[PYTHON] Add cumulative ratio to maplotlib histogram 2 ~ Use FacetGrid ~

I wrote here how to superimpose the cumulative ratio line on pyplot.hist (). This time, I will overlay the cumulative ratio on multiple histograms drawn using seaborn.FacetGrid (). Below is the completed plot.

What you do is the same as here, but if you draw a histogram using seaborn.FacetGrid () or seaborn.distplot () , pyplot.hist () does not explicitly receive bin and frequency information as a return value. Therefore, the bin and frequency information is extracted from the ʻaxobject. Specifically, it retrieves the required information from thepatches object contained in ʻax. The basic code is below

"""Overlay the cumulative ratio line on the FacetGrid histogram"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# % matplotlib inline
sns.set(style="darkgrid", palette="muted", color_codes=True)

#Toy data generation
np.random.seed(0)

size = 200
x1 = np.random.normal(size=size)
group = np.random.choice((0, 1), size=size)
df = pd.DataFrame({'x1': x1, 'group': group})

# seaborn.FacetGrid()Generate multiple histograms with
g = sns.FacetGrid(data=df, col='group', hue='group', size=5)
g = g.map(plt.hist, 'x1', alpha=0.7, label='Frequency')


#Draw a cumulative line for all ax contained in FacetGrid
for ax in g.axes.ravel():

    #Extract patches
    patches = ax.patches

    #Calculate the value of the 2nd axis (x)
    bins_part = [patch.get_xy()[0] + patch.get_width() for patch in patches]
    bins = [patches[0].get_xy()[0]] + bins_part
    x2 = np.convolve(bins, np.ones(2) / 2, mode="same")[1:]

    #Calculate the value of the 2nd axis (y:Accumulation)
    n = [patch.get_height() for patch in patches]
    y2 = np.add.accumulate(n) / sum(n)

    #2nd axis plot
    ax2 = ax.twinx()
    ax2.plot(x2, y2, ls='--', marker='o', color='r',
                     label='Cumulative ratio')
    ax2.grid(visible=False)


plt.tight_layout()
plt.show()

The full code, including the legend, is in Gist. Please go to here.