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)