valid_data[group_col].unique()))
    sns.barplot(data=valid_data, x=group_col, y=target_col, palette=colors, ax=ax)
    
    # 标签位置与美化
    ax.set_title('多维度数据分析', fontsize=16, pad=15)
    ax.set_xlabel('分组维度', fontsize=12)
    ax.set_ylabel('目标指标', fontsize=12)
    plt.xticks(rotation=45, ha='right')
    
    # 添加数据标签
    for p in ax.patches:
        ax.annotate(f'{p.get_height():.1f}', 
                    (p.get_x() + p.get_width() / 2., p.get_height()), 
                    ha='center', va='bottom', fontsize=10)
    
    plt.tight_layout()
    plt.savefig(output_path, dpi=300, bbox_inches='tight')
    plt.close()

def export_and_generate_link(df, output_path):
    # 大文件 Parquet 转换
    parquet_path = output_path.replace('.png', '.parquet').replace('.csv', '.parquet')
    df.to_parquet(parquet_path, index=False)
    
    # 下载链接生成
    csv_data = df.to_csv(index=False).encode('utf-8')
    b64 = base64.b64encode(csv_data).decode()
    href = f'<a href="data:file/csv;base64,{b64}" download="analysis_result.csv">点击下载分析结果 (CSV)</a>'
    display(HTML(href))