如何将 seaborn.distplot() 中的 yticks 从标准化值更改为绝对值？

Question

I am trying to create a gaussian curve (without the bar charts) using the seaborn.displot() method. Unfortunately, I get normalised values on the y-axis instead of the absolute values. How can I resolve this issue?

Here's my code:

height_mu = 165
height_sigma = 15
heights = np.random.normal(height_mu, height_sigma, size=10000)

plt.figure(figsize=(20, 5))
sns.distplot(heights, hist=False)
plt.axvline(165, color='red', label='Mean height (in cm)', linewidth=2)
plt.ylabel("Number of observations")
plt.legend()
plt.grid(which='major', axis='y', color='lightgrey')
plt.show()

Answer 1

There's no option inside seaborn to revert to counts, because once kde is turned on, the norm_hist option is False . Strictly speaking, when a gaussian kernel is applied, you get the density whose values depends on the binwidth and it can be >1 .

To get something similar to counts, you need to first define the bin width (sns.displot does it for you) and use gaussian_kde to perform the density. The values are density and you convert by multiplying the density values by binwidth and number of observations, eg counts_i = n * dens_i * binwidth

As noted by @mwaskom(see comments), may not be the best to show just the kde plot with y-axis as counts.

We can check this:

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

np.random.seed(999)
height_mu = 165
height_sigma = 15
heights = np.random.normal(height_mu, height_sigma, size=10000)
nbins = 50

fig,ax = plt.subplots(1,3,figsize=(10, 4))
sns.distplot(heights, hist=True,norm_hist=False,kde=False,bins=nbins,ax=ax[0])
sns.distplot(heights, hist=False,bins=nbins,ax=ax[1])
ax[1].axvline(165, color='red', label='Mean height (in cm)', linewidth=2)

from scipy.stats import gaussian_kde
dens = gaussian_kde(heights)
xlen,step = np.linspace(heights.min(),heights.max(),num=nbins,retstep=True)
ax[2].plot(xlen,len(heights)*dens(xlen)*step)
ax[2].axvline(165, color='red', label='Mean height (in cm)', linewidth=2)

fig.tight_layout()

The first plot on the left, you have the histogram with counts, 2nd plot the density plot you have, and on the right, the density with the "counts".