Python courses

Defining Specific Data Types In Python, you can define and work with specific data types beyond the built-in types. This includes creating custom data types using classes and utilizing more advanced data structures. Here, we’ll cover defining custom data types, using type hints, and working with complex data structures. Defining Custom Data Types Custom data types can be defined using classes. Classes allow you to create objects with specific attributes and methods, giving you control over the data and behavior of those objects. Creating a Class: To define a custom data type, you use a class. A class can contain attributes (data) and methods (functions) that operate on that data. Example:  class Person:    def __init__(self, name: str, age: int):         self.name = name         self.age = age     def greet(self) -> str:         return f”Hello, my name is {self.name} and I am {self.age} years old.”     def have_birthday(self):         self.age += 1 # Creating an instance of the Person class person1 = Person(“Alice”, 30) print(person1.greet())  # Output: Hello, my name is Alice and I am 30 years old. person1.have_birthday() print(person1.greet())  # Output: Hello, my name is Alice and I am 31 years old.  Class Components: __init__ Method: This is the constructor method that initializes the object’s attributes. Attributes: Variables that belong to the object. Methods: Functions defined inside the class that operate on the object’s data. Using Type Hints for Custom Types Python’s type hints allow you to specify what types are expected for class attributes and methods. This improves code clarity and can be used with type checkers like mypy. Example with Type Hints:  from typing import List class Course:     def __init__(self, title: str, students: List[str]):         self.title = title         self.students = students     def add_student(self, student: str):         self.students.append(student)     def get_student_count(self) -> int:         return len(self.students) # Creating an instance of the Course class course = Course(“Python Programming”, [“Alice”, “Bob”]) course.add_student(“Charlie”) print(course.get_student_count())  # Output: 3  Working with Advanced Data Structures Python offers several advanced data structures beyond the basic built-in types. These include: Named Tuples: Use the collections.namedtuple function to create tuple subclasses with named fields. Example:  from collections import namedtuple Point = namedtuple(‘Point’, [‘x’, ‘y’]) p = Point(10, 20) print(p.x, p.y)  # Output: 10 20 Dataclasses: Introduced in Python 3.7, dataclasses simplify class definitions by automatically adding special methods like __init__() and __repr__().  Example:  From dataclasses import dataclass @dataclass class Book:     title: str     author: str     year: int book = Book(“1984”, “George Orwell”, 1949) print(book)  # Output: Book(title=’1984′, author=’George Orwell’, year=1949) Enums: The enum module allows you to define enumerations, a set of symbolic names bound to unique, constant values. Example:  from enum import Enum class Color(Enum):     RED = 1     GREEN = 2     BLUE = 3 print(Color.RED)        # Output: Color.RED print(Color.RED.name)   # Output: RED print(Color.RED.value)  # Output: 1  Type Aliases Type aliases provide a way to create alternative names for existing types, which can help improve code readability and maintainability. Example:  from typing import List, Tuple # Define a type alias Coordinates = List[Tuple[int, int]] def get_center(points: Coordinates) -> Tuple[int, int]:     x_coords, y_coords = zip(*points)    return (sum(x_coords) // len(points), sum(y_coords) // len(points)) # Using the type alias points = [(1, 2), (3, 4), (5, 6)] center = get_center(points) print(center)  # Output: (3, 4) Practical Examples of Custom Types Example 1: Banking Application Create a class to model a bank account with methods for depositing and withdrawing funds. from typing import List, Tuple # Define a type alias Coordinates = List[Tuple[int, int]] def get_center(points: Coordinates) -> Tuple[int, int]:     x_coords, y_coords = zip(*points)     return (sum(x_coords) // len(points), sum(y_coords) // len(points)) # Using the type alias points = [(1, 2), (3, 4), (5, 6)] center = get_center(points) print(center)  # Output: (3, 4) Example 2: Student Grades Define a class to handle student grades with methods to add grades and calculate the average.  class StudentGrades:     def __init__(self, student_name: str):         self.student_name = student_name         self.grades = []     def add_grade(self, grade: float):         if 0 <= grade <= 100:             self.grades.append(grade)         else:             print(“Grade must be between 0 and 100.”) # Creating and using a student grades record student = StudentGrades(“Alice”) student.add_grade(90) student.add_grade(85) print(student.average_grade())  # Output: 87.5  Summary Custom Data Types: Use classes to create custom data types with specific attributes and methods. Type Hints: Provide hints about expected types in classes and functions to improve code clarity. Advanced Data Structures: Utilize named tuples, data classes, and enumerations for more complex data handling. Type Aliases: Create alternative names for existing types to enhance readability and maintainability. Practical Examples: Apply custom types to model real-world entities and operations, such as banking accounts and student grades.

