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)