7. Weighted Probabilities
By Bernd Klein. Last modified: 24 Mar 2022.
Introduction
In the previous chapter of our tutorial, we introduced the random module. We got to know the functions 'choice' and 'sample'. We used 'choice' to choose a random element from a nonempty sequence and 'sample' to chooses k unique random elements from a population sequence or set. The probality for all elements is evenly distributed, i.e. each element has of the sequences or sets have the same probability to be chosen.
This is exactly what we want, if we simulate the rolling of dice. But what about loaded dice? Loaded dice are designed to favor some results over others for whatever reasons.
In our previous chapter we had a look at the following examples:
from random import choice, sample
print(choice("abcdefghij"))
professions = ["scientist", "philosopher", "engineer", "priest"]
print(choice(professions))
print(choice(("beginner", "intermediate", "advanced")))
# rolling one die
x = choice(range(1, 7))
print("The dice shows: " + str(x))
# rolling two dice:
dice = sample(range(1, 7), 2)
print("The two dice show: " + str(dice))
OUTPUT:
i priest advanced The dice shows: 3 The two dice show: [6, 2]
Like we said before, the chances for the elements of the sequence to be chosen are evenly distributed. So the chances for getting a 'scientist' as a return value of the call choice(professions) is 1/4. This is out of touch with reality. There are surely more scientists and engineers in the world than there are priests and philosophers. Just like with the loaded die, we have again the need of a weighted choice.
We will devise a function "weighted_choice", which returns a random element from a sequence like random.choice, but the elements of the sequence will be weighted.
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Weighted Random Choices
We will define now the weighted choice function. Let's assume that we have three weights, e.g. 1/5, 1/2, 3/10. We can build the cumulative sum of the weights with np.cumsum(weights).
import numpy as np
weights = [0.2, 0.5, 0.3]
cum_weights = [0] + list(np.cumsum(weights))
print(cum_weights)
OUTPUT:
[0, 0.2, 0.7, 1.0]
If we create a random number x between 0 and 1 by using random.random(), the probability for x to lie within the interval [0, cum_weights[0]) is equal to 1/5. The probability for x to lie within the interval [cum_weights[0], cum_weights[1]) is equal to 1/2 and finally, the probability for x to lie within the interval [cum_weights[1], cum_weights[2]) is 3/10.
Now you are able to understand the basic idea of how weighted_choice operates:
import numpy as np
import random
def weighted_choice(objects, weights):
""" returns randomly an element from the sequence of 'objects',
the likelihood of the objects is weighted according
to the sequence of 'weights', i.e. percentages."""
weights = np.array(weights, dtype=np.float64)
sum_of_weights = weights.sum()
# standardization:
np.multiply(weights, 1 / sum_of_weights, weights)
weights = weights.cumsum()
x = random.random()
for i in range(len(weights)):
if x < weights[i]:
return objects[i]
Example:
We can use the function weighted_choice for the following task:
Suppose, we have a "loaded" die with P(6)=3/12 and P(1)=1/12. The probability for the outcomes of all the other possibilities is equally likely, i.e. P(2) = P(3) = P(4) = P(5) = p.
We can calculate p with
1  P(1)  P(6) = 4 x p
that means
p = 1 / 6
How can we simulate this die with our weighted_choice function?
We call weighted_choice with 'faces_of_die' and the 'weights' list. Each call correspondents to a throw of the loaded die.
We can show that if we throw the die a large number of times, for example 10,000 times, we get roughly the probability values of the weights:
from collections import Counter
faces_of_die = [1, 2, 3, 4, 5, 6]
weights = [1/12, 1/6, 1/6, 1/6, 1/6, 3/12]
outcomes = []
n = 10000
for _ in range(n):
outcomes.append(weighted_choice(faces_of_die, weights))
c = Counter(outcomes)
for key in c:
c[key] = c[key] / n
print(sorted(c.values()), sum(c.values()))
OUTPUT:
[0.0868, 0.1636, 0.1643, 0.1654, 0.1666, 0.2533] 1.0
We can also use list of strings with our 'weighted_choice' function.
