Add KDE on to a histogram

Question

I would like to add a density plot to my histogram diagram. I know something about pdf function but I've got confused and other similar questions were not helpful.

from scipy.stats import * 
from numpy import*
from matplotlib.pyplot import*
from random import*

nums = []
N = 100
for i in range(N):
    a = randint(0,9)
    nums.append(a)

bars= [0,1,2,3,4,5,6,7,8,9]
alpha, loc, beta=5, 100, 22

hist(nums,normed= True,bins = bars)


show()

I'm looking for something like this

Answer 1

from scipy import stats
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(41)

N = 100
x = np.random.randint(0, 9, N)
bins = np.arange(10)

kde = stats.gaussian_kde(x)
xx = np.linspace(0, 9, 1000)
fig, ax = plt.subplots(figsize=(8,6))
ax.hist(x, density=True, bins=bins, alpha=0.3)
ax.plot(xx, kde(xx))

Answer 2

Here's a solution using seaborn 0.11.1 and pandas 1.1.5:

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

N = 100
nums = [np.random.randint(i-i, 9) for i in range(N)]
df = pd.DataFrame(nums, columns=["value"])

fig, ax1 = plt.subplots()
sns.kdeplot(data=df, x="value", ax=ax1)
ax1.set_xlim((df["value"].min(), df["value"].max()))
ax2 = ax1.twinx()
sns.histplot(data=df, x="value", discrete=True, ax=ax2)

Note how I use numpy to generate the random values because I need actual values, not generators. The discrete=True in the last line assures that the ticks are centered.