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For example, it has the following code:
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(10**7)
mu = 121
sigma = 21
x = mu + sigma * np.random.randn(1000)
num_bins = 100
fig, ax = plt.subplots()
n, bins, patches = ax.hist(x, num_bins,
density = 1,
color ='green',
alpha = 0.7)
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
ax.plot(bins, y, '--', color ='black')
ax.set_xlabel('X-Axis')
ax.set_ylabel('Y-Axis')
ax.set_title('matplotlib.axes.Axes.hist() Example')
plt.show()
With it is generated the following graph:
My doubt: it is not clear to me how,
x = mu + sigma * np.random.randn(1000)
which is then passed on
n, bins, patches = ax.hist(x,...
makes the histogram look like this. By the matplotlib documentation, the output parameter n "returns the values of the histogram boxes", but what operations are done in the x for the graph to be plotted this way?
I also don’t understand what the "Density = 1" parameter does on the graph.