We define a list of cities and a list with their corresponding populations. The probability of a city to be chosen should be according to their size:
cities = ["Frankfurt",
"Stuttgart",
"Freiburg",
"München",
"Zürich",
"Hamburg"]
populations = [736000, 628000, 228000, 1450000, 409241, 1841179]
total = sum(populations)
weights = [ round(pop / total, 2) for pop in populations]
print(weights)
for i in range(10):
print(weighted_choice(cities, populations))
OUTPUT:
[0.14, 0.12, 0.04, 0.27, 0.08, 0.35] Hamburg Hamburg Frankfurt München Hamburg München München Frankfurt Frankfurt Hamburg
Weighted Random Choice with Numpy
To produce a weighted choice of an array like object, we can also use the choice function of the numpy.random package. Actually, you should use functions from wellestablished module like 'NumPy' instead of reinventing the wheel by writing your own code. In addition the 'choice' function from NumPy can do even more. It generates a random sample from a given 1D array or array like object like a list, tuple and so on. The function can be called with four parameters:
choice(a, size=None, replace=True, p=None)
Parameter  Meaning 

a  a 1dimensional arraylike object or an int. If it is an arraylike object, the function will return a random sample from the elements. If it is an int, it behaves as if we called it with np.arange(a) 
size  This is an optional parameter defining the output shape. If the given shape is, e.g., (m, n, k) , then m * n * k samples are drawn. The Default is None, in which case a single value will be returned. 
replace  An optional boolean parameter. It is used to define whether the output sample will be with or without replacements. 
p  An optional 1dimensional arraylike object, which contains the probabilities associated with each entry in a. If it is not given the sample assumes a uniform distribution over all entries in a. 
We will base our first exercise on the popularity of programming language as stated by the "Tiobe index"^{1}:
from numpy.random import choice
professions = ["scientist",
"philosopher",
"engineer",
"priest",
"programmer"]
probabilities = [0.2, 0.05, 0.3, 0.15, 0.3]
choice(professions, p=probabilities)
OUTPUT:
'scientist'
Let us use the function choice to create a sample from our professions. To get two professions chosen, we set the size
parameter to the shape (2, )
. In this case multiple occurances are possible. The top ten programming languages in August 2019 were:
Programming Language  Percentage in August 2019  Change to August 2018  

Java  16.028%  0.85%  
C  15.154%  +0.19%  
Python  10.020%  +3.03%  
C++  6.057%  1.41%  
C#  3.842%  +0.30%  
Visual  Basic  .NET  3.695%  1.07% 
JavaScript  2.258%  0.15%  
PHP  2.075%  0.85%  
ObjectiveC  1.690%  +0.33%  
SQL  1.625%  0.69%  
Ruby  1.316%  +0.13% 
programming_languages = ["Java", "C", "Python", "C++"]
weights = np.array([16, 15.2, 10, 6.1])
# normalization
weights /= sum(weights)
print(weights)
for i in range(10):
print(choice(programming_languages, p=weights))
OUTPUT:
[0.33826638 0.32135307 0.21141649 0.12896406] Python C C C Python C C C C C
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Weighted Sample
In the previous chapter on random numbers and probability, we introduced the function 'sample' of the module 'random' to randomly extract a population or sample from a group of objects liks lists or tuples. Every object had the same likelikhood to be drawn, i.e. to be part of the sample.
In real life situation there will be of course situation in which every or some objects will have different probabilities. We will start again by defining a function on our own. This function will use the previously defined 'weighted_choice' function.
