  Dataframes are a popular abstraction to represent, prepare, and analyze data.
Despite the remarkable success of dataframe libraries in Rand Python,
dataframes face performance issues even on moderately large datasets. Moreover,
there is significant ambiguity regarding dataframe semantics. In this paper we
lay out a vision and roadmap for scalable dataframe systems. To demonstrate the
potential in this area, we report on our experience building MODIN, a scaled-up
implementation of the most widely-used and complex dataframe API today,
Python's pandas. With pandas as a reference, we propose a simple data model and
algebra for dataframes to ground discussion in the field. Given this
foundation, we lay out an agenda of open research opportunities where the
distinct features of dataframes will require extending the state of the art in
many dimensions of data management. We discuss the implications of signature
data-frame features including flexible schemas, ordering, row/column
equivalence, and data/metadata fluidity, as well as the piecemeal,
trial-and-error-based approach to interacting with dataframes.
