问题描述
关于如何制作双轴图表,然后使用altair在y=x
的每个图表上添加一条线的任何建议?挑战在于,y=x
行必须与特定于每个多面图表中显示的数据的系列比例相匹配。
链接:
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)',# 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','Y']
)
scatter_rate = alt.layer(scatter,rate,data=source)
尝试的解决方案
问题:图表不是双轴的(并且不包括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',)
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
-
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。 - 每个多面图表均包含一行
y=x
,其范围从单个图表的(0,0)到y=max(X)
值。
解决方法
您可以通过正常创建图层并在图层表上调用facet()
方法来做到这一点。唯一的要求是所有层共享相同的源数据。在当前版本的Altair中,无需手动构造构面,也不需要后期的数据绑定:
import altair as alt
from vega_datasets import data
import pandas as pd
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'] = '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'] = 0
source_origin['x_linear'] = 0
source_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',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'
)
,
此解决方案在y=x
处根据每个图表上的数据构建所需的线;但是,点在合并步骤中是重复的,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,axis=1)
source['rate'] = source.y_diff/source.x_diff
source['rate-label'] = 'line y=x'
创建Y = X行数据
source_linear = source.groupby(by=['Series']).agg(x_linear=('X',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','rate']
)
line_plot = alt.Chart().mark_line(color= 'black',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())
合并图表
chart = alt.concat(*(
chart_generator
),columns=3)
chart.display()
,
此解决方案包括费率,但不是双轴w / Y
和另一轴rate
。
import altair as alt
from vega_datasets import data
import pandas as pd
source = data.anscombe().copy()
source['line-label'] = 'x=y'
source = pd.concat([source,axis=1)
source['rate'] = source.y_diff/source.x_diff
source['rate-label'] = 'rate of change'
source['line-label'] = 'line y=x'
source_linear = source.groupby(by=['Series']).agg(x_linear=('X','rate']
)
line_plot = alt.Chart(source).mark_line(color= 'black',shape = alt.Shape('line-label',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,rate).facet(
'Series:N'
).properties(
columns=2
).resolve_scale(
x='independent',y='independent'
).display()