Python - Lambda, Functions, Class/Objects

Lambda

A lambda function in Python is a small, anonymous function that can take any number of arguments but only has one expression. The expression is executed, and its result is returned. Lambda functions are often used for short, one-time operations where a regular function definition would be too verbose.

Key characteristics of lambda functions:

• Anonymous: They don't have a name unless explicitly assigned to a variable.

• Concise: Defined in a single line.

• Single Expression: Can only contain one expression.

• Automatic Return: The result of the expression is automatically returned without a return statement.

Syntax:

lambda arguments: expression

lambda is the keyword, arguments are the input parameters, and expression is the code to be executed.

Example:

add_ten = lambda a: a + 10
print(add_ten(5))  # Output: 15

This lambda function takes one argument a, adds 10 to it, and returns the result.

Use cases for lambda functions:

• Inline operations: Lambda functions are useful when you need a quick function for a short period1.

• Higher-order functions: They are commonly used as arguments to higher-order functions like map(), filter(), and sorted().

  numbers = [1, 2, 3, 4, 5]
  squared = list(map(lambda x: x**2, numbers))
  print(squared)  # Output: [1, 4, 9, 16, 25]

• Creating simple formulas: Lambda functions can be used to define simple formulas.

  c_to_f = lambda c: (c * 9/5) + 32
  print(c_to_f(0))  # Output: 32.0

Comparison with regular Python functions:

Feature	Lambda Functions	Regular Functions
Name	Anonymous (unless assigned)	Named
Syntax	Single line	Multi-line
Return	Implicit	Explicit (return statement)
Complexity	Simple expressions	Can contain complex logic

Lambda functions offer a way to write compact and simple functions, making your code more concise and readable in certain contexts. Here are some common use cases for lambda functions:

• Sorting Lists with Custom Keys: Lambda functions can serve as custom key functions when sorting lists, enabling sorting based on a specific attribute or calculation.

  • Example: Sorting a list of students based on their grades.

• Filtering Lists: Lambda functions can be used with the filter() function to filter a list based on a specific condition.

  • Example: Filtering out even numbers from a list of integers.

• Applying Transformations: They can be used with the map() function to apply a transformation to each element in a list.

  • Example: Calculating the square of each number in a list.

• Small, One-Time-Use Functions: Lambda functions are ideal for creating small functions that don’t need a full function definition.

  • Example: Finding the maximum of two numbers.

• With functools.reduce(): The reduce() function from the functools module can be used with a lambda function to apply a binary operation cumulatively to the elements in a list, reducing the list to a single value.

  • Example: Calculating the product of all numbers in a list.

• Implementing Simple Event Handlers: Lambda functions can create simple event handlers for user interface elements, like buttons in a GUI application.

• Custom Comparison Functions: Lambda functions can be used to create custom comparison functions for data structures, such as heaps.

• Concurrent Programming: They can be used to create simple, one-time-use functions when working with concurrent programming, such as with the ThreadPoolExecutor from the concurrent.futures module.

• Defining simple formulas: Lambda functions are also great for defining simple formulas.

  • Example: Convert Celsius to Fahrenheit.

Lambda functions are particularly useful in functional programming with functions like map() and filter(). They can make code more concise, readable, and efficient.

Functions

In Python, a function is a reusable block of code that performs a specific task. Functions help make code more modular and easier to manage.

Defining a Function

1. Syntax: Use the def keyword followed by the function name, parentheses (), and a colon :.

   def functionName():
       # Function body

2. Body: The function body contains the code to be executed and is defined by indentation.

3. Parameters (Optional): You can pass arguments (parameters) inside the parentheses.

   def functionName(arg1, arg2):
       # Function body

Calling a Function

1. Basic Call: To execute a function, write the function name followed by parentheses.

   functionName()

2. With Arguments: If the function accepts arguments, provide the values inside the parentheses when calling the function.

   functionName(valueForArg1, valueForArg2)

Return Keyword

1. Purpose: The return keyword exits the function and can send a value back to the caller

   def multiplyNum(num1):
       return num1 * 8

2. Usage: The return statement can include an expression1. If no return statement is provided, the function returns None by default.

