Major Difference in 2D kernel Density Plots: Seaborn and R

Question

I am trying to plot data using the 2D kernel density plot of Seaborn's jointplot function (using statsmodels' KDEMultivariate function to calculate a data-driven bandwidth). I've plotted a 2D kernel density in R using the same data and the result looks very good (using the 'ks' package), while the Seaborn plot looks very very different.

I am using the same exact data and the same exact bandwidth for each (taking the bandwidth given by KDEMultivariant and passing that to the R method).

Here is the input.csv data used: https://app.box.com/s/ot7d36t44wrr85pusp5657pc1w2kf5hj

Below are the code used in each and output images from each.

Python / Seaborn:

import matplotlib.pyplot as plt
import statsmodels.api as sm
data = pd.read_csv("input.csv", dtype={'x': float, 'y': float}, skiprows=0)
bw_ml_x = sm.nonparametric.KDEMultivariate(data=data['x'], var_type='c', bw='cv_ml')
bw_ml_y = sm.nonparametric.KDEMultivariate(data=data['y'], var_type='c', bw='cv_ml')        

g = sns.jointplot(x='x', y='y', data=data, kind="kde", stat_func=None, bw=[bw_ml_x.bw, bw_ml_y.bw])

g.plot_joint(plt.scatter, c="w")
g.ax_joint.collections[0].set_alpha(0)

sns.plt.show()

Img for Seaborn plot:

The bandwidth given by bw_ml_x.bw and bw_ml_y.bw is placed in a 2 x 2 R matrix H, where H[1,1] = bw_ml_x.bw, H[2,2] = bw_ml.y.bw, and other values set to zero.

R:

library(ks)
fhat <- kde(x=as.data.frame(data[1], data[2]), H=H)
plot(fhat, display="filled.contour2", cont=seq(10,90,by=10))

Img for R plot:

Answer 1

Looking at your Seaborn/Python plot, many of the points cluster along the (0,n) region and the (1,1) region of your space, just as the KDE of the R plot shows. This indicates that Seaborn and R are looking at the same data; we simply need to reformulate the call to the kde in Seaborn in order to visualize the KDE gradients.

If you modify your Python call to match the documentation for Kernel Density Estimation in Seaborn you'll get a proper 2d-kdf out of Python:

import matplotlib.pyplot as plt
import statsmodels.api as sm
import pandas as pd
import seaborn as sns

data = pd.read_csv("input.csv", dtype={'x': float, 'y': float}, skiprows=0)
bw_ml_x = sm.nonparametric.KDEMultivariate(data=data['x'], var_type='c', bw='cv_ml')
bw_ml_y = sm.nonparametric.KDEMultivariate(data=data['y'], var_type='c', bw='cv_ml')        

g = sns.jointplot(x='x', y='y', data=data, kind="kde")

g.plot_joint(plt.scatter, c="w")
g.ax_joint.collections[0].set_alpha(0)

sns.plt.show()

This accords with the R plot (though the kernel estimators seem to be slightly different, which would account for the variation in gradients between the plots):