3-D arrays

• An array that has 2-D arrays (matrices) as its elements is called 3-D array.

• These are often used to represent a 3rd order tensor.

• Example

• Create a 3-D array with two 2-D arrays, both containing two arrays with the values 1,2,3 and 4,5,6:

• Program:

• import numpy as np

  arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])

  print(arr)

Access 3-D Arrays

• To access elements from 3-D arrays we can use comma separated integers representing the dimensions and the index of the element.

• Program: Access the third element of the second array of the first array:

import numpy as np
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
print(arr[0, 1, 2])

Output: arr[0, 1, 2] prints the value 6

Data Types in NumPy

• NumPy has some extra data types, and refer to data types with one character, like i for integers, u for unsigned integers etc.

• Below is a list of all data types in NumPy and the characters used to represent them.

• i – integer

• b – boolean

• u – unsigned integer

• f – float

• c – complex float

• m – timedelta

• M – datetime

• O – object

• S – string

• U – unicode string

• V – fixed chunk of memory for other type ( void)