Weeks 7–10 — OOP Part 1: Classes & Instances · OOP Part 2: Inheritance & Dunder Methods · Regular Expressions · Intermediate Capstone: Text Analysis Tool

class Dog:
    species = "Canis lupus familiaris"  # class attribute

    def __init__(self, name, age):
        self.name = name   # instance attribute
        self.age = age

    def bark(self):
        return f"{self.name} says: Woof!"

    def __repr__(self):
        return f"Dog(name={self.name!r}, age={self.age})"

fido = Dog("Fido", 3)
print(fido.bark())
print(repr(fido))
print(Dog.species)

class BankAccount:
    def __init__(self, owner, balance=0):
        self.owner = owner
        self._balance = balance   # private by convention

    @property
    def balance(self):
        return self._balance

    @balance.setter
    def balance(self, value):
        if value < 0:
            raise ValueError("Balance cannot be negative")
        self._balance = value

    def deposit(self, amount):
        self.balance += amount

    def withdraw(self, amount):
        if amount > self.balance:
            raise ValueError("Insufficient funds")
        self.balance -= amount

acct = BankAccount("Alice", 1000)
acct.deposit(500)
acct.withdraw(200)
print(acct.balance)  # 1300

from dataclasses import dataclass, field

@dataclass
class Point:
    x: float
    y: float

    def distance_to(self, other):
        return ((self.x-other.x)**2 + (self.y-other.y)**2)**0.5

p1 = Point(0, 0)
p2 = Point(3, 4)
print(p1.distance_to(p2))  # 5.0
print(p1)                  # Point(x=0, y=0)

class Animal:
    def __init__(self, name):
        self.name = name

    def speak(self):
        raise NotImplementedError

class Dog(Animal):
    def speak(self):
        return f"{self.name}: Woof!"

class Cat(Animal):
    def speak(self):
        return f"{self.name}: Meow!"

for a in [Dog("Fido"), Cat("Whiskers")]:
    print(a.speak())

from abc import ABC, abstractmethod

class Shape(ABC):
    @abstractmethod
    def area(self) -> float: ...

    @abstractmethod
    def perimeter(self) -> float: ...

    def __str__(self):
        return f"{self.__class__.__name__}: area={self.area():.2f}"

class Circle(Shape):
    def __init__(self, r): self.r = r
    def area(self): return 3.14159 * self.r ** 2
    def perimeter(self): return 2 * 3.14159 * self.r

class Rectangle(Shape):
    def __init__(self, w, h): self.w, self.h = w, h
    def area(self): return self.w * self.h
    def perimeter(self): return 2 * (self.w + self.h)

shapes = [Circle(5), Rectangle(3, 4)]
for s in shapes: print(s)

class Vector:
    def __init__(self, x, y):
        self.x, self.y = x, y

    def __repr__(self):
        return f"Vector({self.x}, {self.y})"

    def __add__(self, other):
        return Vector(self.x + other.x, self.y + other.y)

    def __eq__(self, other):
        return self.x == other.x and self.y == other.y

    def __abs__(self):
        return (self.x**2 + self.y**2)**0.5

v1 = Vector(1, 2)
v2 = Vector(3, 4)
print(v1 + v2)   # Vector(4, 6)
print(abs(v2))   # 5.0

import re

text = "Call us at 010-1234-5678 or 02-987-6543"

# findall: return all matches
phones = re.findall(r"\d{2,3}-\d{3,4}-\d{4}", text)
print(phones)   # ['010-1234-5678', '02-987-6543']

# sub: replace
masked = re.sub(r"\d{4}$", "****", phones[0])
print(masked)   # 010-1234-****

# Named groups
m = re.search(r"(?P<area>\d{2,3})-(?P<number>\d+-\d+)", text)
print(m.group("area"), m.group("number"))

EMAIL   = r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"
URL     = r"https?://[\w./%-]+"
DATE    = r"\d{4}-\d{2}-\d{2}"
IP_ADDR = r"\b(?:\d{1,3}\.){3}\d{1,3}\b"

for pattern, sample in [
    (EMAIL, "user@example.com"),
    (URL, "https://python.org/docs"),
    (DATE, "2024-05-15"),
]:
    m = re.fullmatch(pattern, sample)
    print(f"{'OK' if m else 'FAIL'} — {sample}")

python analyze.py [OPTIONS] FILE

Options:
  --top N       Show top N words (default 10)
  --format FMT  Output format: text|csv|json (default text)
  --no-stop     Exclude common stop words

$ python analyze.py hamlet.txt --top 5

File: hamlet.txt  (5447 words, 1234 unique)

Top 5 words:
  the     ████████████████  250
  and     ██████████████    215
  to      ████████████      180
  of      ██████████        150
  a       ████████          130

Sentences: 423  (avg 12.9 words)
Readability: 68.4 (Standard)