Skip to content
Introduction to Python
  • AI Chat
  • Code
  • Report
  • Spinner

    Introduction to Python

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

    # Importing course packages; you can add more too!
    import numpy as np
    import math
    
    # Import columns as numpy arrays
    baseball_names = np.genfromtxt(
        fname="baseball.csv",  # This is the filename
        delimiter=",",  # The file is comma-separated
        usecols=0,  # Use the first column
        skip_header=1,  # Skip the first line
        dtype=str,  # This column contains strings
    )
    baseball_heights = np.genfromtxt(
        fname="baseball.csv", delimiter=",", usecols=3, skip_header=1
    )
    baseball_weights = np.genfromtxt(
        fname="baseball.csv", delimiter=",", usecols=4, skip_header=1
    )
    baseball_ages = np.genfromtxt(
        fname="baseball.csv", delimiter=",", usecols=5, skip_header=1
    )
    
    soccer_names = np.genfromtxt(
        fname="soccer.csv",
        delimiter=",",
        usecols=1,
        skip_header=1,
        dtype=str,
        encoding="utf", 
    )
    soccer_ratings = np.genfromtxt(
        fname="soccer.csv",
        delimiter=",",
        usecols=2,
        skip_header=1,
        encoding="utf", 
    )
    soccer_positions = np.genfromtxt(
        fname="soccer.csv",
        delimiter=",",
        usecols=3,
        skip_header=1,
        encoding="utf", 
        dtype=str,
    )
    soccer_heights = np.genfromtxt(
        fname="soccer.csv",
        delimiter=",",
        usecols=4,
        skip_header=1,
        encoding="utf", 
    )
    soccer_shooting = np.genfromtxt(
        fname="soccer.csv",
        delimiter=",",
        usecols=8,
        skip_header=1,
        encoding="utf", 
    )
    
    
    Hidden output

    Take Notes

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

    Add your notes 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')?
    # Print out the weight of the first ten baseball players.
    print(baseball_names[0:10])
    # What is the median weight of all baseball players in the data?
    print('The median weigth of all baseball players in data is: ' + str(np.median(baseball_heights)))
    # Print out the names of all players with a height greater than 80 (heights are in inches)
    
    print(baseball_names[baseball_heights >= 80 ], soccer_names[soccer_heights >= 80 ])
    # Who is taller on average? Baseball players or soccer players? Keep in mind that baseball heights are stored in inches!
    print('The avarege of Baseball heights is:'+ str(np.mean(baseball_heights)))
    print('The avarege of Soccer heights is:'+ str(np.mean(soccer_heights/2.5)))
    print('The taller average is Baseball playes that is :' + str(np.mean(baseball_heights)))
    # The values in soccer_shooting are decimals. Convert them to whole numbers (e.g., 0.98 becomes 98).
    print (str(soccer_shooting*100))
    # Do taller players get higher ratings? Calculate the correlation between soccer_ratings and soccer_heights to find out!
    
    taller_soccer_ratings = soccer_ratings[soccer_heights <= 181.75042387249914]
    shorter_soccer_ratings = soccer_ratings[soccer_heights >= 181.75042387249914] 
    
    
    # Resize the arrays to the same size
    taller_soccer_ratings = taller_soccer_ratings[:len(shorter_soccer_ratings)]
    shorter_soccer_ratings = shorter_soccer_ratings[:len(taller_soccer_ratings)]
    
    #print( taller_soccer_ratings )
    #print( shorter_soccer_ratings )
    print('The Average of taller players ratings is : ' + str(np.mean(taller_soccer_ratings)))
    print('The Average of Shorter players ratings is : ' + str(np.mean(shorter_soccer_ratings)))
    print('The taller players dont get the higher rating')
    
    print( np.corrcoef (taller_soccer_ratings, shorter_soccer_ratings) )
    # What is the average rating for attacking players ('A')?
    print ('The average rating for attacking players is : '+ str ( np.mean (soccer_ratings[soccer_positions == 'A'])))