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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.

Spinner
DataFrameas
df2
variable
WITH CTE1 AS (SELECT * FROM (
SELECT industry, EXTRACT(Year FROM dt.date_joined) AS year, ROW_NUMBER() OVER(PARTITION BY EXTRACT(Year FROM dt.date_joined) ORDER BY COUNT(*) DESC) AS rank_count
FROM public.industries ind
INNER JOIN dates dt
ON ind.company_id = dt.company_id
WHERE EXTRACT(Year FROM dt.date_joined) IN (2019, 2020, 2021)
GROUP BY industry, year) AS FOO 
WHERE rank_count <=3), 


CTE2 AS (SELECT industry, EXTRACT(Year FROM dt.date_joined) AS year, COUNT(*), AVG(fu.valuation)
FROM public.industries ind
INNER JOIN dates dt
ON ind.company_id = dt.company_id
INNER JOIN public.funding fu
ON ind.company_id = fu.company_id
GROUP BY industry, EXTRACT(Year FROM dt.date_joined))

SELECT CTE1.industry AS industry, CTE1.year AS year, CTE2.count AS num_unicorns, ROUND(CTE2.avg,2) AS average_valuation_billions
FROM CTE1 
INNER JOIN CTE2 
ON CTE1.industry = CTE2.industry AND CTE1.year = CTE2.year
ORDER BY  CTE1.rank_count DESC, CTE2.year DESC
Spinner
DataFrameas
df1
variable
SELECT industry, EXTRACT(Year FROM dt.date_joined) AS year, ROW_NUMBER() OVER(PARTITION BY EXTRACT(Year FROM dt.date_joined) ORDER BY COUNT(dt.company_id) DESC) AS rank_count, COUNT(dt.company_id)
FROM public.industries ind
INNER JOIN dates dt
ON ind.company_id = dt.company_id
WHERE EXTRACT(Year FROM dt.date_joined) IN (2019, 2020, 2021)
GROUP BY industry, year
Spinner
DataFrameas
df
variable
WITH CTE1 AS (SELECT industry
FROM public.industries ind
INNER JOIN dates dt
ON ind.company_id = dt.company_id
WHERE EXTRACT(Year FROM dt.date_joined) IN (2019, 2020, 2021)
GROUP BY industry
ORDER BY COUNT(dt.*) DESC
LIMIT 3),

CTE2 AS (SELECT industry, EXTRACT(Year FROM dt.date_joined) AS year, COUNT(*), AVG(fu.valuation) AS avg
FROM public.industries ind
INNER JOIN dates dt
ON ind.company_id = dt.company_id
INNER JOIN public.funding fu
ON ind.company_id = fu.company_id
GROUP BY industry, EXTRACT(Year FROM dt.date_joined))

SELECT * FROM(
SELECT CTE1.industry AS industry, CTE2.year AS year, CTE2.count AS num_unicorns, ROUND(AVG(CTE2.avg)/ 1000000000,2)  AS average_valuation_billions
FROM CTE1 
INNER JOIN CTE2 
ON CTE1.industry = CTE2.industry
WHERE CTE2.year IN (2019, 2020, 2021)
	GROUP BY CTE1.industry, CTE2.year, CTE2.count, CTE2.avg
ORDER BY CTE2.year DESC) AS foo
GROUP BY industry, year, num_unicorns, average_valuation_billions
ORDER BY year DESC, num_unicorns DESC
test = 10 
dollar =20
weekly = test * dollar
monthly = weekly * 4
yearly = monthly * 12

print('month', monthly, '/n', 'yearly', yearly )