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# 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。这个程序会生成一个包含多个子图的仪表板，展示不同类型的数据可视化。 ```python import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from scipy import stats # 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") # 创建示例数据 np.random.seed(42) # 创建时间序列数据 dates = pd.date_range('2023-01-01', periods=100, freq='D') time_series = np.cumsum(np.random.randn(100)) + 50 # 创建分类数据 categories = ['A', 'B', 'C', 'D', 'E'] category_values = np.random.randint(20, 100, size=len(categories)) # 创建散点图数据 x_scatter = np.random.randn(200) * 10 + 50 y_scatter = 0.8 * x_scatter + np.random.randn(200) * 5 + 10 # 创建分布数据 distribution_data = np.concatenate([ np.random.normal(0, 1, 500), np.random.normal(5, 1.5, 500) ]) # 创建DataFrame df = pd.DataFrame({ '日期': dates, '销售额': time_series, '类别': np.random.choice(categories, 100), '数量': np.random.randint(10, 100, 100) }) # 创建仪表板 fig = plt.figure(figsize=(16, 12)) fig.suptitle('数据可视化仪表板', fontsize=20, fontweight='bold') # 1. 时间序列图 ax1 = plt.subplot(3, 3, 1) ax1.plot(dates, time_series, color='steelblue', linewidth=2) ax1.set_title('时间序列趋势', fontsize=14, fontweight='bold') ax1.set_xlabel('日期') ax1.set_ylabel('销售额') ax1.tick_params(axis='x', rotation=45) ax1.grid(True, alpha=0.3) # 添加趋势线 z = np.polyfit(range(len(time_series)), time_series, 1) p = np.poly1d(z) ax1.plot(dates, p(range(len(time_series))), 'r--', alpha=0.7, label='趋势线') ax1.legend() # 2. 柱状图 ax2 = plt.subplot(3, 3, 2) bars = ax2.bar(categories, category_values, color=sns.color_palette("husl", len(categories))) ax2.set_title('分类数据比较', fontsize=14, fontweight='bold') ax2.set_xlabel('类别') ax2.set_ylabel('数值') # 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 + 1, f'{int(height)}', ha='center', va='bottom') # 3. 散点图 ax3 = plt.subplot(3, 3, 3) scatter = ax3.scatter(x_scatter, y_scatter, c=y_scatter, cmap='viridis', alpha=0.6, s=50) ax3.set_title('相关性分析', fontsize=14, fontweight='bold') ax3.set_xlabel('X变量') ax3.set_ylabel('Y变量') # 添加回归线 slope, intercept, r_value, p_value, std_err = stats.linregress(x_scatter, y_scatter) line_x = np.array([x_scatter.min(), x_scatter.max()]) line_y = slope * line_x + intercept ax3.plot(line_x, line_y, 'r-', linewidth=2, label=f'R² = {r_value**2:.3f}') ax3.legend() plt.colorbar(scatter, ax=ax3, label='Y值') # 4. 直方图与密度图 ax4 = plt.subplot(3, 3, 4) ax4.hist(distribution_data, bins=30, density=True, alpha=0.6, color='skyblue', edgecolor='black') ax4.set_title('数据分布', fontsize=14, fontweight='bold') ax4.set_xlabel('数值') ax4.set_ylabel('频率') # 添加核密度估计 kde_x = np.linspace(distribution_data.min(), distribution_data.max(), 1000) kde = stats.gaussian_kde(distribution_data) ax4.plot(kde_x, kde(kde_x), 'r-', linewidth=2, label='密度曲线') ax4.legend() # 5. 箱线图 ax5 = plt.subplot(3, 3, 5) box_data = [np.random.normal(i, 1, 100) for i in range(5)] bp = ax5.boxplot(box_data, labels=['组1', '组2', '组3', '组4', '组5'], patch_artist=True) ax5.set_title('数据分布比较', fontsize=14, fontweight='bold') ax5.set_xlabel('组别') ax5.set_ylabel('数值') # 设置箱线图颜色 colors = sns.color_palette("Set2", 5) for patch, color in zip(bp['boxes'], colors): patch.set_facecolor(color) # 6. 饼图 ax6 = plt.subplot(3, 3, 6) category_counts = df['类别'].value_counts() explode = [0.05] * len(category_counts) wedges, texts, autotexts = ax6.pie(category_counts.values, labels=category_counts.index, autopct='%1.1f%%', startangle=90, explode=explode, colors=sns.color_palette("pastel", len(category_counts))) ax6.set_title('类别占比', fontsize=14, fontweight='bold') # 7. 热力图 ax7 = plt.subplot(3, 3, 7) # 创建相关性矩阵 corr_data = pd.DataFrame({ '变量A': np.random.randn(100), '变量B': np.random.randn(100) * 0.5 + 0.8, '变量C': np.random.randn(100) * 0.3 + 0.5, '变量D': np.random.randn(100) * 0.7 - 0.2, '变量E': np.random.randn(100) * 0.9 + 0.3 }) corr_matrix = corr_data.corr() im = ax7.imshow(corr_matrix, cmap='coolwarm', vmin=-1, vmax=1) ax7.set_title('相关性热力图', fontsize=14, fontweight='bold') ax7.set_xticks(range(len(corr_matrix.columns))) ax7.set_yticks(range(len(corr_matrix.columns))) ax7.set_xticklabels(corr_matrix.columns, rotation=45) ax7.set_yticklabels(corr_matrix.columns) # 添加数值标签 for i in range(len(corr_matrix.columns)): for j in range(len(corr_matrix.columns)): text = ax7.text(j, i, f'{corr_matrix.iloc[i, j]:.2f}', ha="center", va="center", color="white" if abs(corr_matrix.iloc[i, j]) > 0.5 else "black") plt.colorbar(im, ax=ax7) # 8. 面积图 ax8 = plt.subplot(3, 3, 8) x_area = np.linspace(0, 10, 100) y1_area = np.sin(x_area) + 1.5 y2_area = np.cos(x_area) + 1.5 ax8.fill_between(x_area, y1_area, alpha=0.5, label='系列1', color='skyblue') ax8.fill_between(x_area, y2_area, alpha=0.5, label='系列2', color='lightcoral') ax8.plot(x_area, y1_area, 'b-', alpha=0.8, linewidth=1) ax8.plot(x_area, y2_area, 'r-', alpha=0.8, linewidth=1) ax8.set_title('面积图比较', fontsize=14, fontweight='bold') ax8.set_xlabel('X轴') ax8.set_ylabel('Y轴') ax8.legend() # 9. 小提琴图 ax9 = plt.subplot(3, 3,

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