Seaborn plot_marginals several kdeplots

Question

I would like to be able to display several superimposed kde-graphs along the edge of the y-axis (this does not require a graph of the x-axis field). Each kde graph will correspond to a color category (there are 4), so I will have 4 kde each, depicting the distribution of one of the categories. This, as I understand it:

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


%matplotlib inline
%config InlineBackend.figure_format = 'svg'



x = [106405611, 107148674, 107151119, 107159869, 107183396, 107229405, 107231917, 107236097,
 107239994, 107259338, 107273842, 107275873, 107281000, 107287770, 106452671, 106471246, 
 106478110, 106494135, 106518400, 106539079]


y = np.array([  9.09803208,   5.357552  ,   8.98868469,   6.84549005,
         8.17990909,  10.60640521,   9.89935692,   9.24079133,
         8.97441459,   9.09803208,  10.63753055,  11.82336724,
         7.93663794,   8.74819285,   8.07146236,   9.82336724,
         8.4429435 ,  10.53332973,   8.23361968,  10.30035256])


x1 = pd.Series(x, name="$V$")
x2 = pd.Series(y, name="$Distance$")  

col = np.array([2, 4, 4, 1, 3, 4, 3, 3, 4, 1, 4, 3, 2, 4, 1, 1, 2, 2, 3, 1])

g = sns.JointGrid(x1, x2)
g = g.plot_joint(plt.scatter, color=col, edgecolor="black", cmap=plt.cm.get_cmap('RdBu', 11))
cax = g.fig.add_axes([1, .25, .02, .4])
plt.colorbar(cax=cax, ticks=np.linspace(1,11,11))
g.plot_marginals(sns.kdeplot, color="black", shade=True)

Answer 1

To build the distribution of each category, I think the best way is to first merge the data into a framework pandas. You can then scroll through each unique category by filtering the data frame and plotting it using calls sns.kdeplot.

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


x = np.array([106405611, 107148674, 107151119, 107159869, 107183396, 107229405,
              107231917, 107236097, 107239994, 107259338, 107273842, 107275873,
              107281000, 107287770, 106452671, 106471246, 106478110, 106494135,
              106518400, 106539079])

y = np.array([9.09803208,   5.357552  ,   8.98868469,   6.84549005,
              8.17990909,  10.60640521,   9.89935692,   9.24079133,
              8.97441459,   9.09803208,  10.63753055,  11.82336724,
              7.93663794,   8.74819285,   8.07146236,   9.82336724,
              8.4429435 ,  10.53332973,   8.23361968,  10.30035256])

col = np.array([2, 4, 4, 1, 3, 4, 3, 3, 4, 1, 4, 3, 2, 4, 1, 1, 2, 2, 3, 1])

# Combine data into DataFrame
df = pd.DataFrame({'V': x, 'Distance': y, 'col': col})

# Define colormap and create corresponding color palette
cmap = sns.diverging_palette(20, 220, as_cmap=True)
colors = sns.diverging_palette(20, 220, n=4)

# Plot data onto seaborn JointGrid
g = sns.JointGrid('V', 'Distance', data=df, ratio=2)
g = g.plot_joint(plt.scatter, c=df['col'], edgecolor="black", cmap=cmap)

# Loop through unique categories and plot individual kdes
for c in df['col'].unique():
    sns.kdeplot(df['Distance'][df['col']==c], ax=g.ax_marg_y, vertical=True,
                color=colors[c-1], shade=True)
    sns.kdeplot(df['V'][df['col']==c], ax=g.ax_marg_x, vertical=False,
                color=colors[c-1], shade=True)

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g.ax_marg_x.legend_.remove()
g.ax_marg_y.legend_.remove()