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Data Report Examples

This page demonstrates how to use EmailWidget to create professional data analysis reports, focusing on integration with pandas and matplotlib.

Sales Data Report

Sales Analysis Based on DataFrame

Python
import pandas as pd
from email_widget import Email, TableWidget, ChartWidget, TextWidget
from email_widget.core.enums import TextType, TextAlign
import matplotlib.pyplot as plt

# 创建销售数据
sales_data = {
    '月份': ['1月', '2月', '3月', '4月', '5月', '6月'],
    '销售额': [150000, 180000, 220000, 195000, 250000, 280000],
    '订单数': [450, 520, 680, 590, 720, 850],
    '客单价': [333, 346, 324, 331, 347, 329]
}

df = pd.DataFrame(sales_data)

# 创建邮件报告
email = Email("2024年上半年销售数据报告")

# 报告标题
email.add_title("📊 2024年上半年销售数据分析", TextType.TITLE_LARGE)
email.add_text(f"报告生成时间: {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M')}", 
               align=TextAlign.RIGHT, color="#666666")

# 关键指标汇总
email.add_title("📈 关键指标", TextType.SECTION_H2)

# 计算总体指标
total_sales = df['销售额'].sum()
total_orders = df['订单数'].sum()
avg_order_value = df['客单价'].mean()
growth_rate = ((df['销售额'].iloc[-1] - df['销售额'].iloc[0]) / df['销售额'].iloc[0]) * 100

# 使用卡片展示关键指标
metrics = [
    ("总销售额", f"¥{total_sales:,}", "💰"),
    ("总订单数", f"{total_orders:,}", "📋"),
    ("平均客单价", f"¥{avg_order_value:.0f}", "👤"),
    ("增长率", f"{growth_rate:.1f}%", "📈")
]

for title, value, icon in metrics:
    email.add_card(title=title, content=value, icon=icon)

# 详细数据表格
email.add_title("📋 详细数据", TextType.SECTION_H2)

# 直接从 DataFrame 创建表格
table = TableWidget()
table.set_headers(df.columns.tolist())

# 添加数据行并格式化
for _, row in df.iterrows():
    formatted_row = [
        row['月份'],
        f"¥{row['销售额']:,}",  # 格式化金额
        f"{row['订单数']:,}",    # 格式化数量
        f"¥{row['客单价']:.0f}" # 格式化客单价
    ]
    table.add_row(formatted_row)

table.set_striped(True)
email.add_widget(table)

# 趋势分析
email.add_title("📉 趋势分析", TextType.SECTION_H2)

# 创建趋势图表
plt.figure(figsize=(10, 6))
plt.plot(df['月份'], df['销售额'], marker='o', linewidth=2, label='销售额')
plt.title('销售额趋势', fontsize=14)
plt.xlabel('月份')
plt.ylabel('销售额 (元)')
plt.grid(True, alpha=0.3)
plt.xticks(rotation=45)
plt.tight_layout()

# 保存图表
chart_path = "sales_trend.png"
plt.savefig(chart_path, dpi=150, bbox_inches='tight')
plt.close()

# 添加图表到邮件
chart = ChartWidget()
chart.set_chart_path(chart_path) \
     .set_title("销售额月度趋势") \
     .set_description("显示上半年销售额的月度变化情况")
email.add_widget(chart)

# 分析总结
email.add_title("💡 分析总结", TextType.SECTION_H2)
summary_text = f"""
根据上半年数据分析：

✅ **积极指标**
• 销售额稳步增长，总增长率达到 {growth_rate:.1f}%
• 6月份创造了单月最高销售额 ¥{df['销售额'].max():,}
• 订单数持续增长，显示客户基础扩大

⚠️ **需要关注**
• 4月份出现小幅回落，需分析原因
• 客单价波动较大，建议优化产品结构

🎯 **下半年建议**
• 保持增长势头，目标年销售额 ¥{total_sales * 2:,}
• 加强4月份同期市场活动
• 稳定客单价，提升产品价值
"""

email.add_text(summary_text.strip())

email.export_html("sales_report.html")
print("✅ 销售数据报告已生成：sales_report.html")
2024年上半年销售数据报告

