reading-notes

Game of Greed Reading Part 4

Dunder Methods

dunder methods are predefined methods that are included in classes, they start whith double under_score

Enriching a Simple Account Class

Dunder methods in classes can unlock these features:

Initialization of new objects
Object representation
Enable iteration
Operator overloading (comparison)
Operator overloading (addition)
Method invocation
Context manager support (with statement)

Dunder methods examples:

init

The init method is used to create an instance of the class. Example:

def __init__(self,names):

 if names:
    self.names = names.copy()
    for name in names:
        self.versions[name] = 1

### str

The str method is useful when we want to use instances of our class in a print statement. As discussed earlier, it usually returns a memory object. Example:

def __str__(self):

s ="The current softwares and their versions are listed below: \n"
for key,value in self.versions.items():
    s+= f"{key} : v{value} \n"
return s

setitem

We can give instances of our class a similar feature with the help of the setitem method. Example:

def __setitem__(self,name,version):

if name in self.versions:
    self.versions[name] = version

len

In a dictionary, the len method returns the number of elements in a list or the number of key-value pairs in a dictionary.

def __len__(self):

  return len(self.names)

The len method for this class returns the number of softwares.

getitem

The getitem method is like the setitem method, the major difference being that the getitem method is called when we use the [] operator of a dictionary.

contains

The contains method is used when using the in operator. The return value has to be a boolean.

def __contains__(self,name):

if name in self.versions:
    return True
else:
    return False

add

The add method is called when using the + operator. We can define a custom add method for our class.

def __add__(self,p2):

x = self.x + p2.x
y = self.y + p2.y
return point(x,y)

iadd

The iadd method is like the add method. It is invoked when using the += operator

call

When invoking a function like func(), we are invoking the call method.

If we put in place a call method for our class, we can do the following:

p1()

p2()

Below is an example call method:

def __call__(self):

print(f"Called Point {self.x},{self.y}")

repr

repr is a built-in function to compute the “official” string representation of an object. If at all possible, this should look like a valid Python expression that could be used to recreate an object with the same value.

more dunder methods:

table

Probability in Python

that probability theory is mainly concerned with predicting the likelihood of future events, while statistics analyzes the frequency of past events.

### From statistics to probability

he code below simulates 10, 100, 1000, and 1000000 trials, and then calculates the average proportion of heads observed

import random

def coin_trial():

heads = 0

for i in range(100):

if random.random() <= 0.5:

heads +=1

return headsdef simulate(n):

trials = [] for i in range(n):

trials.append(coin_trial())

return(sum(trials)/n)

simulate(10)

>>> 5.4

simulate(100)

>>> 4.83

simulate(1000)

>>> 5.055

simulate(1000000)

>>> 4.999781

The data and the distribution

The normal distribution refers to a particularly important phenomenon in the realm of probability and statistics. The normal distribution looks like this:

normal dist

code example

# Extract the Tokaji scores

tokaji = []

non_tokaji = []

for wine in wines:

if points != '':

points = wine[4]

if wine[9] == "Tokaji":

tokaji.append(float(points))

else:

non_tokaji.append(points)

# Extract the Lambrusco scores

lambrusco = []

non_lambrusco = []

for wine in wines:

if points != '':

points = wine[4]

if wine[9] == "Lambrusco":

lambrusco.append(float(points))

else:

non_lambrusco.append(float(points))

Three Sigma Rule

The Three Sigma rule, also known as the empirical rule or 68-95-99.7 rule, is an expression of how many of our observations fall within a certain distance of the mean.

sigma

the z-scores

The Z-score is a simple calculation that answers the question, “Given a data point, how many standard deviations is it away from the mean?” The equation below is the Z-score equation.

z-score