def weighted_sample(population, weights, k):
"""
This function draws a random sample (without repeats)
of length k from the sequence 'population' according
to the list of weights
"""
sample = set()
population = list(population)
weights = list(weights)
while len(sample) < k:
choice = weighted_choice(population, weights)
sample.add(choice)
index = population.index(choice)
weights.pop(index)
population.remove(choice)
weights = [ x / sum(weights) for x in weights]
return list(sample)
Example using the sample function:
Let's assume we have eight candies, coloured "red", "green", "blue", "yellow", "black", "white", "pink", and "orange". Our friend Peter will have the "weighted" preference 1/24, 1/6, 1/6, 1/12, 1/12, 1/24, 1/8, 7/24 for thes colours. He is allowed to take 3 candies:
candies = ["red", "green", "blue", "yellow", "black", "white", "pink", "orange"]
weights = [ 1/24, 1/6, 1/6, 1/12, 1/12, 1/24, 1/8, 7/24]
for i in range(10):
print(weighted_sample(candies, weights, 3))
OUTPUT:
['orange', 'blue', 'green'] ['blue', 'pink', 'green'] ['orange', 'blue', 'yellow'] ['orange', 'blue', 'yellow'] ['orange', 'blue', 'pink'] ['orange', 'pink', 'yellow'] ['blue', 'green', 'yellow'] ['orange', 'green', 'yellow'] ['blue', 'green', 'yellow'] ['orange', 'pink', 'yellow']
Let's approximate the likelihood for an orange candy to be included in the sample:
n = 100000
orange_counter = 0
for i in range(n):
if "orange" in weighted_sample(candies, weights, 3):
orange_counter += 1
print(orange_counter / n)
OUTPUT:
0.71294
It was completely unnecessary to write this function, because we can use the choice function of NumPy for this purpose as well. All we have to do is assign the shape '(2, )' to the optional parameter 'size'. Let us redo the previous example by substituting weighted_sampe with a call of np.random.choice:
n = 100000
orange_counter = 0
for i in range(n):
if "orange" in np.random.choice(candies,
p=weights,
size=(3,),
replace=False):
orange_counter += 1
print(orange_counter / n)
OUTPUT:
0.71134
In addition, the function 'np.random.choice' gives us the possibility to allow repetitions, as we can see in the following example:
countries = ["Germany", "Switzerland",
"Austria", "Netherlands",
"Belgium", "Poland",
"France", "Ireland"]
weights = np.array([83019200, 8555541, 8869537,
17338900, 11480534, 38413000,
67022000, 4857000])
weights = weights / sum(weights)
for i in range(4):
print(np.random.choice(countries,
p=weights,
size=(3,),
replace=True))
OUTPUT:
['Germany' 'Germany' 'Poland'] ['Poland' 'Netherlands' 'Germany'] ['France' 'France' 'Netherlands'] ['Germany' 'France' 'Netherlands']
Cartesian Choice
The function cartesian_choice is named after the Cartesian product from set theory
Cartesian product
The Cartesian product is an operation which returns a set from multiple sets. The result set from the Cartesian product is called a "product set" or simply the "product".
For two sets A and B, the Cartesian product A × B is the set of all ordered pairs (a, b) where a ∈ A and b ∈ B:
A x B = { (a, b)  a ∈ A and b ∈ B }
If we have n sets A_{1}, A_{2}, ... A_{n}, we can build the Cartesian product correspondingly:
A_{1} x A_{2} x ... x A_{n} = { (a_{1}, a_{2}, ... a_{n})  a_{1} ∈ A_{1}, a_{2} ∈ A_{2}, ... a_{n} ∈ A_{n}]
The Cartesian product of n sets is sometimes called an nfold Cartesian product.
Cartesian Choice: cartesian_choice
We will write now a function cartesian_choice, which takes an arbitrary number of iterables as arguments and returns a list, which consists of random choices from each iterator in the respective order.
Mathematically, we can see the result of the function cartesian_choice as an element of the Cartesian product of the iterables which have been passed as arguments.
import numpy as np
def cartesian_choice(*iterables):
"""
A list with random choices from each iterable of iterables
is being created in respective order.
The result list can be seen as an element of the
Cartesian product of the iterables
"""
res = []
for population in iterables:
res.append(np.random.choice(population))
return res
cartesian_choice(["The", "A"],
["red", "green", "blue", "yellow", "grey"],
["car", "house", "fish", "light"],
["smells", "dreams", "blinks", "shines"])
OUTPUT:
['A', 'yellow', 'light', 'shines']
We define now a weighted version of the previously defined function:
import numpy as np
def weighted_cartesian_choice(*iterables):
"""
An arbitrary number of tuple or lists,
each consisting of population and weights.
