CSD 4523 Python II Lecture Notes Week 1 PDF

CSD 4523 Python II Lecture Notes Week 1 1 General Class Itinerary 2 Class Timing (except for mid term, presentations and final) 3 Weekly Course Objectives Python Overview...

CSD 4523 Python II Lecture Notes Week 1 1 General Class Itinerary 2 Class Timing (except for mid term, presentations and final) 3 Weekly Course Objectives Python Overview Mathematical Functions Operators Data Types Flow Control Functions Variables File I/O Try Exception Blocks OOP/Class Python Features 5 Python Reserved Words (keywords) Can access all python keywords with “help(“keyword”)” in the python console. There are 36 keywords (Python 3.13.1). Except for “False”, “True” and “None”, all keywords are lowercase. Keywords are always available, no importing required. Python keywords are different from Python’s built-in functions and types. The built-in functions and types are also always available, but they aren’t as restrictive as the keywords in their usage. Eg “Print”. These built-in functions can be reassigned. Cannot be used as identifiers. 6 LINES AND INDENTATION Blocks of code are denoted by line indentation, which is rigidly enforced. Convention is to use 4 spaces to indent. (https://peps.python.org/pep-0008/ is the style guide to Python programs). In Python, statements end with a new line. However, a user can continue the statement on a second line using \ to denote that the line should continue. Statements that have the [], {}, or () brackets do not need to use the line continuation character. 7 Quotation and Comments Python accepts single ('), double (") and triple (''' or """) quotes to denote string literals, as long as the same type of quote starts and ends the string. Triple quotes are used to span the string across multiple lines. A hash sign (#) that is not inside a string literal begins a comment. All characters after the # and up to the end of the physical line are part of the comment and the Python interpreter ignores them. You can start comment at the end of a statement. Multiline comments need to start and end with 3 quotes (single or double). 8 Mathematical Functions Math module provides many helpful functions including: Number-theoretic and representative Power and logarithmic Trigonometric Hyperbolic Special Constants (eg pi) Random module provides many functions that are useful for simulations, privacy and security applications. 9 Arithmetic Operators Operator Name Example + Addition: adds two operands x+y – Subtraction: subtracts two operands x–y * Multiplication: multiplies two operands x*y / Division (float): divides the first operand by the second x/y Division (floor): divides the first operand by the second // x // y and returns the integer quotient Modulus: returns the remainder when the first operand % x%y is divided by the second ** Power (Exponent): Returns first raised to power second x ** y 10 Relational Operators Operator Meaning Example == equal to x == y != not equal to x != y > greater than x>y >= greater than or equal to x >= y < less than x>= b ) Method of class D Method of class B Method of class C Method of class A 25 Composition Composition in Python is a design In composition, the objects are typically technique where a class includes objects created and managed by the containing of other classes as attributes, allowing class, and they are closely associated for the creation of more complex objects with the containing class. by combining simpler ones. It enables a "has-a" relationship Composition allows for building complex between classes, where one class "has" objects by reusing existing classes, another class as part of its promoting code reuse and modularity. implementation. 26 Composition Continued Example: A Car class might have objects like an Engine, Wheels, and Seats. These objects In this case, Car "has-a" Engine, Car "has-a" Wheels, etc. belong to the car, but they aren't the car itself. Composition in object-oriented programming is when one class contains objects of other classes. This is different from inheritance where a class "is-a" type of another class. Composition means that one object is made up of other objects. Instead of inheriting behaviors and properties (like in inheritance), composition lets a How Composition Works: class include objects of other classes. These included objects work together to provide more complex functionality. 27 Why Use Composition? More Flexibility: You can compose objects in various ways. You can give the same car a different type of engine (e.g., GasEngine or ElectricEngine) without changing the structure of the Car class. Simpler Code: Rather than creating one massive class that does everything (like a Car class with all the car details), you can break it into smaller parts (like Engine, Wheels, etc.) and combine them. This makes the code easier to manage. Reuse of Components: You can reuse the same smaller components in other classes. For example, the Engine class could be used in both Car and Truck classes without duplicating code. Avoids Inheritance: Avoids deep inheritance hierarchies which can be harder to maintain. 