np.newaxis?The np.newaxis is just an alias for the Python constant None, which means that wherever you use np.newaxis you could also use None:
>>> np.newaxis is None
True
It's just more descriptive if you read code that uses np.newaxis instead of None.
np.newaxis?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)
np.newaxis?There is another very similar functionality in NumPy: np.expand_dims, which can also be used to insert one dimension:
>>> np.expand_dims(a, 1) # like a[:, np.newaxis]
>>> np.expand_dims(a, 0) # like a[np.newaxis, :]
But given that it just inserts 1s in the shape you could also reshape the array to add these dimensions:
>>> a.reshape(a.shape + (1,)) # like a[:, np.newaxis]
>>> a.reshape((1,) + a.shape) # like a[np.newaxis, :]
Most of the times np.newaxis is the easiest way to add dimensions, but it's good to know the alternatives.
np.newaxis?In several contexts is adding dimensions useful:
If the data should have a specified number of dimensions. For example if you want to use matplotlib.pyplot.imshow to display a 1D array.
If you want NumPy to broadcast arrays. By adding a dimension you could for example get the difference between all elements of one array: a - a[:, np.newaxis]. This works because NumPy operations broadcast starting with the last dimension 1.
To add a necessary dimension so that NumPy can broadcast arrays. This works because each length-1 dimension is simply broadcast to the length of the corresponding1 dimension of the other array.
1 If you want to read more about the broadcasting rules the NumPy documentation on that subject is very good. It also includes an example with np.newaxis:
>>> a = np.array([0.0, 10.0, 20.0, 30.0]) >>> b = np.array([1.0, 2.0, 3.0]) >>> a[:, np.newaxis] + b array([[ 1., 2., 3.], [ 11., 12., 13.], [ 21., 22., 23.], [ 31., 32., 33.]])