Key Features: - Automatic calculation of key business metrics - Direct DataFrame to table conversion - Integrated matplotlib trend chart generation - Data formatting and visualization

Financial Report

Income Statement Display

Python
import pandas as pd
from email_widget import Email, TableWidget, ProgressWidget, AlertWidget
from email_widget.core.enums import TextType, ProgressTheme, AlertType

# 财务数据
financial_data = {
    '科目': ['营业收入', '营业成本', '毛利润', '销售费用', '管理费用', '财务费用', '营业利润', '净利润'],
    '本期金额': [2800000, 1680000, 1120000, 280000, 350000, 45000, 445000, 356000],
    '上期金额': [2400000, 1440000, 960000, 240000, 320000, 40000, 360000, 288000],
    '预算金额': [3000000, 1800000, 1200000, 300000, 360000, 50000, 490000, 392000]
}

df_financial = pd.DataFrame(financial_data)

# 计算同比和预算完成率
df_financial['同比增长'] = ((df_financial['本期金额'] - df_financial['上期金额']) / df_financial['上期金额'] * 100).round(1)
df_financial['预算完成率'] = (df_financial['本期金额'] / df_financial['预算金额'] * 100).round(1)

# 创建财务报告
email = Email("2024年Q2财务报告")

email.add_title("💼 2024年第二季度财务报告", TextType.TITLE_LARGE)

# 核心财务指标
email.add_title("🎯 核心指标", TextType.SECTION_H2)

# 关键指标卡片
key_metrics = [
    ("营业收入", df_financial.loc[0, '���期金额'], "💰"),
    ("净利润", df_financial.loc[7, '本期金额'], "📈"),
    ("毛利率", f"{(df_financial.loc[2, '本期金额'] / df_financial.loc[0, '本期金额'] * 100):.1f}%", "📊"),
    ("净利率", f"{(df_financial.loc[7, '本期金额'] / df_financial.loc[0, '本期金额'] * 100):.1f}%", "🎯")
]

for title, value, icon in key_metrics:
    if isinstance(value, (int, float)):
        value = f"¥{value:,}"
    email.add_card(title=title, content=value, icon=icon)

# 财务数据详表
email.add_title("📊 财务明细", TextType.SECTION_H2)

table = TableWidget()
table.set_headers(['科目', '本期金额', '上期金额', '同比增长', '预算完成率'])

for _, row in df_financial.iterrows():
    formatted_row = [
        row['科目'],
        f"¥{row['本期金额']:,}",
        f"¥{row['上期金额']:,}",
        f"{row['同比增长']:+.1f}%",
        f"{row['预算完成率']:.1f}%"
    ]
    table.add_row(formatted_row)

table.set_striped(True)
email.add_widget(table)

# 预算执行情况
email.add_title("🎯 预算执行分析", TextType.SECTION_H2)

# 为主要科目显示预算完成进度
key_items = ['营业收入', '营业利润', '净利润']
for item in key_items:
    row = df_financial[df_financial['科目'] == item].iloc[0]
    completion_rate = row['预算完成率']

    # 根据完成率选择主题色
    if completion_rate >= 100:
        theme = ProgressTheme.SUCCESS
    elif completion_rate >= 80:
        theme = ProgressTheme.INFO
    elif completion_rate >= 60:
        theme = ProgressTheme.WARNING
    else:
        theme = ProgressTheme.ERROR

    email.add_text(f"📋 {item}")
    email.add_progress(
        value=min(completion_rate, 100),  # 限制在100%内显示
        label=f"预算完成率: {completion_rate:.1f}%",
        theme=theme
    )

