Leetcode 3793 - Aggregate Filters in SQL - Find Users with High Token Usage | Everyday Data Science

Description:
Question: https://leetcode.com/problems/find-users-with-high-token-usage/?page=2

SQL Schema:
CREATE TABLE if not exists prompts (
user_id INT,
prompt VARCHAR(255),
tokens INT
)

Truncate table prompts
insert into prompts (user_id, prompt, tokens) values ('1', 'Write a blog outline', '120')
insert into prompts (user_id, prompt, tokens) values ('1', 'Generate SQL query', '80')
insert into prompts (user_id, prompt, tokens) values ('1', 'Summarize an article', '200')
insert into prompts (user_id, prompt, tokens) values ('2', 'Create resume bullet', '60')
insert into prompts (user_id, prompt, tokens) values ('2', 'Improve LinkedIn bio', '70')
insert into prompts (user_id, prompt, tokens) values ('3', 'Explain neural networks', '300')
insert into prompts (user_id, prompt, tokens) values ('3', 'Generate interview Q&A', '250')
insert into prompts (user_id, prompt, tokens) values ('3', 'Write cover letter', '180')
insert into prompts (user_id, prompt, tokens) values ('3', 'Optimize Python code', '220')

Pandas Schema:
data = [[1, 'Write a blog outline', 120], [1, 'Generate SQL query', 80], [1, 'Summarize an article', 200], [2, 'Create resume bullet', 60], [2, 'Improve LinkedIn bio', 70], [3, 'Explain neural networks', 300], [3, 'Generate interview Q&A', 250], [3, 'Write cover letter', 180], [3, 'Optimize Python code', 220]]
prompts = pd.DataFrame({
"user_id": pd.Series(dtype="int"),
"prompt": pd.Series(dtype="string"),
"tokens": pd.Series(dtype="int")
})

#leetcode #datascience #tutorial