Lists: Ordered Collections

Lists are Python's workhorse data structure. They hold items in a specific order, allow duplicates, and can grow or shrink as needed. When you receive a batch of records from an API, process rows from a CSV file, or collect results from a database query, you typically work with lists. Lists are by far the most commonly used data structure in Python. What makes lists so versatile is their flexibility. They can hold any type of data - numbers, strings, other lists, dictionaries, or custom objects. You can mix types within the same list, though in practice keeping types consistent makes code easier to understand and maintain. Indexing is how you access individual elements. The first element is at index 0, the second at index 1, and so on. Python also supports negative indexing: -1 refers to t

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This section is part of the Data Structures: Beginner 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.

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