Incremental Filtering and Transformation on Tabular User Records (Follow-up Chaining)

Problem: Chainable Filtering and Transformation on Tabular User Records (Unknown Number of Follow-ups) You are given many rows of user data records, which can be viewed as a 2D table: Each row corresponds to a user (or a user-related record). Each column corresponds to a field (a user attribute). Task Implement a program/function that, given rules from the interviewer, performs: Filtering: keep only rows satisfying certain conditions. Transformation/processing: further process the filtered result and output the rows that meet the requirements. During the interview there will be an unknown number of follow-ups: Each follow-up adds new filtering/processing requirements on top of the previous result set. You should implement as many of these incremental requirements as possible. Notes Time/space optimization is not the focus. The goal is correctness, fast iteration, and code structure that supports follow-up extensions. Your solution should handle generic row/column operations (e.g., get a field by column, compare values, combine conditions). I/O (unspecified in the original write-up) Input: multiple rows with multiple columns separated by some delimiter (comma/tab/space), may include a header. Output: print all rows that satisfy the current set of rules (often full-row output), and continue chaining rules in follow-ups. Since the original post does not provide concrete fields, exact rules, or I/O examples, this is a high-level summary of the interview-style question rather than a fully specified OJ-ready statement.