The **numpy.sum()** method** “calculates the sum of all the elements in a NumPy array”** or** along a specified axis**.

**Syntax**

`numpy.sum(array, axis=None, dtype=None, out=None)`

**Parameters**

- An
**array**is an array to be summed.

- An
**axis**is an axis along which to sum the elements. If the axis is None, the array details are summed together.

- The
**dtype**is the data type of the resulting array. If dtype is None, the data type of the resulting array is the same as the data type of the input array.

- An
**out**is an output array. If out is not**None,**the result is stored in out.

**Example**

```
import numpy as np
# Sample NumPy array
arr = np.array([[1, 2, 3], [4, 5, 6]])
print("Array:")
print(arr)
# Compute the sum of all elements
total_sum = np.sum(arr)
print("\nSum of all elements:", total_sum)
# Compute the sum along axis 0 (rows)
row_sum = np.sum(arr, axis=0)
print("\nSum along axis 0 (rows):", row_sum)
# Compute the sum along axis 1 (columns)
col_sum = np.sum(arr, axis=1)
print("\nSum along axis 1 (columns):", col_sum)
```

**Output**

```
Array:
[[1 2 3]
[4 5 6]]
Sum of all elements: 21
Sum along axis 0 (rows): [5 7 9]
Sum along axis 1 (columns): [ 6 15]
```

In this code, we created a sample NumPy array.

In the next step, we calculated the sum of all elements, the sum along axis 0 (rows), and the sum along axis 1 (columns) using the **numpy.sum()** function.

The resulting sums are printed, showing how the sum can be computed using the **numpy.sum()** function.