子图 seaborn 联合图用相关性注释

Question

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目标是为带有相关性的联合图注释创建子图。 但是，当plt.show()时，该图是单独显示的。

我可以知道如何解决这个问题吗？

import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
ncols=['ra','rb','a','b','c','d']

df=pd.DataFrame(np.random.rand(100,len(ncols)),columns=ncols)
nvars=['a','b','c','d']
rt=['a','b']
fig, axs = plt.subplots(len(rt),len(nvars))
for idx_rt, nrt in enumerate(rt):
  for idx_var, nvar  in enumerate(nvars):
    g=sns.jointplot(data=df, y=nrt, x=nvar,kind = 'reg',ax=axs[idx_rt,idx_var])
    r, p = stats.pearsonr(df[nrt], df[nvar])
    g.ax_joint.annotate(f'$\\rho = {r:.3f}, p = {p:.3f}$',
      xy=(0.1, 0.9), xycoords='axes fraction',
      ha='left', va='center',
      bbox={'boxstyle': 'round', 'fc': 'powderblue', 'ec': 'navy'})
    

plt.tight_layout()
plt.show()

更新：

尝试2：

f, axes = plt.subplots(3, 3, figsize=(9, 9), sharex=True, sharey=True)
for ax, in axes.flat:
    for idx_rt, nrt in enumerate(rt):
        for idx_var, nvar in enumerate(nvars):
            g = sns.jointplot(data=df, y=nrt, x=nvar, kind='reg', ax=ax)
            r, p = stats.pearsonr(df[nrt], df[nvar])
            g.ax_joint.annotate(f'$\\rho = {r:.3f}, p = {p:.3f}$',
                                xy=(0.1, 0.9), xycoords='axes fraction',
                                ha='left', va='center',
                                bbox={'boxstyle': 'round', 'fc': 'powderblue', 'ec': 'navy'})

生产

类型错误：无法解压不可迭代的 AxesSubplot object

Answer 1

在此答案的基础上，您应该通过组合自定义SeabornFig2Grid class 和 matplotlib GridSpec来取得成功。 这是您的玩具示例：

• 首先定义SeabornFig2Grid class

  class SeabornFig2Grid():

   def __init__(self, seaborngrid, fig, subplot_spec): self.fig = fig self.sg = seaborngrid self.subplot = subplot_spec if isinstance(self.sg, sns.axisgrid.FacetGrid) or \ isinstance(self.sg, sns.axisgrid.PairGrid): self._movegrid() elif isinstance(self.sg, sns.axisgrid.JointGrid): self._movejointgrid() self._finalize() def _movegrid(self): """ Move PairGrid or Facetgrid """ self._resize() n = self.sg.axes.shape[0] m = self.sg.axes.shape[1] self.subgrid = gridspec.GridSpecFromSubplotSpec(n,m, subplot_spec=self.subplot) for i in range(n): for j in range(m): self._moveaxes(self.sg.axes[i,j], self.subgrid[i,j]) def _movejointgrid(self): """ Move Jointgrid """ h= self.sg.ax_joint.get_position().height h2= self.sg.ax_marg_x.get_position().height r = int(np.round(h/h2)) self._resize() self.subgrid = gridspec.GridSpecFromSubplotSpec(r+1,r+1, subplot_spec=self.subplot) self._moveaxes(self.sg.ax_joint, self.subgrid[1:, :-1]) self._moveaxes(self.sg.ax_marg_x, self.subgrid[0, :-1]) self._moveaxes(self.sg.ax_marg_y, self.subgrid[1:, -1]) def _moveaxes(self, ax, gs): #https://stackoverflow.com/a/46906599/4124317 ax.remove() ax.figure=self.fig self.fig.axes.append(ax) self.fig.add_axes(ax) ax._subplotspec = gs ax.set_position(gs.get_position(self.fig)) ax.set_subplotspec(gs) def _finalize(self): plt.close(self.sg.fig) self.fig.canvas.mpl_connect("resize_event", self._resize) self.fig.canvas.draw() def _resize(self, evt=None): self.sg.fig.set_size_inches(self.fig.get_size_inches())

• 然后在同一个网格中绘制你的图

import itertools 
import matplotlib.gridspec as gridspec
# import seaborn as sns; sns.set()
import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
ncols=['ra','rb','a','b','c','d']

df=pd.DataFrame(np.random.rand(100,len(ncols)),columns=ncols)
nvars=['a','b','c','d']
rt=['a','b']

# A JointGrid
fig = plt.figure(figsize=(13,8))
gs = gridspec.GridSpec(len(rt),len(nvars))

for i, idxs in enumerate(itertools.product(rt, nvars)):
    nrt, nvar = idxs
    g=sns.jointplot(data=df, y=nrt, x=nvar,kind = 'reg')#, space=0,ax=axs[idx_rt,idx_var])
    r, p = stats.pearsonr(df[nrt], df[nvar])
    g.ax_joint.annotate(f'$\\rho = {r:.3f}, p = {p:.3f}$',
      xy=(0.1, 0.9), xycoords='axes fraction',
      ha='left', va='center',
      bbox={'boxstyle': 'round', 'fc': 'powderblue', 'ec': 'navy'})
    mg = SeabornFig2Grid(g, fig, gs[i])

gs.tight_layout(fig)
#gs.update(top=0.7)

plt.show()

结果如下：

那是你要找的吗？