In numpy.sum() there is parameter called "keepdims". What does it do?

Question

In numpy.sum() there is parameter called keepdims. What does it do?

As you can see here in the documentation: http://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html

numpy.sum(a, axis=None, dtype=None, out=None, keepdims=False)[source]
Sum of array elements over a given axis.

Parameters: 
...
keepdims : bool, optional
    If this is set to True, the axes which are reduced are left in the result as
    dimensions with size one. With this option, the result will broadcast
    correctly against the input array.
...

Have you tried the examples, with and without this parameter? It should be easy to test in an interactive session. — hpaulj, Sep 11 '16 at 23:15
This parameter makes most sense if you are aware of what `sum` does without it. Are you familiar with how the shape of the result depends on the input array, and the axis choice? — hpaulj, Sep 12 '16 at 00:23

score 93 · Accepted Answer · edited Jun 10 '18 at 04:10

@Ney @hpaulj is correct, you need to experiment, but I suspect you don't realize that summation for some arrays can occur along axes. Observe the following which reading the documentation

>>> a
array([[0, 0, 0],
       [0, 1, 0],
       [0, 2, 0],
       [1, 0, 0],
       [1, 1, 0]])
>>> np.sum(a, keepdims=True)
array([[6]])
>>> np.sum(a, keepdims=False)
6
>>> np.sum(a, axis=1, keepdims=True)
array([[0],
       [1],
       [2],
       [1],
       [2]])
>>> np.sum(a, axis=1, keepdims=False)
array([0, 1, 2, 1, 2])
>>> np.sum(a, axis=0, keepdims=True)
array([[2, 4, 0]])
>>> np.sum(a, axis=0, keepdims=False)
array([2, 4, 0])

You will notice that if you don't specify an axis (1st two examples), the numerical result is the same, but the keepdims = True returned a 2D array with the number 6, whereas, the second incarnation returned a scalar. Similarly, when summing along axis 1 (across rows), a 2D array is returned again when keepdims = True. The last example, along axis 0 (down columns), shows a similar characteristic... dimensions are kept when keepdims = True.
Studying axes and their properties is critical to a full understanding of the power of NumPy when dealing with multidimensional data.

score 8 · Answer 2 · answered May 01 '19 at 14:32

An example showing keepdims in action when working with higher dimensional arrays. Let's see how the shape of the array changes as we do different reductions:

import numpy as np
a = np.random.rand(2,3,4)
a.shape
# => (2, 3, 4)
# Note: axis=0 refers to the first dimension of size 2
#       axis=1 refers to the second dimension of size 3
#       axis=2 refers to the third dimension of size 4

a.sum(axis=0).shape
# => (3, 4)
# Simple sum over the first dimension, we "lose" that dimension 
# because we did an aggregation (sum) over it

a.sum(axis=0, keepdims=True).shape
# => (1, 3, 4)
# Same sum over the first dimension, but instead of "loosing" that 
# dimension, it becomes 1.

a.sum(axis=(0,2)).shape
# => (3,)
# Here we "lose" two dimensions

a.sum(axis=(0,2), keepdims=True).shape
# => (1, 3, 1)
# Here the two dimensions become 1 respectively

In numpy.sum() there is parameter called "keepdims". What does it do?

2 Answers2