# Summing small array faster than NumPy sum function

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.