We will start exploring the first dataset, the Boston dataset, but before delving into numbers, we will upload a series of helpful packages that will be used during the rest of the chapter:
In: import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib as mpl
If you are working from an IPython Notebook, running the following command in a cell will instruct the Notebook to represent any graphic output in the Notebook itself (otherwise, if you are not working on IPython, just ignore the command because it won't work in IDEs such as Python's IDLE or Spyder):
In: %matplotlib inline # If you are using IPython, this will make the images available in the Notebook
To immediately select the variables that we need, we just frame all the data available into a Pandas data structure, DataFrame.
Inspired by a similar data structure present in the R statistical language, a DataFrame renders data vectors of different types easy to handle under...
