Access Array Elements
Array indexing is the same as accessing an array element.
You can access an array element by referring to its index number.
The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc.
Example
import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr[0])
Example
import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr[1])
Example
import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr[2] +
arr[3])
Access 2-D Arrays
To access elements from 2-D arrays we can use comma separated integers representing the dimension and the index of the element.
Think of 2-D arrays like a table with rows and columns, where the dimension represents the row and the index represents the column.
Example
import numpy as np
arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print('2nd element on 1st row: ', arr[0, 1])
Example
import numpy as np
arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print('5th element on
2nd row: ', arr[1, 4])
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.
Example
import numpy as np
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8,
9], [10, 11, 12]]])
print(arr[0, 1, 2])
Example Explained
arr[0, 1, 2] prints the value 6.
And this is why:
The first number represents the first dimension, which contains two arrays: [[1, 2, 3], [4, 5, 6]] and: [[7, 8, 9], [10, 11, 12]] Since we selected 0, we are left with the first array: [[1, 2, 3], [4, 5, 6]]
The second number represents the second dimension, which also contains two arrays: [1, 2, 3] and: [4, 5, 6] Since we selected 1, we are left with the second array: [4, 5, 6]
The third number represents the third dimension, which contains three values: 4 5 6 Since we selected 2, we end up with the third value: 6
Negative Indexing
Use negative indexing to access an array from the end.
Example
import numpy as np
arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print('Last element
from
2nd dim: ', arr[1, -1])
