Possible usecase: Maximum subarray problem.
Steps

Initialize two variables:
max_sum = current_sum = 0

Loop through numbers in list

Set
current_sum = max(0, current_sum + list[i])

Set
max_sum = max(max_sum, current_sum)


Return
max_sum
Caveats ^{1}, ^{2}

Kadane's Algorithm requires at least one positive number, so an input of all negative numbers would be invalid.

If the array contains all nonnegative numbers, then the problem is trivial; a maximum subarray is the entire array.

If the array contains all nonpositive numbers, then a solution is any subarray of size 1 containing the maximal value of the array (or the empty subarray (which has sum 0), if it is permitted).

Several different subarrays may have the same maximum sum.
Solving Best Time to Buy and Sell Stock with Kadane's Algorithm
Slightly tweak the algorithm to track max profit and min price.
def maxProfit(self, prices: List[int]) > int:
max_profit, min_price = 0, float("inf")
for price in prices:
min_price = min(min_price, price)
max_profit = max(max_profit, price  min_price)
return max_profit