Introduction
In Python Numpy library numpy.ndarray.flatten() is used to flatten multi-dimensional arrays into one-dimensional arrays. In this article, we will explore the syntax, functionality, and various examples of np.flatten() function.
Syntax
The syntax for Numpy flatten() is straightforward as shown below :
numpy.ndarray.flatten(order=’C’)
Parameters:
- order (optional): The order parameter determines how elements are arranged in the flattened array. It can take any of the following values:
- ‘C’: This is the default order and represents row-major flattening (also known as C-style flattening). The function traverses the array row by row.
- ‘F’: This represents column-major flattening (Fortran-style flattening). The function traverses the array column by column.
- ‘A’: It flattens in column-major order if the array is laid out in Fortran style, otherwise it flattens in row-major.
- ‘K’: It flattens the array as per how its placed in memory.
Examples of Numpy Flatten Function
Import Library
Let us start with importing the Numpy library as shown below.
In [0]:
import numpy as np
Example 1: Basic Usage of Numpy flatten()
In this example, we have explained the basic usage of the np.flatten() function by flattening a 2D Numpy array into a 1D array.
In [1]:
arr = np.array([[1, 2, 3], [4, 5, 6]]) flattened_arr = arr.flatten() print(flattened_arr)
Out[1]:
[1 2 3 4 5 6]
Example 2: Flattening 3D Numpy Array
In this example, we are showing how to use numpy.flatten() function to convert 3D Numpy array into 1D array.
In [2]:
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]) flattened_arr = arr.flatten() print(flattened_arr)
Out[2]:
[ 1 2 3 4 5 6 7 8 9 10 11 12]
Example 3: Using flatten() with order=’C’
In this example, we use np.flatten() on the 2D Numpy array by using the default order=’C’, resulting in a C-ordered flattened array. Here the function traverses the array row-wise like 1,2,3,4… and so on.
In [3]:
arr = np.array([[1, 2, 3],
[4, 5, 6]])
flattened_c_order = arr.flatten(order='C')
print("C-ordered Flattened Array:", flattened_c_order)
Out[3]:
C-ordered Flattened Array: [1 2 3 4 5 6]
Example 4: Using flatten() with order=’F’
Compared to the previous example, this time when we use order=’F’ the function traverses the array column by column like 1,4,2,5.. and so on. This is the Fortan-Style flattening.
In [4]:
arr = np.array([[1, 2, 3],
[4, 5, 6]])
flattened_f_order = arr.flatten(order='F')
print("F-ordered Flattened Array:", flattened_f_order)
Out[4]:
F-ordered Flattened Array: [1 4 2 5 3 6]
Example 5: Using flatten() with order=’A’
When we use order=’A’, it behaves in two ways depending on whether the input Numpy array is created as row major or column major. We have two sub-examples to show both behaviors –
i) Row Major Array
In a row-major array, the elements are indexed in rows like 1,2,3,4… and so on as per the below example. In this case, when order=’A’ is used it traverses the elements row-wise to flatten the array.
In [5]:
arr = np.array([[1, 2, 3],
[4, 5, 6]])
flattened_arr_c = arr.flatten(order='C')
print("A-ordered Flattened Array:", flattened_arr_a)
Out[5]:
A-ordered Flattened Array: [1 2 3 4 5 6]
ii) Column Major Array
In a column-major array, the elements the indexed in a columnar manner like 1,4,2,5… and so on as per the below example. When order=’A’ is used it traverses column-wise to flatten the array.
arr = np.array([[1, 2, 3],
[4, 5, 6]], order='F') # Creating a column-major (Fortran-style) array
flattened_arr_a = arr.flatten(order='A')
print("A-ordered Flattened Array:", flattened_arr_a)
A-ordered Flattened Array: [1 4 2 5 3 6]
