Plotting two distributions in seaborn.jointplot

Question

I have two pandas dataframes I would like to plot in the same seaborn jointplot. It looks something like this (commands are don in an IPython shell; ipython --pylab):

import pandas as pd
import seaborn as sns
iris = sns.load_dataset('iris')
df = pd.read_csv('my_dataset.csv')
g = sns.jointplot('sepal_length', 'sepal_width', iris)

The keys in the two dataframes are identical.
How do I plot my values in the same plot (different color of course)? And even more detailed: How do I plot both dataset, but only having the distribution of the first on at the top and side? I.e. only plot the dots.

Answer 1

A better solution, in my opinion, is to use the axes handles for the joint and marginal distributions that sns.joinplot returns. Using those (the names are ax_joint, ax_marg_x and ax_marg_y) is also possible to draw on the marginal distributions plots.

import seaborn as sns
import numpy as np

data1 = np.random.randn(100)
data2 = np.random.randn(100)
data3 = np.random.randn(100)
data4 = np.random.randn(100)

df1 = pd.DataFrame({'col1': data1, 'col2':data2})
df2 = pd.DataFrame({'col1': data3, 'col2':data4})

axs = sns.jointplot('col1', 'col2', data=df1)
axs.ax_joint.scatter('col1', 'col2', data=df2, c='r', marker='x')

# drawing pdf instead of histograms on the marginal axes
axs.ax_marg_x.cla()
axs.ax_marg_y.cla()
sns.distplot(df1.col1, ax=axs.ax_marg_x)
sns.distplot(df1.col2, ax=axs.ax_marg_y, vertical=True)

Answer 2

Here is one way to do it by modifying the underlying data of sns.JointGrid.

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

# simulate some artificial data
# ========================================
np.random.seed(0)
data1 = np.random.multivariate_normal([0,0], [[1,0.5],[0.5,1]], size=200)
data2 = np.random.multivariate_normal([0,0], [[1,-0.8],[-0.8,1]], size=100)

# both df1 and df2 have bivaraite normals, df1.size=200, df2.size=100
df1 = pd.DataFrame(data1, columns=['x1', 'y1'])
df2 = pd.DataFrame(data2, columns=['x2', 'y2'])


# plot
# ========================================   
graph = sns.jointplot(x=df1.x1, y=df1.y1, color='r')

graph.x = df2.x2
graph.y = df2.y2
graph.plot_joint(plt.scatter, marker='x', c='b', s=50)