How to Plot Multiple Seaborn Jointplot in Subplot

How to plot multiple Seaborn Jointplot with annotation in Subplot

Building on top of this answer you should succeed by combining a custom SeabornFig2Grid class and matplotlib GridSpec. Here's your toy example:

first define the 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())

then draw your plots in the same grid

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()

The results is as follows:
Sample Image

Is that what you were looking for?

Seaborn: Subplot of jointplots doesn't work

From your question, I was able to first create each graph with a joint plot. I did a lot of research around turning that into a subplot, and found an inspiring answer here, which I applied. It brilliantly solved your problem. Thank you! @ImportanceOfBeingErnest

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

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())
        
sns.set(style="darkgrid")
iris = sns.load_dataset("iris")

g0 = sns.JointGrid(x="sepal_width", y="sepal_length", data=iris)
g0.plot_joint(sns.scatterplot, sizes=(10, 200), size=iris['petal_length'], legend='brief')
g0.plot_marginals(sns.histplot, kde=True, color='k')

g1 = sns.JointGrid(x="sepal_width", y="sepal_length", data=iris)
g1.plot_joint(sns.scatterplot, sizes=(10, 200), size=iris['petal_width'], legend='brief')
g1.plot_marginals(sns.histplot, kde=True, color='k')

fig = plt.figure(figsize=(13,8))
gs = gridspec.GridSpec(1, 2)

mg0 = SeabornFig2Grid(g0, fig, gs[0])
mg1 = SeabornFig2Grid(g1, fig, gs[1])

gs.tight_layout(fig)

plt.show()

Sample Image

Plotting 2 seaborn KDE joint plots in the same row

jointplot is a figure-level function and can't coexist in a figure with other plots, but there is the kdeplot function which can draw a 2-d KDE onto a specific Axes. See this example.

Joint plot for groupby datas on seaborn

You can use groupby on your dataset column, then use sns.jointgrid(), and then finally add your scatter plot and KDE plot to the jointgrid.

Here is an example using a random seed generator with numpy. I made three "datasets" and random x,y values. See the Seaborn jointgrid documentation for ways to customize colors, etc.

### Build an example dataset
np.random.seed(seed=1)
ds = (np.arange(3)).tolist()*10
x = np.random.randint(100, size=(60)).tolist()
y = np.random.randint(20, size=(60)).tolist()
df = pd.DataFrame(data=zip(ds, x, y), columns=["ds", "x", "y"])

### The plots
for _ds, group in df.groupby('ds'):
    group = group.copy()
    g = sns.JointGrid(data=group, x='x', y='y')
    g.plot(sns.scatterplot, sns.kdeplot)

Sample Image

How to plot multiple Seaborn Barplots in a subplot?

It's easier when you flatten the axes:

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

fig, axes = plt.subplots(2, 2)
axes = axes.flatten()

Population1980 = pd.DataFrame({'Age': ['0-4','5-9','10-14','15-19','20-24','25-29','30-34','35-39','40-44','45-49','50-54','55-59','60-64','65-69','70-74','75-79','80-84','85-89','90-94','95-99','100+'], 
                       'Male': [-49228000, -61283000, -64391000, -52437000, -42955000, -44667000, -31570000, -23887000, -22390000, -20971000, -17685000, -15450000, -13932000, -11020000, -7611000, -4653000, -1952000, -625000, -116000, -14000, -1000], 
                       'Female': [52367000, 64959000, 67161000, 55388000, 45448000, 47129000, 33436000, 26710000, 25627000, 23612000, 20075000, 16368000, 14220000, 10125000, 5984000, 3131000, 1151000, 312000, 49000, 4000, 0]})

Population2020 = pd.DataFrame({'Age': ['0-4','5-9','10-14','15-19','20-24','25-29','30-34','35-39','40-44','45-49','50-54','55-59','60-64','65-69','70-74','75-79','80-84','85-89','90-94','95-99','100+'],
                               'Male': [-39476000, -40415000, -38913000, -38239000, -40884000, -46466000, -62296000, -48746000, -46985000, -58664000, -61097000, -48782000, -38597000, -37623000, -23525000, -14337000, -9298000, -4739000, -1574000, -359000, -62000],
                               'Female': [44456000, 46320000, 45350000, 44103000, 46274000, 51523000, 66443000, 51346000, 49289000, 61173000, 62348000, 49958000, 38917000, 36527000, 21425000, 12207000, 6884000, 2843000, 731000, 116000, 13000]})

Population2050 = pd.DataFrame({'Age': ['0-4','5-9','10-14','15-19','20-24','25-29','30-34','35-39','40-44','45-49','50-54','55-59','60-64','65-69','70-74','75-79','80-84','85-89','90-94','95-99','100+'],
                               'Male': [-31222000, -32130000, -32532000, -33006000, -33639000, -35628000, -38650000, -39462000, -37812000, -37015000, -39486000, -44586000, -58817000, -44365000, -39900000, -43830000, -36255000, -19327000, -7942000, -2883000, -497000],
                               'Female': [33392000, 34351000, 34764000, 35250000, 36576000, 39416000, 43473000, 45150000, 43954000, 42485000, 44282000, 48656000, 61036000, 44548000, 38445000, 39264000, 28884000, 13627000, 4539000, 1207000, 123000]})

AgeClass = ['100+','95-99','90-94','85-89','80-84','75-79','70-74','65-69','60-64','55-59','50-54','45-49','40-44','35-39','30-34','25-29','20-24','15-19','10-14','5-9','0-4']
labels = ['80M', '60M', '40M', '20M', '0', '20M', '40M', '60M']

bar_plot = sns.barplot(x='Male', y='Age', data=Population1980, order=AgeClass, orient='h', ax=axes[0], palette='OrRd', lw=0)
bar_plot = sns.barplot(x='Female', y='Age', data=Population1980, order=AgeClass, orient='h', ax=axes[0], palette='PuBu', lw=0)
bar_plot.set_xticklabels(labels)
#bar_plot.set(xlabel="Population by sex", ylabel="Age-Group", title = "1980")

bar_plot = sns.barplot(x='Male', y='Age', data=Population2020, order=AgeClass, palette='OrRd', lw=0, ax=axes[1])
bar_plot = sns.barplot(x='Female', y='Age', data=Population2020, order=AgeClass, palette='PuBu', lw=0, ax=axes[1])
bar_plot.set_xticklabels(labels)
#bar_plot.set(xlabel="Population by sex", ylabel="Age-Group", title = "2020")

bar_plot = sns.barplot(x='Male', y='Age', data=Population2050, order=AgeClass, palette='OrRd', lw=0, ax=axes[2])
bar_plot = sns.barplot(x='Female', y='Age', data=Population2050, order=AgeClass, palette='PuBu', lw=0, ax=axes[2])
bar_plot.set_xticklabels(labels)
#bar_plot.set(xlabel="Population by sex", ylabel="Age-Group", title = "2050")

The reason to flatten is that axes is an array of 2 x 2, so you have to use 2 indexes to get the ax you want. This is easier with axes.flatten(), because converts the array from 2 x 2 to 1 x 4 dimension, so, you only need one index.