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data platforms web analytics

Microsoft PowerBI Google Analytics Power Virtual Agent MixPanel Alteryx Designer Amplitude Tableau, Snowflake Adobe Analytics Fivetran Snowflake ThoughtSpot Data Studio Excel Metabase Looker

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Create list areas

areas = [11.25, 18.0, 20.0, 10.75, 9.50]

Print out the index of the element 20.0

print(areas.index(20.0))

Print out how often 9.50 appears in areas

print(areas.count(9.50))

# Add your code snippets here
areas = [11.25, 18.0, 20.0, 10.75, 9.50]
print(areas.index(20.0))
print(areas.count(9.50))

Explore Datasets

Use the arrays imported in the first cell to explore the data and practice your skills!

  • Print out the weight of the first ten baseball players.
  • What is the median weight of all baseball players in the data?
  • Print out the names of all players with a height greater than 80 (heights are in inches).
  • Who is taller on average? Baseball players or soccer players? Keep in mind that baseball heights are stored in inches!
  • The values in soccer_shooting are decimals. Convert them to whole numbers (e.g., 0.98 becomes 98).
  • Do taller players get higher ratings? Calculate the correlation between soccer_ratings and soccer_heights to find out!
  • What is the average rating for attacking players ('A')?
# Build cars DataFrame
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]
cars_dict = { 'country':names, 'drives_right':dr, 'cars_per_cap':cpc }
cars = pd.DataFrame(cars_dict)
print(cars)