如何将系列特定线 y=x 添加到 Altair 中的多面（或类似）双轴图表？

Question

y=x关于如何在使用 Altair时对双轴图表进行分面，然后向每个图表添加一条线，有什么建议吗？挑战在于，该线y=x需要与每个多面图表中显示的数据特定的系列比例相匹配。

链接：

1. Altair github 关于 Facets 的问题线程

2. Altair github 轴显示上的问题线程

下面是重现该问题的代码。

import altair as alt

from vega_datasets import data

source = data.anscombe().copy()

source['line-label'] = 'x=y'

source = pd.concat([source,source.groupby('Series').agg(x_diff=('X','diff'), y_diff=('Y','diff'))],axis=1)

source['rate'] = source.y_diff/source.x_diff

source['rate-label'] = 'rate-of-change'

base = alt.Chart().encode(

    x='X:O',

)

scatter = base.mark_circle(size=60, opacity=0.30).encode(

    y='Y:Q',

    color=alt.Color('Series:O', scale=alt.Scale(scheme='category10')),

    tooltip=['Series','X','Y']

)

line_x_equals_y = alt.Chart().mark_line(color= 'black', strokeDash=[3,8]).encode(

    x=alt.X('max(X)',axis=None),

    y=alt.Y('max(X)',axis=None), # note: it's intentional to set max(X) here so that X and Y are equal.

    color = alt.Color('line-label') # note: the intent here is for the line label to show up in the legend

    )

rate = base.mark_line(strokeDash=[5,3]).encode(

    y=alt.Y('rate:Q'),

    color = alt.Color('rate-label',),

    tooltip=['rate','X','Y']

)

scatter_rate = alt.layer(scatter, rate, data=source)

尝试过的解决方案

问题：图表不是双轴（并且这不包括line_x_equals_y）

scatter_rate.facet('Series',columns=2).resolve_axis(
        x='independent',
        y='independent',
        )

问题：Javascript 错误

alt.layer(scatter_rate, line_x_equals_y, data=source).facet('Series',columns=2).resolve_axis(

        x='independent',

        y='independent',

        )

问题：Javascript 错误

chart_generator =  (alt.layer(line_x_equals_y, scatter_rate, data = source, title=f"Series {val}").transform_filter(alt.datum.Series == val).resolve_scale(y='independent',x='independent') \

             for val in source.Series.unique()) 

alt.concat(*(

    chart_generator

), columns=2)

目标

1. scatter_rate是一个多面（按系列）双轴图表，带有适合值范围的单独刻度。

2. y=x每个多面图表都包含一条从 (0,0) 到y=max(X)各个图表的值的线。

Answer 1

您可以通过正常创建图层并调用facet()图层图表上的方法来完成此操作。唯一的要求是所有层共享相同的源数据；无需手动构建facet，并且在当前版本的Altair中无需为facet进行后期数据绑定：import altair as altfrom vega_datasets import dataimport pandas as pdsource = data.anscombe().copy()source['line-label'] = 'x=y'source = pd.concat([source,source.groupby('Series').agg(x_diff=('X','diff'), y_diff=('Y','diff'))],axis=1)source['rate'] = source.y_diff/source.x_diffsource['rate-label'] = 'line y=x'source_linear = source.groupby(by=['Series']).agg(x_linear=('X','max'), y_linear=('X', 'max')).reset_index().sort_values(by=['Series'])source_origin = source_linear.copy()source_origin['y_linear'] = 0source_origin['x_linear'] = 0source_linear = pd.concat([source_origin,source_linear]).sort_values(by=['Series'])source = source.merge(source_linear,on='Series').drop_duplicates()scatter = alt.Chart(source).mark_circle(size=60, opacity=0.60).encode(    x='X:Q',    y='Y:Q',    color='Series:N',    tooltip=['X','Y','rate'])rate = alt.Chart(source).mark_line(strokeDash=[5,3]).encode(    x='X:Q',    y='rate:Q',    color = 'rate-label:N')line_plot = alt.Chart(source).mark_line(color= 'black', strokeDash=[3,8]).encode(    x=alt.X('x_linear', title = ''),    y=alt.Y('y_linear', title = ''),    shape = alt.Shape('rate-label', title = 'Break Even'),    color = alt.value('black'))alt.layer(scatter, rate, line_plot).facet(    'Series:N').properties(    columns=2).resolve_scale(    x='independent',    y='independent')