Example

def greet(name):
    return f"Hello, {name}!"

message = greet("Alice")
print(message)  # Output: Hello, Alice!

In this example, the function greet takes a name as input and returns a greeting. The returned string is then stored in the message variable and printed.

In Python, you can pass multiple arguments to a function in several ways. Here are the most common methods:

Positional Arguments

You can define a function with multiple parameters and pass arguments in the same order when calling the function.

Example:

def add(a, b, c):
    return a + b + c

result = add(1, 2, 3)  # Passes 1, 2, and 3 as arguments
print(result)  # Output: 6

Using *args for Variable-Length Arguments

If you want to allow a function to accept any number of positional arguments, you can use the *args syntax. This collects all extra positional arguments into a tuple.

Example:

def add_all(*args):
    return sum(args)

result = add_all(1, 2, 3, 4)  # Can pass any number of arguments
print(result)  # Output: 10

Using **kwargs for Keyword Arguments

To allow passing multiple keyword arguments (name-value pairs), use the **kwargs syntax. This collects all extra keyword arguments into a dictionary.

Example:

def print_info(**kwargs):
    for key, value in kwargs.items():
        print(f"{key}: {value}")

print_info(name="Alice", age=30, city="New York")

This will output:

textname: Alice
age: 30
city: New York

Combining Positional and Keyword Arguments

You can combine both *args and **kwargs in a single function definition.

Example:

def mixed_args(arg1, arg2, *args, **kwargs):
    print(f"arg1: {arg1}, arg2: {arg2}")
    print("Additional args:", args)
    print("Keyword args:", kwargs)

mixed_args(1, 2, 3, 4, name="Bob", age=25)

This will output:

textarg1: 1, arg2: 2
Additional args: (3, 4)
Keyword args: {'name': 'Bob', 'age': 25}

Unpacking Lists or Tuples

You can also pass a list or tuple as multiple arguments using the unpacking operator *.

Example:

numbers = [1, 2, 3]
result = add(*numbers)  # Unpacks the list into separate arguments
print(result)  # Output: 6

These methods provide flexibility in how you define and call functions in Python, allowing you to handle varying numbers of inputs effectively.

Class and Object

In Python, classes and objects are fundamental concepts of object-oriented programming (OOP). They allow you to create structured and modular code by encapsulating data and behavior.

Class

A class is a blueprint for creating objects. It defines a set of attributes (data) and methods (functions) that the created objects will have. Classes allow you to group related functionalities together.

Defining a Class

To define a class, use the class keyword followed by the class name and a colon. The class body is indented below the definition.

Example:

class Bike:
    def __init__(self, name, gear):
        self.name = name  # Instance attribute
        self.gear = gear  # Instance attribute

    def display_info(self):
        return f"Name: {self.name}, Gears: {self.gear}"

In this example, Bike is a class with an initializer method (__init__) that sets the attributes name and gear. The method display_info returns a string containing the bike's information.

Object

An object is an instance of a class. When you create an object, you are creating a specific instance that has its own unique data based on the class definition.

Creating an Object

You can create an object by calling the class as if it were a function.

Example:

# Create an object of the Bike class
bike1 = Bike("Mountain Bike", 5)

# Accessing attributes and methods
print(bike1.display_info())  # Output: Name: Mountain Bike, Gears: 5

Here, bike1 is an object of the Bike class with specific values for its attributes. You can access its methods and attributes using the dot notation (e.g., bike1.display_info()).

Summary

• Class: A blueprint for creating objects that encapsulates data (attributes) and behavior (methods).

• Object: An instance of a class that contains specific data defined by the class.

Example of Class and Object in Context

Here’s a complete example demonstrating both concepts:

class Car:
    def __init__(self, make, model):
        self.make = make  # Instance attribute
        self.model = model  # Instance attribute

    def start_engine(self):
        return f"The engine of {self.make} {self.model} is now running."

