[카테고리:] Data Work

[USVC] Drawing Supply Chain 1 – Small Sample

/******************************************************************
— Title : [Python; NetworkX] Supply Chain analysis
— Key word : networkx, Node, Edge, Centrality, Supply Chain, Value Chain
*******************************************************************/

Data

  • About 200 major firms listed on Compustat data
  • Data Set will soon encompass all the firms with CIK code
  • Customer information extracted from 10-k disclosure data

Graph

  • Drawn from the basic networkx graph tool (nx.draw())
  • year : ordered in years ; 2000, 2005, 2010, 2015
  • Size of node : in_degree_centrality
  • Color of node : out_degree_centrality

2000(2)2005(2)2010(2)2015(2)

Sample Code :

(Reference : https://briandew.wordpress.com/2016/06/15/trade-network-analysis-why-centrality-matters/)

import networkx as nx
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

def draw_G(G, year):
    oc = nx.out_degree_centrality(G)
    for key in oc.keys():
        oc[key] = oc[key]*10
    nx.set_node_attributes(G, name= 'cent', values = oc)
    ic = nx.in_degree_centrality(G)
    nx.set_node_attributes(G, name= 'in', values = ic)
    node_size = [float(G.node[v]['in'])*20000 + 1 for v in G]
    node_color = [float(G.node[v]['cent']) for v in G]
    pos = nx.spring_layout(G, k=30, iterations=8)
    nodes = nx.draw_networkx_nodes(G, pos, node_size=node_size, node_color = node_color, alpha=0.5)
#nodes = nx.draw_networkx_nodes(G, pos, node_color=node_color, alpha=0.5)
    edges = nx.draw_networkx_edges(G, pos, edge_color='black', arrows=True, width=0.3)
    nx.draw_networkx_labels(G, pos, font_size=5)
    plt.text(0,-1.2, 'Node color is out_degree_centrality', fontsize=7)
    plt.title('Compustat firms Supply Chain (year : ' + str(year) + ')', fontsize=12)
    cbar = plt.colorbar(mappable=nodes, cax=None, ax=None, fraction=0.015, pad=0.04)
    cbar.set_clim(0, 1)
    plt.margins(0,0)
    plt.axis('off')
    plt.savefig(str(year)+ 'Supply Chain.png', dpi=1000)
    plt.show()

Numbers(Statistics)

  • Longest path :
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