code: # np_baseball is available
  
  # Import numpy
  import numpy as np
  
  # Create np_height_in from np_baseball
  np_height_in = 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)))
  
apis: numpy.median numpy.array numpy.std numpy.mean numpy.corrcoef