  Python has become a standard scientific computing language with fast-growing
support of machine learning and data analysis modules, as well as an increasing
usage of big data. The Dynamic Distributed Dimensional Data Model (D4M) offers
a highly composable, unified data model with strong performance built to handle
big data fast and efficiently. In this work we present an implementation of D4M
in Python. $D4M.py$ implements all foundational functionality of D4M and
includes Accumulo and SQL database support via Graphulo. We describe the
mathematical background and motivation, an explanation of the approaches made
for its fundamental functions and building blocks, and performance results
which compare $D4M.py$'s performance to D4M-MATLAB and D4M.jl.
