To start, let me arouse your interest by showing you some analytical and graphical capabilities of Python. I have explained the code just briefly in this section; you will learn more about Python programming in the rest of this chapter. The following code imports the libraries required for this demonstration:

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 

Then we need some data. I am using the same data from the AdventureWorksDW2014 demo database and the dbo.vTargetMail view as in the R chapters, Chapter 13, Supporting R in SQL Server, and Chapter 14, Data Exploration and Predictive Modeling with R, of this book. The following code reads this data from the CSV file:

TM = pd.read_csv("C:SQL2017DevGuideChapter15_TM.csv") 

Now I can do a quick cross tabulation of the NumberCarsOwned variable using the TotalChildren variable, with the help of the following code:

obb = pd.crosstab(TM.NumberCarsOwned, TM.TotalChildren) 
obb 

And here are the first results, a pivot table of the previously mentioned variables:

    TotalChildren       0     1     2     3     4    5
    NumberCarsOwned                                   
    0                 990  1668   602   419   449  110
    1                1747  1523   967   290   286   70
    2                1752   162  1876  1047  1064  556
    3                 384   130   182   157   339  453
    4                 292   136   152   281   165  235
  

Now, let me show you the results of the pivot table in a graph. I need just the following two lines:

obb.plot(kind = 'bar') 
plt.show() 

You can see the graph in the following figure:

It is quite simple to create even more complex graphs. The following code shows the distribution of the Age variable in histograms and with a kernel density plot:

(TM['Age'] - 20).hist(bins = 25, normed = True,
                      color = 'lightblue') 
(TM['Age'] - 20).plot(kind='kde', style='r--', xlim = [0, 80]) 
plt.show() 

You can see the results in the following figure. Note that in the code, I subtracted 20 from the actual age, to get a slightly younger population than exists in the demo database:

I hope that you are interested in learning Python after this brief introduction of its capabilities.