answer:```python def binary_search(lst, target): left, right = 0, len(lst) - 1 while left <= right: mid = (left + right) // 2 if lst[mid] == target: return mid elif lst[mid] < target: left = mid + 1 else: right = mid - 1 return -1 # Example usage: print(binary_search([1, 3, 5, 7, 9], 5)) # Output: 2 print(binary_search([1, 3, 5, 7, 9], 2)) # Output: -1 # Explanation: # This function performs a binary search on a sorted list of integers to find the index of a target element. # # Here's a step-by-step breakdown: # # 1. Initialize two pointers, 'left' and 'right', to the start and end of the list, respectively. # # 2. Enter a while loop that continues as long as 'left' is less than or equal to 'right'. # # 3. Calculate the middle index 'mid' of the current range [left, right]. # # 4. Compare the middle element 'lst[mid]' to the target element. # # - If they match, return the middle index 'mid' as the target element has been found. # # - If the middle element is less than the target, move the 'left' pointer to 'mid + 1' to search the right half. # # - If the middle element is greater than the target, move the 'right' pointer to 'mid - 1' to search the left half. # # 5. If the while loop ends without finding the target element, return -1 to indicate that the target is not in the list. ``` This binary search function has a time complexity of O(log n), where n is the length of the input list, making it much more efficient than a linear search (O(n)) for large sorted lists.

answer:To find the median, we need to understand that the median is the middle value in a dataset when it is arranged in ascending order. In this case, the data is presented as ranges, making it difficult to pinpoint an exact median. However, we can estimate the median range. Since the total percentage of customers is 100%, and the percentages are already arranged in ascending order, we can find the middle range. The cumulative percentage up to the 26-50 range is 22% + 31% = 53%, which means the median likely falls within the 26-50 range. To find the exact median, more data would be needed, such as the actual spending values within this range, to determine the precise midpoint. As it stands, we can conclude that the median falls within the 26-50 range but cannot determine the exact value without more detailed information.

answer:Unfortunately, the data only provides a lower bound for the top 12% of spenders, indicating that they spent over 100. Without additional information, we cannot determine the exact amount or range of their spending. The top 12% could be spending just over 100 or significantly more, and we cannot make any further inferences with the given data. To gain more insight into their spending habits, additional data would be necessary.

answer:Given that the data is categorical and represents the distribution of customer spending, I would recommend a bar chart or a histogram as the best representation for this data. Since the data is already grouped into discrete spending ranges, a bar chart would effectively display the proportion of customers in each range. As the audience is a business, they are likely interested in understanding their customer spending habits to inform marketing, pricing, or sales strategies. A bar chart would allow them to quickly visualize the distribution of spending, identifying the most common spending ranges and the proportion of high-value customers. This visualization would provide actionable insights, such as identifying opportunities to upsell or cross-sell to customers in the lower spending ranges or targeting high-value customers with specific marketing campaigns. Additionally, a bar chart is a straightforward and easy-to-understand visualization, making it accessible to a broad business audience, including stakeholders who may not have extensive data analysis experience.