Stacked density plots with pandas and seaborn

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 I want to use:

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

Notice that the frequency will need to be normalized (height of the histogram), as the number of data points and distributions differ significantly and the distributions will have different 'y scales'.

data.plot.hist();

This is the code of seaborn that generates the figure I used in the beginning.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})

# Create the data
rs = np.random.RandomState(1979)
x = rs.randn(150)
g = np.tile(list("ABC"), 50)
df = pd.DataFrame(dict(x=x, g=g))
m = df.g.map(ord)

# Initialize the FacetGrid object
pal = sns.cubehelix_palette(10, rot=-.25, light=.7)
g = sns.FacetGrid(df, row="g", hue="g", aspect=5, height=1, palette=pal)

# Draw the densities in a few steps
g.map(sns.kdeplot, "x", clip_on=False, shade=True, alpha=1, lw=1.5, bw=.2)
g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw=.2)
g.map(plt.axhline, y=0, lw=2, clip_on=False)


# Define and use a simple function to label the plot in axes coordinates
def label(x, color, label):
    ax = plt.gca()
    ax.text(0, .3, label, fontweight="bold", color=color,
            ha="left", va="center", transform=ax.transAxes)


g.map(label, "x")

# Set the subplots to overlap
g.fig.subplots_adjust(hspace=-.0025)

# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[])
g.despine(bottom=True, left=True)

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()