weighted_cartesian_choice returns a list
with a chocie from each population
"""
res = []
for population, weights in iterables:
# normalize weight:
weights = np.array(weights) / sum(weights)
lst = np.random.choice(population, p=weights)
res.append(lst)
return res
determiners = (["The", "A", "Each", "Every", "No"],
[0.3, 0.3, 0.1, 0.1, 0.2])
colours = (["red", "green", "blue", "yellow", "grey"],
[0.1, 0.3, 0.3, 0.2, 0.2])
nouns = (["water", "elephant", "fish", "light", "programming language"],
[0.3, 0.2, 0.1, 0.1, 0.3])
nouns2 = (["of happiness", "of chocolate", "of wisdom", "of challenges", "of air"],
[0.5, 0.2, 0.1, 0.1, 0.1])
verb_phrases = (["smells", "dreams", "thinks", "is made of"],
[0.4, 0.3, 0.2, 0.1])
print("It may or may not be true:")
for i in range(10):
res = weighted_cartesian_choice(determiners,
colours,
nouns,
verb_phrases,
nouns2)
print(" ".join(res) + ".")
OUTPUT:
It may or may not be true: The green light smells of happiness. The red light dreams of happiness. The grey water thinks of challenges. A green programming language thinks of air. Each blue programming language smells of happiness. The blue water thinks of happiness. The blue programming language smells of chocolate. The yellow water smells of chocolate. No grey light dreams of challenges. A blue light smells of challenges.
We check in the following version, if the "probabilities" are all right:
import random
def weighted_cartesian_choice(*iterables):
"""
A list with weighted random choices from each iterable of iterables
is being created in respective order
"""
res = []
for population, weight in iterables:
lst = weighted_choice(population, weight)
res.append(lst)
return res
determiners = (["The", "A", "Each", "Every", "No"],
[0.3, 0.3, 0.1, 0.1, 0.2])
colours = (["red", "green", "blue", "yellow", "grey"],
[0.1, 0.3, 0.3, 0.2, 0.2])
nouns = (["water", "elephant", "fish", "light", "programming language"],
[0.3, 0.2, 0.1, 0.1, 0.3])
nouns2 = (["of happiness", "of chocolate", "of wisdom", "of challenges", "of air"],
[0.5, 0.2, 0.1, 0.1, 0.1])
verb_phrases = (["smells", "dreams", "thinks", "is made of"],
[0.4, 0.3, 0.2, 0.1])
print("It may or may not be true:")
sentences = []
for i in range(10000):
res = weighted_cartesian_choice(determiners,
colours,
nouns,
verb_phrases,
nouns2)
sentences.append(" ".join(res) + ".")
words = ["smells", "dreams", "thinks", "is made of"]
from collections import Counter
c = Counter()
for sentence in sentences:
for word in words:
if word in sentence:
c[word] += 1
wsum = sum(c.values())
for key in c:
print(key, c[key] / wsum)
OUTPUT:
It may or may not be true: smells 0.3963 thinks 0.1997 dreams 0.3023 is made of 0.1017
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Random Seed
A random seed,  also called "seed state", or just "seed"  is a number used to initialize a pseudorandom number generator. When we called random.random() we expected and got a random number between 0 and 1. random.random() calculates a new random number by using the previously produced random number. What about the first time we use random in our program? Yes, there is no previously created random number. If a random number generator is called for the first time, it will have to create a first "random" number.
If we seed a pseudorandom number generator, we provide a first "previous" value. A seed value corresponds to a sequence of generated values for a given random number generator. If you use the same seed value again, you get and you can rely on getting the same sequence of numbers again.
The seed number itself doesn't need to be randomly chosen so that the algorithm creates values which follow a probability distribution in a pseudorandom manner. Yet, the seed matters in terms of security. If you know the seed, you could for example generate the secret encryption key which is based on this seed.
Random seeds are in many programming languages generated from the state of the computer system, which is in lots of cases the system time.
This is true for Python as well. Help on random.seed says that if you call the function with None or no argument it will seed "from current time or from an operating system specific randomness source if available."
import random
help(random.seed)
OUTPUT:
Help on method seed in module random: seed(a=None, version=2) method of random.Random instance Initialize internal state from hashable object. None or no argument seeds from current time or from an operating system specific randomness source if available. If *a* is an int, all bits are used. For version 2 (the default), all of the bits are used if *a* is a str, bytes, or bytearray. For version 1 (provided for reproducing random sequences from older versions of Python), the algorithm for str and bytes generates a narrower range of seeds.
