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(SQL) Project: Uncovering the World's Oldest Businesses
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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
    DataFrameavailable as
    df
    variable
    -- overview of businessess table
    
    SELECT * FROM businesses
    Spinner
    DataFrameavailable as
    df1
    variable
    -- overview of new_businessess table
    
    SELECT * FROM new_businesses
    Spinner
    DataFrameavailable as
    df2
    variable
    -- overview of countries table
    
    SELECT * FROM countries
    Spinner
    DataFrameavailable as
    df3
    variable
    -- overview of categorie table
    
    SELECT * FROM categories
    Spinner
    DataFrameavailable as
    df12
    variable
    -- see how many countries in each continent are in the countries table
    
    SELECT DISTINCT continent
    FROM countries
    Spinner
    DataFrameavailable as
    df5
    variable
    -- see how many countries in each continent are in the countries table
    
    SELECT continent, COUNT (*) 
    FROM countries
    GROUP BY continent
    Spinner
    DataFrameavailable as
    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
    DataFrameavailable as
    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
    DataFrameavailable as
    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
    DataFrameavailable as
    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
    DataFrameavailable as
    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
    DataFrameavailable as
    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'