# NumPy Sum – A Complete Guide Hello and welcome to this tutorial on the Numpy sum method. In this tutorial, we will be learning about the NumPy sum method and also seeing a lot of examples regarding the same. So let us begin!

Also read: NumPy Cos – A Complete Guide

## What is NumPy Sum?

The sum method in NumPy is a function that returns the sum of the array. It can be the sum of the whole array, sum along the rows or sum along the columns. We will see the examples for each of these in the upcoming section of this tutorial.

Also read: Numpy Sin – A Complete Guide

## Syntax of NumPy sum

Let us first have a look at the syntax of the NumPy sum function.

```numpy.sum(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)
```

Returns:
An array with the same shape as a which contains the sum along the axis given and the specified axis removed. If the axis=None, a scalar is returned which is the sum of the whole array.

## Examples of Numpy.sum() method

Let’s now get right into using the numpy.sum method so we can understand the outputs.

### Numpy.sum() of the entire array

1-dimensional array

```import numpy as np

a = [2, 5, 3, 8, 4]

sum = np.sum(a)
print("a =", a)
print("Sum of the array =", sum)
```

Output:

```a = [2, 5, 3, 8, 4]
Sum of the array = 22
```

Sum of the array = 2+5+3+8+4 = 17.

2-dimensional array

```import numpy as np

a = [[2, 5, 4], [3, 2, 1]]

sum = np.sum(a)
print("a =", a)
print("Sum of the array =", sum)
```

Output:

```a = [[2, 5, 4], [3, 2, 1]]
Sum of the array = 17
```

Sum of the array = 2+5+4+3+2+1 = 17

### Numpy.sum() along the axis

Column-wise sum

```import numpy as np

a = [[2, 5, 4],
[3, 2, 1]]

# sum along axis=0 i.e. columns
sum = np.sum(a, axis=0)
print("a =", a)
print("Sum of the array =", sum)
```

Output:

```a = [[2, 5, 4], [3, 2, 1]]
Sum of the array = [5 7 5]
```

Column 0 sum = 2+3 = 5
Column 1 sum= 5+2 = 7
Column 2 sum = 4+1 = 5

Row-wise sum

```import numpy as np

a = [[2, 5, 4],
[3, 2, 1]]

# sum along axis=1 i.e. rows
sum = np.sum(a, axis=1)
print("a =", a)
print("Sum of the array =", sum)
```

Output:

```a = [[2, 5, 4], [3, 2, 1]]
Sum of the array = [11  6]
```

Row 0 sum = 2+5+4 = 11
Row 1 sum = 3+2+1 = 6

### Numpy.sum() of an empty array

```import numpy as np

a = []
b = [[]]

sum_a = np.sum(a)
print("a =", a)
print("Sum of the 1-d empty array =", sum_a)

sum_b = np.sum(b)
print("b =", b)
print("Sum of the 2-d empty array =", sum_b)
```

Output:

```a = []
Sum of the 1-d empty array = 0.0
b = [[]]
Sum of the 2-d empty array = 0.0
```

The sum of an empty array is the neutral element i.e. 0.

## Return Numpy.sum() of the array as float data type

This is the same as the above examples except that here the returned values are of float data type.

Sum of the entire array

```import numpy as np

a = [[3, 12, 4], [3, 5, 1]]

sum = np.sum(a, dtype=float)
print("a =", a)
print("Sum of the array =", sum)
```

Output:

```a = [[3, 12, 4], [3, 5, 1]]
Sum of the array = 28.0
```

Column-wise sum

```import numpy as np

a = [[3, 12, 4],
[3, 5, 1]]

# sum along axis=0 i.e. columns
sum = np.sum(a, dtype=float, axis=0)
print("a =", a)
print("Sum of the array =", sum)
```

Output:

```a = [[3, 12, 4], [3, 5, 1]]
Sum of the array = [ 6. 17.  5.]
```

Row-wise sum

```import numpy as np

a = [[3, 12, 4],
[3, 5, 1]]

# sum along axis=1 i.e. rows
sum = np.sum(a, dtype=float, axis=1)
print("a =", a)
print("Sum of the array =", sum)
```

Output:

```a = [[3, 12, 4], [3, 5, 1]]
Sum of the array = [19.  9.]
```

## Conclusion

That’s all! In this tutorial, we learned about the Numpy sum method and practiced different types of examples using the same.