When I initially challenged myself with building a dataframe implementation, I began by looking at what already existed for inspiration. Spark was my initial reference. Then pandas. Then Polars, R’s data.table, Julia’s DataFrames.jl. Each one had its own take on the API. Early on, my main challenge was trying to figure out which operations I actually needed to implement to call what I had a “dataframe”. The surface area felt enormous. Do I need pivot and melt as separate things? Is apply different from map? What about transform, agg, applymap, pipe? Some of these seemed like the same operation wearing different hats. Others seemed genuinely distinct. I didn’t have a good framework for telling them apart.