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/* Ollivander's Inventory */ | |
SELECT w2.id, t.age, t.price, t.power | |
FROM Wands AS w2 | |
JOIN Wands_Property AS p2 ON w2.code = p2.code | |
INNER JOIN ( | |
SELECT p.age AS age, MIN(w1.coins_needed) AS price, w1.power AS power | |
FROM Wands AS w1 | |
JOIN Wands_Property AS p ON w1.code = p.code | |
WHERE p.is_evil = 0 | |
GROUP BY p.age, w1.power |
/* Weather Observation Station 20 */ | |
SELECT ROUND(AVG(t.LAT_N), 4) | |
FROM ( | |
SELECT | |
LAT_N | |
, ROW_NUMBER() OVER (ORDER BY LAT_N ASC, ID ASC) AS row_num_asc | |
, ROW_NUMBER() OVER (ORDER BY LAT_N DESC, ID DESC) AS row_num_desc | |
FROM STATION) AS t | |
WHERE | |
t.row_num_asc IN (t.row_num_desc-1, t.row_num_desc, t.row_num_desc+1) |
/* New Companies */ | |
SELECT | |
c.company_code | |
, c.founder | |
, COUNT(DISTINCT l.lead_manager_code) | |
, COUNT(DISTINCT s.senior_manager_code) | |
, COUNT(DISTINCT m.manager_code) | |
, COUNT(DISTINCT e.employee_code) | |
FROM Company AS c | |
LEFT JOIN Lead_Manager AS l ON c.company_code = l.company_code |
/* Employee Salaries */ | |
SELECT name FROM Employee WHERE months < 10 AND salary > 2000 ORDER BY employee_id ASC | |
/* Employee Names */ | |
SELECT name FROM Employee ORDER BY name | |
/* Higher Than 75 Marks */ | |
SELECT Name FROM STUDENTS WHERE Marks > 75 ORDER BY RIGHT(Name, 3), ID | |
/* Weather Observation Station 12 */ |
class Solution: | |
def majorityElement(self, nums: List[int]) -> int: | |
result = {} | |
for num in nums: | |
if num not in result.keys(): | |
result[num] = 1 | |
else: | |
result[num] += 1 | |
for k in result: | |
if result[k] > len(nums)/2: |
#!/bin/python3 | |
import math | |
import os | |
import random | |
import re | |
import sys | |
# | |
# Complete the 'timeInWords' function below. |
# Import your libraries | |
import pandas as pd | |
# Start writing code | |
cities_population.head() | |
# remove row with non-positive area | |
cities_population = cities_population[cities_population['area'] > 0] | |
# calculate population density |
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