如何嵌入由 seaborn lmplot 生成的插图？

Question

在 seaborn 中sns.lmplot返回FacetGrid对象。我想绘制一个插图。这是一个自包含的“工作”示例：from io import StringIOimport pandas as pd%matplotlib inlinedf_string='time\tsex\tage\tval1\tval2\n1\tM\t18\t0.285837375\t4.402793733\n2\tM\t18\t0.234239365\t2.987464305\n3\tM\t18\t0.820418465\t3.23991295\n4\tM\t18\t0.826027695\t9.707366329\n5\tM\t18\t0.625449525\t2.971235344\n6\tM\t18\t0.485980081\t5.517575471\n7\tM\t18\t0.136163546\t3.620177216\n8\tM\t18\t0.784944053\t5.116294718\n9\tM\t18\t0.981526403\t6.348155198\n10\tM\t18\t0.822237037\t4.682176522\n1\tF\t22\t0.104339381\t5.434133736\n2\tF\t22\t0.788797127\t0.843869877\n3\tF\t22\t0.997986894\t8.765048753\n4\tF\t22\t0.51167857\t2.054679646\n5\tF\t22\t0.328416139\t6.581617426\n6\tF\t22\t0.317804112\t1.584234393\n7\tF\t22\t0.489944956\t8.564257177\n8\tF\t22\t0.207348127\t1.346020575\n9\tF\t22\t0.727347344\t7.487993859\n10\tF\t22\t0.252917798\t8.822904862\n11\tF\t22\t0.690106636\t6.728470474\n12\tF\t22\t0.508078197\t2.489437246\n'df = pd.read_csv(StringIO(df_string), sep='\t')# running a moving averagedf_tmp = df.groupby(['sex', 'age']).rolling(min_periods=1, window=3, center=True).mean()df_tmp.plot()import seaborn as snsimport numpy as npimport matplotlib.pylab as pltfrom mpl_toolkits.axes_grid.inset_locator import inset_axes, mark_insetdf_to_plot = df_tmp.reset_index()g = sns.lmplot(x='time',y='val1',hue="sex",x_estimator=np.mean,height=10, aspect=1,                   data=df_to_plot, logx= True, legend_out=True, truncate=True)g.axes[0][0].xaxis.set_label_text('t [sec]')g.set(yscale="log")   ax = g.axes[0][0]axins = inset_axes(ax, "30%", "40%")g_inset = sns.lmplot(x='time',y='val1',hue="sex",x_estimator=np.mean, data=df_to_plot, legend_out=False)

Answer 1

由于FacetGrid产生自己的图形，lmplot不能在轴内使用。您将需要根据需要绘制尽可能多的regplots。

from io import StringIO

import pandas as pd

df_string="""time\tsex\tage\tval1\tval2\n1\tM\t18\t0.285837375\t4.402793733\n

2\tM\t18\t0.234239365\t2.987464305\n

3\tM\t18\t0.820418465\t3.23991295\n

4\tM\t18\t0.826027695\t9.707366329\n

5\tM\t18\t0.625449525\t2.971235344\n

6\tM\t18\t0.485980081\t5.517575471\n

7\tM\t18\t0.136163546\t3.620177216\n

8\tM\t18\t0.784944053\t5.116294718\n

9\tM\t18\t0.981526403\t6.348155198\n

10\tM\t18\t0.822237037\t4.682176522\n

1\tF\t22\t0.104339381\t5.434133736\n

2\tF\t22\t0.788797127\t0.843869877\n

3\tF\t22\t0.997986894\t8.765048753\n

4\tF\t22\t0.51167857\t2.054679646\n

5\tF\t22\t0.328416139\t6.581617426\n

6\tF\t22\t0.317804112\t1.584234393\n

7\tF\t22\t0.489944956\t8.564257177\n

8\tF\t22\t0.207348127\t1.346020575\n

9\tF\t22\t0.727347344\t7.487993859\n

10\tF\t22\t0.252917798\t8.822904862\n

11\tF\t22\t0.690106636\t6.728470474\n

12\tF\t22\t0.508078197\t2.489437246\n"""

df = pd.read_csv(StringIO(df_string), sep='\t')

import seaborn as sns

import numpy as np

import matplotlib.pylab as plt

from mpl_toolkits.axes_grid1.inset_locator import inset_axes

fig, ax = plt.subplots()

for (n, grp) in df.groupby("sex"):

    sns.regplot(x='time',y='val1', x_estimator=np.mean,

                   data=grp, logx= True, truncate=True)

ax.xaxis.set_label_text('t [sec]')

ax.set(yscale="log")   

axins = inset_axes(ax,  "30%", "40%" ,loc="lower right", borderpad=3)

for (n, grp) in df.groupby("sex"):

    sns.regplot(x='time',y='val1', x_estimator=np.mean,

                   data=grp, truncate=True, ax=axins)

plt.show()