Python numpy cumsum() function returns the cumulative sum of the elements along the given axis.

## Python numpy cumsum() syntax

The cumsum() method syntax is:

```
cumsum(array, axis=None, dtype=None, out=None)
```

- The
**array**can be ndarray or array-like objects such as nested lists. - The
**axis**parameter defines the axis along which the cumulative sum is calculated. If the axis is not provided then the array is flattened and the cumulative sum is calculated for the result array. - The
**dtype**parameter defines the output data type, such as float and int. - The
**out**optional parameter is used to specify the array for the result.

## Python numpy cumsum() Examples

Let’s look at some examples of calculating cumulative sum of numpy array elements.

### 1. Cumulative Sum of Numpy Array Elements without axis

```
import numpy as np
array1 = np.array(
[[1, 2],
[3, 4],
[5, 6]])
total = np.cumsum(array1)
print(f'Cumulative Sum of all the elements is {total}')
```

**Output**: `Cumulative Sum of all the elements is [ 1 3 6 10 15 21]`

Here, the array is first flattened to [ 1 2 3 4 5 6]. Then the cumulative sum is calculated, resulting in [ 1 3 6 10 15 21].

### 2. Cumulative Sum along the axis

```
import numpy as np
array1 = np.array(
[[1, 2],
[3, 4],
[5, 6]])
total_0_axis = np.cumsum(array1, axis=0)
print(f'Cumulative Sum of elements at 0-axis is:\n{total_0_axis}')
total_1_axis = np.cumsum(array1, axis=1)
print(f'Cumulative Sum of elements at 1-axis is:\n{total_1_axis}')
```

Output:

```
Cumulative Sum of elements at 0-axis is:
[[ 1 2]
[ 4 6]
[ 9 12]]
Cumulative Sum of elements at 1-axis is:
[[ 1 3]
[ 3 7]
[ 5 11]]
```

### 3. Specifying data type for the cumulative sum array

```
import numpy as np
array1 = np.array(
[[1, 2],
[3, 4],
[5, 6]])
total_1_axis = np.cumsum(array1, axis=1, dtype=float)
print(f'Cumulative Sum of elements at 1-axis is:\n{total_1_axis}')
```

Output:

```
Cumulative Sum of elements at 1-axis is:
[[ 1. 3.]
[ 3. 7.]
[ 5. 11.]]
```

**Reference**: API Doc