How to draw a normal curve on seaborn displot

Question

distplot was deprecated in favour of displot.

The previous function had the option to draw a normal curve.

import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats

ax = sns.distplot(df.extracted, bins=40, kde=False, fit=stats.norm)

the fit=stats.norm doesn't work with displot anymore. In the answer to this question, I see the approach to plot the normal later, however it is done on some random data averaged around 0.

Fructification

Answer 1

• seaborn.displot is a figure-level plot where the kind parameter specifies the approach. When kind='hist' the parameters for seaborn.histplot are available.
  • For axes-level plots see How to add a standard normal pdf over a seaborn histogram
• seaborn.axisgrid.FacetGrid.map expects dataframe column names, as such, to map the pdf onto seaborn.displot, the data needs to be in a dataframe.
• An issue is that x_pdf is calculated for each axes:
  • x0, x1 = p1.axes[0][0].get_xlim()
  • If the axes are different for multiple Facets (sharex=False), then there's not a way to get xlim for each axes within .map.
• References:
  • seaborn histplot and displot output doesn't match
  • Building structured multi-plot grids
• Tested in python 3.8.11, pandas 1.3.2, matplotlib 3.4.2, seaborn 0.11.2

Single Facet

• .map can be used

import pandas as pd
import seaborn as sns
import numpy as np
import scipy

# data
np.random.seed(365)
x1 = np.random.normal(10, 3.4, size=1000)  # mean of 10
df = pd.DataFrame({'x1': x1})

# display(df.head(3))
          x1
0  10.570932
1  11.779918
2  12.779077

# function for mapping the pdf
def map_pdf(x, **kwargs):
    mu, std = scipy.stats.norm.fit(x)
    x0, x1 = p1.axes[0][0].get_xlim()  # axes for p1 is required to determine x_pdf
    x_pdf = np.linspace(x0, x1, 100)
    y_pdf = scipy.stats.norm.pdf(x_pdf, mu, std)
    plt.plot(x_pdf, y_pdf, c='r')


p1 = sns.displot(data=df, x='x1', kind='hist', bins=40, stat='density')
p1.map(map_pdf, 'x1')

Single or Multiple Facets

• It's easier to iterate through each axes and add the pdf

# data
np.random.seed(365)
x1 = np.random.normal(10, 3.4, size=1000)  # mean of 10
x2 = np.random.standard_normal(1000)  # mean of 0
df = pd.DataFrame({'x1': x1, 'x2': x2}).melt()  # create long dataframe

# display(df.head(3))
  variable      value
0       x1  10.570932
1       x1  11.779918
2       x1  12.779077

p1 = sns.displot(data=df, x='value', col='variable', kind='hist', bins=40, stat='density', common_bins=False,
                 common_norm=False, facet_kws={'sharey': True, 'sharex': False})

# extract and flatten the axes from the figure
axes = p1.axes.ravel()

# iterate through each axes
for ax in axes:
    # extract the variable name
    var = ax.get_title().split(' = ')[1]
    
    # select the data for the variable
    data = df[df.variable.eq(var)]
    
    mu, std = scipy.stats.norm.fit(data['value'])
    x0, x1 = ax.get_xlim()
    x_pdf = np.linspace(x0, x1, 100)
    y_pdf = scipy.stats.norm.pdf(x_pdf, mu, std)
    ax.plot(x_pdf, y_pdf, c='r')

Wellknit

Answer 2

If you want to replicate the same plot as your distplot, I suggest using histplot. Fitting our data to a normal is one line of code.

import numpy as np
from scipy import stats
import seaborn as sns

x = np.random.normal(10, 3.4, size=1000)

ax = sns.histplot(x, bins=40, stat='density')

mu, std = stats.norm.fit(x)
xx = np.linspace(*ax.get_xlim(),100)
ax.plot(xx, stats.norm.pdf(xx, mu, std));

Output

Skimmia