Indexing

Indexing gets one value out of an array. Positions start at 0 from the left and at -1 from the right. A 2-D array needs a row and a column. A 3-D array needs a block, a row, and a column.

1-D indexing

a[i] gives one value. Negative indices count from the end. An index past either end raises IndexError.

import numpy as np

a = np.array([40, 15, 72, 8, 63])
print(a[0], a[2], a[-1], a[-2])

try:
    print(a[5])
except IndexError as error:
    print("IndexError:", error)

Output:

40 72 63 8
IndexError: index 5 is out of bounds for axis 0 with size 5

2-D indexing

Write m[row, col]. m[row][col] gives the same value in two steps, but the comma form is the usual one. Both indices can be negative.

import numpy as np

m = np.array([[11, 12, 13, 14],
              [21, 22, 23, 24],
              [31, 32, 33, 34]])
print(m[1, 2])
print(m[1][2])
print(m[-1, 0])
print(m[0, -1])

Output:

23
23
31
14

3-D indexing

A 3-D array is a stack of 2-D blocks. Write t[block, row, col].

import numpy as np

t = np.arange(1, 13).reshape(2, 2, 3)
print(t)
print(t[1, 0, 2])
print(t[0, -1, -1])

Output:

[[[ 1  2  3]
  [ 4  5  6]]

 [[ 7  8  9]
  [10 11 12]]]
9
6

Change a value

An index on the left of = replaces that one value. The new value is converted to the array's dtype, so a float in an integer array loses its decimals.

import numpy as np

m = np.array([[1, 2], [3, 4]])
m[0, 1] = 20
m[-1, -1] = 9.8
print(m)

Output:

[[ 1 20]
 [ 3  9]]

Key takeaways

• Indices start at 0, and -1 is the last value

• 2-D: m[row, col]. 3-D: t[block, row, col]

• An index past the end raises IndexError

• Assigned values take the array's dtype

• Next: Slicing

• Previous: Random Arrays