correlation_matrix = data.corr()sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')plt....
You can calculate the correlation matrix of any dataset using the corr method of pandas, which returns a DataFrame. If you pass it to the imshow function of Express, you get a heatmap: import plotly.express as px # Create heatmap using Plotly Express fig = px.imshow( diamonds.corr...
import plotly.express as px import numpy as np # 生成示例数据 np.random.seed(42) corr_matrix = np.random.rand(10, 10) # 创建热力图 fig = px.imshow(corr_matrix, labels=dict(x="X-axis", y="Y-axis", color="Correlation"), title='Heatmap with Annotations') # 显示图表 fig.show()...
importplotly.expressaspximportnumpyasnp # 生成示例数据 np.random.seed(42)corr_matrix=np.random.rand(10,10)# 创建热力图 fig=px.imshow(corr_matrix,labels=dict(x="X-axis",y="Y-axis",color="Correlation"),title='Heatmap with Annotations')# 显示图表 fig.show() 复制 labels参数允许我们自定义...
import plotly.express as px import numpy as np np.random.seed( 42 ) corr_matrix = np.random.rand( 10 , 10 ) fig = px.imshow(corr_matrix, labels= dict (x= "X-axis" , y= "Y-axis" , color= "Correlation" ), title= 'Heatmap with Annotations' ) ...
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')plt.title('Correlation Matrix')plt.show()4. 结果展示与洞见发现 - 通过可视化展示数据分析结果，清晰传达洞见。可简单统计摘要或复杂交互式可视化，例如使用 Plotly。import plotly.express as px fig = px.scatter(data, x='column1'...
ENlegend: { data: [ // "最高分", "最低分" { name: "最高分", t...
import plotly.express as px import numpy as np # 生成示例数据 np.random.seed(42) corr_matrix = np.random.rand(10, 10) # 创建热力图 fig = px.imshow(corr_matrix, labels=dict(x="X-axis", y="Y-axis", color="Correlation"), title='Heatmap with Annotations') ...
import plotly.express as px import numpy as np # 生成示例数据 np.random.seed(42) corr_matrix = np.random.rand(10, 10) # 创建热力图 fig = px.imshow(corr_matrix, labels=dict(x="X-axis", y="Y-axis", color="Correlation"), title='Heatmap with Annotations') ...
importplotly.expressaspximportnumpyasnp np.random.seed(42)corr_matrix = np.random.rand(10,10)fig = px.imshow(corr_matrix,labels= dict (x='X-axis', y='Y-axis', color='Correlation'),title='Heatmap with Annotations') fig.show() ...