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Joining Data in SQL
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).
Note: When using sample integrations such as those that contain course data, you have read-only access. You can run queries, but cannot make any changes such as adding, deleting, or modifying the data (e.g., creating tables, views, etc.).
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Add notes about the concepts you've learned and SQL cells with queries you want to keep.
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DataFrameas
world_info
variable
-- Add your own queries here
SELECT *
FROM world.languages
LIMIT 5-- Select fields with aliases
DataFrameas
df
variable
SELECT c.code AS country_code, name, year, inflation_rate
FROM world.countries AS c
-- Join to economies (alias e)
INNER JOIN world.economies AS e
-- Match on code field using table aliases
ON e.code=c.codeDataFrameas
df1
variable
SELECT c.name AS country, l.name AS language, official
FROM world.countries AS c
INNER JOIN world.languages AS l
-- Match using the code column
USING(code)DataFrameas
df2
variable
-- Select country and language names, aliased
SELECT c.name AS country, l.name AS language
-- From countries (aliased)
FROM world.languages AS l
-- Join to languages (aliased)
INNER JOIN world.countries AS c
-- Use code as the joining field with the USING keyword
USING(code);DataFrameas
df3
variable
SELECT c.name AS country, l.name AS language
FROM world.countries AS c
INNER JOIN world.languages AS l
USING(code)
ORDER BY country;
DataFrameas
df4
variable
-- Select relevant fields
SELECT name, year,fertility_rate
-- Inner join countries and populations, aliased, on code
FROM world.countries AS c
INNER JOIN world.populations AS p
ON c.code=p.country_code;
DataFrameas
df5
variable
-- Select fields
SELECT name, fertility_rate, e.year,e.unemployment_rate
FROM world.countries AS c
INNER JOIN world.populations AS p
ON c.code = p.country_code
-- Join to economies (as e)
INNER JOIN world.economies as e
-- Match on country code
ON p.country_code=e.code;DataFrameas
df6
variable
SELECT name, e.year, fertility_rate, unemployment_rate
FROM world.countries AS c
INNER JOIN world.populations AS p
ON c.code = p.country_code
INNER JOIN world.economies AS e
ON c.code = e.code
-- Add an additional joining condition such that you are also joining on year
AND p.year= e.year;