Advancing into Analytics

Chapter 12. Data Manipulation and Visualization in Python

In Chapter 8 you learned how to manipulate and visualize data, with heavy help from the tidyverse suite of packages. Here we’ll demonstrate similar techniques on the same star dataset, this time in Python. In particular, we’ll use pandas and seaborn to manipulate and visualize data, respectively. This isn’t a comprehensive guide to what these modules, or Python, can do with data analysis. Instead, it’s enough to get you exploring on your own.

As much as possible, I’ll mirror the steps and perform the same operations that we did in Chapter 8. Because of this familiarity, I’ll focus less on the whys of manipulating and visualizing data than I will on hows of doing it in Python. Let’s load the necessary modules and get started with star. The third module, matplotlib, is new for you and will be used to complement our work in seaborn. It comes installed with Anaconda. Specifically, we’ll be using the pyplot submodule, aliasing it as plt.

In [1]:  import pandas as pd
         import seaborn as sns
         import matplotlib.pyplot as plt

         star = pd.read_excel('datasets/star/star.xlsx')
         star.head()
Out[1]:
   tmathssk  treadssk             classk  totexpk   sex freelunk   race  \
0       473       447        small.class        7  girl       no  white
1       536       450        small.class       21  girl       no  black
2       463       439  regular.with.aide        0   boy      yes  black
3       559       448            regular       16   boy       no  white
4       489       447        small.class        5   boy      yes  white

   schidkn
0       63
1       20
2       19
3       69
4       79

Column-Wise Operations

In Chapter 11 you learned that pandas will ...