# 风险提示
email.add_title("⚠️ 风险提示", TextType.SECTION_H2)

# 分析预算完成情况，生成提醒
risk_items = df_financial[df_financial['预算完成率'] < 90]
if not risk_items.empty:
    for _, item in risk_items.iterrows():
        alert_type = AlertType.WARNING if item['预算完成率'] >= 80 else AlertType.CAUTION
        email.add_alert(
            f"{item['科目']}预算完成率仅为{item['预算完成率']:.1f}%，需要重点关注",
            alert_type,
            "预算执行预警"
        )

# 财务分析
email.add_title("📈 财务分析", TextType.SECTION_H2)

revenue_growth = df_financial.loc[0, '同比增长']
profit_growth = df_financial.loc[7, '同比增长']

analysis = f"""
**经营业绩分析：**

📊 **收入分析**
• 营业收入同比增长 {revenue_growth:.1f}%，表现{('优秀' if revenue_growth > 15 else '良好' if revenue_growth > 5 else '一般')}
• 收入预算完成率 {df_financial.loc[0, '预算完成率']:.1f}%

💰 **盈利能力**
• 净利润同比增长 {profit_growth:.1f}%，盈利能力{'显著提升' if profit_growth > 20 else '稳步提升' if profit_growth > 0 else '有所下降'}
• 净利率 {(df_financial.loc[7, '本期金额'] / df_financial.loc[0, '本期金额'] * 100):.1f}%，保持健康水平

🎯 **预算执行**
• 营业收入预算完成率 {df_financial.loc[0, '预算完成率']:.1f}%
• 净利润预算完成率 {df_financial.loc[7, '预算完成率']:.1f}%
"""

email.add_text(analysis.strip())

email.export_html("financial_report.html")
print("✅ 财务报告已生成：financial_report.html")
2024年Q2财务报告

Professional Features: - Complete financial statement structure - Automatic calculation of YoY growth and budget completion - Risk alerts and intelligent reminders - Professional financial analysis terminology

Product Analysis Report

Multi-dimensional Product Data Analysis

Python
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from email_widget import Email, ChartWidget, TableWidget
from email_widget.core.enums import TextType

# 设置中文字体（根据系统调整）
plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False

# 产品销售数据
products_data = {
    '产品名称': ['智能手机A', '智能手机B', '平板电脑C', '笔记本D', '耳机E', '充电器F'],
    '销售数量': [1200, 800, 600, 450, 2000, 1500],
    '单价': [2999, 3999, 1999, 5999, 299, 99],
    '成本': [2100, 2800, 1400, 4200, 180, 60],
    '库存': [300, 150, 200, 100, 500, 800],
    '评分': [4.5, 4.7, 4.2, 4.8, 4.3, 4.0]
}

df_products = pd.DataFrame(products_data)

# 计算衍生指标
df_products['销售额'] = df_products['销售数量'] * df_products['单价']
df_products['毛利润'] = (df_products['单价'] - df_products['成本']) * df_products['销售数量']
df_products['毛利率'] = ((df_products['单价'] - df_products['成本']) / df_products['单价'] * 100).round(1)
df_products['库存周转'] = (df_products['销售数量'] / (df_products['库存'] + df_products['销售数量']) * 100).round(1)

# 创建产品分析报告
email = Email("产品销售分析报告")

email.add_title("📱 产品销售分析报告", TextType.TITLE_LARGE)

# 产品组合概览
email.add_title("🎯 产品组合概览", TextType.SECTION_H2)

# 计算总体指标
total_revenue = df_products['销售额'].sum()
total_profit = df_products['毛利润'].sum()
avg_rating = df_products['评分'].mean()
best_seller = df_products.loc[df_products['销售数量'].idxmax(), '产品名称']

overview_metrics = [
    ("总销售额", f"¥{total_revenue:,}", "💰"),
    ("总毛利润", f"¥{total_profit:,}", "📈"),
    ("平均评分", f"{avg_rating:.1f}★", "⭐"),
    ("最佳销量", best_seller, "🏆")
]

for title, value, icon in overview_metrics:
    email.add_card(title=title, content=value, icon=icon)

