Beta

## 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'`

)?

```
# 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)
```