Fitting a log-normal function on array values using Python

Question

I am trying to fit log-normal pdf on the matrix generated using the inbuilt log-normal function but it doesn't fit. I was wondering why it is off. The plot is attached for reference.

import numpy as np
import matplotlib.pyplot as plt
mu, sigma = 0.2, 0.5 # mean and standard deviation
A=np.random.lognormal(mean=0.2, sigma=0.5, size=(10, 10))

count, bins, ignored = plt.hist(A, 100, density=True, align='mid')
x = np.linspace(min(bins), max(bins), 10000)

pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))/ (x * sigma * np.sqrt(2 * np.pi)))
plt.plot(x, pdf, linewidth=2, color='r')
plt.axis('tight')
plt.show()