scipy.stats.norm
gives easy access to the pdf of a normal distribution with
known parameters; by default it corresponds to the standard normal, mu=0
, sigma=1
.
- This answer works regardless of where the data mean is located (e.g.
mu=0
or mu=10
)
- Tested in
python 3.8.11
, matplotlib 3.4.2
, seaborn 0.11.2
- This question and answer are for axes-level plots; for figure-level plots, see How to draw a normal curve on seaborn displot
Imports and Data
import numpy as np
import seaborn as sns
from scipy import stats
import matplotlib.pyplot as plt
# data
np.random.seed(365)
x = np.random.standard_normal(1000)
ax = sns.histplot(x, kde=False, stat='density', label='samples')
# calculate the pdf
x0, x1 = ax.get_xlim() # extract the endpoints for the x-axis
x_pdf = np.linspace(x0, x1, 100)
y_pdf = scipy.stats.norm.pdf(x_pdf)
ax.plot(x_pdf, y_pdf, 'r', lw=2, label='pdf')
ax.legend()
- For this to correspond correctly to your sampled data, the histogram should
display densities and not counts, so use norm_hist=True
in the seaborn.distplot
call.
ax = sns.distplot(x, kde = False, norm_hist=True, hist_kws={'ec': 'k'}, label='samples')
# calculate the pdf
x0, x1 = ax.get_xlim() # extract the endpoints for the x-axis
x_pdf = np.linspace(x0, x1, 100)
y_pdf = scipy.stats.norm.pdf(x_pdf)
ax.plot(x_pdf, y_pdf, 'r', lw=2, label='pdf')
ax.legend()
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