The following steps will serve as a guideline to create an honest coin simulator in Python programming language:

Create a new notebook using the Google Colab environment as described in section 1.2: Designing the solution.

To build a coin simulator is necessary to define a list of possible events after throwing a coin ['Head','Tail'], a list with the corresponding probabilities of happening for each event  [ph, pt], and the number n of times the experiment will be repeated. All these commands can be summarized as:

import numpy as np

n = 1000

ph = 0.5

pt = 0.5

coin_seq = np.random.choice(['Head','Tail'], p=[ph, pt], size=n)

coin_seq

The following values will be stored in variable coin_seq:

array(['Head', 'Tail', 'Head', 'Head', 'Head', 'Tail', 'Tail', 'Head', 'Head', 'Head', 'Head', 'Head', 'Head', 'Tail', 'Head', 'Tail', 'Head', 'Head', 'Head', 'Tail', 'Head', 'Tail', 'Head', 'Tail', 'Tail', 'Tail', 'Tail', 'Tail', 'Head', 'Head', 'Head', 'Head', 'Tail', 'Tail', 'Tail', 'Tail', 'Head', 'Tail', 'Head', 'Head', 'Tail', 'Head', 'Head', 'Head', 'Tail', 'Head', 'Tail', 'Tail', 'Tail', 'Head', 'Tail', 'Head', 'Head', 'Head', 'Tail', 'Tail', 'Head', 'Tail', 'Head', 'Tail', 'Head', 'Head', 'Head', 'Head', 'Tail', 'Tail', 'Head', 'Tail', 'Tail', 'Tail', 'Tail', 'Tail', 'Head', 'Tail', 'Head', 'Tail', 'Head', 'Head', 'Tail', 'Head', 'Tail', 'Tail', 'Tail', 'Head', 'Head', 'Tail', 'Tail', 'Tail', 'Tail', 'Head', 'Head', 'Tail', 'Head', 'Tail', 'Tail', 'Head', 'Head', 'Tail', 'Tail', 'Tail', 'Head', 'Tail', 'Tail', 'Tail', 'Head', 'Head', 'Head', 'Tail', 'Head', 'Tail', 'Tail', 'Head', 'Tail', 'Head', 'Head', 'Tail', 'Head', 'Head', 'Tail', 'Tail', 'Tail', 'Tail', 'Head', 'Tail', 'Tail', 'Head', 'Tail', 'Tail', 'Head', 'Tail', 'Head', 'Tail', 'Head', 'Head', 'Head', 'Head', 

After the previous command, the variable coin_seq contains the results of the n repetitions of the experiment. Now, these results could be counted in terms of the number of occurrences of each event: 'Head' and 'Tail'. For this task, the command Counter() from the collections package is useful since it returns a dictionary with the name of the event and its frequency. A dictionary is a data structure where each element has two parts: the key which is the name of the element, and the value is the part that stores the value related to the name stored in the key part:

import collections

coin_counter = collections.Counter(coin_seq)

print(coin_counter)

Counter({'Head': 503, 'Tail': 497})

To extract the keys from the variable coin_seq  the command keys() could be used:

coin_counter.keys()

dict_keys(['Head', 'Tail']) 

To extract the keys from the variable coin_seq  the command values() could be used:

coin_counter.values()

dict_values([503, 497]) 

The command plt.bar from package matplotlib.pyplot as plt helps to create a related bar plot. The number of times each event occurs is the height of each bar, and the command face = list(coin_counter.keys()) stores in the variable face the frequency of each event. The name of each event is described in the x-axis label using the command freq = list(coin_counter.values()).

import matplotlib.pyplot as plt

face = list(coin_counter.keys())

freq = list(coin_counter.values())

print(freq)

plt.bar(face, freq, color ='maroon', width = 0.4)

plt.xlabel("Face")

plt.ylabel("Frequency")

plt.title("Number of occurrences of each face after throw a coin "+str(n)+" times")

plt.show()

The following values and graph will appear: