有一种方法可以将散点图覆盖分组的盒子图上的散点图，因此它们不会使用Plotly Graph_Objects来抵消它们？

Question

可以通过退出框模式并使每个标签与众不同，从而获得所需的输出。这是因为盒子和旋风和散点图的X轴将相同。

import plotly.graph_objects as go

def create_multiple_boxplots(summary_stats_list, labels, types, title="Multiple Boxplots"):
    fig = go.Figure()
    
    color_map = {"Rainy": "blue", "Sunny": "green"}
    
    i=0
    for stats, label, type_ in zip(summary_stats_list, labels, types):
        fig.add_trace(go.Box(
            name=type_,
            q1=[stats['Q1']],
            median=[stats['Median']],
            q3=[stats['Q3']],
            lowerfence=[stats['Min']],
            upperfence=[stats['Max']],
            mean=[stats['Mean']],
            boxpoints='all' if 'Outliers' in stats else False,
            jitter=0.3,
            pointpos=-1.8,
            marker=dict(color=color_map[type_]),  # Assign color based on type
            legendgroup=type_,
            showlegend=True if i < 2 else False,
            x=[label],
            # y=stats.get('Outliers', [])
        ))
        # Add outlier points separately

        fig.add_trace(go.Scatter(
            x=[label] * len(stats['Outliers']),
            y=stats['Outliers'],
            mode='markers',
            marker=dict(color=color_map[type_], size=8, symbol='circle-open'),
            name=f"Outliers - {type_}",
            legendgroup=type_,
            showlegend=False
        ))
        i+=1
    
    fig.update_layout(title=title, yaxis_title="Value", boxmode='group')
    
    fig.show()

# Example summary statistics
data_summaries = [
    {"Min": 5, "Q1": 10, "Median": 15, "Q3": 20, "Max": 25, "Mean": 16, "Outliers": [2, 27]},
    {"Min": 6, "Q1": 11, "Median": 16, "Q3": 21, "Max": 26, "Mean": 17, "Outliers": [3, 28]},
    {"Min": 4, "Q1": 9, "Median": 14, "Q3": 19, "Max": 24, "Mean": 15, "Outliers": [1, 26]},
    {"Min": 7, "Q1": 12, "Median": 17, "Q3": 22, "Max": 27, "Mean": 18, "Outliers": [4, 29]}
]
labels = ["Happy", "Happy", "Sad", "Sad"]
types = ["Rainy", "Sunny", "Rainy", "Sunny"]

create_multiple_boxplots(data_summaries, labels, types)

Answer 1

要在仍处于盒子模式的情况下进行散点图，请添加一个偏移组，该组将绘制盒子和旋风图中心的散点图。

import plotly.graph_objects as go

def create_multiple_boxplots(summary_stats_list, labels, types, title="Multiple Boxplots"):
    fig = go.Figure()
    
    color_map = {"Rainy": "blue", "Sunny": "green"}
    
    i=0
    for stats, label, type_ in zip(summary_stats_list, labels, types):
        fig.add_trace(go.Box(
            name=type_,
            q1=[stats['Q1']],
            median=[stats['Median']],
            q3=[stats['Q3']],
            lowerfence=[stats['Min']],
            upperfence=[stats['Max']],
            mean=[stats['Mean']],
            boxpoints='all' if 'Outliers' in stats else False,
            jitter=0.3,
            pointpos=-1.8,
            marker=dict(color=color_map[type_]),  # Assign color based on type
            legendgroup=type_,
            showlegend=True if i < 2 else False,
            x=[label],
        ))
        fig.add_trace(go.Scatter(
            x=[label] * len(stats['Outliers']),
            y=stats['Outliers'],
            mode='markers',
            marker=dict(color=color_map[type_], size=8, symbol='circle-open'),
            name=f"Outliers - {type_}",
            legendgroup=type_,
            showlegend=False
        ))
        i+=1
    
    fig.update_layout(title=title, yaxis_title="Value")#, boxmode='group') # update
    
    fig.show()

# Example summary statistics
data_summaries = [
    {"Min": 5, "Q1": 10, "Median": 15, "Q3": 20, "Max": 25, "Mean": 16, "Outliers": [2, 27]},
    {"Min": 6, "Q1": 11, "Median": 16, "Q3": 21, "Max": 26, "Mean": 17, "Outliers": [3, 28]},
    {"Min": 4, "Q1": 9, "Median": 14, "Q3": 19, "Max": 24, "Mean": 15, "Outliers": [1, 26]},
    {"Min": 7, "Q1": 12, "Median": 17, "Q3": 22, "Max": 27, "Mean": 18, "Outliers": [4, 29]}
]
labels = ["Happy", "Happy_", "Sad", "Sad_"] # update
types = ["Rainy", "Sunny", "Rainy", "Sunny"]

create_multiple_boxplots(data_summaries, labels, types)