# 3.12
# Average versus median
#You now know how to use numpy functions to get a better feeling for your data. It basically comes down to importing numpy and then calling several simple functions on the numpy arrays:

#import numpy as np
#x = [1, 4, 8, 10, 12]
#np.mean(x)
#np.median(x)
#The baseball data is available as a 2D numpy array with 3 columns (height, weight, age) and 1015 rows. The name of this numpy array is np_baseball. After restructuring the data, however, you notice that some height values are abnormally high. Follow the instructions and discover which summary statistic is best suited if you're dealing with so-called outliers.

#INSTRUCTIONS
#100 XP
#Create numpy array np_height that is equal to first column of np_baseball.
#Print out the mean of np_height.
#Print out the median of np_height.

# np_baseball is available

# Import numpy
import numpy as np

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

# Print out the mean of np_height
print(np.mean(np_height))

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