Advancing into Analytics

Chapter 13. Capstone: Python for Data Analytics

At the end of Chapter 8 you extended what you learned about R to explore and test relationships in the mpg dataset. We’ll do the same in this chapter, using Python. We’ve conducted the same work in Excel and R, so I’ll focus less on the whys of our analysis in favor of the hows of doing it in Python.

To get started, let’s call in all the necessary modules. Some of these are new: from scipy, we’ll import the stats submodule. To do this, we’ll use the from keyword to tell Python what module to look for, then the usual import keyword to choose a sub-module. As the name suggests, we’ll use the stats submodule of scipy to conduct our statistical analysis. We’ll also be using a new package called sklearn, or scikit-learn, to validate our model on a train/test split. This package has become a dominant resource for machine learning and also comes installed with Anaconda.

In [1]: import pandas as pd
        import seaborn as sns
        import matplotlib.pyplot as plt
        from scipy import stats
        from sklearn import linear_model
        from sklearn import model_selection
        from sklearn import metrics

With the usecols argument of read_csv() we can specify which columns to read into the DataFrame:

In [2]: mpg = pd.read_csv('datasets/mpg/mpg.csv',usecols=
           ['mpg','weight','horsepower','origin','cylinders'])
        mpg.head()

Out[2]:
     mpg  cylinders  horsepower  weight origin
 0  18.0          8         130    3504    USA
 1  15.0          8         165    3693    USA
 2  18.0          8         150    3436    USA
 3  16.0          8         150    3433    USA
 4  17.0          8         140    3449    USA ...