Let’s say I have the following 2D numpy array consisting of four rows and three columns:

```
>>> a = numpy.arange(12).reshape(4,3)
>>> print(a)
[[ 0 1 2]
[ 3 4 5]
[ 6 7 8]
[ 9 10 11]]
```

What would be an efficient way to generate a 1D array that contains the sum of all columns (like `[18, 22, 26]`

)? Can this be done without having the need to loop through all columns?

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:

### Answer：

Use `numpy.sum`

. for your case, it is

```
sum = a.sum(axis=0)
```

### Answer：

Use the `axis`

argument:

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

### Answer：

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 numpy’s `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.show(
# 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)',
)
```

### Answer：

Then NumPy `sum`

function takes an optional axis argument that specifies along which axis you would like the sum performed:

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

Or, equivalently:

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

### Answer：

```
a.sum(0)
```

should solve the problem. It is a 2d `np.array`

and you will get the sum of all column. `axis=0`

is the dimension that points downwards and `axis=1`

the one that points to the right.