Answer 2

y=x该解决方案为每个图表上的数据按比例构建所需的线；但是，点在合并步骤中重复，我不确定如何添加双轴速率。获取数据source = data.anscombe().copy()source['line-label'] = 'x=y'source = pd.concat([source,source.groupby('Series').agg(x_diff=('X','diff'), y_diff=('Y','diff'))],axis=1)source['rate'] = source.y_diff/source.x_diffsource['rate-label'] = 'line y=x'创建Y=X线数据source_linear = source.groupby(by=['Series']).agg(x_linear=('X','max'), y_linear=('X', 'max')).reset_index().sort_values(by=['Series'])source_origin = source_linear.copy()source_origin['y_linear'] = 0source_origin['x_linear'] = 0source_linear = pd.concat([source_origin,source_linear]).sort_values(by=['Series'])合并线性数据source = source.merge(source_linear,on='Series').drop_duplicates()构建图表scatter = alt.Chart().mark_circle(size=60, opacity=0.60).encode(    x=alt.X('X', title='X'),    y=alt.Y('Y', title='Y'),    #color='year:N',    tooltip=['X','Y','rate'])line_plot = alt.Chart().mark_line(color= 'black', strokeDash=[3,8]).encode(    x=alt.X('x_linear', title = ''),    y=alt.Y('y_linear', title = ''),    shape = alt.Shape('rate-label', title = 'Break Even'),    color = alt.value('black'))手动分面图chart_generator =  (alt.layer(scatter, line_plot, data = source, title=f"{val}: Duplicated Points w/ Line at Y=X").transform_filter(alt.datum.Series == val) \             for val in source.Series.unique())组合图表chart = alt.concat(*(    chart_generator), columns=3)chart.display()

Answer 3

该解决方案包括速率，但不是Y一个轴和rate另一个轴上的双轴。import altair as altfrom vega_datasets import dataimport pandas as pdsource = data.anscombe().copy()source['line-label'] = 'x=y'source = pd.concat([source,source.groupby('Series').agg(x_diff=('X','diff'), y_diff=('Y','diff'))],axis=1)source['rate'] = source.y_diff/source.x_diffsource['rate-label'] = 'rate of change'source['line-label'] = 'line y=x'source_linear = source.groupby(by=['Series']).agg(x_linear=('X','max'), y_linear=('X', 'max')).reset_index().sort_values(by=['Series'])source_origin = source_linear.copy()source_origin['y_linear'] = 0source_origin['x_linear'] = 0source_linear = pd.concat([source_origin,source_linear]).sort_values(by=['Series'])source = source.merge(source_linear,on='Series').drop_duplicates()scatter = alt.Chart(source).mark_circle(size=60, opacity=0.60).encode(    x=alt.X('X', title='X'),    y=alt.Y('Y', title='Y'),    color='Series:N',    tooltip=['X','Y','rate'])line_plot = alt.Chart(source).mark_line(color= 'black', strokeDash=[3,8]).encode(    x=alt.X('x_linear', title = ''),    y=alt.Y('y_linear', title = ''),    shape = alt.Shape('line-label', title = 'Break Even'),    color = alt.value('black'))rate =  alt.Chart(source).mark_line(strokeDash=[5,3]).encode(    x=alt.X('X', axis=None, title = 'X'),    y=alt.Y('rate:Q'),    color = alt.Color('rate-label',),    tooltip=['rate','X','Y'])alt.layer(scatter, line_plot, rate).facet(    'Series:N').properties(    columns=2).resolve_scale(    x='independent',    y='independent').display()