点颜色的散点图表示seaborn FacetGrid中的连续变量

Question

我试图在python中使用seaborn生成多面板图,我希望我的多面板图中的点的颜色由连续变量指定.以下是我尝试使用"iris"数据集的示例:

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
iris = sns.load_dataset('iris')

g = sns.FacetGrid(iris, col = 'species', hue = 'petal_length', palette = 'seismic')
g = g.map(plt.scatter, 'sepal_length', 'sepal_width', s = 100, alpha = 0.5)
g.add_legend()

这使得下图:

这很好,但传说太长了.我想像这些值的1/4一样(理想情况下)或禁止显示颜色条.例如,这样的事情可能是可以接受的,但我仍然想把它分成三个物种.

plt.scatter(iris.sepal_length, iris.sepal_width, alpha = .8, c = iris.petal_length, cmap = 'seismic')
cbar = plt.colorbar()

关于如何充分利用这两个情节的任何想法？

编辑:这个主题似乎是一个好的开始.

https://github.com/mwaskom/seaborn/issues/582

不知何故,对于这个用户,只需在其他所有运行之后附加plt.colorbar似乎以某种方式工作.但在这种情况下似乎没有帮助.

Answer 1

这FacetGrid hue是绝对的,而不是连续的.这需要一些工作来获得散点图的连续色图FacetGrid(与imshow链接的Github问题不同,matplotlib不会保留对"当前活动的散点图映射器"的引用,因此魔术调用plt.colorbar不会选取应用于点颜色的映射.

g = sns.FacetGrid(iris, col='species', palette = 'seismic')

def facet_scatter(x, y, c, **kwargs):
    """Draw scatterplot with point colors from a faceted DataFrame columns."""
    kwargs.pop("color")
    plt.scatter(x, y, c=c, **kwargs)

vmin, vmax = 0, 7
cmap = sns.diverging_palette(240, 10, l=65, center="dark", as_cmap=True)

g = g.map(facet_scatter, 'sepal_length', 'sepal_width', "petal_length",
          s=100, alpha=0.5, vmin=vmin, vmax=vmax, cmap=cmap)

# Make space for the colorbar
g.fig.subplots_adjust(right=.92)

# Define a new Axes where the colorbar will go
cax = g.fig.add_axes([.94, .25, .02, .6])

# Get a mappable object with the same colormap as the data
points = plt.scatter([], [], c=[], vmin=vmin, vmax=vmax, cmap=cmap)

# Draw the colorbar
g.fig.colorbar(points, cax=cax)

Answer 2

由于您是在询问散点图的图例，因此可以采用@mwaskom 的解决方案来生成具有散点图的图例，如下所示：

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
iris = sns.load_dataset('iris')

g = sns.FacetGrid(iris, col='species', palette = 'seismic')

def facet_scatter(x, y, c, **kwargs):
    kwargs.pop("color")
    plt.scatter(x, y, c=c, **kwargs)

vmin, vmax = 0, 7
cmap = plt.cm.viridis

norm=plt.Normalize(vmin=vmin, vmax=vmax)

g = g.map(facet_scatter, 'sepal_length', 'sepal_width', "petal_length",
          s=100, alpha=0.5, norm=norm, cmap=cmap)

# Make space for the colorbar
g.fig.subplots_adjust(right=.9)

lp = lambda i: plt.plot([], color=cmap(norm(i)), marker="o", ls="", ms=10, alpha=0.5)[0]
labels = np.arange(0,7.5,0.5)
h = [lp(i) for i in labels]
g.fig.legend(handles=h, labels=labels, fontsize=9)

plt.show()