sales@lix.by
г.Минск, ул. Тимирязева, д.67, пом. 428
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Hiksemi

# 使用Python实现简单的数据可视化下面是一个使用Python的matplotlib和seaborn库创建数据可视化的示例。这个例子展示了如何创建多种类型的图表，并包含了一些最佳实践。 ```python import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from datetime import datetime, timedelta # 设置中文字体和样式 plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS', 'DejaVu Sans'] plt.rcParams['axes.unicode_minus'] = False sns.set_style("whitegrid") sns.set_palette("husl") # 创建示例数据 np.random.seed(42) # 1. 时间序列数据 dates = [datetime(2023, 1, 1) + timedelta(days=i) for i in range(90)] sales = np.cumsum(np.random.randn(90) * 1000 + 5000) website_traffic = np.cumsum(np.random.randn(90) * 500 + 3000) # 2. 分类数据 categories = ['电子产品', '服装', '食品', '家居', '图书'] category_sales = np.random.randint(1000, 10000, size=len(categories)) category_profit = category_sales * np.random.uniform(0.1, 0.3, size=len(categories)) # 3. 相关性数据 customer_data = pd.DataFrame({ '年龄': np.random.randint(18, 70, 100), '年收入(万)': np.random.randint(10, 100, 100), '消费频率': np.random.randint(1, 30, 100), '平均消费额': np.random.randint(50, 500, 100), '满意度': np.random.randint(1, 10, 100) }) # 创建多子图布局 fig = plt.figure(figsize=(16, 12)) fig.suptitle('销售数据分析仪表板', fontsize=16, fontweight='bold') # 1. 时间序列图 - 销售额和网站流量 ax1 = plt.subplot(2, 3, 1) ax1.plot(dates, sales, 'b-', linewidth=2, label='销售额') ax1.set_xlabel('日期') ax1.set_ylabel('销售额 (元)', color='b') ax1.tick_params(axis='y', labelcolor='b') ax1.set_title('销售额趋势', fontweight='bold') ax1.legend(loc='upper left') # 添加第二个y轴 ax1b = ax1.twinx() ax1b.plot(dates, website_traffic, 'r--', linewidth=2, label='网站流量') ax1b.set_ylabel('网站流量', color='r') ax1b.tick_params(axis='y', labelcolor='r') ax1b.legend(loc='upper right') # 2. 柱状图 - 各品类销售额 ax2 = plt.subplot(2, 3, 2) bars = ax2.bar(categories, category_sales, color=sns.color_palette("husl", len(categories))) ax2.set_xlabel('产品类别') ax2.set_ylabel('销售额 (元)') ax2.set_title('各品类销售额', fontweight='bold') # 在柱子上添加数值标签 for bar in bars: height = bar.get_height() ax2.text(bar.get_x() + bar.get_width()/2., height + 50, f'{int(height):,}', ha='center', va='bottom', fontsize=9) # 3. 饼图 - 销售额占比 ax3 = plt.subplot(2, 3, 3) explode = (0.05, 0, 0, 0, 0) # 突出显示第一个部分 wedges, texts, autotexts = ax3.pie(category_sales, explode=explode, labels=categories, autopct='%1.1f%%', shadow=True, startangle=90) ax3.set_title('销售额品类占比', fontweight='bold') # 4. 散点图 - 年龄与消费关系 ax4 = plt.subplot(2, 3, 4) scatter = ax4.scatter(customer_data['年龄'], customer_data['平均消费额'], c=customer_data['满意度'], s=customer_data['消费频率']*5, alpha=0.6, cmap='viridis') ax4.set_xlabel('年龄') ax4.set_ylabel('平均消费额 (元)') ax4.set_title('年龄与消费关系', fontweight='bold') # 添加颜色条 cbar = plt.colorbar(scatter, ax=ax4) cbar.set_label('满意度 (1-10)') # 5. 箱线图 - 各年龄段消费分布 ax5 = plt.subplot(2, 3, 5) # 创建年龄分组 customer_data['年龄分组'] = pd.cut(customer_data['年龄'], bins=[18, 30, 40, 50, 70], labels=['18-30', '31-40', '41-50', '51-70']) sns.boxplot(x='年龄分组', y='平均消费额', data=customer_data, ax=ax5) ax5.set_xlabel('年龄分组') ax5.set_ylabel('平均消费额 (元)') ax5.set_title('各年龄段消费分布', fontweight='bold') # 6. 热力图 - 相关性矩阵 ax6 = plt.subplot(2, 3, 6) correlation_matrix = customer_data[['年龄', '年收入(万)', '消费频率', '平均消费额', '满意度']].corr() sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', square=True, ax=ax6, cbar_kws={'shrink': 0.8}) ax6.set_title('变量相关性热力图', fontweight='bold') # 调整布局 plt.tight_layout() plt.subplots_adjust(top=0.92) # 保存图表 plt.savefig('data_visualization_dashboard.png', dpi=300, bbox_inches='tight') # 显示图表 plt.show() # 创建单独的趋势分析图 fig2, (ax7, ax8) = plt.subplots(1, 2, figsize=(14, 5)) # 7. 移动平均线 window_size = 7 sales_series = pd.Series(sales, index=dates) moving_avg = sales_series.rolling(window=window_size).mean() ax7.plot(dates, sales, 'b-', alpha=0.5, label='日销售额') ax7.plot(dates, moving_avg, 'r-', linewidth=2, label=f'{window_size}日移动平均') ax7.fill_between(dates, sales, moving_avg, where=(sales > moving_avg), color='green', alpha=0.3, interpolate=True) ax7.fill_between(dates, sales, moving_avg, where=(sales <= moving_avg), color='red', alpha=0.3, interpolate=True) ax7.set_xlabel('日期') ax7.set_ylabel('销售额 (元)') ax7.set_title('销售额与移动平均线', fontweight='bold') ax7.legend() ax7.grid(True, alpha=0.3) # 8. 直方图 - 消费频率分布 ax8.hist(customer_data['消费频率'], bins=15, edgecolor='black', alpha=0.7) ax8.set_xlabel('消费频率 (次/月)') ax8.set_ylabel('客户数量') ax8.set_title('客户消费频率分布', fontweight='bold') ax8.axvline(customer_data['消费频率'].mean(), color='red', linestyle='--', linewidth=2, label=f'均值: {customer_data["消费频率"].mean():.1f}') ax8.legend() plt.tight_layout() plt.savefig('trend_analysis.png', dpi=300, bbox_inches='tight') plt.show() # 输出数据统计摘要 print("=" * 60) print("数据统计摘要") print("=" * 60) print(f"总销售额: {sales[-1]:,.2f} 元") print(f"平均日销售额: {sales.mean():,.2f} 元") print(f"最高日销售额: {sales.max():,.2f} 元") print(f"最低日销售额: {sales.min():,.2f} 元") print(f"销售额标准差: {sales.std():,.2f} 元") print("\n品类销售排名:") for i, (cat, sale) in enumerate(sorted(zip(categories, category_sales), key=lambda x: x[1], reverse=True), 1): print(f"{i}. {

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