single-sheet-reading-and-analysis

Skill Steps

Step1 导入依赖并配置中英文字体，防止图表乱码

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

Step2 加载数据与基础清洗，包含合并单元格处理与正则提取

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

Step3 数据分类映射与多维度评分/分级算法

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

Step4 交叉分析与统计汇总（频数、占比、总计行）

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

Step5 图表美化与高分辨率输出

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()

Step6 大文件 Parquet 转换与下载链接生成

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))