Reshape Data: Pivot - Video Solutions

Problem Overview: You receive tabular data where one column represents row identifiers, another represents column categories, and a third contains values. The task is to reshape the table so category values become columns while preserving the row identifier as the index.

Approach 1: Using Pandas Library in Python (Time: O(n), Space: O(n))

This approach relies on the built-in DataFrame.pivot() operation from the Pandas library. The key idea is that pivoting converts unique values from one column into new columns while aligning values using another column as the index. You pass the index column, the column that should become headers, and the value column. Pandas internally groups records and places each value in the correct row–column position. This is the most direct solution when working with tabular datasets in Python and avoids manual iteration. Prefer this when the environment already uses Python data analysis tools or when solving problems involving dataframe transformations.

Approach 2: Manual Aggregation and Restructuring in C (Time: O(n), Space: O(n))

Without high-level libraries, you reshape the data by iterating through each record and placing values into a newly structured table. Maintain a mapping from row identifier to row index and from category to column index. As you scan the dataset, perform a constant-time lookup and assign the value to the corresponding cell in a 2D structure. The core operations are iteration, lookup, and assignment. This mirrors how a pivot works internally and demonstrates how tabular reshaping can be implemented using basic structures like arrays or hash maps. This approach is useful in low-level environments where you must control memory layout or build the pivot table manually.

Recommended for interviews: The manual aggregation approach shows deeper understanding. Interviewers want to see how you transform row-based records into a structured grid using indexing or hashing. Mention that a library pivot operation exists, then explain the internal logic: iterate through records, map identifiers to indices, and populate a matrix-style structure. That demonstrates both practical awareness and algorithmic thinking.