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Uncovering the World's Oldest Businesses

Use joining techniques to discover the oldest businesses in the world.

An essential part of business is planning for the future and ensuring that the business survives changing market conditions.

In this project, you'll explore data from BusinessFinancing.co.uk on the world's oldest businesses. You'll use joining and data manipulation techniques to answer questions about these historic businesses.

Staffelter Hof Winery is Germany's oldest business, established in 862 under the Carolingian dynasty. It has continued to serve customers through dramatic changes in Europe, such as the Holy Roman Empire, the Ottoman Empire, and both world wars. What characteristics enable a business to stand the test of time?

To help answer this question, BusinessFinancing.co.uk researched the oldest company still in business in almost every country and compiled the results into several CSV files. This dataset has been cleaned.

Having useful information in different files is a common problem. While it's better to keep different types of data separate for data storage, you'll want all the data in one place for analysis. You'll use joining and data manipulation to work with this data and better understand the world's oldest businesses.

The Data

businesses and new_businesses

ColumnDescription
businessName of the business (varchar)
year_foundedYear the business was founded (int)
category_codeCode for the business category (varchar)
country_codeISO 3166-1 three-letter country code (char)

countries

ColumnDescription
country_codeISO 3166-1 three-letter country code (varchar)
countryName of the country (varchar)
continentName of the continent the country exists in (varchar)

categories

ColumnDescription
category_codeCode for the business category (varchar)
categoryDescription of the business category (varchar)
Spinner
DataFrameas
df
variable
-- overview of businessess table

SELECT * FROM businesses
Spinner
DataFrameas
df1
variable
-- overview of new_businessess table

SELECT * FROM new_businesses
Spinner
DataFrameas
df2
variable
-- overview of countries table

SELECT * FROM countries
Spinner
DataFrameas
df3
variable
-- overview of categorie table

SELECT * FROM categories
Spinner
DataFrameas
df12
variable
-- see how many countries in each continent are in the countries table

SELECT DISTINCT continent
FROM countries
Spinner
DataFrameas
df5
variable
-- see how many countries in each continent are in the countries table

SELECT continent, COUNT (*) 
FROM countries
GROUP BY continent
Spinner
DataFrameas
df14
variable
-- join between old business table (businesses) and countries table 

SELECT continent, c.country, business, year_founded
    FROM businesses AS b
    INNER JOIN countries AS c 
    ON b.country_code = c.country_code
Spinner
DataFrameas
df13
variable
-- each businesses' oldest year founded grouped by continent

SELECT continent, MIN(year_founded) AS year_founded
    FROM businesses
    JOIN countries 
    USING (country_code)
    GROUP BY continent
	ORDER BY year_founded
Spinner
DataFrameas
df7
variable
-- list of continent, business category based on oldest year founded

SELECT continent, category, year_founded
FROM countries AS c
JOIN businesses AS b
USING (country_code)
JOIN categories
USING (category_code)
WHERE (continent, year_founded) IN (
    SELECT continent, MIN(year_founded)
    FROM countries AS c
    JOIN businesses AS b
    USING (country_code)
    GROUP BY continent)
ORDER BY year_founded;
Spinner
DataFrameas
df19
variable
-- types of category and counts of how many they are

SELECT category, COUNT(1)
FROM categories
GROUP BY 1
ORDER BY COUNT DESC
Spinner
DataFrameas
df20
variable
-- count of each category of business

WITH all_info AS (
SELECT business, year_founded, category
FROM businesses AS b
INNER JOIN categories AS ca
ON ca.category_code = b.category_code)

SELECT category, COUNT(*)
FROM all_info
GROUP BY category
ORDER BY COUNT DESC
Spinner
DataFrameas
df21
variable
-- example of 1 category

WITH all_info AS (
SELECT business, year_founded, category
FROM businesses AS b
INNER JOIN categories AS ca
ON ca.category_code = b.category_code)

SELECT * FROM all_info
WHERE category = 'Media'