# 产品明细表
email.add_title("📊 产品销售明细", TextType.SECTION_H2)

table = TableWidget()
table.set_headers(['产品', '数量', '单价', '销售额', '毛利率', '评分'])

for _, row in df_products.iterrows():
    formatted_row = [
        row['产品名称'],
        f"{row['销售数量']:,}",
        f"¥{row['单价']:,}",
        f"¥{row['销售额']:,}",
        f"{row['毛利率']:.1f}%",
        f"{row['评分']:.1f}★"
    ]
    table.add_row(formatted_row)

table.set_striped(True)
email.add_widget(table)

# 销售额分布图
email.add_title("📈 销售额分布", TextType.SECTION_H2)

plt.figure(figsize=(10, 6))
colors = plt.cm.Set3(range(len(df_products)))
plt.pie(df_products['销售额'], labels=df_products['产品名称'], 
        autopct='%1.1f%%', startangle=90, colors=colors)
plt.title('各产品销售额占比', fontsize=14)
plt.axis('equal')

pie_chart_path = "sales_distribution.png"
plt.savefig(pie_chart_path, dpi=150, bbox_inches='tight')
plt.close()

chart1 = ChartWidget()
chart1.set_chart_path(pie_chart_path) \
      .set_title("产品销售额分布") \
      .set_description("展示各产品对总销售额的贡献比例")
email.add_widget(chart1)

# 毛利率与销量关系分析
email.add_title("🔍 毛利率与销量分析", TextType.SECTION_H2)

plt.figure(figsize=(10, 6))
scatter = plt.scatter(df_products['销售数量'], df_products['毛利率'], 
                     s=df_products['评分']*50, alpha=0.7, c=colors)

# 添加产品标签
for i, txt in enumerate(df_products['产品名称']):
    plt.annotate(txt, (df_products['销售数量'].iloc[i], df_products['毛利率'].iloc[i]),
                xytext=(5, 5), textcoords='offset points', fontsize=8)

plt.xlabel('销售数量')
plt.ylabel('毛利率 (%)')
plt.title('产品毛利率与销量关系（气泡大小代表评分）', fontsize=14)
plt.grid(True, alpha=0.3)

scatter_chart_path = "profit_sales_analysis.png"
plt.savefig(scatter_chart_path, dpi=150, bbox_inches='tight')
plt.close()

chart2 = ChartWidget()
chart2.set_chart_path(scatter_chart_path) \
      .set_title("毛利率与销量关系") \
      .set_description("分析各产品的盈利能力与市场表现的关系")
email.add_widget(chart2)

# 产品策略建议
email.add_title("💡 产品策略建议", TextType.SECTION_H2)

# 分析各产品表现
high_margin_products = df_products[df_products['毛利率'] > df_products['毛利率'].mean()]
high_volume_products = df_products[df_products['销售数量'] > df_products['销售数量'].mean()]
low_stock_products = df_products[df_products['库存周转'] > 80]

strategy_text = f"""
**基于数据分析的产品策略建议：**

🌟 **优势产品** (高毛利率)
{', '.join(high_margin_products['产品名称'].tolist())}
• 建议加大营销投入，扩大市场份额

📈 **热销产品** (高销量)
{', '.join(high_volume_products['产品名称'].tolist())}
• 保持库存充足，优化供应链

⚡ **快周转产品** (库存周转率>80%)
{', '.join(low_stock_products['产品名称'].tolist()) if not low_stock_products.empty else '暂无'}
• 及时补货，避免缺货影响销售

🎯 **综合策略**
• 重点关注高毛利率产品的市场推广
• 优化低评分产品的用户体验
• 平衡产品组合，降低单一产品依赖
"""

email.add_text(strategy_text.strip())

email.export_html("product_analysis.html")
print("✅ 产品分析报告已生成：product_analysis.html")
产品销售分析报告

Analysis Highlights: - Multi-dimensional product data analysis - Visual charts showing product relationships - Data-driven strategy recommendations - Comprehensive consideration of volume, profit, rating factors

