Recursion vs Iteration When Each Wins in DSA Python - Interactive Comparison Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate factorial using recursion.

DSA Python

def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n[1]1)

Drag options to blanks, or click blank then click option'

A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of subtraction in the recursive call.

The factorial function calls itself with n-1 to reduce the problem size until it reaches 0.

2fill in blank

medium

Complete the code to calculate factorial using iteration.

DSA Python

def factorial_iter(n):
    result = 1
    for i in range(1, n[1]1):
        result *= i
    return result

Drag options to blanks, or click blank then click option'

A+

B*

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using n instead of n+1 in the range, missing the last multiplication.

The loop runs from 1 up to n inclusive, so we use n+1 in range.

3fill in blank

hard

Fix the error in the recursive Fibonacci function to avoid infinite recursion.

DSA Python

def fib(n):
    if n == 0 or n == 1:
        return n
    else:
        return fib(n[1]1) + fib(n[1]2)

Drag options to blanks, or click blank then click option'

A+

B-

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of subtraction causing infinite recursion.

Subtract 1 and 2 from n to move towards the base cases and stop recursion.

4fill in blank

hard

Fill both blanks to create a recursive function that sums numbers from 1 to n.

DSA Python

def sum_to_n(n):
    if n == 0:
        return 0
    else:
        return n [1] sum_to_n(n [2] 1)

Drag options to blanks, or click blank then click option'

A+

B-

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication or division instead of addition.

Adding 1 instead of subtracting 1.

The function adds n to the sum of numbers up to n-1, reducing n each call.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps numbers to their factorials using iteration.

DSA Python

def factorial_dict(n):
    return {i: [1] for i in range(1, n [2] 1)}

def factorial_iter(num):
    result = 1
    for j in range(1, num [3] 1):
        result *= j
    return result

Drag options to blanks, or click blank then click option'

Afactorial_iter(i)

B+

Dfactorial_iter(num)

Attempts:

3 left

💡 Hint

Common Mistakes

Using factorial_iter(num) instead of factorial_iter(i).

Using n or num without adding 1 in range.

The dictionary comprehension uses factorial_iter(i) for each i in range 1 to n+1. The loops use n+1 and num+1 to include n.

Practice

1. Which scenario is best suited for using recursion instead of iteration?

easy

A. When the problem requires simple repeated loops

B. When you want to avoid function call overhead

C. When memory usage must be minimal

D. When the problem can be divided into similar smaller problems

Solution

1. Step 1: Understand recursion use case

   Recursion works best when a problem can be broken down into smaller similar problems, like tree traversals or factorial calculation.

2. Step 2: Compare with iteration scenarios

   Iteration is better for simple repeated tasks without the need for breaking down problems.

3. Final Answer:

   When the problem can be divided into similar smaller problems -> Option D

4. Quick Check:

   Recursion = divide and conquer [OK]

Hint: Recursion fits problems breaking into smaller similar parts [OK]

Common Mistakes:

• Thinking iteration is always faster
• Ignoring function call overhead in recursion
• Confusing recursion with simple loops

2. Which of the following is the correct syntax for a recursive function in Python?

easy

A. def func(): return func()

B. def func(n): if n == 0: return 1 else: return n * func(n-1)

C. def func(n): while n > 0: n -= 1 return n

D. def func(n): for i in range(n): return i

Solution

1. Step 1: Identify recursive function structure

   A recursive function calls itself with a smaller input and has a base case to stop recursion.

2. Step 2: Check each option

   def func(n): if n == 0: return 1 else: return n * func(n-1) correctly defines a base case (n == 0) and recursive call (func(n-1)). Others either lack base case or use iteration.

3. Final Answer:

   def func(n): if n == 0: return 1 else: return n * func(n-1) -> Option B

4. Quick Check:

   Base case + self call = recursion [OK]

Hint: Look for base case and self call in function [OK]

Common Mistakes:

• Missing base case causing infinite recursion
• Using loops instead of recursive calls
• Incorrect indentation or syntax errors

3. What is the output of the following Python code?

def factorial_iter(n):
    result = 1
    for i in range(1, n+1):
        result *= i
    return result

print(factorial_iter(4))

medium

A. 24

B. 10

C. 120

D. Error

Solution

1. Step 1: Trace the loop for factorial_iter(4)

   Loop runs i=1 to 4, multiplying result: 1*1=1, 1*2=2, 2*3=6, 6*4=24.

2. Step 2: Return and print result

   Function returns 24, which is printed.

3. Final Answer:

   24 -> Option A

4. Quick Check:

   4! = 24 [OK]

Hint: Factorial of 4 is 24, check loop multiplication [OK]

Common Mistakes:

• Confusing factorial with sum
• Off-by-one errors in loop range
• Expecting recursion output from iteration

4. Identify the error in this recursive function to compute sum of numbers from 1 to n:

def sum_recursive(n):
    if n == 0:
        return 0
    else:
        return n + sum_recursive(n)

medium

A. Recursive call uses same argument causing infinite recursion

B. Function should return n * sum_recursive(n-1)

C. Should use iteration instead

D. Missing base case

Solution

1. Step 1: Check base case and recursive call

   Base case exists for n == 0. Recursive call uses sum_recursive(n) without reducing n.

2. Step 2: Identify infinite recursion cause

   Since n does not decrease, function calls itself endlessly causing stack overflow.

3. Final Answer:

   Recursive call uses same argument causing infinite recursion -> Option A

4. Quick Check:

   Recursive argument must change to avoid infinite calls [OK]

Hint: Recursive call argument must reduce towards base case [OK]

Common Mistakes:

• Forgetting to reduce argument in recursion
• Confusing multiplication with addition in sum
• Ignoring base case importance

5. You need to process a large dataset with a depth of 10,000 recursive calls. Which approach is better and why?

hard

A. Use recursion with no base case for speed

B. Use recursion because it is easier to write

C. Use iteration to avoid stack overflow errors

D. Use iteration but expect high memory usage

Solution

1. Step 1: Understand recursion limits

   Python has a recursion depth limit (usually ~1000). 10,000 calls exceed this causing stack overflow.

2. Step 2: Choose iteration for large depth

   Iteration uses loops and constant stack space, avoiding overflow and handling large datasets safely.

3. Final Answer:

   Use iteration to avoid stack overflow errors -> Option C

4. Quick Check:

   Large depth = iteration to prevent overflow [OK]

Hint: Large recursion depth? Switch to iteration to avoid errors [OK]

Common Mistakes:

• Ignoring recursion depth limits
• Assuming recursion is always simpler
• Not considering memory and stack constraints