Stacked density plots with pandas and seaborn

Question

I am trying to obtain the following plot from a pandas data frame.

I am not sure how to combine seaborn with pandas for that task.

This is the dataframe

Answer 1

Here is a function to create a grid of kde plots ("joyplot") with one plot per dataframe column.

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde


def joyplot_from_dataframe(data, cmap=None):
    mi, ma = np.nanmin(data.values), np.nanmax(data.values)
    minx = mi - (ma-mi)/5
    maxx = ma + (ma-mi)/5
    x = np.linspace(minx,maxx, 1000)

    n = len(data.columns)

    if not cmap:
        cmap = plt.cm.get_cmap("Blues")
    colors = cmap(np.linspace(.2,1,n))

    fig, axes = plt.subplots(nrows = n, sharex=True)

    for c, ax, color in zip(data.columns, axes, colors):
        y = data[c].values
        y = y[~np.isnan(y)]
        kde = gaussian_kde(y)
        ax.fill_between(x, kde(x), color=color)
        ax.yaxis.set_visible(False)
        for spine in ["left", "right", "top"]:
            ax.spines[spine].set_visible(False)
        ax.spines["bottom"].set_linewidth(2)
        ax.spines["bottom"].set_color(color)
        ax.margins(y=0)
        ax.tick_params(bottom=False)

    return fig, axes

Use it as

import pandas as pd

data = pd.DataFrame({'a': np.random.randn(1000) + 1,
              'b': np.random.randn(1000),
              'c': np.random.rand(1000) + 10},        
             columns=['a', 'b', 'c'])

data.a[data.a.sample(100).index] = np.NaN
data.b[data.b.sample(800).index] = np.NaN


joyplot_from_dataframe(data)    
plt.show()