# How to compute cross-correlation of two given NumPy arrays?

• Last Updated : 08 Dec, 2020

In the Numpy program, we can compute cross-correlation of two given arrays with the help of correlate(). In this first parameter and second parameter pass the given arrays it will return the cross-correlation of two given arrays.

Syntax : numpy.correlate(a, v, mode = ‘valid’)

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Parameters :
a, v : [array_like] Input sequences.
mode : [{‘valid’, ‘same’, ‘full’}, optional] Refer to the convolve docstring. Default is ‘valid’.

Return : [ndarray] Discrete cross-correlation of a and v.

Example 1:

In this example, we will create two NumPy arrays and the task is to compute cross-correlation using correlate().

## Python3

 `import` `numpy as np``array1 ``=` `np.array([``0``, ``1``, ``2``])``array2 ``=` `np.array([``3``, ``4``, ``5``])`` ` `# Original array1``print``(array1)`` ` `# Original array2``print``(array2)`` ` `# ross-correlation of the arrays``print``(``"\nCross-correlation:\n"``,``      ``np.correlate(array1, array2))`

Output:

```[0 1 2]
[3 4 5]

Cross-correlation:
```

Example 2:

## Python3

 `import` `numpy as np``array1 ``=` `np.array([``1``,``2``])``array2 ``=` `np.array([``1``,``2``])`` ` `# Original array1``print``(array1)`` ` `# Original array2``print``(array2)``# Cross-correlation of the arrays``print``(``"\nCross-correlation:\n"``,``      ``np.correlate(array1, array2))`

Output:

```[1 2]
[1 2]

Cross-correlation:
```

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