The seed functions allows you to get a determined sequence of random numbers. You can repeat this sequence, whenever you need it again, e.g. for debugging purposes.
import random
random.seed(42)
for _ in range(10):
print(random.randint(1, 10), end=", ")
print("\nLet's create the same random numbers again:")
random.seed(42)
for _ in range(10):
print(random.randint(1, 10), end=", ")
OUTPUT:
2, 1, 5, 4, 4, 3, 2, 9, 2, 10, Let's create the same random numbers again: 2, 1, 5, 4, 4, 3, 2, 9, 2, 10,
Random Numbers in Python with Gaussian and Normalvariate Distribution
We want to create now 1000 random numbers between 130 and 230 that have a gaussian distribution with the mean value mu set to 550 and the standard deviation sigma is set to 30.
from random import gauss
n = 1000
values = []
frequencies = {}
while len(values) < n:
value = gauss(180, 30)
if 130 < value < 230:
frequencies[int(value)] = frequencies.get(int(value), 0) + 1
values.append(value)
print(values[:10])
OUTPUT:
[173.49123947564414, 183.47654360102564, 186.96893210720162, 214.90676059797428, 199.69909520396007, 183.31521532331496, 157.85035192965537, 149.56012897536849, 187.39026585633607, 219.33242481612143]
The following program plots the random values, which we have created before. We haven't covered matplotlib so far, so it's not necessary to understand the code:
%matplotlib inline
import matplotlib.pyplot as plt
freq = list(frequencies.items())
freq.sort()
plt.plot(*list(zip(*freq)))
OUTPUT:
[<matplotlib.lines.Line2D at 0x7fbdf4df2580>]
We do the same now with normvariate instead of gauss:
from random import normalvariate
n = 1000
values = []
frequencies = {}
while len(values) < n:
value = normalvariate(180, 30)
if 130 < value < 230:
frequencies[int(value)] = frequencies.get(int(value), 0) + 1
values.append(value)
freq = list(frequencies.items())
freq.sort()
plt.plot(*list(zip(*freq)))
OUTPUT:
[<matplotlib.lines.Line2D at 0x7fbdf4560430>]
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Exercise With Zeros and Ones
It might be a good idea to write the following function as an exercise yourself. The function should be called with a parameter p, which is a probabilty value between 0 and 1. The function returns a 1 with a probability of p, i.e. ones in p percent and zeros in (1  p) percent of the calls:
import random
def random_ones_and_zeros(p):
""" p: probability 0 <= p <= 1
returns a 1 with the probability p
"""
x = random.random()
if x < p:
return 1
else:
return 0
Let's test our little function:
n = 1000000
sum(random_ones_and_zeros(0.8) for i in range(n)) / n
OUTPUT:
0.800609
It might be a great idea to implement a task like this with a generator. If you are not familar with the way of working of a Python generator, we recommend to consult our chapter on generators and iterators of our Python tutorial.
import random
def random_ones_and_zeros(p):
while True:
x = random.random()
yield 1 if x < p else 0
def firstn(generator, n):
for i in range(n):
yield next(generator)
n = 1000000
sum(x for x in firstn(random_ones_and_zeros(0.8), n)) / n
OUTPUT:
0.799762
Our generator random_ones_and_zeros can be seen as a sender, which emits ones and zeros with a probability of p and (1p) respectively.
We will write now another generator, which is receiving this bitstream. The task of this new generator is to read the incoming bitstream and yield another bitstream with ones and zeros with a probability of 0.5 without knowing or using the probability p. It should work for an arbitrary probability value p.^{2}
def ebitter(bitstream):
while True:
bit1 = next(bitstream)
bit2 = next(bitstream)
if bit1 + bit2 == 1:
bit3 = next(bitstream)
if bit2 + bit3 == 1:
yield 1
else:
yield 0
def ebitter2(bitstream):
bit1 = next(bitstream)
bit2 = next(bitstream)
bit3 = next(bitstream)
while True:
if bit1 + bit2 == 1:
if bit2 + bit3 == 1:
yield 1
else:
yield 0
bit1, bit2, bit3 = bit2, bit3, next(bitstream)
n = 1000000
sum(x for x in firstn(ebitter(random_ones_and_zeros(0.8)), n)) / n
OUTPUT:
0.49975
n = 1000000
sum(x for x in firstn(ebitter2(random_ones_and_zeros(0.8)), n)) / n
OUTPUT:
0.500011
Underlying theory:
Our first generator emits a bitstream B_{0}, B_{1}, B_{2},...