Defining Data Types In Python, although the language is dynamically typed, meaning you don’t need to explicitly declare the type of a variable, you can influence the data type of variables by initializing them with specific values or using type conversion functions. You can also use type annotations to document the expected types in functions and variables. Assigning Values and Defining Types When you assign a value to a variable in Python, the variable’s type is automatically determined based on the assigned value. Here are some examples: Examples: Integers: x = 10 # x is of type int Floats: y = 3.14 # y is of type float Strings: z = “Hello” # z is of type str Lists: a = [1, 2, 3] # a is of type list Dictionaries: b = {“name”: “Alice”, “age”: 30} # b is of type dict  Converting Data Types Python provides several built-in functions to convert variables from one type to another. These functions are particularly useful when you need to ensure that data is of the correct type before performing operations. Conversion Functions: int(): Converts to an integer.  x = int(3.7)      # x = 3 y = int(“42”)    # y = 42  float(): Converts to a float. a = float(3)     # a = 3.0 b = float(“2.5”) # b = 2.5 str(): Converts to a string. i = str(100)     # i = “100” j = str(3.14)    # j = “3.14” list(): Converts to a list. k = list(“abc”)  # k = [‘a’, ‘b’, ‘c’] l = list((1, 2)) # l = [1, 2] tuple(): Converts to a tuple. m = tuple([1, 2, 3]) # m = (1, 2, 3) set(): Converts to a set. n = set([1, 2, 2, 3]) # n = {1, 2, 3} frozenset(): Converts to an immutable set. o = frozenset([1, 2, 2, 3]) # o = frozenset({1, 2, 3}) dict(): Converts to a dictionary (often used with key-value pairs). p = dict([(1, ‘a’), (2, ‘b’)]) # p = {1: ‘a’, 2: ‘b’}  Using Type Annotations Although Python is dynamically typed, type annotations (introduced in PEP 484) allow you to provide hints about the types expected in functions and variables. This helps with code clarity and can be used by static type checkers. Annotations for Variables: Since Python 3.6, you can add type annotations to variables. However, note that these annotations are mainly for documentation and do not change the dynamic behavior of typing in Python. age: int = 30 height: float = 1.75 name: str = “Alice”  Annotations for Functions: Type annotations for functions specify the types of parameters and return values.  def add(a: int, b: int) -> int:     return a + b def greet(name: str) -> str:     return f”Hello, {name}” Practical Examples of Type Conversion Example 1: User Input Conversion User inputs are typically processed as strings. You may need to convert them to other types for numeric or logical operations.  # Ask the user for a number age = input(“How old are you? “)  # age is a string age = int(age)  # Convert to integer print(f”You are {age} years old.”)  Example 2: Handling Types in Functions When working with different data types in a function, you can use conversions to ensure that operations are performed correctly.  def convert_and_add(value1: str, value2: str) -> float:     try:         # Convert strings to floats         number1 = float(value1)         number2 = float(value2)         return number1 + number2     except ValueError:         return “Error: The provided values are not numbers.” print(convert_and_add(“3.5”, “4.5”))  # 8.0 print(convert_and_add(“3.5”, “abc”))  # Error: The provided values are not numbers.   Summary Assigning Values: The type of a variable is automatically defined based on the value assigned. Type Conversion: Use built-in functions like int(), float(), str(), list(), tuple(), etc., to convert between types. Type Annotations: Use these to provide hints about the expected types, improving code clarity and static type checking. Practical Handling: Convert types as needed and use annotations to clarify types in functions.

