import numpy as np

a = np.arange(15).reshape(3, 5)

print(a)

###

# This will create the following array:

# [

# [ 0 1 2 3 4]

# [ 5 6 7 8 9]

# [10 11 12 13 14]

# ]

#

# Notice that we have 3 arrays of length 5 each.

# Read the array as follows:

# The last value in the shape tuple is the length of the innermost array

# The value to the left in the tuple i.e. 3 in (3,5) is the number of such arrays

###

b = np.arange(30).reshape(3, 5, 2)

print(b)

###

# This will create the following array

# [

# [[ 0 1]

# [ 2 3]

# [ 4 5]

# [ 6 7]

# [ 8 9]]

#

# [[10 11]

# [12 13]

# [14 15]

# [16 17]

# [18 19]]

#

# [[20 21]

# [22 23]

# [24 25]

# [26 27]

# [28 29]]

# ]

# Notice that the reading method above still holds. i.e. in shape tuple (3,5,2)

# 2 is the length of the innermost array

# The value to its left, i.e. 5, is the number of the innermost array

# The value further to the left, i.e. 3, is the number of arrays of arrays of length 2

# Indexing the arrays

print(a[0]) # this should return the first row i.e. [0 1 2 3 4]

print(a[0:2]) # this should return row 0 and row 1. Row 2 is not included

# in order to specify an individual element, row and column indexes,

# which are called axes in numpy arrays, should be specified as comma separated values

print("a[0,1] = {}".format(a[0,1])) # this should return 1

print("a[0,1] = {}".format(a[0][1])) # this is the same as a[0,1]

# So, in order to access 15 in array b, we can do the following

print("b[1,2,1] = {}".format(b[1,2,1]))