Clarification of log-normal distribution using Python

Question

What's the meaning of count, bins, ignored in the code below which I found on the numpy website (https://numpy.org/doc/stable/reference/random/generated/numpy.random.lognormal.html).

import numpy as np
import matplotlib.pyplot as plt

mu, sigma = 0.2, 0.5 # mean and standard deviation

s = np.random.lognormal(mu, sigma, 1000)
count, bins, ignored = plt.hist(s, 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()

Answer 1

count is the value for the density in each bin (in this example 100 values). bins contains the bin edges (101 in this example), where each pair of edges [i,i+1] are the edges of the bin. ignored is not important for the purpose of that plot. According to the documentation of plt.hist, it is a "Container of individual artists used to create the histogram or list of such containers if there are multiple input datasets".