We check now an arbitrary pair of consecutive Bits B_{i}, B_{i+1}, ...
Such a pair can have the values 01, 10, 00 or 11. The probability P(01) = (p1) x p and probability P(10) = p x (p1), so that the combined probabilty that the two consecutive bits are either 01 or 10 (or the sum of the two bits is 1) is 2 x (p1) x p
Now we look at another bit B_{i+2}. What is the probability that both
B_{i} + B_{i+1} = 1
and
B_{i+1} + B_{i+2} = 1?
The possible outcomes satisfying these conditions and their corresponding probabilities can be found in the following table:
Probability  B_{i}  B_{i+1}  B_{i+2} 

p^{2} x (1p)  0  1  0 
p x (1  p)^{2}  1  0  1 
We will denote the outcome sum(B_{i}, B_{i+1})=1 asX_{1} and correspondingly the outcome sum(B_{i+1}, B_{i+2})=1 as X_{2}
So, the joint probability P(X_{1}, X_{2}) = p^{2} x (1p) + p x (1  p)^{2} which can be rearranged to p x (1p)
The conditional probability of X_{2} given X_{1}:
P(X_{2}  X_{1}) = P(X_{1}, X_{2}) / P(X_{2})
P(X_{2}  X_{1}) = p x (1p) / 2 x p x (1p) = 1 / 2
Synthetical Sales Figures
In this subchapter we want to create a data file with sales figures. Imagine that we have a chain of shops in various European and Canadian cities: Frankfurt, Munich, Berlin, Zurich, Hamburg, London, Toronto, Strasbourg, Luxembourg, Amsterdam, Rotterdam, The Hague
We start with an array 'sales' of sales figures for the year 1997:
import numpy as np
sales = np.array([1245.89, 2220.00, 1635.77, 1936.25, 1002.03, 2099.13, 723.99, 990.37, 541.44, 1765.00, 1802.84, 1999.00])
The aim is to create a comma separated list like the ones you get from Excel. The file should contain the sales figures, we don't know, for all the shops, we don't have, spanning the year from 1997 to 2016.
We will add random values to our sales figures year after year. For this purpose we construct an array with growthrates. The growthrates can vary between a minimal percent value (min_percent) and maximum percent value (max_percent):
min_percent = 0.98 # corresponds to 1.5 %
max_percent = 1.06 # 6 %
growthrates = (max_percent  min_percent) * np.random.random_sample(12) + min_percent
print(growthrates)
OUTPUT:
[1.00519741 0.98570126 0.99862155 0.98566266 1.02450406 0.98716687 1.04642595 1.05124614 1.05304526 0.99729833 1.03868868 0.99865257]
To get the new sales figures after a year, we multiply the sales array "sales" with the array "growthrates":
sales * growthrates
OUTPUT:
array([1252.36539657, 2188.25678613, 1633.51517486, 1908.48932696, 1026.58380204, 2072.19158526, 757.60192692, 1041.12263919, 570.16082349, 1760.23154653, 1872.58949714, 1996.30648468])
To get a more sustainable sales development, we change the growthrates only every four years.
This is our complete program, which saves the data in a file called sales_figures.csv:
import numpy as np
fh = open("sales_figures.csv", "w")
fh.write("Year, Frankfurt, Munich, Berlin, Zurich, Hamburg, London, Toronto, Strasbourg, Luxembourg, Amsterdam, Rotterdam, The Hague\n")
sales = np.array([1245.89, 2220.00, 1635.77, 1936.25, 1002.03, 2099.13, 723.99, 990.37, 541.44, 1765.00, 1802.84, 1999.00])
for year in range(1997, 2016):
line = str(year) + ", " + ", ".join(map(str, sales))
fh.write(line + "\n")
if year % 4 == 0:
min_percent = 0.98 # corresponds to 1.5 %
max_percent = 1.06 # 6 %
growthrates = (max_percent  min_percent) * np.random.random_sample(12) + min_percent
#growthrates = 1 + (np.random.rand(12) * max_percent  negative_max) / 100
sales = np.around(sales * growthrates, 2)
fh.close()
The result is in the file sales_figures.csv.
