Code for post about networkx features - https://tomron.net/2020/01/17/3-interesting-features-of-networkx/

networkx_post.py

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


# Multigraph example

G = nx.MultiGraph()
G.add_nodes_from([1, 2, 3])
G.add_edges_from([(1, 2), (1, 3), (1, 2)])

print(G.degree())
#[(1, 3), (2, 2), (3, 1)]
H = nx.Graph()
H.add_nodes_from([1, 2, 3])
H.add_edges_from([(1, 2), (1, 3), (1, 2)])

print(H.degree())
#[(1, 2), (2, 1), (3, 1)]


# Pandas example

df = pd.DataFrame([[1, 1, 4], [2, 1, 5], [3, 2, 6], [1, 1, 3]], columns=['source', 'destination', 'weight'])

print(df)
#    source  destination  weight
# 0       1            1       4
# 1       2            1       5
# 2       3            2       6
# 3       1            1       3

G = nx.from_pandas_edgelist(df, 'source', 'destination', ['weight'], create_using=nx.MultiGraph)

print(nx.info(G))

# Name:
# Type: MultiGraph
# Number of nodes: 3
# Number of edges: 4
# Average degree:   2.6667


# Graph generators

# Complete graph

G = nx.complete_graph(n=9)

print(len(G.edges()), len(G.nodes()))
# 36 9

H = nx.complete_graph(n=9, create_using=nx.DiGraph)

print(len(H.edges()), len(H.nodes()))
# 72 9

J = nx.empty_graph(n=9)
print(len(J.edges()), len(J.nodes()))
# 0 9

K = nx.star_graph(n=9)
print(len(K.edges()), len(K.nodes()))

# 9 10


##

G1 = nx.binomial_graph(n=9, p=0.5, seed=1)

G2 = nx.binomial_graph(n=9, p=0.5, seed=1)

G3 = nx.binomial_graph(n=9, p=0.5)

print(G1.edges()==G2.edges(), G1.edges()==G3.edges())
# True False


G = nx.random_regular_graph(d=4, n=10)

nx.draw(G)
plt.show()


G = nx.random_tree(n=10)
nx.draw(G)
plt.show()