Using NumPy, `sum()`

function is an efficient technique to compute the sum of a `large`

array, but it may not be the fastest way to `sum`

a small array. In fact, for small arrays, it may be faster to use alternative methods that are optimized for small sizes. In this article, we will explore some of the ways to sum a small array faster than sum() function.

#### 1. Built-in Python "sum()" function:

For small arrays, the `built-in`

Python `sum(`

) function can be faster than NumpP’s `sum()`

funnction. Here’s an example given below:

In the above code, we first import the NumPy library and assign it the alias `np`

. We use `np.arange`

function to create the array. We then apply the built-in `sum()`

function to calculate the sum of the array.

#### 2. Using "add.reduce()" function:

The `numpy.add.reduce()`

function computes the sum of elements along a specified axis of an array. It can be used to sum an array faster than `np.sum()`

.

Here’s an example:

In the above code, we first import the NumPy library and assign it the alias `np`

. We use `np.arange`

function to create the array. We then apply `np.add.reduce()`

function to calculate the sum of the array.

#### 3. Using "sum()" function with axis parameter:

When summing a small 1-dimensional array, we can use the axis parameter of `np.sum()`

to specify the axis along which to compute the sum in faster way.

Here’s an example:

In the above code, we first import the NumPy library and assign it the alias `np`

. We use `np.arange`

function to create the array. We then apply `np.sum()`

function with axis parameter to calculate the sum of the array.

#### 4. Using "ndarray.sum()" function:

The `ndarray.sum()`

function in NumPy is used to compute the sum of elements in an ndarray (n-dimensional array).

For small arrays, the `ndarray.sum()`

method can be faster than NumPy `sum()`

fnction. This method directly computes the sum of the array without creating an intermediate array. Here’s an example:

In the above code, we first import the NumPy library and assign it the alias `np`

. We use `np.arange`

function to generate the array. We then apply `ndarray.sum()`

function with to calculate the sum of the array.