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Project: Analyzing Unicorn Companies (Portfolio)
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    Did you know that the average return from investing in stocks is 10% per year (not accounting for inflation)? But who wants to be average?!

    You have been asked to support an investment firm by analyzing trends in high-growth companies. They are interested in understanding which industries are producing the highest valuations and the rate at which new high-value companies are emerging. Providing them with this information gives them a competitive insight as to industry trends and how they should structure their portfolio looking forward.

    You have been given access to their unicorns database, which contains the following tables:

    dates

    ColumnDescription
    company_idA unique ID for the company.
    date_joinedThe date that the company became a unicorn.
    year_foundedThe year that the company was founded.

    funding

    ColumnDescription
    company_idA unique ID for the company.
    valuationCompany value in US dollars.
    fundingThe amount of funding raised in US dollars.
    select_investorsA list of key investors in the company.

    industries

    ColumnDescription
    company_idA unique ID for the company.
    industryThe industry that the company operates in.

    companies

    ColumnDescription
    company_idA unique ID for the company.
    companyThe name of the company.
    cityThe city where the company is headquartered.
    countryThe country where the company is headquartered.
    continentThe continent where the company is headquartered.

    The output

    Your query should return a table in the following format:

    industryyearnum_unicornsaverage_valuation_billions
    industry12021------
    industry22020------
    industry32019------
    industry12021------
    industry22020------
    industry32019------
    industry12021------
    industry22020------
    industry32019------

    Where industry1, industry2, and industry3 are the three top-performing industries.

    Unknown integration
    DataFrameavailable as
    df2
    variable
    -- step-1
    -- top industries(measured by num of industries)
    
    SELECT i.industry, 
            COUNT(*)
    FROM industries AS i
    INNER JOIN dates AS d
    ON i.company_id = d.company_id
    WHERE EXTRACT(year FROM d.date_joined) in ('2019', '2020', '2021')
    GROUP BY industry
    ORDER BY count DESC
    LIMIT 3;
    
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
    Unknown integration
    DataFrameavailable as
    df3
    variable
    -- step-2, yearly rankings data
    
    SELECT 
    	industry, 
    	EXTRACT(year FROM d.date_joined) AS year,
    	COUNT(*) AS num_unicorns, 
    	AVG(valuation) AS avg_valuation
    FROM industries AS i
    INNER JOIN dates AS d
    ON i.company_id = d.company_id
    INNER JOIN funding AS f
    ON d.company_id = f.company_id
    GROUP BY industry, EXTRACT(year FROM d.date_joined)
    ;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
    Unknown integration
    DataFrameavailable as
    df
    variable
    -- final step 
    -- combining all query and setting the previous two as CTE 
    
    WITH top_industries AS
    (
    SELECT i.industry, 
            COUNT(i.*) AS industry_count
    FROM industries AS i
    INNER JOIN dates AS d
    ON i.company_id = d.company_id
    WHERE EXTRACT(year FROM d.date_joined) in ('2019', '2020', '2021')
    GROUP BY industry
    ORDER BY industry_count DESC
    LIMIT 3
    ), 
    
    yearly_rankings AS 
    (
    SELECT industry, 
    	EXTRACT(year FROM d.date_joined) AS year,
    	COUNT(i.*) AS num_unicorns, 
    	AVG(f.valuation) AS avg_valuation	
    FROM industries AS i
    INNER JOIN dates AS d
    ON i.company_id = d.company_id
    INNER JOIN funding AS f
    ON d.company_id = f.company_id
    GROUP BY industry, EXTRACT(year FROM d.date_joined)
    )
    -- Final Query
    SELECT 
    		industry, 
    		year, 
    		num_unicorns, 
        	ROUND(AVG(avg_valuation / 1000000000), 2) AS average_valuation_billions	
    FROM yearly_rankings
    WHERE year IN (2019, 2020, 2021) AND 
    		industry IN (
    			SELECT industry
    			FROM top_industries)
    GROUP BY industry, year, num_unicorns
    ORDER BY year DESC, num_unicorns DESC
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
    import matplotlib.pyplot as plt
    import seaborn as sns
    import pandas as pd
    
    
    # Filter the DataFrame for the specified industries
    selected_industries = ['Fintech', 'Internet software & services', 'E-commerce & direct-to-consumer']
    filtered_df = df[df['industry'].isin(selected_industries)]
    
    # Grouped bar plot for the number of unicorns by industry and year
    plt.figure(figsize=(12, 8))
    sns.barplot(x='year', y='num_unicorns', hue='industry', data=filtered_df)
    plt.title('Number of Unicorns by Industry and Year')
    plt.xlabel('Year')
    plt.ylabel('Number of Unicorns')
    plt.legend(title='Industry', bbox_to_anchor=(1, 1))
    plt.show()