Python/Plotly:如何使用要显示的信息自定义悬停模板?

Python/Plotly: How to customize hover-template on with what information to show?(Python/Plotly:如何使用要显示的信息自定义悬停模板?)

问题描述

这是我的数据集:

在按年锁定我的数据框并按月分组后，我继续计算百分比增加/减少作为新列；它最终看起来像这样:

After locking my dataframe by year and grouping by month, I proceed with calculating percentage increase/decrease as a new column; it ends up looking like this:

现在对于我的 Plotly 情节，我使用它来显示轨迹并添加一些悬停信息:

Now for my Plotly plot I use this to display traces and add some hover info:

fig.add_trace(go.Scatter(x=group_dfff.Months, y=group_dfff.Amount, name=i,
                        hovertemplate='Price: $%{y:.2f}'+'<br>Week: %{x}'))

现在你可以看到有一个参数 hovertemplate 我可以在其中传递我的 x 和 y...但是，我不知道如何包含我的 PERC_CHANGE 也包含其中的价值.

Now as you can see there is an argument hovertemplate where I can pass my x and y... However, I can't figure out how to include my PERC_CHANGE values in it too.

问题:如何在 hovertemplate 中包含其他想要的列的值?具体来说，我如何包含 PERC_CHANGE 值，因为我在下面显示了所需的输出:

Question: How to include other wanted columns' values inside the hovertemplate? Specifically, How do I include PERC_CHANGE values as I shown desired output below:

我解决了我的具体问题，请查看下面的图片(添加第 3 个元素，请参阅评论)，但是问题仍然存在，因为我看不到如何为第 4 个、第 5 个等元素执行此操作.

I solved my specific problem, check pic below (adding 3rd element it is, please see comments), however question remains the same as I do not see how to do this for 4th, 5th and so on elements.

非常感谢您的帮助！

推荐答案

对于 Plotly Express，您需要在创建图形时使用 custom_data 参数.例如:

For Plotly Express, you need to use the custom_data argument when you create the figure. For example:

fig = px.scatter(
    data_frame=df, 
    x='ColX', 
    y='ColY', 
    custom_data=['Col1', 'Col2', 'Col3']
)

然后使用 update_traces 和 hovertemplate 对其进行修改，将其引用为 customdata.例如:

and then modify it using update_traces and hovertemplate, referencing it as customdata. For example:

fig.update_traces(
    hovertemplate="<br>".join([
        "ColX: %{x}",
        "ColY: %{y}",
        "Col1: %{customdata[0]}",
        "Col2: %{customdata[1]}",
        "Col3: %{customdata[2]}",
    ])
)

这需要大量的试验和错误才能弄清楚，因为它没有很好的记录，并且 custom_data 和 customdata 之间的不一致令人困惑.

This took a lot of trial and error to figure out, as it isn't well-documented, and the inconsistency between the custom_data and customdata is confusing.

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