密度散点图(Density Scatter Plot),也称为密度点图或核密度估计散点图,是一种数据可视化技术,主要用于展示大量数据点在二维平面上的分布情况。与传统散点图相比,它使用颜色或阴影来表示数据点的密度,从而更直观地展示数据的分布情况。密度散点图能更好地揭示数据的集中趋势和分布模式,尤其是在数据量非常大时,避免...
plot:线型图 scatter:散点图 使用前: import matplotlib.pyplot as plt 1. 使用: plt.plot(x, y) plt.scatter(x, y) 1. 2. 除了带了个前缀,和matlab里绘图也没啥差别。 举例 a=1 b=0 x = torch.linspace(-1, 1, 100) y = a*x.pow(2)+b+0.1*torch.rand(x.size()) plt.scatter(x, y...
matlab% 三维轨迹动画figure;axis equal;grid on;view(3);for t = 1:100:length(time)plot3(x(1:t), y(1:t), z(1:t), 'b-', 'LineWidth',2);hold on;scatter3(x(t), y(t), z(t), 100, 'r', 'filled');hold off;axis([-1e6 1e6 -1e6 1e6 0 2e6]);drawnow;end 4.2 Py...
plt.title('Simple Scatter') ''' # 面向对象方式绘制 fig,ax = plt.subplots() ax.plot( [3,5,8,10,32,12,9,6,21,22,23,25,25], [5,4,2,12,44,10,2,8,21,22,23,24,25], 'o' #点类型为o ) ax.set_title('Simple Scatter') plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 9...
# pip install shapimportshap# load JS visualization code to notebookshap.initjs()# 用SHAP值解释模型的预测,相同的语法适用于LightGBM、CatBoost和scikit-learn模型explainer=shap.TreeExplainer(xgb)shap_values=explainer.shap_values(X_test)shap_values###shap_values1=np.array(shap_values).reshape(23,36)...
def scatterplot(x_data, y_data, x_label="", y_label="", title="", color = "r", yscale_log=False): # Create the plot object _, ax = plt.subplots() # Plot the data, set the size (s), color and transparency (alpha)
df['Sum_Vectorized']=df['A']+df['B']end_time=time.time()print(f"向量化运算耗时: {end_time - start_time:.4f} 秒")# 耗时显著减少 # 正确:使用apply(适用于更复杂但无直接向量化的操作,axis=1表示按行)# df['Custom_Result']=df.apply(lambda row:row['A']*2ifrow['B']>50000elserow[...
Scatter(x=df['Height'], y=residuals, mode='lines', line=dict(dash='dash'), name='误差线')) # 添加区间(可根据需要调整) ci_low = regression_result['CI[2.5%]'].values[0] ci_high = regression_result['CI[97.5%]'].values[0] fig.add_trace(go.Scatter(x=df['Height'], y=model_...
Python编程:从入门到实践 - matplotlib篇 - plot & scatter matplotlib篇 plot & scatter #filename.py 获取当前文件名方法importsys#当前文件名print(sys.argv[0])#去除后缀后的文件名print(sys.argv[0].split('.')[0]) #mpl_squares.py 简单的平方折线图importmatplotlib.pyplot as pltimportsys...
Use thescatter()method to draw a scatter plot diagram: importmatplotlib.pyplotasplt x =[5,7,8,7,2,17,2,9,4,11,12,9,6] y =[99,86,87,88,111,86,103,87,94,78,77,85,86] plt.scatter(x, y) plt.show() Result: Run example » ...