normal distribution

>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 30, density=True)
>>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
...          linewidth=2, color='r')

Here is what the above code is Doing:
1. We’re creating a random sample of data from a normal distribution.
2. We’re plotting a histogram of the data.
3. We’re plotting a normal distribution curve on top of the histogram.

The normal distribution curve is a good fit for the data.

Now let’s look at a sample of data that is not from a normal distribution.