Frequency Counting

Many problems become trivial once you count occurrences. Frequency maps answer questions like "how many of each?" and "what appears most/least?" Building Frequency Maps The basic pattern for counting involves iterating through items and incrementing a counter for each unique value. Find Most Common Element Once you have frequencies, finding the most common element is straightforward. Check Anagrams Two strings are anagrams if they have exactly the same character frequencies. Frequency maps unlock a surprising number of problems beyond simple counting. Once you build the habit of reaching for a dictionary whenever you see "how many" or "which is most common," many problems become almost trivial. In practice, Python provides a built-in shortcut for frequency maps. Frequency maps are one of t

About This Interactive Section

This section is part of the Problem Solving: Advanced 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.