Grouping the data – aggregation, filtering, and transformation
In this section, you will learn how to aggregate data over categorical variables. This is a very common practice when the data consists of categorical variables. This analysis enables us to conduct a category-wise analysis and take further decisions regarding the modelling.
To illustrate the concepts of grouping and aggregating data better, let's create a simple dummy data frame that has a rich mix of both numerical and categorical variables. Let's use whatever we have explored till now about random numbers to create this data frame, as shown in the following snippet:
import numpy as np import pandas as pd a=['Male','Female'] b=['Rich','Poor','Middle Class'] gender= seb= for i in ...