英文:
How do I prevent a second plot on an axis from rescaling a seaborn FacetGrid?
问题
我正在尝试在分类小提琴图上叠加一个分类条带图。我绘制了小提琴图，然后是条带图，但坐标轴重新缩放到了条带图。我想保留小提琴图的原始坐标轴。
以下是小提琴图的代码片段：
import pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsdata = pd.DataFrame.from_dict({'y_violin': list(range(16)),'y_strip': [8] * 16,'x': [1, 1, 0, 0] * 4,'col': [1, 1, 1, 1, 0, 0, 0, 0] * 2,'hue': [1] * 8 + [0] * 8,})g = sns.catplot(kind='violin', data=data,x='x', y='y_violin', col='col', hue='hue',inner='points', dodge=False,)plt.show()
这将生成以下图像：
小提琴图
然后，我尝试在每个坐标轴上绘制更多点：
for col, ax in g.axes_dict.items():sns.stripplot(data=data.loc[data.col == col],x='x', y='y_strip',jitter=False, legend=False, palette=['#00FF00'], ax=ax)plt.show()
新的点位于正确的位置，但图形已重新缩放。如何保持第一次绘图的原始缩放？
重新缩放
英文:
I am trying to overlay a categorical strip plot on top of a categorical violin plot. I plot the violin plot, and then the strip plot, but the axes are rescaled to the strip plot. I would like to keep the original axes from the violin plots.
Here is the code snippet for the violin plot:
import pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsdata = pd.DataFrame.from_dict({'y_violin': list(range(16)),'y_strip': [8] * 16,'x': [1, 1, 0, 0] * 4,'col': [1, 1, 1, 1, 0, 0, 0, 0] * 2,'hue': [1] * 8 + [0] * 8,})g = sns.catplot(kind = 'violin', data = data,x = 'x', y = 'y_violin', col = 'col', hue = 'hue',inner = 'points', dodge = False,)plt.show()
which produces the following image:
Violin plot
Then, I try to plot some more points on each axis:
for col, ax in g.axes_dict.items():sns.stripplot(data = data.loc[data.col == col],x = 'x', y = 'y_strip',jitter = False, legend = False, palette = ['#00FF00'], ax = ax)plt.show()
The new points are in the correct location, however the figure is rescaled. How do I keep the original scaling from the first plotting?
Rescaled
答案1
得分: 2
以下是翻译好的部分：
一种选项是将set_ylim设置为它们的原始值：
for col, ax in g.axes_dict.items():ylim = ax.get_ylim()sns.stripplot(data=data.loc[data.col == col],x='x', y='y_strip',jitter=False, legend=False, palette=['#00FF00'], ax=ax)ax.set_ylim(ylim)
输出：
另外，最好使用.map_dataframe而不是使用循环：
g = sns.catplot(kind='violin', data=data, x='x', y='y_violin', col='col', hue='hue', inner='points', dodge=False)ylim = g.axes.flat[0].get_ylim()g.map_dataframe(sns.stripplot, x='x', y='y_strip', jitter=False, legend=False, color='#00FF00')g.set(ylim=ylim)
英文:
One option is to set_ylim to their original values:
for col, ax in g.axes_dict.items():ylim = ax.get_ylim()sns.stripplot(data = data.loc[data.col == col],x = 'x', y = 'y_strip',jitter = False, legend = False, palette = ['#00FF00'], ax = ax)ax.set_ylim(ylim)
Output:
As an aside, it's better to use .map_dataframe than to use a loop.
g = sns.catplot(kind='violin', data=data, x='x', y='y_violin', col='col', hue='hue', inner='points', dodge=False)ylim = g.axes.flat[0].get_ylim()g.map_dataframe(sns.stripplot, x='x', y='y_strip', jitter=False, legend=False, color='#00FF00')g.set(ylim=ylim)
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