## Time for action – drawing the lognormal distribution

Let's visualize the lognormal distribution and its PDF with a histogram:

1. Generate random numbers using the `normal()` function from the `random` NumPy module:

```N=10000
lognormal_values = np.random.lognormal(size=N)```
2. Draw the histogram and theoretical PDF with a center value of `0` and standard deviation of `1`:

```_, bins, _ = plt.hist(lognormal_values, np.sqrt(N), normed=True, lw=1)
sigma = 1
mu = 0
x = np.linspace(min(bins), max(bins), len(bins))
pdf = np.exp(-(numpy.log(x) - mu)**2 / (2 * sigma**2))/ (x * sigma * np.sqrt(2 * np.pi))
plt.plot(x, pdf,lw=3)
plt.show()```

The fit of the histogram and theoretical PDF is excellent, as you can see in the following diagram:

### What just happened?

We visualized the lognormal distribution using the `lognormal()` function from the `random` NumPy module. We did this by drawing the curve of the theoretical PDF and a histogram of randomly generated values (see `lognormaldist.py`):

```import numpy as np
import matplotlib.pyplot as...```