How to plot multiple Seaborn Jointplot in Subplot

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Question :

How to plot multiple Seaborn Jointplot in Subplot

I’m having problem placing Seaborn Jointplot inside a multicolumn subplot.

import pandas as pd
import seaborn as sns

df = pd.DataFrame({'C1': {'a': 1,'b': 15,'c': 9,'d': 7,'e': 2,'f': 2,'g': 6,'h': 5,'k': 5,'l': 8},
          'C2': {'a': 6,'b': 18,'c': 13,'d': 8,'e': 6,'f': 6,'g': 8,'h': 9,'k': 13,'l': 15}})

fig = plt.figure();   
ax1 = fig.add_subplot(121);  
ax2 = fig.add_subplot(122);

sns.jointplot("C1", "C2", data=df, kind='reg', ax=ax1)
sns.jointplot("C1", "C2", data=df, kind='kde', ax=ax2)

Notice how only a portion of the jointplot is placed inside the subplot and the rest left inside another two plot frames. What I’d want is to have both the distributions also inserted inside the subplots.

Can anyone help with this?

Asked By: Afloz

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Answer #1:

Moving axes in matplotlib is not as easy as it used to be in previous versions. The below is working with the current version of matplotlib.

As has been pointed out at several places (this question, also this issue) several of the seaborn commands create their own figure automatically. This is hardcoded into the seaborn code, so there is currently no way to produce such plots in existing figures. Those are PairGrid, FacetGrid, JointGrid, pairplot, jointplot and lmplot.

There is a seaborn fork available which would allow to supply a subplot grid to the respective classes such that the plot is created in a preexisting figure. To use this, you would need to copy the axisgrid.py from the fork to the seaborn folder. Note that this is currently restricted to be used with matplotlib 2.1 (possibly 2.0 as well).

An alternative could be to create a seaborn figure and copy the axes to another figure. The principle of this is shown in this answer and could be extended to Searborn plots. The implementation is a bit more complicated that I had initially expected. The following is a class SeabornFig2Grid that can be called with a seaborn grid instance (the return of any of the above commands), a matplotlib figure and a subplot_spec, which is a position of a gridspec grid.

Note: This is a proof of concept, it may work for most easy cases, but I would not recommend using it in production code.

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
import numpy as np

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())

The usage of this class would look like this:

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns; sns.set()
import SeabornFig2Grid as sfg


iris = sns.load_dataset("iris")
tips = sns.load_dataset("tips")

# An lmplot
g0 = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips, 
                palette=dict(Yes="g", No="m"))
# A PairGrid
g1 = sns.PairGrid(iris, hue="species")
g1.map(plt.scatter, s=5)
# A FacetGrid
g2 = sns.FacetGrid(tips, col="time",  hue="smoker")
g2.map(plt.scatter, "total_bill", "tip", edgecolor="w")
# A JointGrid
g3 = sns.jointplot("sepal_width", "petal_length", data=iris,
                   kind="kde", space=0, color="g")


fig = plt.figure(figsize=(13,8))
gs = gridspec.GridSpec(2, 2)

mg0 = sfg.SeabornFig2Grid(g0, fig, gs[0])
mg1 = sfg.SeabornFig2Grid(g1, fig, gs[1])
mg2 = sfg.SeabornFig2Grid(g2, fig, gs[3])
mg3 = sfg.SeabornFig2Grid(g3, fig, gs[2])

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

plt.show()

enter image description here

Note that there might be several drawbacks from copying axes and the above is not (yet) tested thoroughly.

Answer #2:

It can not be easily done without hacking. jointplot calls JointGrid method, which in turn creates a new figure object every time it is called.

Therefore, the hack is to make two jointplots (JG1 JG2), then make a new figure, then migrate the axes objects from JG1 JG2 to the new figure created.

Finally, we adjust the sizes and the positions of subplots in the new figure we just created.

JG1 = sns.jointplot("C1", "C2", data=df, kind='reg')
JG2 = sns.jointplot("C1", "C2", data=df, kind='kde')

#subplots migration
f = plt.figure()
for J in [JG1, JG2]:
    for A in J.fig.axes:
        f._axstack.add(f._make_key(A), A)

#subplots size adjustment
f.axes[0].set_position([0.05, 0.05, 0.4,  0.4])
f.axes[1].set_position([0.05, 0.45, 0.4,  0.05])
f.axes[2].set_position([0.45, 0.05, 0.05, 0.4])
f.axes[3].set_position([0.55, 0.05, 0.4,  0.4])
f.axes[4].set_position([0.55, 0.45, 0.4,  0.05])
f.axes[5].set_position([0.95, 0.05, 0.05, 0.4])

It is a hack because we are now using _axstack and _add_key private methods, which might and might not stay the same as they are now in matplotlib future versions.

enter image description here

Answered By: CT Zhu

Answer #3:

If you get into trouble despite the elegant solution of @ImportanceOfBeingErnest, you can still save seaborn plots to memory as images and use them to build your custom figure. Use other formats than ‘.png’ if you seek a higher resolution.

Here is the example is shown above using this nasty (but working) approach:

import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import seaborn as sns

# data
iris = sns.load_dataset("iris")
tips = sns.load_dataset("tips")

############### 1. CREATE PLOTS
# An lmplot
g0 = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips, 
                palette=dict(Yes="g", No="m"))
# A PairGrid
g1 = sns.PairGrid(iris, hue="species")
g1.map(plt.scatter, s=5)
# A FacetGrid
g2 = sns.FacetGrid(tips, col="time",  hue="smoker")
g2.map(plt.scatter, "total_bill", "tip", edgecolor="w")
# A JointGrid
g3 = sns.jointplot("sepal_width", "petal_length", data=iris,
                   kind="kde", space=0, color="g")

############### 2. SAVE PLOTS IN MEMORY TEMPORALLY
g0.savefig('g0.png')
plt.close(g0.fig)

g1.savefig('g1.png')
plt.close(g1.fig)

g2.savefig('g2.png')
plt.close(g2.fig)

g3.savefig('g3.png')
plt.close(g3.fig)

############### 3. CREATE YOUR SUBPLOTS FROM TEMPORAL IMAGES
f, axarr = plt.subplots(2, 2, figsize=(25, 16))

axarr[0,0].imshow(mpimg.imread('g0.png'))
axarr[0,1].imshow(mpimg.imread('g1.png'))
axarr[1,0].imshow(mpimg.imread('g3.png'))
axarr[1,1].imshow(mpimg.imread('g2.png'))

# turn off x and y axis
[ax.set_axis_off() for ax in axarr.ravel()]

plt.tight_layout()
plt.show()

The four subplots are shown together in the following image

Answered By: Pedro Herruzo

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