How does numpy newaxis work and when to use it
I'm trying to use
numpy.newaxis
and the result gives me a 2-d plot frame with x-axis from 0 to 1.
However, when I try using numpy.newaxis to slice a vector,
vector[0:4,]
[ 0.04965172 0.04979645 0.04994022 0.05008303]
vector[:, np.newaxis][0:4,]
[[ 0.04965172]
[ 0.04979645]
[ 0.04994022]
[ 0.05008303]]
Is it the same thing except that it changes a row vector to a column vector?
Generally, what is the use of numpy.newaxis and in which circumstances should we use it?
newaxis is also called as a pseudo-index that allows the temporary addition of an axis into a multiarray.
np.newaxis uses the slicing operator to recreate the array while numpy. reshape reshapes the array to the desired layout (assuming that the dimensions match; And this is must for a reshape to happen).
The np.newaxis is generally used with slicing. It indicates that you want to add an additional dimension to the array.
The position of the np.newaxis represents where I want to add dimensions.
>>> import numpy as np
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> a.shape
(10,)In the first example I use all elements from the first dimension and add a second dimension:
>>> a[:, np.newaxis]
array([[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
[9]])
>>> a[:, np.newaxis].shape
(10, 1)
The second example adds a dimension as first dimension and then uses all elements from the first dimension of the original array as elements in the second dimension of the result array:
>>> a[np.newaxis, # The output has 2 [] pairs!
array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
>>> a[np.newaxis, .shape
(1, 10)Similarly you can use multiple np.newaxis to add multiple dimensions:
>>> a[np.newaxis, :, np.newaxis] # note the 3 [] pairs in the output
array([[[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
[9]]])
>>> a[np.newaxis, :, np.newaxis].shape
(1, 10, 1)Answer (1)
numpy.newaxis is a powerful tool in the NumPy library that allows you to increase the dimensions of an existing array. This can be very useful when you need to perform operations that require arrays to have compatible shapes, such as broadcasting.
How numpy.newaxis Works
numpy.newaxis is essentially an alias for None and is used to create a new axis in an array, increasing its dimensions. By inserting numpy.newaxis (or None) into the array's indexing syntax, you can add a new axis of size 1 at the specified position.
Here's how it works:
1D Array Example:import numpy as np
a = np.array([1, 2, 3])
print(a.shape) # (3,)
# Add a new axis to make it a 2D column vector
b = a[:, np.newaxis]
print(b.shape) # (3, 1)
print(b)
# Output:
# [[1]
# [2]
# [3]]2D Array Example:
a = np.array([[1, 2, 3], [4, 5, 6]])
print(a.shape) # (2, 3)
# Add a new axis to make it a 3D array
b = a[np.newaxis, :,
print(b.shape) # (1, 2, 3)
print(b)
# Output:
# [[[1, 2, 3],
# [4, 5, 6]]]In the first example, a[:, np.newaxis] turns the 1D array a into a 2D column vector. In the second example, a[np.newaxis, :, 
turns the 2D array a into a 3D array with the new axis at the beginning.
When to Use numpy.newaxis
numpy.newaxis is useful in various scenarios, particularly when you need to:
Change the Shape of Arrays for Broadcasting:
Broadcasting is a feature that allows NumPy to perform element-wise operations on arrays of different shapes. By adding an axis, you can make arrays compatible for broadcasting.
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
# Make `a` a column vector and `b` a row vector for broadcasting
a = a[:, np.newaxis] # Shape becomes (3, 1)
b = b[np.newaxis, # Shape becomes (1, 3)
c = a + b
print(c)
# Output:
# [[5, 6, 7],
# [6, 7, 8],
# [7, 8, 9]]Vectorization of Functions:
When applying a function that operates on arrays, you might need to adjust the dimensions of the inputs to match the expected shape.
def f(x, y):
return x + y
x = np.array([1, 2, 3])
y = np.array([10, 20, 30])
result = f(x[:, np.newaxis], y[np.newaxis, )
print(result)
# Output:
# [[11, 21, 31],
# [12, 22, 32],
# [13, 23, 33]]Handling High-Dimensional Data:
In machine learning and data processing, you often work with high-dimensional data. Adding an axis can be necessary to properly align data structures.
images = np.random.rand(10, 64, 64) # 10 images of 64x64 pixels
# Add a channel axis for grayscale images
images = images[:, :, :, np.newaxis]
print(images.shape) # (10, 64, 64, 1)