Performing Bivariate Analysis in Python
Bivariate analysis uncovers insights embedded within two variables of interest. When performing this analysis, we are typically interested in how these two variables are distributed or related. Bivariate analysis can sometimes be more complex than univariate analysis because it involves the analysis of categorical and (or) numerical values. This means that in bivariate analysis, we can have three possible combinations of variables namely: numerical-numerical, numerical-categorical, and categorical-categorical.
It is important to understand the various chart options that cater to these combinations. These chart options can help us understand the underlying distribution of our data and identify any hidden patterns within the dataset.
In this chapter, we will cover the following key topics:
- Analyzing two variables using a scatter plot
- Creating crosstab/two-way tables on bivariate data
- Analyzing two variables using a pivot table...
