code: def degree_pearson_correlation_coefficient(
      G, x="out", y="in", weight=None, nodes=None
  ):
      """Compute degree assortativity of graph.
  
      Assortativity measures the similarity of connections
      in the graph with respect to the node degree.
  
      This is the same as degree_assortativity_coefficient but uses the
      potentially faster scipy.stats.pearsonr function.
  
      Parameters
      ----------
      G : NetworkX graph
  
      x: string ('in','out')
         The degree type for source node (directed graphs only).
  
      y: string ('in','out')
         The degree type for target node (directed graphs only).
  
      weight: string or None, optional (default=None)
         The edge attribute that holds the numerical value used
         as a weight.  If None, then each edge has weight 1.
         The degree is the sum of the edge weights adjacent to the node.
  
      nodes: list or iterable (optional)
          Compute pearson correlation of degrees only for specified nodes.
          The default is all nodes.
  
      Returns
      -------
      r : float
         Assortativity of graph by degree.
  
      Examples
      --------
      >>> G = nx.path_graph(4)
      >>> r = nx.degree_pearson_correlation_coefficient(G)
      >>> print(f"{r:3.1f}")
      -0.5
  
      Notes
      -----
      This calls scipy.stats.pearsonr.
  
      References
      ----------
      .. [1] M. E. J. Newman, Mixing patterns in networks
             Physical Review E, 67 026126, 2003
      .. [2] Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M.
         Edge direction and the structure of networks, PNAS 107, 10815-20 (2010).
      """
      import scipy as sp
  
      xy = node_degree_xy(G, x=x, y=y, nodes=nodes, weight=weight)
      x, y = zip(*xy)
      return sp.stats.pearsonr(x, y)[0]
  
file_path: networkx/networkx/algorithms/assortativity/correlation.py
imports: import networkx
  from networkx.algorithms.assortativity.mixing import attribute_mixing_matrix, degree_mixing_matrix
  from networkx.algorithms.assortativity.pairs import node_degree_xy
label_desc: Use image node_id 2 for calling a global function with example usage: degree_pearson_correlation_coefficient(G, x, y, weight, nodes) and returns: sp