Python | Flatten a 2D Numpy Array into 1D Array

Given a 2D NumPy array, the task is to convert it into a 1D array. Flattening helps when you want to convert matrix-style data into a single list-like structure for further processing. For Example:

Input: [[1, 2], [3, 4]]
Output: [1 2 3 4]

Let's explore different ways to flatten a 2D NumPy array into a 1D array.

Using np.ravel()

ravel() tries to return a view of the original array (not a copy). It reads values row-wise and presents them as a 1D array, making it the most efficient method.

import numpy as np

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

[1 2 3 4 5 6]

Using np.ndarray.flatten()

flatten() creates a new copy of the data and converts the array into a row-major 1D array.

import numpy as np

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

[1 2 3 4 5 6]

Using reshape(-1)

reshape(-1) converts any array into a 1D array. The -1 tells NumPy to automatically compute the required length.

import numpy as np

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

[1 2 3 4 5 6]

Explanation:

• a.reshape(-1) reshapes the structure into one dimension.
• -1 NumPy figures out the correct final size on its own.

Using np.concatenate()

concatenate() joins all inner arrays (rows) one after another, forming a single continuous 1D array.

import numpy as np

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

[1 2 3 4 5 6]

Explanation: np.concatenate(a) combines each row from a into one long array.