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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.

    # 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", 
    )

    Take Notes

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

    Add your notes here

    # 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')?
    #1
    baseball_weights[:10]
    #2
    print(np.median(baseball_weights))
    #3
    baseball_names[baseball_heights>80]
    #4
    a=np.mean(soccer_heights)
    b=np.mean(baseball_heights*2.54)
    c=[a if a>b else b]
    print(c)
    #5
    soccer_shooting*100
    #6
    np.corrcoef(soccer_heights,soccer_ratings)
    #7
    np.mean(soccer_ratings[soccer_positions=='A'])
    #Just to check the data
    import pandas as pd
    df=pd.read_csv('baseball.csv')
    df