Putting it all together with pipelines

Now that we've used pipelines and data transformation techniques, we'll walk through a more complicated example that combines several of the previous recipes into a pipeline.

Getting ready

In this section, we'll show off some more of pipeline's power. When we used it earlier to impute missing values, it was only a quick taste; here, we'll chain together multiple pre-processing steps to show how pipelines can remove extra work. Let's briefly load the iris dataset and seed it with some missing values:

from sklearn.datasets import load_iris
from sklearn.datasets import load_iris
import numpy as np
iris = load_iris()
iris_data = iris.data
mask = np.random.binomial(1, .25, iris_data.shape).astype(bool)
iris_data[mask] = np.nan
iris_data[:5]

array([[ nan,  3.5,  1.4,  0.2],
       [ 4.9,  3. ,  1.4,  nan],
       [ nan,  3.2,  nan,  nan],
       [ nan,  nan,  1.5,  0.2],
       [ nan,  3.6,  1.4,  0.2]])

How to do it...

The goal of this chapter is to first impute...