Obtaining the Data Type In Python, you often need to check or confirm the data type of a variable. This can be crucial for debugging, ensuring data consistency, or performing type-specific operations. Python provides several built-in functions to help you determine the data type of a variable. Using the type() Function The type() function is the most common way to obtain the data type of an object. It returns the type of the object as a class type. Syntax:  type(object) Examples: Basic Types: a = 10 print(type(a))  # <class ‘int’> b = 3.14 print(type(b))  # <class ‘float’> c = “Hello” print(type(c))  # <class ‘str’> d = [1, 2, 3] print(type(d))  # <class ‘list’> Complex Types:  e = {“name”: “Alice”, “age”: 30} print(type(e))  # <class ‘dict’> f = (1, 2, 3) print(type(f))  # <class ‘tuple’> g = {1, 2, 3} print(type(g))  # <class ‘set’> h = None print(type(h))  # <class ‘NoneType’>  Using the isinstance() Function The isinstance() function checks if an object is an instance or subclass of a particular class or type. It is useful for type-checking and validating the type of an object at runtime. Syntax:  isinstance(object, classinfo)  object: The object to check. classinfo: The type, class, or tuple of classes/types to check against. Examples: Basic Type Checking:  x = 42 print(isinstance(x, int))  # True print(isinstance(x, float))  # False y = [1, 2, 3] print(isinstance(y, list))  # True print(isinstance(y, dict))  # False  Checking Multiple Types:  z = “Hello” print(isinstance(z, (int, float, str)))  # True, because z is a str Using issubclass() The issubclass() function checks if a class is a subclass of another class. This function is used in object-oriented programming to determine class hierarchy. Syntax:  issubclass(class, classinfo)  class: The class to check. classinfo: The class or tuple of classes to check against. Examples: Basic Subclass Checking:  class Animal:     pass class Dog(Animal):     pass print(issubclass(Dog, Animal))  # True print(issubclass(Dog, object))  # True  Checking Against Multiple Classes:  print(issubclass(Dog, (Animal, object)))  # True  Type Hints and Annotations While Python is dynamically typed, you can use type hints and annotations (introduced in PEP 484) to provide hints about the expected types of variables, function parameters, and return values. This helps with code clarity and can be used by static type checkers. Examples: Function Annotations:  def greet(name: str) -> str:     return “Hello, ” + name # Type hint indicates that ‘name’ should be a str and the function returns a str  Variable Annotations (from Python 3.6):  age: int = 30 height: float = 5.9  Type Checking with mypy: mypy is a static type checker for Python. You can use it to check type hints in your code. Install it using pip: pip install mypy Run mypy on your script: mypy script.py Practical Usage Examples Example 1: Validating User Input When processing user input, you might need to ensure that the input is of the expected type. def process_input(data):     if isinstance(data, int):         return data * 2     elif isinstance(data, str):         return data.upper()     else:         return “Unsupported type” print(process_input(10))      # 20 print(process_input(“hello”)) # HELLO print(process_input([1, 2]))  # Unsupported type Example 2: Function Overloading In Python, you can use type hints to simulate function overloading, providing different behaviors based on input types.  from typing import Union def process_value(value: Union[int, str]) -> str:     if isinstance(value, int):         return f”Integer: {value}”     elif isinstance(value, str):         return f”String: {value}” print(process_value(100))   # Integer: 100 print(process_value(“abc”)) # String: abc  Summary type(): Use to get the type of an object. Returns the type as a class type. isinstance(): Use to check if an object is an instance of a class or a subclass of it. Useful for type-checking. issubclass(): Use to check if a class is a subclass of another class. Useful for class hierarchy checks. Type Hints: Provide hints about expected types in functions and variables for improved code clarity and static type checking.