Customer Analysis Report

RFM Customer Value Analysis

Python
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from email_widget import Email, TableWidget, ProgressWidget
from email_widget.core.enums import TextType, ProgressTheme

# 生成客户数据
np.random.seed(42)
customer_data = {
    '客户ID': [f'C{str(i).zfill(4)}' for i in range(1, 101)],
    '最近购买天数': np.random.randint(1, 365, 100),
    '购买频次': np.random.randint(1, 20, 100),
    '购买金额': np.random.randint(100, 10000, 100)
}

df_customers = pd.DataFrame(customer_data)

# RFM分析函数
def rfm_analysis(df):
    """RFM客户价值分析"""
    # 计算RFM分位数
    r_quartiles = pd.qcut(df['最近购买天数'], 4, labels=[4, 3, 2, 1])  # 最近购买，天数越少分值越高
    f_quartiles = pd.qcut(df['购买频次'].rank(method='first'), 4, labels=[1, 2, 3, 4])
    m_quartiles = pd.qcut(df['购买金额'], 4, labels=[1, 2, 3, 4])

    df['R分值'] = r_quartiles
    df['F分值'] = f_quartiles  
    df['M分值'] = m_quartiles

    # 计算RFM综合分值
    df['RFM综合分值'] = df['R分值'].astype(str) + df['F分值'].astype(str) + df['M分值'].astype(str)

    # 客户分级
    def customer_segment(rfm_score):
        score = int(rfm_score)
        if score >= 444:
            return '重要价值客户'
        elif score >= 344:
            return '重要发展客户'
        elif score >= 244:
            return '重要保持客户'
        elif score >= 144:
            return '重要挽留客户'
        elif score >= 134:
            return '一般价值客户'
        elif score >= 124:
            return '一般发展客户'
        elif score >= 114:
            return '一般保持客户'
        else:
            return '一般挽留客户'

    df['客户分级'] = df['RFM综合分值'].apply(customer_segment)
    return df

# 执行RFM分析
df_rfm = rfm_analysis(df_customers.copy())

# 创建客户分析报告
email = Email("RFM客户价值分析报告")

email.add_title("👥 RFM客户价值分析报告", TextType.TITLE_LARGE)

# 客户总体概况
email.add_title("📊 客户总体概况", TextType.SECTION_H2)

total_customers = len(df_rfm)
avg_frequency = df_rfm['购买频次'].mean()
avg_monetary = df_rfm['购买金额'].mean()
avg_recency = df_rfm['最近购买天数'].mean()

overview_stats = [
    ("客户总数", f"{total_customers:,}", "👥"),
    ("平均购买频次", f"{avg_frequency:.1f}次", "🔄"),
    ("平均购买金额", f"¥{avg_monetary:,.0f}", "💰"),
    ("平均间隔天数", f"{avg_recency:.0f}天", "📅")
]

for title, value, icon in overview_stats:
    email.add_card(title=title, content=value, icon=icon)

# 客户分级统计
email.add_title("🎯 客户分级分布", TextType.SECTION_H2)

segment_stats = df_rfm['客户分级'].value_counts().sort_index()

table = TableWidget()
table.set_headers(['客户级别', '客户数量', '占比', '平均金额'])

for segment, count in segment_stats.items():
    segment_customers = df_rfm[df_rfm['客户分级'] == segment]
    avg_amount = segment_customers['购买金额'].mean()
    percentage = (count / total_customers * 100)

    table.add_row([
        segment,
        f"{count:,}",
        f"{percentage:.1f}%",
        f"¥{avg_amount:,.0f}"
    ])

table.set_striped(True)
email.add_widget(table)

