Now we'll explore crosstabulation—a powerful analytical technique that compares variables against each other to reveal hidden patterns in your data. Unlike visualization methods that emphasize visual storytelling, crosstabulation produces concrete numerical relationships that form the foundation of robust data-driven decisions.
In this analysis, we'll examine how salary levels influence employee retention rates—a critical business metric that directly impacts recruitment costs, institutional knowledge, and organizational stability. This type of comparative analysis is essential for identifying which variables should serve as features in predictive models, helping you build more accurate frameworks for workforce planning and risk assessment.
Salary represents one of the most significant factors in employee decision-making, often serving as a primary predictor of whether talent chooses to stay or seek opportunities elsewhere. By running a crosstabulation analysis using Pandas' built-in functionality, we can compute a comprehensive frequency table that reveals the distribution of values across multiple variables and uncovers meaningful relationships between compensation and retention patterns.
The beauty of crosstabulation lies in its simplicity and directness—it returns a clean DataFrame that presents raw numerical relationships without the interpretive layer that visualization sometimes adds. This makes it invaluable for stakeholders who need to see exact figures and statistical relationships before making strategic decisions about compensation structures or retention initiatives.
The implementation process is straightforward and efficient. We'll create what we can call a "left versus salary crosstab"—a descriptive name that clearly identifies the relationship we're examining for future reference and documentation purposes.
Using Pandas' crosstab function, we'll systematically compare the "left" column against the "salary" column from our HRData dataset. This generates a matrix that shows exactly how many employees in each salary category chose to leave or remain with the organization, providing the quantitative foundation for informed workforce management strategies.
While our initial output provides valuable insights, you'll notice that the salary columns—high, low, and medium—appear in alphabetical rather than logical order, which can obscure meaningful patterns and make interpretation more challenging for stakeholders. This common formatting issue can significantly impact how decision-makers interpret and act on your analysis, so our next step will focus on reordering this data to create a more intuitive, human-readable format that clearly demonstrates the salary-retention relationship.