Sorting and Filtering

Sorting and filtering are fundamental data operations that are often combined to answer analytical questions. Python provides flexible tools for both: the sorted() function with custom keys enables sophisticated ordering, while comprehensions and the filter() function provide powerful selection capabilities. Mastering the combination of these operations enables you to write complex data queries that rival SQL in expressiveness. Sorting with Custom Keys Multi-Level Sorting Filter, Sort, and Slice Real data queries often combine filtering, sorting, and limiting results. The pattern is: filter with a comprehension to select relevant records, sort with sorted() to order them, then slice with [:n] to limit the result count. This pipeline approach mirrors SQL queries and is natural to read. iter

About This Interactive Section

This section is part of the Data Structures: Intermediate lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

How DataDriven Lessons Work

DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.