Visualizing density function - difference between displot() and plot()

I visualize density function (PDF) using two plotting approaches: displot() and plot(). I don't understand why displot() doesn't produce normally distributed plot wheras plot() do this perfectly. Density plots should look alike but they don't. What's wrong with displot() here?

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

data_x= np.arange(-4, 4, 0.001)
norm_pdf = norm.pdf(data_x)
sns.displot(data = norm_pdf, x = data_x, kind='kde')

from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np

data_x= np.arange(-4, 4, 0.001)
plt.plot(data_x, norm.pdf(data_x))
plt.show()

displot (or the underlying kdeplot) creates an approximation of a probability density function (pdf) to resemble the function that might have generated the given random data. As input, you'll need random data. The function will mimic these data as a sum of Gaussian bell shapes (a "kernel density estimation" with a Gaussian kernel).

Here is an example using 8000 random points as input. You'll notice the curve resembles the normal pdf, but is also a bit "bumpier" (that's how randomness looks like).

data_x = norm.rvs(size=8000)
sns.kdeplot(x=data_x)

When you call kdeplot (or displot(..., kind='kde')) with both data= and x=, while x= isn't a columnname in a dataframe, data= gets ignored. So, you are using 8000 evenly distributed values between -4 and 4. The kde of such data looks like a flat line between -4 and 4. But as the kde supposes the underlying function locally resembles a Gaussian, the start and end are smoothed out.

data_x = np.arange(-4, 4, 0.001)
sns.kdeplot(x=data_x)