Built-in Data Types Python provides several built-in data types that are essential for handling various kinds of data. These types are fundamental to programming in Python and include numeric types, sequence types, collection types, associative types, and special types. Numeric Types int (Integer) Description: Represents whole numbers without any decimal point. Can be positive, negative, or zero. Examples:  a = 42 b = -7  Supported Operations: Addition, subtraction, multiplication, division, modulo, exponentiation, and comparison operations. Conversion: Convert other types to integers using the int() function.  x = int(3.14)  # x = 3 y = int(“123”) # y = 123 float (Floating-point number) Description: Represents real numbers with decimal points. Examples:  c = 3.14 d = -0.001  Supported Operations: Addition, subtraction, multiplication, division, exponentiation, and comparison operations. Conversion: Convert other types to floating-point numbers using the float() function.  x = float(10)    # x = 10.0 y = float(“2.5”) # y = 2.5  complex (Complex number) Description: Represents complex numbers in the form a + bj, where a is the real part and b is the imaginary part. Examples:  e = 1 + 2j f = -3 – 4j  Supported Operations: Addition, subtraction, multiplication, division, and comparison for real and imaginary parts. Conversion: Create complex numbers using the complex() function.  x = complex(2, 3)  # x = 2 + 3j  Sequence Types str (String) Description: Represents a sequence of characters. Strings are immutable in Python, meaning their values cannot be changed once created. Examples:  g = “Hello, World!” h = ‘Python’  Supported Operations: Concatenation, repetition, slicing, and various methods for text manipulation (e.g., upper(), lower(), replace()). Conversion: Convert other types to strings using the str() function.  x = str(123)     # x = ‘123’ y = str(3.14)    # y = ‘3.14 list Description: Represents an ordered, mutable collection of items that can be of different types. Examples:  i = [1, 2.5, “Python”, True] j = [3, [1, 2], “hello”]  Supported Operations: Indexing, appending, removing, and modifying elements, and various methods like append(), remove(), extend(). Conversion: Convert other types to lists using the list() function.  x = list(“hello”)  # x = [‘h’, ‘e’, ‘l’, ‘l’, ‘o’]  tuple Description: Represents an ordered, immutable collection of items. Tuples are often used to group related values.  x = tuple([1, 2, 3])  # x = (1, 2, 3)  Examples:  k = (1, 2.5, “Python”, True) l = (3, (1, 2), “hello”)  Supported Operations: Indexing, but no modification (immutable). Methods include count() and index(). Conversion: Convert other types to tuples using the tuple() function. range Description: Represents an immutable sequence of numbers, typically used for iteration in loops. Examples:  m = range(5)     # represents the sequence [0, 1, 2, 3, 4] n = range(2, 10) # represents the sequence [2, 3, 4, 5, 6, 7, 8, 9]  Supported Operations: Indexing and conversion to lists or tuples for manipulation. Collection Types set Description: Represents an unordered collection of unique elements. Useful for removing duplicates. Examples:  o = {1, 2, 3, 4} p = {2, 4, 6, 8}  Supported Operations: Union, intersection, difference, addition, and removal of elements. Methods include add(), remove(), discard(), union(). Conversion: Convert other types to sets using the set() function.  x = set([1, 2, 2, 3])  # x = {1, 2, 3}  frozenset Description: Represents an immutable set. Similar to set, but cannot be modified after creation. Examples:  q = frozenset([1, 2, 3, 4])  Supported Operations: Union, intersection, difference, but no modification. Methods include union(), intersection(). Conversion: Convert other types to frozensets using the frozenset() function.  x = frozenset([1, 2, 2, 3])  # x = frozenset({1, 2, 3}) Associative Types dict (Dictionary) Description: Represents a collection of key-value pairs. Dictionaries are used to store data in a way that is easy to look up by key. Examples:  r = {“name”: “Alice”, “age”: 30} s = {1: “one”, 2: “two”, 3: “three”} Supported Operations: Access by key, addition, deletion, and modification of key-value pairs. Methods include keys(), values(), items(). Conversion: Create dictionaries from key-value pairs.  python x = dict([(1, ‘a’), (2, ‘b’)])  # x = {1: ‘a’, 2: ‘b’}   2.5. Special Type NoneType Description: Represents the absence of a value or a null value. The only value of this type is None. Examples:  t = None  Supported Operations: Comparison operations (is, is not), but no arithmetic or modification operations. Summary Python provides several built-in data types that are fundamental to data handling in your programs. These include: Numeric Types: int, float, complex Sequence Types: str, list, tuple, range Collection Types: set, frozenset Associative Type: dict Special Type: NoneType

