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# NumPy | cumsum method

NumPy
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schedule Jul 1, 2022
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Numpy's `cumsum(~)` method returns an array holding the cumulative sums of the input array.

# Parameters

1. `a` | `array_like`

The input array.

2. `axis` | `None` or `int` | `optional`

The allowed values are as follows:

Axis

Meaning

0

Cumulative sum is computed column-wise

1

Cumulative sum is computed row-wise

None

Cumulative sum is computed using entire array

By default, `axis=None`.

3. `dtype` | `string` or `type` | `optional`

The desired data-type of the returned array. By default, the data-type is the same as that of the input array.

4. `out` | `Numpy array` | `optional`

A Numpy array to place the results in.

# Return value

A Numpy array holding the cumulative sum of the input elements.

# Examples

## 1D array

To compute the cumulative sum of a 1D array:

``` x = np.array([1,2,3])np.cumsum(x) array([1, 3, 6]) ```

Here, the we are performing the following computations:

```  1 = 1 1 + 2 = 3 1 + 2 + 3 = 6 ```

## 2D array

Consider the following 2D array:

``` x = np.array([[1,2], [3,4]])x array([[1, 2], [3, 4]]) ```

### All values

To compute the cumulative sums of all values:

``` x = np.array([[1,2], [3,4]])np.cumsum(x) array([ 1, 3, 6, 10]) ```

### Column-wise

To compute the cumulative sums column-wise, set `axis=0`:

``` x = np.array([[1,2], [3,4]])np.cumsum(x, axis=0) array([[1, 2], [4, 6]]) ```

### Row-wise

To compute the cumulative sums row-wise, set `axis=1`:

``` x = np.array([[1,2], [3,4]])np.cumsum(x, axis=1) array([[1, 3], [3, 7]]) ```

## Specifying a datatype

To obtain an array of data-type `float`:

``` x = np.array([1,2,3])np.cumsum(x, dtype=float) array([1., 3., 6.]) ```

Here, the `.` means that the numbers are floats.

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