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pythonnumpy
Isha Garg
Isha Garg

Reputation: 351

Why does the shape remains same when I sum a square numpy array along either directions?

I was expecting the shape to be (1,3) when I sum along axis=0 i.e. rows. But the shape remains same in both cases. Why is that?

>>> arr = np.arange(9).reshape(3,3)
>>> arr
array([[0, 1, 2],
   [3, 4, 5],
   [6, 7, 8]])
>>> arr.sum(1)
array([ 3, 12, 21])
>>> arr.sum(1).shape
(3,)
>>> arr.sum(0)
array([ 9, 12, 15])
>>> arr.sum(0).shape
(3,)

Upvotes: 0

Views: 1217

Answers (4)

B. M.
B. M.

Reputation: 18628

numpy.arrays have a logic which is not the same than Matlab or even mathematics. From here :

Handling of vectors (one-dimensional arrays) For array, the vector shapes 1xN, Nx1, and N are all different things. Operations like A[:,1] return a one-dimensional array of shape N, not a two-dimensional array of shape Nx1. Transpose on a one-dimensional array does nothing.

Numpy story began not with linear algebra, so a one dimension object is always horizontal, cannot be transposed, an so on. It is confusing first time with a different background, but with a lot advantages in other fields. in numpy 2-dim arrays are lines (dim0) of columns(dim1), like for matrix, but selecting a line or a column return always ... a line !

As an example :

In [1]: m=np.arange(6).reshape(3,2)

In [2]: m
Out[2]: 
  array([[0, 1],
         [2, 3],
         [4, 5]])

In [3]: m[0,:]  
Out[3]: array([0, 1])

In [4]: m[:,0]
Out[4]: array([0, 2, 4])

This convention accepted, nothing is very difficult.

Upvotes: 0

Moses Koledoye
Moses Koledoye

Reputation: 78556

numpy.sum returns:

An array with the same shape as a, with the specified axis removed.

With one axis removed in both cases, you are left with a singleton tuple.

2 axes - 1 specified axis = 1 axis

However, passing keepdims as True in both gives different shapes, retaining all the axes in the original array with a corresponding change of length along the specified axis:

>>> arr.sum(axis=0, keepdims=True)
array([[ 9, 12, 15]])
>>> arr.sum(axis=1, keepdims=True)
array([[ 3],
       [12],
       [21]])

Upvotes: 5

FHTMitchell
FHTMitchell

Reputation: 12157

Because summing along the axis of a ND array yields a (N-1)D array. This makes sense if you consider that 

np.sum([1,2,3]) == 6  # a 0D 'array'

If you want to turn your arr.sum(1) into a (1, 3) or (3, 1) 2D array, then use

s = arr.sum(0)[np.newaxis, :]  # (1, 3)

or

s = arr.sum(1)[:, np.newaxis]  # (3, 1)

Upvotes: 1

Raniz
Raniz

Reputation: 11113

According to the documentation this is what you'll get:

Returns:

sum_along_axis : ndarray

An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.

The shape of arr is indeed (3,3) and is two-dimensional. If you remove one axis you'll be left with a shape of (3,) - which is one-dimensional.

An array with shape (1,3) still has two axes.

Upvotes: 0

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