Numpy ndarray.flat Attribute

The Numpy ndarray.flat Attribute which returns a 1-D iterator over the array. This iterator allows us to iterate over the array as if it were flattened but it does not create a new array.

This Attribute is especially useful when we need to iterate over elements of a multidimensional array in a flat manner.

Syntax

Here is the syntax of Numpy ndarray.flat() Attribute −

numpy.ndarray.flat

Parameter

The Numpy ndarray.flat attribute does not take any parameters.

Return Value

This attribute returns a 1-D iterator over the array. This iterator can be used to access and modify elements of the array.

Example 1

Following is the example of Numpy ndarray.flat Attribute, which shows how to iterate over each element of a 2D array using flat Attribute −

import numpy as np

# Creating a 2D numpy array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])

# Using flat to iterate over elements
for item in array_2d.flat:
    print(item)

Output

1
2
3
4
5
6

Example 2

This example doubles each element in the array by iterating through the flat iterator and modifying each element.

 
import numpy as np

# Creating a 2D numpy array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])

# Modifying elements using flat iterator
for index, value in enumerate(array_2d.flat):
    array_2d.flat[index] = value * 2

print(array_2d)

After execution of above code, we get the following result

[[ 2  4  6]
 [ 8 10 12]]

Example 3

Here in this example we access specific elements of a 3D array using the flat iterator, showing how the flat iterator flattens the multidimensional array into a single dimension for easy access −

import numpy as np

# Creating a 3D numpy array
array_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

# Accessing specific elements using flat
print(array_3d.flat[0])  # First element
print(array_3d.flat[5])  # Sixth element
print(array_3d.flat[-1]) # Last element

Output

1
6
8