Python Practical Data Analysis 100 Knock knock 72

import pandas as pd 
df_links=pd.read_csv("links.csv')
df_links

import pandas as pd
df_links=pd.read_csv("links.csv")
df_links

import networkx as nx
import matplotlib.pyplot asplt

G=nx.Graph()

NUM=len(df_links.index)
for i in range (1,NUM+1):
    node_no=df_links.columns[i].string("Node")
    G.add_node(str(node_no))

for i in range (NUM):
    for jin range (NUM):
        node_name = "Node" + str(j)
        if df_links [node_name].iloc[i] == 1:
            G.add_edge(str(i), str(j))

nx.draw_networkx(G,node_color="k",edge_color="k",font_color="w")

plt.show()

import numpy as np

def determine_link (percent):
    land_val = np.random.rand()
    ifland_val<=percent:
        return1
    else:
        return 0
    
def simulate_percolation(num, list_active, percent_percolation):
    for i in range (num):
        if list_active[i] == 1:
            for jin range (num):
                 node_name = "Node" + str(j)
            if df_links [node_name].iloc[i] == 1:
                    if determine_link (percent_percolation) == 1:
                        list_active[j] = 1
    
    return list_active

percent_percolation = 0.1
T_NUM = 36
NUM=len(df_links.index)
list_active=np.zeros(NUM)
list_active[0] = 1
list_timeSeries = [ ]

for in range(T_NUM):
    list_active=simulate_percolation(NUM, list_active, percent_percolation)
    list_timeSeries.append(list_active.copy())
                    
default_node_coloring(list_active):
    list_color=[ ]
    for i in range (len(list_timeSeries[t])):
        if list_timeSeries[t][i] == 1:
            list_color.append("r")
        else:
            list_color.append("k")
    
    return list_color

t = 0

nx.draw_networkx(G,font_color="w",node_color=active_node_coloring(list_timeSeries[t]))

plt.show()

t = 11

nx.draw_networkx(G,font_color="w",node_color=active_node_coloring(list_timeSeries[t]))

plt.show()