Summary

Quite a long chapter! Isn't it? But, this chapter will form the core of anything you learn and implement in data-science. Let us wrap-up the chapter by summarizing the key takeaways from the chapter:

• Data can be sub-setted in a variety of ways: by selecting a column, selecting few rows, selecting a combination of rows and columns; using .ix method and [ ] method, and creating new columns.

• Random numbers can be generated in a number of ways. There are many methods like randint(), raandarrange() in the random library of numpy. There are also methods like shuffle and choice to randomly select an element out of a list. Randn() and uniform() are used to generate random numbers following normal and uniform probability distributions. Random numbers can be used to run simulations and generate dummy data frames.

• The groupby() method creates a groupby element on which aggregate, transform, and filter operations can be applied. This is a good method to summarize data for each categorical variable...