What does the code in Example 1 measure?

It measures and prints the execution time of a loop that counts from 1 to 9.

What is the time complexity of the integer to string conversion in Example 2?

The time complexity is O(log n), where n is the input number, as it processes one digit per iteration until the number becomes zero.

It iterates through the list twice, using one loop to calculate the sum and another to calculate the product.

Explanation

Code measures and prints execution time of a loop that counts from 1 to 9
Uses time.time() function to capture timestamps before and after the loop
Records start time before loop begins and end time after loop completes
Calculates difference between end and start times to show how long loop took
Prints individual numbers 1 through 9 followed by total execution time in seconds

Output

Explanation

Converts an integer to its string representation by repeatedly extracting digits using modulo and integer division operations
Uses a digit mapping string '0123456789' to convert numeric values to their character equivalents
Builds the result string by prepending each digit character to the front of the existing result
Processes the input number by taking remainder when divided by 10 to get last digit, then integer dividing by 10 to remove it
Has logarithmic time complexity O(log n) where n is the input number, since it processes one digit per iteration until number becomes zero

Output

Explanation

Code calculates sum and product of all elements in list L by iterating through it twice
Uses basic for loops with accumulation variables sum and product initialized to 0 and 1 respectively
Prints final values 10 (1+2+3+4) and 24 (1×2×3×4) to console
First loop adds each element to running sum total
Second loop multiplies each element to running product total
Both operations have linear time complexity O(n) where n is list length

Output

Explanation

This code uses nested loops to iterate through each element of list A and pair it with every element of list B
The inner loop runs completely for each iteration of the outer loop, creating all possible combinations of elements from both lists
Key APIs used include basic for loops and the print function to display the paired values
Expected output shows each element from A paired with every element from B in sequence (1,5), (1,6), (1,7), (1,8), (2,5), etc.
Time complexity is O(n²) because for each of the n elements in A, we iterate through all n elements in B

Output

Explanation

Code uses nested loops to iterate through elements of list A, then list B, then a range of 5 numbers
For each combination of i from A and j from B, it prints the pair (i,j) five times due to the innermost loop
Key APIs/functions: range(), print(), nested for-loops for iteration
Expected output shows each element from A paired with each element from B, repeated 5 times total per pair
Time complexity is O(n²) where n is the size of input lists since the constant 5 doesn't affect asymptotic growth

Output

Explanation

This code reverses the elements of a list L in-place by swapping elements from both ends toward the center
It uses range(0, len(L)//2) to iterate only halfway through the list, avoiding redundant swaps
The swapping is done using a temporary variable temp to hold one value while exchanging positions
Key APIs used include len() to get list length, range() for iteration, and direct index assignment
Expected output is [5, 4, 3, 2, 1] - the original list reversed in place

Output

Explanation

The code initializes three variables n=10, j=10, k=0 and then uses nested loops to calculate a value for k
The outer loop runs from i=n//2 to n-1 (so i goes from 5 to 9), and the inner loop has a strange range call that will likely cause issues
The inner loop range(2,n,pow(2,j)) is problematic because pow(2,j) uses the loop variable j which is also the loop iterator, making the step size unpredictable
The calculation k = k + n/2 adds 5 to k during each iteration of the inner loop
The code attempts to demonstrate O(n log n) complexity but contains a logic error in the inner loop's range parameters that would likely cause runtime issues

Output

Explanation

Code performs integer division of a=10 by b=3, storing result in div variable
Conditional check if b is less than or equal to -1, which is false so nothing prints
Calculates and prints value of a - div - b which equals 10 - (-3) - 3 = 10
Uses floor division operator // which gives quotient without remainder
Time complexity is O(1) meaning execution time is constant regardless of input size

Output

Explanation

This code calculates the sum of all digits in the number 345 by repeatedly extracting the last digit using modulo operator (%) and adding it to a running total
The while loop continues until n becomes 0, with n being integer divided by 10 in each iteration to remove the last digit
Key functions used: modulo (%) for extracting last digit, integer division (//) for removing last digit, and print() for displaying result
Expected output is 12 (3+4+5=12) as the sum of digits in 345
Time complexity is logarithmic O(log n) since the number of iterations equals the number of digits in n

Output

Explanation

This code implements a recursive Fibonacci function that calculates the nth Fibonacci number by calling itself with smaller values
The base cases return 1 when n is 0 or 1, while larger values recursively compute the sum of the two preceding Fibonacci numbers
Key functions used include recursive function calls and simple conditional logic (if/else statements)
The time complexity is exponential O(2^n) due to repeated calculations of the same Fibonacci numbers
Side effect is that it becomes very slow for large inputs due to the exponential growth in function calls