Due to the emergence of massive real-world graphs, whose sizes may extend to terabytes, new tools must be developed to enable data scientists to handle such graphs efficiently. These graphs may include social networks, computer networks, and genomes. In this paper, we propose a novel graph package Arachne to make large-scale graph analytics more effortless and more efficient based on the open-source Arkouda framework, which has been developed to allow users to perform massively parallel computations on distributed data with an interface similar to NumPy. In this package, we developed a fundamental sparse graph data structure and several useful graph algorithms around our data structure to build a basic algorithmic library. Benchmarks and tools have also been developed to evaluate and demonstrate the provided graph algorithms. The graph algorithms we have implemented thus far include breadth-first search (BFS), connected components (CC), k-Truss (KT), Jaccard coefficients (JC), triangle counting (TC), and triangle centrality (TCE). Their corresponding experimental results based on realworld and synthetic graphs are presented.