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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

Converting array from one metric to another and using arithmetic to calculate Bmi Add notes about the concepts you've learned and code cells with code you want to keep.

# Import numpy
import numpy as np

# Define height_in and weight_lb
height_in = [65, 68, 70, 71, 73]
weight_lb = [150, 160, 170, 180, 190]

# 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)

# Create array from weight_lb with metric units: np_weight_kg
np_weight_kg = np.array(weight_lb) * 0.453592
print(np_weight_kg)

# Calculate the BMI: bmi
bmi = np_weight_kg / np_height_m ** 2

# Print out bmi
print(bmi)

# Create the light array
light = bmi < 21

# Print out light
print(light)

# Print out BMIs of all baseball players whose BMI is below 21
print(bmi[light])

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')?