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Introduction to Python
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    • Introduction to Python

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

    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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