Day1. Data Filtering - Big Countries

Problem

Table: World

Column Name	Type
name	varchar
continent	varchar
area	int
population	int
gdp	bigint

name is the primary key (column with unique values) for this table.
Each row of this table gives information about the name of a country, the continent to which it belongs, its area, the population, and its GDP value.

A country is big if:

it has an area of at least three million (i.e., 3000000 km2), or
it has a population of at least twenty-five million (i.e., 25000000).
Write a solution to find the name, population, and area of the big countries.

Return the result table in any order.

The result format is in the following example.

Example 1:

Input:
World table:

name	continent	area	population	gdp
Afghanistan	Asia	652230	25500100	20343000000
Albania	Europe	28748	2831741	12960000000
Algeria	Africa	2381741	37100000	188681000000
Andorra	Europe	468	78115	3712000000
Angola	Africa	1246700	20609294	100990000000

Output:

name	population	area
Afghanistan	25500100	652230
Algeria	37100000	2381741

Solution

# my solution
import pandas as pd

def big_countries(world: pd.DataFrame) -> pd.DataFrame:
    world = world[(world['area'] >= 3000000) | (world['population'] >= 25000000)]
    return world[['name','population','area']]

# check result

data = [['Afghanistan', 'Asia', 652230, 25500100, 20343000000], ['Albania', 'Europe', 28748, 2831741, 12960000000], ['Algeria', 'Africa', 2381741, 37100000, 188681000000], ['Andorra', 'Europe', 468, 78115, 3712000000], ['Angola', 'Africa', 1246700, 20609294, 100990000000]]
world = pd.DataFrame(data, columns=['name', 'continent', 'area', 'population', 'gdp']).astype({'name':'object', 'continent':'object', 'area':'Int64', 'population':'Int64', 'gdp':'Int64'})

big_countries(world)

reference

leetcode - 30 Days of Pandas / Big countries