# height_in and weight_lb are available as a regular lists

# Import numpy

import numpy as np

# Calculate the BMI: bmi

np_height_m = np.array(height_in) * 0.0254

np_weight_kg = np.array(weight_lb) * 0.453592

bmi = np_weight_kg / np_height_m ** 2

# 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])

# Create baseball, a list of lists

baseball = [[180, 78.4],

[215, 102.7],

[210, 98.5],

[188, 75.2]]

# Import numpy

import numpy as np

# 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)

# baseball is available as a regular list of lists

# Import numpy package

import numpy as np

# Create np_baseball (2 cols)

np_baseball = np.array(baseball)

# Print out the 50th row of np_baseball

print(np_baseball[49,:])

# Select the entire second column of np_baseball: np_weight_lb

np_weight_lb = np_baseball[:,1]

# Print out height of 124th player

print(np_baseball[123,0])

# baseball is available as a regular list of lists

# updated is available as 2D numpy array

# Import numpy package

import numpy as np

# Create np_baseball (3 cols)

np_baseball = np.array(baseball)

# Print out addition of np_baseball and updated

print(np_baseball)

print(updated)

print(np_baseball+updated)

# Create numpy array: conversion

conversion = np.array([0.0254, 0.453592, 1])

print(conversion)

# Print out product of np_baseball and conversion

print(conversion * np_baseball)

# np_baseball is available

# Import numpy

import numpy as np

# Create np_height_in from np_baseball

np_height_in = np_baseball[:,0]

print(np_baseball[:,0])

# Print out the mean of np_height_in

print(np.mean(np_height_in))

# Print out the median of np_height_in

print(np.median(np_height_in))

# np_baseball is available

# Import numpy

import numpy as np

# Print mean height (first column)

avg = np.mean(np_baseball[:,0])

print("Average: " + str(avg))

# Print median height. Replace 'None'

med = np.median(np_baseball[:,0])

print("Median: " + str(med))

# Print out the standard deviation on height. Replace 'None'

stddev = np.std(np_baseball[:,0])

print("Standard Deviation: " + str(stddev))

# Print out correlation between first and second column. Replace 'None'

corr = np.corrcoef(np_baseball[:,0], np_baseball[:,1])

print("Correlation: " + str(corr))

# heights and positions are available as lists

# Import numpy

import numpy as np

# Convert positions and heights to numpy arrays: np_positions, np_heights

np_positions = np.array(positions)

np_heights = np.array(heights)

# Heights of the goalkeepers: gk_heights

gk_heights = np_heights[np_positions == 'GK']

# Heights of the other players: other_heights

other_heights = np_heights[np_positions != 'GK']

# Print out the median height of goalkeepers. Replace 'None'

print("Median height of goalkeepers: " + str(np.median(gk_heights)))

# Print out the median height of other players. Replace 'None'

print("Median height of other players: " + str(np.median(other_heights)))