Introduction to Data Types What is a Data Type? Data types define the nature of values that variables can hold and the operations that can be performed on these values. In other words, a data type determines how Python interprets and manipulates values. Classification of Data Types In Python, data types can be classified into several categories: Numeric Types Sequence Types Collection Types Associative Types Special Types Importance of Data Types Data types play several important roles in programming: Data Storage: They determine how data is stored in memory. For instance, integers (int) and floating-point numbers (float) are stored differently. Allowed Operations: Each data type supports different operations. For example, you can add numbers but concatenate strings. Validation and Conversion: Data types help in validating data and allow conversions between types when needed. Declaring and Initializing Variables In Python, declaring and initializing variables happens in a single step. You do not need to specify the data type when declaring a variable; Python infers the type based on the assigned value.  x = 10        # x is an int y = 3.14      # y is a float name = “Alice” # name is a str  Dynamic vs. Static Typing Dynamic Languages: Python is a dynamically-typed language, meaning the data type is determined at runtime and can change dynamically. For example, you can reassign a variable from an int to a string (str).  var = 42      # var is an int var = “Hello” # now var is a str  Static Languages: In statically-typed languages (like C++ or Java), the data type is determined at compile time and cannot change. Data Types and Memory Data types directly impact how memory is allocated and managed: Simple Data Types: Like integers and floats, which are generally represented using a fixed amount of memory. Complex Data Types: Like lists and dictionaries, which can contain multiple elements and require more complex memory management. Type Aliases and Static Typing Although Python is a dynamically-typed language, you can use type aliases to specify more complex data types and make code more readable. Type Aliases with typing The typing module allows you to create aliases for complex types, making the code more readable and maintainable.  from typing import List, Tuple # Alias for a list of integers IntList = List[int] # Alias for a tuple containing a str and an int StrIntTuple = Tuple[str, int]  Useful Functions for Data Types type() Allows you to check the data type of a variable.  a = 10 print(type(a))  # <class ‘int’>  isinstance() Checks if a variable is of a particular type or a subtype.  a = 10 print(isinstance(a, int))  # True print(isinstance(a, float))  # False  Data Types in Memory In Python, objects are stored in memory with automatic memory management (garbage collection). Key points include: Immutability: Some types, like strings (str) and tuples (tuple), are immutable, meaning their values cannot be changed once created. Mutability: Other types, like lists (list) and dictionaries (dict), are mutable and can be modified after creation. Naming Conventions and Best Practices Naming: Variable names should be meaningful and describe the type of data or the intended use. For instance, use count for an integer representing a number and name for a string. Using Data Types: Choosing the appropriate data type for each situation improves code readability and efficiency. Use lists for mutable sequences and tuples for immutable sequences, for example. Summary Data types in Python define the nature of values that variables can hold and the operations that can be performed on these values. Python is a dynamically-typed language with automatic memory management. Understanding data types is essential for writing efficient and maintainable code.