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.

Asked By: Borut Flis

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Source

Answer 1

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

Answered By: Michael Szczesny

Answer 2

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')

Answered By: Trenton McKinney

How to draw a normal curve on seaborn displot

Question:

Answers:

Single Facet

Single or Multiple Facets