Practice day 8 of Learning Python for Data Science

Test your understanding of Python Data Structure, which we learned in our previous lesson of Day 8 of Learning Python for Data Science, with these targeted practice questions.

Welcome back to Day 8 of Learning Python for Data Science journey! In the last article, we explored:

✅ Local Scope
✅ Enclosing Scope
✅ Global Scope
✅ Built-in Scope

Now, it’s time to solve the practice questions given in the previous article.
Each question is followed by a detailed explanation and output.

Understanding Python Functions and Object-Oriented Programming (OOP)

1. What are the advantages of using functions in Python?

Functions provide modularity, code reusability, and better readability. They help in breaking complex programs into smaller, manageable parts, reducing redundancy and making debugging easier.

2. How does the return statement work in Python functions?

The return statement is used to send a value back to the caller from a function. It terminates the function execution and allows the returned value to be used elsewhere.

def square(num):
    return num * num

result = square(5)
print(result)  # Output: 25

3. What is the difference between arguments and parameters in Python functions?

• Parameters: Variables listed in a function’s definition.
• Arguments: Values passed to a function when calling it.

def greet(name):  # 'name' is a parameter
    print("Hello, " + name)

greet("Alice")  # "Alice" is an argument

4. Explain the concept of lambda functions with an example.

Lambda functions are anonymous functions that can have multiple arguments but only one expression.

square = lambda x: x * x
print(square(4))  # Output: 16

5. What is the scope of a variable inside a function?

A variable inside a function has local scope and cannot be accessed outside the function unless explicitly returned or defined as global.

def example():
    local_var = "I exist only inside this function"
    print(local_var)

example()
# print(local_var)  # This would cause an error

6. How do you define a class in Python?

A class is defined using the class keyword and serves as a blueprint for objects.

class Car:
    def __init__(self, brand):
        self.brand = brand

my_car = Car("Toyota")
print(my_car.brand)  # Output: Toyota

7. What is the purpose of the __init__ method in a class?

The __init__ method initializes an object’s attributes when an instance of the class is created.

8. How do you create an object from a class in Python?

An object is created by calling the class as if it were a function.

my_car = Car("Ford")

9. What is the difference between instance variables and class variables?

• Instance variables: Unique to each instance of a class.
• Class variables: Shared among all instances of a class.

class Example:
    class_var = "Shared"
    def __init__(self, value):
        self.instance_var = value

10. What are the key principles of Object-Oriented Programming (OOP)?

1. Encapsulation: Hiding data to prevent direct access.
2. Abstraction: Showing only essential details.
3. Inheritance: Deriving new classes from existing ones.
4. Polymorphism: Using a single interface for different data types.

11. What is the purpose of defining functions in Python?

Functions improve modularity, code reusability, and clarity.

12. How do lambda functions differ from regular functions?

Lambda functions are anonymous and can only contain a single expression, while regular functions can have multiple statements.

13. Write a function that takes a list of numbers and returns the sum.

def sum_list(numbers):
    return sum(numbers)

print(sum_list([1, 2, 3, 4]))  # Output: 10

14. Modify the factorial_iterative() function to handle invalid inputs.

def factorial_iterative(n):
    if not isinstance(n, int) or n < 0:
        return "Invalid input"
    result = 1
    for i in range(1, n + 1):
        result *= i
    return result

15. How does the map() function work, and when should you use it?

map() applies a function to all items in an iterable.

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

16. Write a lambda function to check if a number is prime.

is_prime = lambda x: all(x % i != 0 for i in range(2, int(x**0.5) + 1)) if x > 1 else False
print(is_prime(7))  # Output: True

17. Create a function that filters out words shorter than 4 letters from a list.

def filter_words(words):
    return list(filter(lambda word: len(word) >= 4, words))

print(filter_words(["apple", "is", "good"]))  # Output: ['apple', 'good']

18. Implement a recursive function to compute the Fibonacci sequence.

def fibonacci(n):
    if n <= 0:
        return "Invalid input"
    if n == 1:
        return 0
    if n == 2:
        return 1
    return fibonacci(n-1) + fibonacci(n-2)

print(fibonacci(6))  # Output: 5

19. What are the advantages of using higher-order functions in data science?

Higher-order functions improve code flexibility, conciseness, and readability by allowing functions to be passed as arguments.

20. Compare map() and filter(), providing an example for each.

• map() applies a function to all elements:

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

• filter() removes elements based on a condition:

nums = [1, 2, 3, 4, 5]
even_nums = list(filter(lambda x: x % 2 == 0, nums))
print(even_nums)  # Output: [2, 4]

This article covers the fundamental aspects of functions and OOP in Python. Mastering these concepts will help in writing efficient and modular code for real-world applications.

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