Choosing the Right Data Structure

Selecting the right data structure makes programs easier to understand and can improve performance.

A Practical Example

Consider a small dataset containing customer purchase amounts. You can use a list to store the values, a set to identify unique amounts, and a dictionary to calculate summary statistics.

purchase_amounts = [1200, 2500, 1200, 3400, 1800, 2500]

unique_amounts = set(purchase_amounts)

purchase_summary = {

"total_purchases": len(purchase_amounts),

"unique_amount_count": len(unique_amounts),

"total_amount": sum(purchase_amounts),

"average_amount": (

sum(purchase_amounts) / len(purchase_amounts)

)

}

print(purchase_summary)

The dictionary stores the calculated results under descriptive keys, while the set helps identify distinct purchase amounts.

For real-world data science, these structures provide a foundation for understanding how information is organized and transformed. As datasets grow, NumPy and Pandas offer specialized structures that support efficient numerical calculations and tabular data analysis.