This post deals with the ways to increase the dimension of an array in NumPy. NumPy provides us with two different built-in functions to increase the dimension of an array i.e.,
1D array will become 2D array 2D array will become 3D array 3D array will become 4D array 4D array will become 5D array
Method 1: Using numpy.newaxis()
The first method is to use numpy.newaxis object. This object is equivalent to use None as a parameter while declaring the array. The trick is to use the numpy.newaxis object as a parameter at the index location in which you want to add the new axis.
Example:
Python3
import numpy as np arr = np.arange(5*5).reshape(5, 5)print(arr.shape) # promoting 2D array to a 5D array# arr[None, ..., None, None]arr_5D = arr[np.newaxis, ..., np.newaxis, np.newaxis] print(arr_5D.shape) |
Output:
(5, 5) (1, 5, 5, 1, 1)
Method 2: Using numpy.expand_dims()
The second method is to use numpy.expand_dims() function that has an intuitive axis kwarg. This function takes two parameters. The first is the array of which you want to increase the dimension of and the second is index/indexes of array on which you want to create a new axis.
Example:
Python3
import numpy as np x = np.zeros((3, 4))y = np.expand_dims(x, axis=1).shapeprint(y) |
Output:
(3, 1, 4)
Example 2: Simultaneously insert many new axes in the array.
Python3
import numpy as np arr = np.arange(5*5).reshape(5,5)print(arr.shape) newaxes = (0, 3, -1)arr_5D = np.expand_dims(arr, axis=newaxes)print(arr_5D.shape) |
Output:
(5, 5) (1, 5, 5, 1, 1)
