# python – How to calculate the sum of all columns of a 2D numpy array (efficiently)

## python – How to calculate the sum of all columns of a 2D numpy array (efficiently)

Check out the documentation for `numpy.sum`, paying particular attention to the `axis` parameter. To sum over columns:

``````>>> import numpy as np
>>> a = np.arange(12).reshape(4,3)
>>> a.sum(axis=0)
array([18, 22, 26])
``````

Or, to sum over rows:

``````>>> a.sum(axis=1)
array([ 3, 12, 21, 30])
``````

Other aggregate functions, like `numpy.mean`, `numpy.cumsum` and `numpy.std`, e.g., also take the `axis` parameter.

From the Tentative Numpy Tutorial:

Many unary operations, such as computing the sum of all the elements
in the array, are implemented as methods of the `ndarray` class. By
default, these operations apply to the array as though it were a list
of numbers, regardless of its shape. However, by specifying the `axis`
parameter you can apply an operation along the specified axis of an
array:

Other alternatives for summing the columns are

``````numpy.einsum(ij->j, a)
``````

and

``````numpy.dot(a.T, numpy.ones(a.shape[0]))
``````

If the number of rows and columns is in the same order of magnitude, all of the possibilities are roughly equally fast:

If there are only a few columns, however, both the `einsum` and the `dot` solution significantly outperform numpys `sum` (note the log-scale):

Code to reproduce the plots:

``````import numpy
import perfplot

def numpy_sum(a):
return numpy.sum(a, axis=1)

def einsum(a):
return numpy.einsum(ij->i, a)

def dot_ones(a):
return numpy.dot(a, numpy.ones(a.shape[1]))

perfplot.save(
out1.png,
# setup=lambda n: numpy.random.rand(n, n),
setup=lambda n: numpy.random.rand(n, 3),
n_range=[2**k for k in range(15)],
kernels=[numpy_sum, einsum, dot_ones],
logx=True,
logy=True,
xlabel=len(a),
)
``````

#### python – How to calculate the sum of all columns of a 2D numpy array (efficiently)

Use the `axis` argument:

``````>> numpy.sum(a, axis=0)
array([18, 22, 26])
``````