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Joining Data with SQL

Here you can access every table used in the course. To access each table, you will need to specify the world schema in your queries (e.g., world.countries for the countries table, and world.languages for the languages table).

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DataFrameas
world_info
variable
-- Add your own queries here
SELECT *
FROM world.languages 
LIMIT 5
-- Select all columns from cities
SELECT *
FROM cities;
SELECT * 
FROM cities
-- Inner join to countries
INNER JOIN countries
-- Match on country codes
ON cities.country_code = countries.code;
-- Select name fields (with alias) and region 
SELECT cities.name as city,countries.name as country, countries.region
FROM cities
INNER JOIN countries
ON cities.country_code = countries.code;
-- Select country and language names, aliased
SELECT c.name AS country, l.name AS language
-- From countries (aliased)
FROM countries as c
-- Join to languages (aliased)
INNER JOIN languages as l
-- Use code as the joining field with the USING keyword
USING(code);
SELECT c.name AS country, l.name AS language, official
FROM countries AS c
INNER JOIN languages AS l
-- Match using the code column
USING (code)
-- Select fields with aliases
SELECT c.code AS country_code, name, year, inflation_rate
FROM countries AS c
-- Join to economies (alias e)
INNER JOIN economies AS e
-- Match on code field using table aliases
ON c.code = e.code
-- Select fields
SELECT c.name, p.year, p.fertility_rate, e.year, e.unemployment_rate
FROM countries AS c
INNER JOIN populations AS p
ON c.code = p.country_code
-- Join to economies (as e)
INNER JOIN economies AS e
USING(code)
-- Match on country code