We will use this file in our chapter on reading and writing in Numpy.
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Exercises
### Solutions to our exercises

from random import randint outcomes = [ randint(1, 6) for _ in range(10000)] even_pips = [ x for x in outcomes if x % 2 == 0] greater_two = [ x for x in outcomes if x > 2] combined = [ x for x in outcomes if x % 2 == 0 and x > 2] print(len(even_pips) / len(outcomes)) print(len(greater_two) / len(outcomes)) print(len(combined) / len(outcomes))
OUTPUT:
0.5061 0.6719 0.3402

At first we will write the function "process_datafile" to process our data file:
def process_datafile(filename): """ process_datafile > (universities, enrollments, total_number_of_students) universities: list of University names enrollments: corresponding list with enrollments total_number_of_students: over all universities """ universities = [] enrollments = [] with open(filename) as fh: total_number_of_students = 0 fh.readline() # get rid of descriptive first line for line in fh: line = line.strip() *praefix, undergraduates, postgraduates, total = line.rsplit() university = praefix[1:] total = int(total.replace(",", "")) enrollments.append(total) universities.append(" ".join(university)) total_number_of_students += total return (universities, enrollments, total_number_of_students)
Let's start our function and check the results:
universities, enrollments, total_students = process_datafile("../data/universities_uk.txt") for i in range(14): print(universities[i], end=": ") print(enrollments[i]) print("Total number of students onrolled in the UK: ", total_students)
OUTPUT:
Open University in England: 123490 University of Manchester: 37925 University of Nottingham: 33270 Sheffield Hallam University: 33100 University of Birmingham: 32335 Manchester Metropolitan University: 32160 University of Leeds: 30975 Cardiff University: 30180 University of South Wales: 29195 University College London: 28430 King's College London: 27645 University of Edinburgh: 27625 Northumbria University: 27565 University of Glasgow: 27390 Total number of students onrolled in the UK: 2299380
We want to enroll now a virtual student randomly to one of the universities. To get a weighted list suitable for our weighted_choice function, we have to normalize the values in the list enrollments:
normalized_enrollments = [ students / total_students for students in enrollments] # enrolling a virtual student: print(weighted_choice(universities, normalized_enrollments))
OUTPUT:
University of Liverpool
We have been asked by the exercise to "enroll" 100,000 fictional students. This can be easily accomplished with a loop:
from collections import Counter outcomes = [] n = 100000 for i in range(n): outcomes.append(weighted_choice(universities, normalized_enrollments)) c = Counter(outcomes) print(c.most_common(20))
OUTPUT:
[('Open University in England', 5456), ('University of Manchester', 1663), ('University of Birmingham', 1433), ('University of Nottingham', 1432), ('Sheffield Hallam University', 1421), ('Manchester Metropolitan University', 1381), ('University of South Wales', 1365), ('Cardiff University', 1311), ('University College London', 1291), ('University of Leeds', 1290), ("King's College London", 1251), ('Northumbria University', 1205), ('University of Sheffield', 1204), ('University of Edinburgh', 1195), ('Nottingham Trent University', 1184), ('University of Glasgow', 1182), ('University of Oxford', 1181), ('University of Central Lancashire', 1162), ('University of Plymouth', 1161), ('University of Warwick', 1147)]
</li>

The bunch of amazons is implemented as a list, while we choose a set for Pysseusses favorites. The weights at the beginning are 1/11 for all, i.e. 1/len(amazons).