28  Composition vs Inheritance Use Composition when: You want to model a "has-a" relationship, allows mixing and matching components. You need to combine different functionalities from separate classes. You want to minimize coupling between classes and avoid deep inheritance hierarchies. Use Inheritance when: You have a clear "is-a" relationship. You have a clear hierarchical relationship. You want to extend the behavior of an existing class. 29 When to Use Composition or Inheritance: 1 Problem: You have a Dog and Cat that both share behaviors like eat() and sleep(). Answer: Inheritance Reason: Both Dog and Cat are types of Mammals, so they should inherit common behaviors like eat() and sleep() from a Mammal class. This is an "is-a" relationship (Dog is a Mammal, Cat is a Mammal). 30 When to Use Composition or Inheritance: 2 Problem: A Car has an Engine and a GPS. The Car doesn’t inherit from the Engine or GPS but uses them. Answer: Composition Reason: The car has-a engine and has-a GPS, but the car isn’t a type of engine or GPS. This is a "has-a" relationship where the car can be composed of these components. 31 When to Use Composition or Inheritance: 3 Problem: A Manager is an Employee who also has additional responsibilities (e.g., managing a team). Answer: Inheritance Reason: A Manager is a specialized type of Employee. The manager can inherit the base behavior (e.g., work()), and then add specialized functionality for team management. This is an "is-a" relationship. 32 When to Use Composition or Inheritance: 4 Problem: You have a GameCharacter that has multiple abilities, such as Strength, Intelligence, and Agility, and you want to combine these abilities dynamically for different characters. Answer: Composition Reason: The GameCharacter has-a strength, intelligence, and agility. These are separate components that can be composed to create different characters with unique abilities. This allows flexibility in defining characters by mixing and matching abilities. 33 When to Use Composition or Inheritance: 5 Problem: You need a PaymentSystem that can process payments using different methods, like CreditCard or PayPal. Answer: Composition Reason: The PaymentSystem uses different payment methods (like CreditCard or PayPal) but doesn't inherit from them. The payment methods are interchangeable components that the payment system can compose. 34  Composition Example Run Composition example – smartphone.py 35 CSD 4523 Python II Lecture Notes Week 6 1 Weekly Course Objectives Operator overloading Iterators and Iterables Generators Assignment #3 Week 6 Lab 2 Built-in Operator Functions 2+24 2.0 + 2  4.0 “ab” + “cd”  “abcd” [1, 2] + [3, 4]  [1, 2, 3, 4] 2 + ‘b’  ERROR instance1 + instance2  ERROR int.__add__(2, 2)  2 3 What Is Operator Overloading? Operator overloading in Python refers to the ability to define custom behavior for operators when they are used with instances of user-defined classes. Python allows you to redefine the behavior of many built-in operators by implementing special methods with double underscores (often called dunder methods or magic methods). For example, if you define a method named __add__() in your class, Python will call this method when the + operator is used with instances of your class. Operator overloading allows you to make your classes behave more like built-in types, enabling more expressive and readable code. However, it's important to use operator overloading judiciously and provide clear and intuitive behavior to avoid confusion for other developers who might use your code. 4 Why Use Operator Overloading? Improves Code Readability: Polymorphism: Demonstrates Customization: Allows the Instead of defining methods like how different types can creation of user-defined add() or multiply(), overloading respond to the same operator behaviors for objects when operators allows you to use in unique ways, reinforcing using operators, tailored to the operators (+, *) naturally, polymorphism in object- problem at hand. making the code more intuitive. oriented programming. 5 Examples Of Methods That Can Be Overloaded Arithmetic Operators: Unary Operators: __add__ for addition (+) __neg__ for negation (-) __sub__ for subtraction (-) __pos__ for unary plus (+) __mul__ for multiplication (*) __abs__ for absolute value (abs()) __truediv__ for division (/) __invert__ for bitwise inversion (~) __floordiv__ for floor division (//) __mod__ for modulo (%) Container Methods: __pow__ for exponentiation (**) __len__ for determining the length (len()) __getitem__ for getting an item by index or key ([], slicing) Comparison Operators: __setitem__ for setting an item by index or key (assignment to []) __eq__ for equality (==) __delitem__ for deleting an item by index or key (del statement) __ne__ for inequality (!