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# Compute the covariance matrix of two given NumPy arrays

• Last Updated : 29 Aug, 2020

In NumPy for computing the covariance matrix of two given arrays with help of numpy.cov(). In this, we will pass the two arrays and it will return the covariance matrix of two given arrays.

Syntax: numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None)

Example 1:

## Python

 `import` `numpy as np`` ` ` ` `array1 ``=` `np.array([``0``, ``1``, ``1``])``array2 ``=` `np.array([``2``, ``2``, ``1``])`` ` `# Original array1``print``(array1)`` ` `# Original array2``print``(array2)`` ` `# Covariance matrix``print``(``"\nCovariance matrix of the said arrays:\n"``,``      ``np.cov(array1, array2))`

Output:

```[0 1 1]
[2 2 1]

Covariance matrix of the said arrays:
[[ 0.33333333 -0.16666667]
[-0.16666667  0.33333333]]
```

Example 2:

## Python

 `import` `numpy as np`` ` ` ` `array1 ``=` `np.array([``2``, ``1``, ``1``, ``4``])``array2 ``=` `np.array([``2``, ``2``, ``1``, ``1``])`` ` `# Original array1``print``(array1)`` ` `# Original array2``print``(array2)`` ` `# Covariance matrix``print``(``"\nCovariance matrix of the said arrays:\n"``, ``      ``np.cov(array1, array2))`

Output:

```[2 1 1 4]
[2 2 1 1]

Covariance matrix of the said arrays:
[[ 2.         -0.33333333]
[-0.33333333  0.33333333]]
```

Example 3:

## Python

 `import` `numpy as np`` ` ` ` `array1 ``=` `np.array([``1``, ``2``])``array2 ``=` `np.array([``1``, ``2``])`` ` `# Original array1``print``(array1)`` ` `# Original array2``print``(array2)`` ` `# