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A social network is an interactive platform for massive communication. While analyzing a social network, its pictorial representation as a graph is required to explore and visualize it. Graph theory concepts are applied to social networks which are helpful in numerical formulations also. Social network graph theory is applicable to formulate and study various statistical parameters that can help in analyzing the social patterns of these networks. Various Python implementation commands from importing data to the computation of data for network statistics like the clustering coefficient and the degree distribution of nodes are described in this paper. These network statistics are significant as they can be used to unveil differences in the pattern of interconnections in a social network.