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Introduction to Python
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• ## .mfe-app-workspace-kj242g{position:absolute;top:-8px;}.mfe-app-workspace-11ezf91{display:inline-block;}.mfe-app-workspace-11ezf91:hover .Anchor__copyLink{visibility:visible;}Introduction to Python

Run the hidden code cell below to import the data used in this course.

### 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'`)?
```.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}```# Print out the weight of the first ten baseball players.
print(baseball_weights[:10])``````
``````# What is the median weight of all baseball players in the data?
print(np.median(baseball_weights * 0.453592))``````
Hidden code
``````# Who is taller on average? Baseball players or soccer players? Keep in mind that baseball heights are stored in inches!
avg_height_baseball = np.round(np.mean(baseball_heights * 2.54), 2)
avg_height_soccer = np.round(np.mean(soccer_heights), 2)

print(f'Average height of a baseball player: {avg_height_baseball}')
print(f'Average height of a soccer player: {avg_height_soccer}')``````
``````# The values in soccer_shooting are decimals. Convert them to whole numbers (e.g., 0.98 becomes 98).
soccer_shooting = soccer_shooting * 10
soccer_shooting``````
``````# Do taller players get higher ratings? Calculate the correlation between soccer_ratings and soccer_heights to find out!
x = soccer_ratings
y = soccer_heights

print(np.corrcoef(soccer_ratings, soccer_heights))

# Две переменные могут быть связаны таким образом, что при возрастании значений одной из них значения другой убывают.
# Это и показывает отрицательный коэффициент корреляции.
# Про такие переменные говорят, что они отрицательно коррелированы.``````
``````# What is the average rating for attacking players ('A')?
average_rating_A_players = np.mean(soccer_ratings[soccer_positions == 'A'])
print(average_rating_A_players)``````