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def two_sum(nums: list[int], target: int) -> list[int]:
prev_map = {} # value -> index

for i, n in enumerate(nums):
    diff = target - n
    if diff in prev_map:
        return [prev_map[diff], i]
    prev_map[n] = i
    
return []

Objective

In this article, we'll tackle a commonly asked SQL interview question that involves analyzing sales data to produce a summarized monthly revenue report for different regions. Given a table named sales with columns id, region, amount, and sale_date, the goal is to write a SQL...

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SQL Interview Question: Extracting Distinct Payment Methods

Objective

Construct an SQL query to extract all distinct payment methods from the payments table. The resulting list should be ordered alphabetically by the payment method names.

Additional Information

• The `payments...

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Apply seasonal decomposition to monthly sales data using Python to remove recurring seasonal patterns and extract the underlying trend component.

SELECT
a.paper_id,
a.author_id,
a.name,
ROW_NUMBER() OVER (
PARTITION BY a.paper_id
ORDER BY a.author_id
) AS row_number
FROM {{ ref("authors") }} a
INNER JOIN {{ ref("research_papers") }} rp
ON a.paper_id = rp.paper_id

Master data aggregation in PySpark. Learn how to join tables, group data by categories, calculate sums, and sort your results descending to find top-performing sectors.

SELECT
s.name AS star_name,
s.color AS star_color,
s.type AS star_type,
p.name AS planet_name,
p.type AS planet_type,
s.distance AS distance_star_earth,
p.distance AS distance_planet_star
FROM {{ ref("planets") }} p
INNER JOIN {{ ref("stars") }} s
ON p.star_id = s.id

Master relational joins and mathematical operations in PySpark. Learn how to perform an inner join to filter unmatched records and multiply columns together to calculate the Ideal Gas Law result.

Objective

To effectively handle inventory within a warehouse, it's pivotal to have a clear summary of inventory details. This SQL query strives to generate a concise summary that includes the warehouse name, product type, count of different items, the total quantity of items, and their cumulati...

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def top_k_frequent(nums: list[int], k: int) -> list[int]:
count = {}
freq = [[] for i in range(len(nums) + 1)]

for n in nums:
    count[n] = 1 + count.get(n, 0)
    
for n, c in count.items():
    freq[c].append(n)
    
res = []
for i in range(len(freq) - 1, 0, -1):
    for n in freq[i]:
        res.append(n)
        if len(res) == k:
            return res
            
return res

Definition for singly-linked list.

class ListNode:

def init(self, val=0, next=None):

self.val = val

self.next = next

def remove_nth_from_end(head: Optional[ListNode], n: int) -> Optional[ListNode]:
dummy = ListNode(0, head)
left = dummy
right = head

while n > 0 and right:
    right = right.next
    n -= 1
    
while right:
    left = left.next
    right = right.next
    
left.next = left.next.next

return dummy.next