从 Seaborn regplot 中提取均值和置信区间

Question

Given that regplot calculates means in intervals and bootstraps to find confidence intervals for each bin, it seems like a waste to have to recalculate them manually for further study, so:

Question: How do I access the calculated means and confidence intervals of a regplot?

Example: This code produces a nice plot of bin means with CIs:

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

# just some random numbers to get started
fig, ax = plt.subplots()
x = np.random.uniform(-2, 2, 1000)
y = np.random.normal(x**2, np.abs(x) + 1)

# Manual binning to retain control
binwidth=4./10
x_bins=np.arange(-2+binwidth/2,2,binwidth)
sns.regplot(x=x, y=y, x_bins=x_bins, fit_reg=None)
plt.show()

Result: Regplot showing binned data w. CIs

Not that calculating the means bin by bin isn't easily doable, but the CIs are calculated using random numbers. It would be nice to have the exact same numbers accessible as are plotted, so how do I access them? There must be some sort of get_*-method I'm overlooking.

Answer 1

Set-up

Setting up as in your MWE:

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

# Random numbers for plotting
x = np.random.uniform(-2, 2, 1000)
y = np.random.normal(x**2, np.abs(x) + 1)

# Manual binning to retain control
binwidth = 4 / 10
x_bins = np.arange(binwidth/2 - 2, 2, binwidth)
sns.regplot(x=x, y=y, x_bins=x_bins, fit_reg=None)

This gives our starting point as:

Extracting the Confidence Intervals

We can extract the confidence intervals by looping over the plotted lines and extracting the miniumum and maximum values (corresponding to the upper and lower CIs respectively):

ax = plt.gca()
lower = [line.get_ydata().min() for line in ax.lines]
upper = [line.get_ydata().max() for line in ax.lines]

As a sanity check we can plot these extracted points on top of our original data (shown here by red crosses):

plt.scatter(x_bins, lower, marker='x', color='C3', zorder=3)
plt.scatter(x_bins, upper, marker='x', color='C3', zorder=3)

Extracting the Means

The values of the means can be extracted from ax.collections as:

means = ax.collections[0].get_offsets()[:, 1]

Again, as a sanity check we can overlay our extracted values on the original plot:

plt.scatter(x_bins, means, color='C1', marker='x', zorder=3)