在Seaborn FacetGrid中绘制多个DataFrame列 [英] Plot multiple DataFrame columns in Seaborn FacetGrid

问题描述

我正在使用以下代码

import seaborn as sns

g = sns.FacetGrid(dataframe, col='A', hue='A')
g.map(plt.plot, 'X', 'Y1')
plt.show()

制作一个像这样的海洋刻面图:

to make a seaborn facet plot like this:

现在，我想在该图上添加另一行，并在y轴上添加一个不同的变量，称为Y2.结果应类似于垂直叠加通过

Now I would like to add another row to this plot with a different variable, call it Y2, on the y axis. The result should look similar to vertically stacking the two plots obtained by

g = sns.FacetGrid(dataframe, col='A', hue='A')
g.map(plt.plot, 'X', 'Y1')
plt.show()

g = sns.FacetGrid(dataframe, col='A', hue='A')
g.map(plt.plot, 'X', 'Y2')
plt.show()

，但在一个图中，没有重复的x轴和标题("A =< value>")，也没有创建新的FacetGrid对象.

but in a single plot, without the duplicate x axis and titles ("A=<value>") and without creating a new FacetGrid object.

请注意

g = sns.FacetGrid(dataframe, col='A', hue='A')
g.map(plt.plot, 'X', 'Y1')
g.map(plt.plot, 'X', 'Y2')
plt.show()

之所以无法实现，是因为对于每个A值，Y1和Y2的曲线都显示在同一子图中.

does not achive this, because it results in both the curve for Y1 and Y2 being displayed in the same subplot for each value of A.

推荐答案

我使用以下代码创建了一个看起来与您匹配的综合数据集:

I used the following code to create a synthetic dataset which appears to match yours:

import pandas
import numpy
import seaborn as sns
import matplotlib.pyplot as plt

# Generate synthetic data
omega = numpy.linspace(0, 50)

A0s = [1., 18., 40., 100.]

dfs = []
for A0 in A0s:
    V_w_dr = numpy.sin(A0*omega)
    V_w_tr = numpy.cos(A0*omega)
    dfs.append(pandas.DataFrame({'omega': omega,
                                 'V_w_dr': V_w_dr,
                                 'V_w_tr': V_w_tr,
                                 'A0': A0}))
dataframe = pandas.concat(dfs, axis=0)

然后，您可以做您想做的事(感谢)sharey='row', margin_titles=True的注释中的@mwaskom):

Then you can do what you want (thanks to @mwaskom in the comments for )sharey='row', margin_titles=True):

melted = dataframe.melt(id_vars=['A0', 'omega'], value_vars=['V_w_dr', 'V_w_tr'])
g = sns.FacetGrid(melted, col='A0', hue='A0', row='variable', sharey='row', margin_titles=True)
g.map(plt.plot, 'omega', 'value')

这导致

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