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Introduction to Python

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

1 hidden cell

Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

#list()= para modificar una lista sin alterar la anterior

# Add your code snippets here

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')?
# string to experiment with: place
place = "poolhouse"

# Use upper() on place: place_up

place_up=place.upper()
# Print out place and place_up

print(place_up)
# Print out the number of o's in place
print(place.count('o'))
print(place)
# 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))
# Print out how often 9.50 appears in areas
print(areas.count(9.50))

# Create list areas
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Use append twice to add poolhouse and garage size
areas.append(24.5)
areas.append(15.45)

# Print out areas
print(areas)

# Reverse the orders of the elements in areas
areas.reverse()

# Print out areas
print(areas)
In [7]:
from scipy.linalg import inv as my_inv
In [8]:
my_inv([[1,2], [3,4]])
Out[8]:

array([[-2. ,  1. ],
       [ 1.5, -0.5]])

#nampay --> NumPy

# Import numpy
import numpy as np

# Create a numpy array from height_in: np_height_in
np_height_in = np.array(height_in)

# Print out np_height_in

print(np_height_in)
# Convert np_height_in to m: np_height_m
np_height_m= np_height_in*0.0254

# Print np_height_m
print(np_height_m)
# Import numpy
import numpy as np

# Store weight and height lists as numpy arrays
np_weight_lb = np.array(weight_lb)
np_height_in = np.array(height_in)

# Print out the weight at index 50
print(np_weight_lb[50])

# Print out sub-array of np_height_in: index 100 up to and including index 110
print(np_height_in[100:111])
# Import numpy
import numpy as np

# Create baseball, a list of lists
baseball = [[180, 78.4],
            [215, 102.7],
            [210, 98.5],
            [188, 75.2]]

# Create a 2D numpy array from baseball: np_baseball

np_baseball= np.array(baseball)
# Print out the type of np_baseball

print(type(np_baseball))
# Print out the shape of np_baseball
print(np_baseball.shape)
# Import numpy package
import numpy as np

# Create a 2D numpy array from baseball: np_baseball

np_baseball=np.array(baseball)
# Print out the shape of np_baseball
print(np_baseball.shape)
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