digraph.ipynb

Kernel: Python 3

Generator functions

Code examples from Think Complexity, 2nd edition.

Copyright 2019 Allen Downey, MIT License

%matplotlib inline

import networkx as nx
import numpy as np

# TODO: remove this when NetworkX is fixed
from warnings import simplefilter
import matplotlib.cbook
simplefilter("ignore", matplotlib.cbook.mplDeprecation)

Exercise: Write a function named next_two_digraph that takes a sequence of nodes and returns a NetworkX DiGraph object that represents a directed graph where each node is connected to its successor, and the successor of its successor, wrapping around to the beginning.

# Solution goes here

nodes = list(range(10))
dig = next_two_digraph(nodes)

nx.draw_circular(dig, 
        node_color='C0', 
        node_size=1000, 
        with_labels=True)

dig = next_two_digraph([1, 2, 3, 4])
dig.edges()

Exercise: Suppose you have a Python list of integers called sample where each element of the list is the number of friends of a randomly-chosen person in a social network.

Write a few lines of code to do the following:

• Make a Pmf object that represents the distribution of the values in sample.

• Compute the number of people in the sample with exactly 2 friends.

• Compute the average degree in the network.

sample = np.random.zipf(1.8, size=100)

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Exercise: Continuing the previous example, write a few lines of code to do the following:

• Make a Cdf object that represents the distribution of the values in sample.

• Compute the number of people in the sample with strictly less than 10 friends.

• Compute the 75th percentile of the number of friends, that is, the quantile that corresponds to the cumulative probability 0.75.

sample = np.random.zipf(1.8, size=1000)

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Bonus: Use the Cdf to compute the median and interquartile range (which is the difference between the 75th and 25th percentiles).

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