Numpy ndarray.flatten() function in Python

The flatten() function is used to convert a multi-dimensional NumPy array into a one-dimensional array. It creates a new copy of the data so that original array stays unchanged. If your array has rows and columns or even more dimensions, then flatten() line up every single value into a straight list, one after another. Example:

import numpy as np

a = np.array([[5, 6], [7, 8]])
res = a.flatten()
print(res)

[5 6 7 8]

Explanation: This code creates a 2D NumPy array and uses flatten() to convert it into a 1D array in row-major order. It returns a new array [5, 6, 7, 8] without modifying the original.

Syntax of ndarray.flatten()

array.flatten(order=’C’)

Parameters: order ({‘C’, ‘F’, ‘A’, ‘K’}) Optional

• ‘C’: Row-major (C-style) order.
• ‘F’: Column-major (Fortran-style) order.
• ‘A’: Column-major if the array is Fortran contiguous, otherwise row-major.
• ‘K’: Flatten in the order the elements occur in memory.

Returns: A 1D copy of the input array, flattened according to the specified order.

Examples of ndarray.flatten()

Example 1: In this example, we will use the flatten() function with the ‘F’ parameter to flatten a 2D NumPy array in column-major order (also known as Fortran-style order). This means the function will read the elements column by column, instead of row by row.

import numpy as np

a = np.array([[5, 6], [7, 8]])
res = a.flatten('F')
print(res)

[5 7 6 8]

Explanation: flatten(‘F’) convert it into a 1D array in column-major order (Fortran-style). The result is [5, 7, 6, 8], with elements listed column by column.

Example 2: In this example, we will demonstrate how to flatten two 2D NumPy arrays and then concatenate them into a single 1D array using the concatenate() function from NumPy.

import numpy as np

a = np.array([[1, 2, 3], [4, 5, 6]])
b = np.array([[7, 8, 9], [10, 11, 12]])

res = np.concatenate((a.flatten(), b.flatten()))
print(res)

[ 1  2  3  4  5  6  7  8  9 10 11 12]

Explanation: This code flattens two 2D NumPy arrays a and b into 1D arrays and then concatenates them. The result is a single 1D array combining the flattened elements of both arrays.

Example 3: In this example, we demonstrate how to flatten a 2D NumPy array and then initialize a new 1D array of the same shape filled with zeros

import numpy as np

a = np.array([[1, 2, 3], [4, 5, 6]])
res = np.zeros_like(a.flatten())
print(res)

[0 0 0 0 0 0]

Explanation: This code creates a new array of the same shape, initialized with zeros using np.zeros_like(). The result is a 1D array of zeros matching the flattened shape of a.

Example 4: In this example, we create a 2D NumPy. Then we apply the max() method to find the maximum value among all the elements in the flattened array.

import numpy as np

a = np.array([[4, 12, 8],[5, 9, 10],[7, 6, 11]])
res = a.flatten().max()
print(res)

12

Explanation: This code flattens the 2D NumPy array a into a 1D array and then finds the maximum value using .max(). The result is 12, which is the largest value in the flattened array.

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Article Tags :

• Machine Learning
• python
• Python numpy-ndarray
• Python-numpy

Practice Tags :

• Machine Learning
• python