# Creating objects
car1 = Car("Toyota", "Corolla")
car2 = Car("Honda", "Civic")

# Using methods
print(car1.start_engine())  # Output: The engine of Toyota Corolla is now running.
print(car2.start_engine())  # Output: The engine of Honda Civic is now running.

In this example, we define a Car class with attributes for make and model, along with a method to start the engine. We then create two objects (car1 and car2) from this class and call their methods to demonstrate how classes and objects work together in Python

Within a Python class, you can define different types of methods to implement various functionalities. The most common types are instance methods, class methods, and static methods1.

Instance Methods

• Instance methods are the most commonly used methods within a class. They operate on specific instances (objects) of the class and can modify the object's state.

• The first parameter of an instance method is conventionally named self, which refers to the instance of the class.

• Instance methods are accessed through class objects.

  class Cricket:
      teamName = None  # Class variable
  
      def setTeamName(self, name):  # Instance method
          self.teamName = name
  
      def getTeamName(self):  # Instance method
          return self.teamName
  
  c = Cricket()
  c.setTeamName('India')
  print(c.getTeamName())  # Output: India

Class Methods

• Class methods are bound to the class itself rather than an instance of the class. They can access or modify the class state.

• To define a class method, you use the @classmethod decorator before the method definition.

• The first parameter of a class method is conventionally named cls, which refers to the class itself.

• Class methods are often used to create factory methods, which return class objects for different use cases.

• Class methods can be called using either ClassName.method_name() or through an object of the class.

  class Student:
      school_name = 'ABC School'  # Class variable
  
      def __init__(self, name, age):
          self.name = name
          self.age = age
  
      @classmethod
      def change_school(cls, school_name):
          cls.school_name = school_name
  
  Student.change_school('XYZ School')
  print(Student.school_name)  # Output: XYZ School

Static Methods

• Static methods are general utility functions that perform tasks in isolation and don't have access to the class or instance state.

• To define a static method, you use the @staticmethod decorator before the method definition1.
• Static methods do not take self or cls as their first parameter.

• They are typically used when a method doesn't need to access any class-specific or instance-specific information

• Static methods can be accessed using the class name or class objects

  class Cricket:
      @staticmethod
      def utility():
          print("This is a static method.")
  
  c1 = Cricket()
  c1.utility()  # Output: This is a static method.
  Cricket.utility()  # Output: This is a static method.

Here's a breakdown of the key differences between instance methods, class methods, and static methods in Python:

1. Instance Methods

• Binding: Bound to an instance of the class. They require the creation of an object of the class before they can be called.

• Access to Data: Can access and modify instance-specific data. They can access both class-level and object attributes.

• Parameter: Requires self as the first parameter, which refers to the instance of the class.

• Usage: Used for instance-specific logic and operations that rely on the instance of the current class. They act on an object's attributes and can modify the object state by changing the value of instance variables.

2. Class Methods

• Binding: Bound to the class itself, not to a specific instance.

• Access to Data: Can access or modify the class state. They can only access class-level attributes.

• Parameter: Requires cls as the first parameter, which refers to the class itself.

• Usage: Often used as factory methods that return a class object for different use cases. They can modify the class state by changing the value of a class variable, which applies across all class objects.

3. Static Methods

• Binding: Not bound to either the instance of the class or the class itself.

• Access to Data: Cannot access or modify instance or class data.

• Parameter: Does not require self or cls as the first parameter.

• Usage: Used for general utility functions that perform a task in isolation. They are helpful in utilities such as conversion from one type to another. They are useful for operations that don't require any data from an instance of the class.

Here's a table summarizing the key differences:

Feature	Instance Method	Class Method	Static Method
Binding	Instance-bound	Class-bound	Not bound
Access to Data	Accesses instance and class data	Accesses class-level data only	No access to instance or class data
Parameter	self	cls	No special first parameter
Usage	Instance-specific logic	Factory methods, modifying class state	General utility functions
Modification	Can modify the object state	Can modify the class state	Cannot modify the class or object state
Need of Object	Object of its class to be created before it can be called	No Object needed
Invoking	Using object reference	By using class reference	By using class reference