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This approach involves writing a SQL query to filter and compute the sum of all total investment values in 2016 (tiv_2016) for policyholders, ensuring they satisfy the given criteria.
In essence, we utilize SQL's aggregation functions alongside grouping features to systematically filter the data for our specific needs. This is accomplished by creating temporary tables/subqueries to collect and filter specific required datasets.
Time Complexity: O(n log n) due to sorting in aggregation and grouping.
Space Complexity: O(n) to store intermediate datasets in memory.
1SELECT ROUND(SUM(tiv_2016), 2) AS tiv_2016
2FROM (
3 SELECT tiv_2015, tiv_2016, lat, lon
4 FROM Insurance
5 WHERE tiv_2015 IN (
6 SELECT tiv_2015
7 FROM Insurance
8 GROUP BY tiv_2015
9 HAVING COUNT(DISTINCT pid) > 1
10 )
11 AND CONCAT(lat, '-', lon) IN (
12 SELECT CONCAT(lat, '-', lon)
13 FROM Insurance
14 GROUP BY lat, lon
15 HAVING COUNT(DISTINCT pid) = 1
16 )
17) AS filtered_records;This SQL solution utilizes subqueries to filter polynomial datasets based on constraints.
tiv_2015 is shared by more than one policyholder using HAVING COUNT(DISTINCT pid) > 1.HAVING COUNT(DISTINCT pid) = 1.tiv_2016 values and rounds it to two decimal places using SQL's ROUND function.This approach involves using in-memory computations through programming languages with efficient data manipulation capabilities. We load the data into memory and use data structures like dictionaries and sets to maintain unique conditions and compute the resultant sum.
Utilize hashmaps to track matching tiv_2015 values and cities uniquely defined by (lat, lon).
Time Complexity: O(n) as it involves two traversals over the dataset.
Space Complexity: O(n) due to the additional space required for hashmaps/databases for storing intermediate results.
1def calculate_investment(records):
2 from collections import defaultdict
3
This approach involves sorting the array and then using two pointers to find pairs that satisfy the condition. Sorting helps to bring potential pairs closer, making it easier to identify valid combinations without unnecessary checks.
Time Complexity: O(n log n) due to sorting.
Space Complexity: O(1) because sorting is done in-place.
1#include
By utilizing a hash table, we can store the elements of the array and check for complements as we traverse the list. This allows us to achieve an O(n) time complexity for finding pairs that sum up to the given value.
Time Complexity: O(n), as each insertion and lookup in the hash table is O(1).
Space Complexity: O(n) for storing the hash table values.
1#
This solution involves several parts:
tiv_2015_count keeps track of how many policyholders share the same tiv_2015 value.city_set and unique_cities are used to ensure uniqueness of the city based on the (lat, lon) combination.tiv_2016 value of those eligible policyholders.This C program sorts an array and uses two pointers: one starting from the beginning and the other from the end. It checks the sum of these two elements and adjusts the pointers based on the result until a pair that sums to the given value is found.
This program leverages a hash table to keep track of numbers we've seen so far. For each number, we calculate its complement to achieve the given sum and check if that complement has already been seen.