Building Machine Learning Systems with Python - Richert, Coelho

Chapter 1
We are missing only 8 out of 743 entries, so we can afford to remove them. Remember
that we can index a SciPy array with another array. sp.isnan(y) returns an array of
Booleans indicating whether an entry is not a number. Using ~, we logically negate
that array so that we choose only those elements from x and y where y does contain
valid numbers.
x = x[~sp.isnan(y)]
y = y[~sp.isnan(y)]
To get a first impression of our data, let us plot the data in a scatter plot using
Matplotlib. Matplotlib contains the pyplot package, which tries to mimic Matlab's
interface—a very convenient and easy-to-use one (you will find more tutorials on
plotting at http://matplotlib.org/users/pyplot_tutorial.html).
import matplotlib.pyplot as plt
plt.scatter(x,y)
plt.title("Web traffic over the last month")
plt.xlabel("Time")
plt.ylabel("Hits/hour")
plt.xticks([w*7*24 for w in range(10)],
['week %i'%w for w in range(10)])
plt.autoscale(tight=True)
plt.grid()
plt.show()
In the resulting chart, we can see that while in the first weeks the traffic stayed more
or less the same, the last week shows a steep increase:
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