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Python | Flatten a 2d numpy array into 1d array

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Given a 2d numpy array, the task is to flatten a 2d numpy array into a 1d array. Below are a few methods to solve the task.

Method #1 : Using np.flatten() 

Python3




# Python code to demonstrate
# flattening a 2d numpy array
# into 1d array
 
import numpy as np
 
ini_array1 = np.array([[1, 2, 3], [2, 4, 5], [1, 2, 3]])
 
# printing initial arrays
print("initial array", str(ini_array1))
 
# Multiplying arrays
result = ini_array1.flatten()
 
# printing result
print("New resulting array: ", result)


Output:

initial array [[1 2 3]
 [2 4 5]
 [1 2 3]]
New resulting array:  [1 2 3 2 4 5 1 2 3]

Time complexity: O(n), where n is the total number of elements in the 2D numpy array.
Auxiliary space: O(n), as the result array is also of size n. The flatten function returns a flattened 1D array, which is stored in the “result” variable.

Method #2: Using np.ravel() 

Python3




# Python code to demonstrate
# flattening a 2d numpy array
# into 1d array
 
import numpy as np
 
ini_array1 = np.array([[1, 2, 3], [2, 4, 5], [1, 2, 3]])
 
# printing initial arrays
print("initial array", str(ini_array1))
 
# Multiplying arrays
result = ini_array1.ravel()
 
# printing result
print("New resulting array: ", result)


Output:

initial array [[1 2 3]
 [2 4 5]
 [1 2 3]]
New resulting array:  [1 2 3 2 4 5 1 2 3]

Method #3: Using np.reshape() 

Python3




# Python code to demonstrate
# flattening a 2d numpy array
# into 1d array
 
import numpy as np
 
ini_array1 = np.array([[1, 2, 3], [2, 4, 5], [1, 2, 3]])
 
# printing initial arrays
print("initial array", str(ini_array1))
 
# Multiplying arrays
result = ini_array1.reshape([1, 9])
 
# printing result
print("New resulting array: ", result)


Output:

initial array [[1 2 3]
 [2 4 5]
 [1 2 3]]
New resulting array:  [[1 2 3 2 4 5 1 2 3]]

Time Complexity: O(n), where n is the total number of elements in the 2D numpy array.
Auxiliary Space: O(n), as the program creates a new 1D list with the same number of elements as the original 2D array.



Last Updated : 03 Feb, 2023
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