Every loop cycle corresponds to a new day. Every time we start a new loop cycle, we will draw "n" samples of Pythonistas to calculate the ratio of the number of times the sample is equal to the king's favorites divided by the number of times the sample doesn't match the king's idea of daughterinlaws. This corresponds to the probability "prob". We stop the first time, the probability is equal or larger than 0.9.
import time amazons = ["Airla", "Barbara", "Eos", "Glykeria", "Hanna", "Helen", "Agathangelos", "Iokaste", "Medousa", "Sofronia", "Andromeda"] weights = [ 1/len(amazons) for _ in range(len(amazons)) ] Pytheusses_favorites = {"Iokaste", "Medousa", "Sofronia", "Andromeda"} n = 1000 counter = 0 prob = 1 / 330 days = 0 factor1 = 1 / 13 factor2 = 1 / 12 start = time.perf_counter() while prob < 0.9: for i in range(n): the_chosen_ones = weighted_sample(amazons, weights, 4) if set(the_chosen_ones) == Pytheusses_favorites: counter += 1 prob = counter / n counter = 0 weights[:7] = [ p  p*factor1 for p in weights[:7] ] weights[7:] = [ p + p*factor2 for p in weights[7:] ] weights = [ x / sum(weights) for x in weights] days += 1 print(time.perf_counter()  start) print("Number of days, he has to wait: ", days)
OUTPUT:
2.2348596939700656 Number of days, he has to wait: 32
Teh value for the number of days differs, if n is not large enough.
The following is a solutions without roundoff errors. We will use Fraction from the module fractions.
import time from fractions import Fraction amazons = ["Airla", "Barbara", "Eos", "Glykeria", "Hanna", "Helen", "Agathangelos", "Iokaste", "Medousa", "Sofronia", "Andromeda"] weights = [ Fraction(1, 11) for _ in range(len(amazons)) ] Pytheusses_favorites = {"Iokaste", "Medousa", "Sofronia", "Andromeda"} n = 1000 counter = 0 prob = Fraction(1, 330) days = 0 factor1 = Fraction(1, 13) factor2 = Fraction(1, 12) start = time.perf_counter() while prob < 0.9: #print(prob) for i in range(n): the_chosen_ones = weighted_sample(amazons, weights, 4) if set(the_chosen_ones) == Pytheusses_favorites: counter += 1 prob = Fraction(counter, n) counter = 0 weights[:7] = [ p  p*factor1 for p in weights[:7] ] weights[7:] = [ p + p*factor2 for p in weights[7:] ] weights = [ x / sum(weights) for x in weights] days += 1 print(time.perf_counter()  start) print("Number of days, he has to wait: ", days)
OUTPUT:
52.22739774401998 Number of days, he has to wait: 32
We can see that the solution with fractions is beautiful but very slow. Whereas the greater precision doesn't play a role in our case.
So far, we haven't used the power of Numpy. We will do this in the next implementation of our problem:
import time import numpy as np amazons = ["Airla", "Barbara", "Eos", "Glykeria", "Hanna", "Helen", "Agathangelos", "Iokaste", "Medousa", "Sofronia", "Andromeda"] weights = np.full(11, 1/len(amazons)) Pytheusses_favorites = {"Iokaste", "Medousa", "Sofronia", "Andromeda"} n = 1000 counter = 0 prob = 1 / 330 days = 0 factor1 = 1 / 13 factor2 = 1 / 12 start = time.perf_counter() while prob < 0.9: for i in range(n): the_chosen_ones = weighted_sample(amazons, weights, 4) if set(the_chosen_ones) == Pytheusses_favorites: counter += 1 prob = counter / n counter = 0 weights[:7] = weights[:7]  weights[:7] * factor1 weights[7:] = weights[7:] + weights[7:] * factor2 weights = weights / np.sum(weights) #print(weights) days += 1 print(time.perf_counter()  start) print("Number of days, he has to wait: ", days)
OUTPUT:
2.709868110017851 Number of days, he has to wait: 32
Footnotes:
^{1} The TIOBE index or The TIOBE Programming Community index is  according to the website "an indicator of the popularity of programming languages. The index is updated once a month. The ratings are based on the number of skilled engineers worldwide, courses and third party vendors. Popular search engines such as Google, Bing, Yahoo!, Wikipedia, Amazon, YouTube and Baidu are used to calculate the ratings. It is important to note that the TIOBE index is not about the best programming language or the language in which most lines of code have been written."
^{2} I am thankful to Dr. Hanno Baehr who introduced me to the problem of "Random extraction" when participating in a Python training course in Nuremberg in January 2014. Hanno outlined some bits of the theoretical framework. During a night session in a pub called "Zeit & Raum" (english: "Time & Space") I implemented a corresponding Python program to back the theoretical solution empirically.
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