Building Machine Learning Systems with Python - Richert, Coelho

Getting Started with Python Machine Learning
Using SciPy's genfromtxt(), we can easily read in the data.
import scipy as sp
data = sp.genfromtxt("web_traffic.tsv", delimiter="\t")
We have to specify tab as the delimiter so that the columns are correctly determined.
A quick check shows that we have correctly read in the data.
>>> print(data[:10])
[[ 1.00000000e+00 2.27200000e+03]
[ 2.00000000e+00 nan]
[ 3.00000000e+00 1.38600000e+03]
[ 4.00000000e+00 1.36500000e+03]
[ 5.00000000e+00 1.48800000e+03]
[ 6.00000000e+00 1.33700000e+03]
[ 7.00000000e+00 1.88300000e+03]
[ 8.00000000e+00 2.28300000e+03]
[ 9.00000000e+00 1.33500000e+03]
[ 1.00000000e+01 1.02500000e+03]]
>>> print(data.shape)
(743, 2)
We have 743 data points with two dimensions.
Preprocessing and cleaning the data
It is more convenient for SciPy to separate the dimensions into two vectors, each
of size 743. The first vector, x, will contain the hours and the other, y, will contain
the web hits in that particular hour. This splitting is done using the special index
notation of SciPy, using which we can choose the columns individually.
x = data[:,0]
y = data[:,1]
There is much more to the way data can be selected from a SciPy array.
Check out http://www.scipy.org/Tentative_NumPy_Tutorial
for more details on indexing, slicing, and iterating.
One caveat is that we still have some values in y that contain invalid values, nan.
The question is, what can we do with them? Let us check how many hours contain
invalid data.
>>> sp.sum(sp.isnan(y))
8
[ 20 ]