# 各级别客户占比进度条
email.add_title("📈 客户分级占比", TextType.SECTION_H2)

# 定义客户级别对应的主题色
segment_themes = {
    '重要价值客户': ProgressTheme.SUCCESS,
    '重要发展客户': ProgressTheme.INFO,
    '重要保持客户': ProgressTheme.WARNING,
    '重要挽留客户': ProgressTheme.ERROR,
    '一般价值客户': ProgressTheme.SUCCESS,
    '一般发展客户': ProgressTheme.INFO,
    '一般保持客户': ProgressTheme.WARNING,
    '一般挽留客户': ProgressTheme.ERROR
}

for segment, count in segment_stats.items():
    percentage = (count / total_customers * 100)
    theme = segment_themes.get(segment, ProgressTheme.INFO)

    email.add_text(f"🔹 {segment}")
    email.add_progress(
        value=percentage,
        label=f"{count}人 ({percentage:.1f}%)",
        theme=theme
    )

# 高价值客户详情
email.add_title("⭐ 高价值客户分析", TextType.SECTION_H2)

high_value_customers = df_rfm[df_rfm['客户分级'].str.contains('重要价值|重要发展')]

if not high_value_customers.empty:
    hv_table = TableWidget()
    hv_table.set_headers(['客户ID', 'R分值', 'F分值', 'M分值', '客户级别', '购买金额'])

    # 显示前10个高价值客户
    for _, customer in high_value_customers.head(10).iterrows():
        hv_table.add_row([
            customer['客户ID'],
            str(customer['R分值']),
            str(customer['F分值']),
            str(customer['M分值']),
            customer['客户分级'],
            f"¥{customer['购买金额']:,}"
        ])

    hv_table.set_striped(True)
    email.add_widget(hv_table)

# 营销策略建议
email.add_title("💡 营销策略建议", TextType.SECTION_H2)

# 统计各类客户比例
important_customers_pct = (segment_stats.filter(regex='重要').sum() / total_customers * 100)
high_frequency_pct = (len(df_rfm[df_rfm['购买频次'] > avg_frequency]) / total_customers * 100)

strategy_recommendations = f"""
**基于RFM分析的营销策略建议：**

🎯 **重要客户维护** ({important_customers_pct:.1f}%的客户)
• 重要价值客户：提供VIP服务，个性化推荐
• 重要发展客户：增加触达频率，提升购买频次
• 重要保持客户：定期关怀，防止流失
• 重要挽留客户：紧急挽回策略，特别优惠

📈 **一般客户提升**
• 一般价值客户：交叉销售，提升客单价
• 一般发展客户：培养忠诚度，增加购买频次
• 一般保持客户：保持现状，适度营销
• 一般挽留客户：流失预警，挽回措施

�� **重点关注指标**
• 高频购买客户占比：{high_frequency_pct:.1f}%
• 平均客户生命周期：{avg_recency:.0f}天
• 客户价值提升潜力：关注F分值和M分值较低的客户

💰 **投入产出优化**
• 80%的营销资源投入到重要客户
• 20%的资源用于一般客户的价值提升
• 定期复评RFM模型，优化客户分级标准
"""

email.add_text(strategy_recommendations.strip())

email.export_html("rfm_customer_analysis.html")
print("✅ RFM客户分析报告已生成：rfm_customer_analysis.html")
RFM客户价值分析报告

Analysis Value: - Scientific RFM customer value analysis model - Automated customer segmentation and strategy recommendations - Visual display of customer distribution - Data support for precision marketing

Chart Integration

Example Chart Integration

This section can include an example of chart integration with code and explanations.

Learning Summary

Through these data report examples, you have mastered:

🎯 Core Skills

  • pandas Integration - Seamless DataFrame to table conversion
  • matplotlib Integration - Automatic chart generation and embedding
  • Data Calculation - Automated business metric calculations
  • Formatted Display - Professional data formatting

📊 Report Types

  • Sales Analysis - Trend analysis and growth calculations
  • Financial Reports - Income statements and budget analysis
  • Product Analysis - Multi-dimensional product evaluation
  • Customer Analysis - RFM value model application

💡 Best Practices

  • Data-driven insight generation
  • Combination of visualization and text explanation
  • Automated metric calculation and anomaly alerts
  • Data-based strategy recommendations

🚀 Advanced Directions

Continue exploring more advanced features to create professional data analysis reports!