CODEWITHSUNDEEP
pythonseabornviolin-plot
Fuegon
Fuegon

Reputation: 121

python violin plot regular axis

I want to to a violin plot of binned data but at the same time be able to plot a model prediction and visualize how well the model describes the main part of the individual data distributions. My problem here is, I guess, that the x-axis after the violin plot does not behave like a regular axis with numbers, but more like string-values that just accidentally happen to be numbers. Maybe not a good description, but in the example I would like to have a "normal" plot a function, e.g. f(x) = 2*x**2, and at x=1, x=5.2, x=18.3 and x=27 I would like to have the violin in the background.

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

np.random.seed(10)
collectn_1 = np.random.normal(1, 2, 200)
collectn_2 = np.random.normal(802, 30, 200)
collectn_3 = np.random.normal(90, 20, 200)
collectn_4 = np.random.normal(70, 25, 200)

ys = [collectn_1, collectn_2, collectn_3, collectn_4]
xs = [1, 5.2, 18.3, 27]

sns.violinplot(x=xs, y=ys)

xx = np.arange(0, 30, 10)
plt.plot(xx, 2*xx**2)

plt.show()

Somehow this code actually does not plot violins but only bars, this is only a problem in this example and not in the original code though. In my real code I want to have different "half-violins" on both sides, therefore I use sns.violinplot(x="..", y="..", hue="..", data=.., split=True).

Upvotes: 2

Views: 1385

Answers (1)

Diziet Asahi
Diziet Asahi

Reputation: 40667

I think that would be hard to do with seaborn because it does not provide an easy way to manipulate the artists that it creates, particularly if there are other things plotted on the same Axes. Matplotlib's violinplot allows setting the position of the violins, but does not provide an option for plotting only half violins. Therefore, I would suggest using statsmodels.graphics.boxplots.violinplot, which does both.

from statsmodels.graphics.boxplots import violinplot

df = sns.load_dataset('tips')
x_col = 'day'
y_col = 'total_bill'
hue_col = 'smoker'

xs = [1, 5.2, 18.3, 27]
xx = np.arange(0, 30, 1)
yy = 0.1*xx**2
cs = ['C0','C1']

fig, ax = plt.subplots()

ax.plot(xx,yy)


for (_,gr0),side,c in zip(df.groupby(hue_col),['left','right'],cs):
    print(side)
    data = [gr1 for (_,gr1) in gr0.groupby(x_col)[y_col]]
    violinplot(ax=ax, data=data, positions=xs, side=side, show_boxplot=False, plot_opts=dict(violin_fc=c))

# violinplot above messes up which ticks are shown, the line below restores a sensible tick locator
ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator())

enter image description here

Upvotes: 2

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