Data Preparation and Wrangling
“Success is getting what you want..
Happiness is wanting what you get.”
– Dale Carnegie
So far, we have seen gathered a fair amount of expertise in NumPy and Pandas. We have also seen how to acquire data from multiple sources. The next step in the data science lifecycle is preparing the data for different data analysis tasks. This chapter is devoted to understanding the data, handling missing entries, and performing wrangling to aid meaningful extraction of information.
TipChapter Objectives
In this chapter, we will learn the following:
Understanding datasets using Pandas
Handling missing values
Several data transformation techniques