简短的回答

在{"x": [x2, <regression x>], "y": [y1, <regression y>], ...}中"x"和"y"的列表中添加更多数据以显示趋势线.当然，您需要以某种方式计算这些值.下面的建议显示了一种完全可以做到这一点的方法.

详细内容

单击按钮选项后看不到趋势线的原因是没有趋势线的定义和/或数据，例如:

args=[{"x": [x2], "y": [y1], "trendline":["ols"], "trendline_scope":["overall"]},
                           {"title": "NFCC vs Misperception"}]

这并不是很明显，但您可以很容易地为单击按钮创建的布局中的更多轨迹添加数据，方法是在与"x"和"y"关联的列表中添加更多数据，如下所示:

args=[{"x": [x2, x21], "y": [y1, y11], "trendline":["ols"], "trendline_scope":["overall"]},
                           {"title": "NFCC vs Misperception"}]

How您 Select 创建的数据由您自己决定.但坚持到Plotly Express，您可以创建和检索OLS回归的数据，如下所示:

px.scatter(x=x2, y=y1, trendline='ols').data[1].x

以及:

px.scatter(x=x2, y=y1, trendline='ols').data[1].y

使用它，第一个按钮的args将如下所示:

args=[{"x": [x2, px.scatter(x=x2, y=y1, trendline='ols').data[1].x], "y": [y1, px.scatter(x=x2, y=y1, trendline='ols').data[1].y], "trendline":["ols"], "trendline_scope":["overall"]},
    {"title": "NFCC vs Misperception"}]

下面是您的前两个按钮的完整设置，其中包含一些随机数据:

图1:

插图2:

如果您愿意将您的数据101与df.to_dict()的输出共享，我可能会抽出时间来写一份完整的解决方案.

代码:

import plotly.graph_objects as go
import plotly.express as px
import numpy as np
import pandas as pd
# making a figure

df = pd.DataFrame({'crttotal': np.random.random(8),
                   'nfcc_mean': np.random.random(8),
                   'bficonmean': np.random.random(8),
                   'bfiopmean': np.random.random(8),
                   'avg_misperception_score': np.random.random(8),
                   'avg_ambiguous_score': np.random.random(8)})

fig = go.Figure()
x1 = df['crttotal']
x2 = df['nfcc_mean']
x3 = df['bficonmean']
x4 = df['bfiopmean']

y1 = df['avg_misperception_score']
y2 = df['avg_ambiguous_score']

fig = px.scatter(df, x=x2, y=y1, trendline="ols", trendline_scope="overall")
fig.show()
# making the dropdown

fig.update_layout(
    updatemenus=[
        go.layout.Updatemenu(
            type="dropdown",
            buttons=list([

                dict(label="NFCC vs Misperception",
                     method="update",
                     args=[{"x": [x2, px.scatter(x=x2, y=y1, trendline='ols').data[1].x], "y": [y1, px.scatter(x=x2, y=y1, trendline='ols').data[1].y], "trendline":["ols"], "trendline_scope":["overall"]},
                           {"title": "NFCC vs Misperception"}]),
                dict(label="CRT vs Misperception",
                     method="update",
                     args=[{"x": [x1, px.scatter(x=x1, y=y1, trendline='ols').data[1].x], "y": [y1, px.scatter(x=x1, y=y1, trendline='ols').data[1].y], "trendline":["ols"], "trendline_scope":["overall"]},
                           {"title": "CRT vs Misperception"}]),
                # dict(label="bficonmean vs Misperception",
                #      method="update",
                #      args=[{"x": [x3], "y": [y1], "trendline":["ols"], "trendline_scope":["overall"]},
                #            {"title": "bficonmean vs Misperception"}]),
                # dict(label="bfiopmean vs Misperception",
                #      method="update",
                #      args=[{"x": [x4], "y": [y1], "trendline":["ols"], "trendline_scope":["overall"]},
                #            {"title": "bfiopmean vs Misperception"}]),

                # dict(label="CRT vs Ambiguity",
                #      method="update",
                #      args=[{"x": [x1], "y": [y2], "trendline":["ols"], "trendline_scope":["overall"]},
                #            {"title": "CRT vs Ambiguity"}]),
                # dict(label="NFCC vs Ambiguity",
                #      method="update",
                #      args=[{"x": [x2], "y": [y2], "trendline":["ols"], "trendline_scope":["overall"]},
                #            {"title": "NFCC vs Ambiguity"}]),
                # dict(label="bficonmean vs Ambiguity",
                #      method="update",
                #      args=[{"x": [x3], "y": [y2], "trendline":["ols"], "trendline_scope":["overall"]},
                #            {"title": "bficonmean vs Ambiguity"}]),
                # dict(label="bfiopmean vs Ambiguity",
                #      method="update",
                #      args=[{"x": [x4], "y": [y2], "trendline":["ols"], "trendline_scope":["overall"]},
                #            {"title": "bfiopmean vs Ambiguity"}])

            ])
        )
    ]
)

# set the title
fig.update_layout(title="Dropdown")

# fig.show()