=) __lt__ for less than (=) Callable Objects: Attribute Access: __call__ for making instances callable __getattr__ for attribute access __setattr__ for attribute assignment Context Managers: __delattr__ for attribute deletion __enter__ and __exit__ for defining context managers (with statement) 6 Overloading Operator ‘+’ Example class Customers: def __init__(self, p1, p2): self.p1 = p1 self.p2 = p2 def __add__(self, second): p1 = self.p1 + second.p1 p2 = self.p2 + second.p2 return Customers(p1, p2) customer1 = Customers(10,20) customer2 = Customers(30,40) total_customers = customer1 + customer2 print(total_customers.p1, total_customers.p2) print(2+2) print('b' + 'c’) OUTPUT: 40 60 Note, the ‘+’ changes only for the 4 Customers class, the int.__add__ bc and str.__add__ are not impacted. 7 Overloading Operator ‘>’ Example class Customers: def __init__(self, p1, p2): self.p1 = p1 self.p2 = p2 def __gt__(self, second): t1 = self.p1 + self.p2 t2 = second.p1 + second.p2 if t1 > t2: return True else: return False customer1 = Customers(10,20) customer2 = Customers(30,40) if customer1 > customer2: print("Customer 1 is a big spender!") else: print("Customer 2 is a big spender!") OUTPUT: Customer 2 is a big spender! 8 Printing Instances of Classes class Customers: def __init__(self, p1, p2): self.p1 = p1 self.p2 = p2 customer1 = Customers(10,20) customer2 = Customers(30,40) customer3 = customer2 print(customer1) print(customer2) print(customer3) OUTPUT: What is the output? 9 Printing Instances of Classes class Customers: def __init__(self, p1, p2): self.p1 = p1 self.p2 = p2 customer1 = Customers(10,20) customer2 = Customers(30,40) customer3 = customer2 print(customer1) print(customer2) print(customer3) OUTPUT: 10 How print() works (sort of) print() accepts a variable number of arguments (that's why you can do print(a, b, c)). For each argument, it calls the str() function on it. This is the crucial step where __str__ (and potentially __repr__) come into play. str(customer1) will: Call customer1.__str__() if it exists. If __str__() doesn't exist, call customer.__repr__() if that exists. If neither exists, use the default object representation (module, class, memory address). So, print() effectively converts everything to its string representation before outputting it. 11 __str__() Method A special method you can define in your classes. Controls how your object is represented as a string. Called by print() and str(). Must return a string. Intended for user-friendly, readable output. 12 Overloading ‘__str__’ Example class Customers: def __init__(self, p1, p2): self.p1 = p1 self.p2 = p2 def __str__(self): return f"({self.p1}, {self.p2})" customer1 = Customers(10,20) customer2 = Customers(30,40) a=2 print(customer1) print(a) OUTPUT: (10, 20) 2 13 Pitfalls to Avoid with Operator Overloading Logical Consistency: Ensure Avoid Overusing Operator that overloaded operators Overloading: Not all methods behave consistently with should be overloaded, as expectations (e.g., a + b should excessive use may reduce code be commutative if the clarity. operation should be commutative). 14 Overloading Best Practices 15 Real World Applications Frameworks: In Django or Flask, Numeric libraries like NumPy use custom classes often overload operator overloading extensively for operators to provide custom element-wise operations on arrays. behaviors in domain-specific contexts. Artificial Intelligence: Frameworks Game development: Representing like TensorFlow and PyTorch use and manipulating game entities (eg operator overloading for building vectors for movement). computational graphs and performing tensor operations. 16 What are Iterables An iterable is any object in Python that can be iterated over, meaning you can loop through its elements one by one. Iterable objects include lists, tuples, dictionaries, sets, strings, and more. Essentially, any object that has an __iter__() method, which returns an iterator, is considered iterable. Iterables can be used directly in a for loop or with functions like sum(), max(), min(), sorted(), etc. 17 What Are Iterators An iterator is an object that represents a stream of data and can be used to traverse through elements of an iterable. Iterators provide two essential methods: __iter__() and __next__(). The __iter__() method returns the iterator object itself, and the __next__() method returns the next item in the stream. When there are no more elements to iterate over, __next__() raises a StopIteration exception. However, in practice, you rarely call these methods directly; instead, you use iterators implicitly, often with a for loop. Every iterator object in Python is also an iterable because it implements the __iter__() method, which returns the iterator object itself. 18 Iterators and Iterables An iterable is any object that you can iterate over, while an iterator is the object that performs the iteration over the iterable. All iterators are iterables, but not all iterables are iterators. Iterators maintain the state of the iteration, allowing you to resume the iteration where you left off, while iterables do not necessarily maintain such state. Iterator is like a verb while iterables are like a noun. The iterable (noun) is like a book that contains words (elements).The iterator (verb) is like reading through the book, one word at a time (one element at a time). 