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Diana Kanu/

Analyzing unicorn company data

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Analyzing unicorn company data

In this workspace, we'll be exploring the relationship between total funding a company receives and its valuation.

Unknown integration
DataFrameavailable as
df
variable
select *
from companies
inner join funding
using (company_id)
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
import plotly.express as px
px.scatter(df,x='funding', y='valuation', log_x= True, log_y= True, hover_name='company')
  • higher funding should lead to higher valuation
  • there are some companies with a funding > valuation. tough luck!
Unknown integration
DataFrameavailable as
df
variable
select c.company_id, c.company, c.country,i.industry
from companies as c
inner join industries as i
on c.company_id = i.company_id
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
import plotly.express as px
fig = px.bar(df,x='industry', y='company')
fig.show()
  • looks like most companies are under the fintech industry
  • the travel insustry seems to be the least explored.
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