Iterating Over Elements of a NumPy Array

NumPy Array - Iterate Over Elements

To iterate over elements of a NumPy array, you can use the numpy.nditer iterator object.

numpy.nditer provides Python’s standard iterator interface to visit each element in the NumPy array. You can use a for loop to traverse the elements of this iterator object.

Note: NumPy arrays with any number of dimensions can be iterated using numpy.nditer.

Examples

1. Iterate Over Elements of a 2D NumPy Array

In the following example, we have a 2D array, and we use numpy.nditer to print all the elements of the array.

Python Program

import numpy as np

# 2D array
a = (np.arange(8) * 2).reshape(2, 4)

# Print the array
print("The array:\n", a)

print("\nIterating over all the elements of the array:")
# Iterate over elements of the array
for x in np.nditer(a):
    print(x, end=' ')

Explanation:

  1. We import the numpy library as np.
  2. We create a 2D array a using np.arange(8) multiplied by 2 and reshape it to dimensions (2, 4).
  3. We print the array to visualize its structure.
  4. Using numpy.nditer, we iterate over all the elements of the array and print them in a single line.

Output:

The array:
 [[ 0  2  4  6]
 [ 8 10 12 14]]

Iterating over all the elements of the array:
0 2 4 6 8 10 12 14

2. Iterate Over Elements of a 3D NumPy Array

In this example, we demonstrate how to iterate through a 3D NumPy array.

Python Program

import numpy as np

# 3D array
a = np.arange(27).reshape(3, 3, 3)

# Print the array
print("The 3D array:\n", a)

print("\nIterating over all the elements of the 3D array:")
# Iterate over elements of the array
for x in np.nditer(a):
    print(x, end=' ')

Explanation:

  1. We create a 3D array a with dimensions (3, 3, 3) using np.arange(27).
  2. We print the 3D array to see its structure.
  3. Using numpy.nditer, we iterate over all the elements in the 3D array, regardless of its dimensionality.

Output:

The 3D 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]]]

Iterating over all the elements of the 3D 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

3. Iterate Over a NumPy Array with a Custom Order

In this example, we demonstrate how to iterate through a 2D array in a column-major order (F-style).

Python Program

import numpy as np

# 2D array
a = (np.arange(8) * 2).reshape(2, 4)

# Print the array
print("The array:\n", a)

print("\nIterating over the elements in column-major order:")
# Iterate over elements in column-major order
for x in np.nditer(a, order='F'):
    print(x, end=' ')

Explanation:

  1. We create a 2D array a with dimensions (2, 4).
  2. We print the array to observe its layout.
  3. Using numpy.nditer with the order='F' parameter, we iterate over the elements in column-major (Fortran-style) order instead of the default row-major (C-style) order.

Output:

The array:
 [[ 0  2  4  6]
 [ 8 10 12 14]]

Iterating over the elements in column-major order:
0 8 2 10 4 12 6 14

Summary

In this NumPy Tutorial, we learned how to iterate over the elements of a NumPy array using numpy.nditer, covering:

  • Iterating over a 2D array.
  • Iterating over a 3D array.
  • Iterating with a custom order (column-major).

Using numpy.nditer, you can efficiently traverse arrays of any dimensionality and customize the iteration order as needed.

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