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NumPy | cumsum method

schedule Aug 10, 2023
Last updated
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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:

[0] 1 = 1
[1] 1 + 2 = 3
[2] 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.

robocat
Published by Isshin Inada
Edited by 0 others
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