  The Python ecosystem represents a global, data rich, technology-enabled
network. By analyzing Python's dependency network, its top 14 most imported
libraries and cPython (or core Python) libraries, this research finds clear
evidence the Python network can be considered a problem solving network.
Analysis of the contributor network of the top 14 libraries and cPython reveals
emergent specialization, where experts of specific libraries are isolated and
focused while other experts link these critical libraries together, optimizing
both local and global information exchange efficiency. As these networks are
expanded, the local efficiency drops while the density increases, representing
a possible transition point between exploitation (optimizing working solutions)
and exploration (finding new solutions). These results provide insight into the
optimal functioning of technology-enabled social networks and may have larger
implications for the effective functioning of modern organizations.
