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

    • 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.

    video 2: variables: .case- sensitive heght = 1.79 BMI = weight /height 2 h= 1.79 w= 74.3 bmi =w/h2 print(bmi) type(bmi) variables: int : integer float : float type() gives us type of variable string: str boolean: bool differetn type: different behavior!!!

    Video 3: list: contain all variables list contain int, str, float, bol list [a, b, c] fam = ["liz", 1.73, "emma", 1.68, "mom", 1.71, "dad", 1.89 ] list also contain sub list fam2 = [["liz", 1.73], ["emma", 1.68], ["mom", 1.71], ["dad", 189]]

    # 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", 
)

    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')?
    print(baseball_weights)

    #print out the weight of the firts ten baseball player
print(baseball_weights[:11])

#meadian of weight of baseball player
print(np.median(baseball_weights))
    #Print out the names of all players with a height greater than 80 (heights are in inches).
print(baseball_heights)
print(baseball_names[baseball_heights[:]>80])
#the tallest player name
print(np.mean([baseball_names[baseball_heights[:]]]))