<gh_stars>1-10
# Create list baseball
import numpy as np
baseball = [180, 215, 210, 210, 188, 176, 209, 200]

# Import the numpy package as np

# Create a numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out type of np_baseball
print(type(np_baseball))

# height is available as a regular list

# Import numpy

# 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)


# height and weight are available as regular lists

# Import numpy

# Create array from height_in with metric units: np_height_m
np_height_m = np.array(height_in) * 0.0254

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

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


# height and weight are available as a regular lists

# Import numpy

# 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)


# height and weight are available as a regular lists

# Import numpy

# Store weight and height lists as numpy arrays
np_weight_lb = np.array(weight_lb)
np_height_in = np.array(height_in)

# Print out the weight at index 50
print(np_weight_lb[50])

# Print out sub-array of np_height_in: index 100 up to and including index 110
print(np_height_in[100:111])


# Create baseball, a list of lists
baseball = [[180, 78.4],
            [215, 102.7],
            [210, 98.5],
            [188, 75.2]]

# Import numpy

# 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))


# baseball is available as a regular list of lists

# Import numpy package

# Create a 2D numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out the shape of np_baseball
print(np_baseball.shape)


# baseball is available as a regular list of lists

# Import numpy package

# 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
# baseball is available as a regular list of lists
# updated is available as 2D numpy array

# Import numpy package

# Create np_baseball (3 cols)
np_baseball = np.array(baseball)

# Print out addition of np_baseball and updated
print(np_baseball+updated)

# Create numpy array: conversion
conversion = np.array([0.0254, 0.453592, 1])


# np_baseball is available

# Import numpy

# Create np_height_in from np_baseball
np_height_in = np_baseball[:, 0]

# Print out the mean of np_height_in
print(np_height_in.mean())

# Print out the median of np_height_in
print(np.median(np_height_in))


# np_baseball is available

# Import numpy

# 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

# 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)))




# Create list baseball
import numpy as np
baseball = [180, 215, 210, 210, 188, 176, 209, 200]

# Import the numpy package as np

# Create a numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out type of np_baseball
print(type(np_baseball))

# height is available as a regular list

# Import numpy

# 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)


# height and weight are available as regular lists

# Import numpy

# Create array from height_in with metric units: np_height_m
np_height_m = np.array(height_in) * 0.0254

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

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


# height and weight are available as a regular lists

# Import numpy

# 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)


# height and weight are available as a regular lists

# Import numpy

# Store weight and height lists as numpy arrays
np_weight_lb = np.array(weight_lb)
np_height_in = np.array(height_in)

# Print out the weight at index 50
print(np_weight_lb[50])

# Print out sub-array of np_height_in: index 100 up to and including index 110
print(np_height_in[100:111])


# Create baseball, a list of lists
baseball = [[180, 78.4],
            [215, 102.7],
            [210, 98.5],
            [188, 75.2]]

# Import numpy

# 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))


# baseball is available as a regular list of lists

# Import numpy package

# Create a 2D numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out the shape of np_baseball
print(np_baseball.shape)


# baseball is available as a regular list of lists

# Import numpy package

# 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
# baseball is available as a regular list of lists
# updated is available as 2D numpy array

# Import numpy package

# Create np_baseball (3 cols)
np_baseball = np.array(baseball)

# Print out addition of np_baseball and updated
print(np_baseball+updated)

# Create numpy array: conversion
conversion = np.array([0.0254, 0.453592, 1])


# np_baseball is available

# Import numpy

# Create np_height_in from np_baseball
np_height_in = np_baseball[:, 0]

# Print out the mean of np_height_in
print(np_height_in.mean())

# Print out the median of np_height_in
print(np.median(np_height_in))


# np_baseball is available

# Import numpy

# 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

# 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)))
# Create list baseball # Import the numpy package as np # Create a numpy array from baseball: np_baseball # Print out type of np_baseball # height is available as a regular list # Import numpy # Create a numpy array from height_in: np_height_in # Print out np_height_in # Convert np_height_in to m: np_height_m # Print np_height_m # height and weight are available as regular lists # Import numpy # Create array from height_in with metric units: np_height_m # Create array from weight_lb with metric units: np_weight_kg # Calculate the BMI: bmi # height and weight are available as a regular lists # Import numpy # Calculate the BMI: bmi # Create the light array # Print out light # height and weight are available as a regular lists # Import numpy # Store weight and height lists as numpy arrays # Print out the weight at index 50 # Print out sub-array of np_height_in: index 100 up to and including index 110 # Create baseball, a list of lists # Import numpy # Create a 2D numpy array from baseball: np_baseball # Print out the type of np_baseball # baseball is available as a regular list of lists # Import numpy package # Create a 2D numpy array from baseball: np_baseball # Print out the shape of np_baseball # baseball is available as a regular list of lists # Import numpy package # Create np_baseball (2 cols) # Print out the 50th row of np_baseball # Select the entire second column of np_baseball: np_weight_lb # Print out height of 124th player # baseball is available as a regular list of lists # updated is available as 2D numpy array # Import numpy package # Create np_baseball (3 cols) # Print out addition of np_baseball and updated # Create numpy array: conversion # np_baseball is available # Import numpy # Create np_height_in from np_baseball # Print out the mean of np_height_in # Print out the median of np_height_in # np_baseball is available # Import numpy # Print mean height (first column) # Print median height. Replace 'None' # Print out the standard deviation on height. Replace 'None' # Print out correlation between first and second column. Replace 'None' # heights and positions are available as lists # Import numpy # Convert positions and heights to numpy arrays: np_positions, np_heights # Heights of the goalkeepers: gk_heights # Heights of the other players: other_heights # Print out the median height of goalkeepers. Replace 'None' # Print out the median height of other players. Replace 'None'