19 Iterable vs Iterator Aspect Iterable Iterator Definition Can be looped over Produces next value Methods __iter__() __iter__() and __next__() Example List, string Returned by calling iter() Stateful? No Yes 20 Example of an iterable and iterator # List is an iterable but not an iterator my_list = [1, 2, 3] # Attempt to call next() on the list will raise an error # print(next(my_list)) # Raises TypeError # But we can convert the list to an iterator my_list_iterator = iter(my_list) # Now we can call next() on the iterator print(next(my_list_iterator)) # Output: 1 print(next(my_list_iterator)) # Output: 2 21 Example of a Custom Iterator class MyIterator: # Using the MyIterator class def __init__(self, limit): # Create an instance of MyIterator with a limit of 5 self.limit = limit my_iter = MyIterator(5) self.count = 0 # Iterate using a for loop def __iter__(self): for num in my_iter: return self print(num) def __next__(self): if self.count < self.limit: self.count += 1 return self.count else: raise StopIteration 22  Lists versus Iterators Feature List Iterator Memory Usage Stores all elements in memory Stores only current state/logic Evaluation Eager (all elements created at once) Lazy (elements generated on demand) Size Larger (especially for long sequences) Smaller Access Random access by index (fast - O(1)) Sequential access using next() Iterators themselves are not mutable in the Mutability Usually mutable (can be modified) sense of changing elements. You consume them. Reusability Can be iterated over multiple times Typically consumed after one iteration (unless designed to be resettable) 23 When to use Which Use Lists When: You need random access to elements by index. You need to modify the sequence (add, remove, change elements). The sequence is relatively small and memory usage isn't a major concern. You need to iterate over the sequence multiple times without resetting. Use Iterators (or Generators) When: You're dealing with very large sequences that don't fit in memory. You're generating an infinite sequence. You only need to process each element once (sequential access). You want to save computation time by generating values only when needed (lazy evaluation). You are creating a custom data source. The difference in memory usage is the most significant distinction. Lists store everything upfront; iterators generate data on demand. This makes iterators incredibly powerful for working with large datasets or infinite sequences where storing everything in memory would be impossible or highly inefficient. 24 What Are Generators They are a special type of iterable This lazy evaluation makes Generators in Python are a that generates values lazily, as generators memory efficient and convenient and more efficient they are requested, rather than suitable for working with large way to create iterators. generating them all at once and datasets or infinite sequences. storing them in memory. Each time the generator's iterator When a generator function is is advanced, the generator Generators are defined using a called, it returns a generator function resumes execution from function that contains one or object, which can then be iterated where it last left off until it more yield statements. using a for loop, or by calling the encounters a yield statement, at next() function on it. which point it yields the value and pauses execution. 25 Yield Statement 1. ‘yield’ is only used in the context of generator functions or generator expressions. It cannot be used outside these contexts. 2. When a generator function is called, it returns a generator object, which is an iterator. The generator object can be iterated using a for loop or by calling the next() function on it. 3. It's used to pause execution and return a value to the caller without terminating the function. 4. Each time the generator's iterator is advanced, the generator function resumes execution from where it last left off until it encounters another yield statement or reaches the end of the function. 5. yield allows for lazy evaluation of values, making generators memory-efficient and suitable for working with large datasets or infinite sequences. 26 Generators Advantages Memory Efficiency: Generators produce Lazy Evaluation: Values are generated values one at a time on-the-fly, saving only when requested, allowing for memory compared to storing all values efficient processing of large datasets or in memory at once, which is typical for infinite sequences. lists or other data structures. Pipeline Processing: Generators can be Conciseness: Generator functions are chained together in a pipeline, allowing often more concise and readable than for efficient processing of data in stages the equivalent code using a regular without storing intermediate results in iterator or list comprehension. memory. 27 Iterables, Iterators And Generators Demonstration Demo in Jupiter notebooks 28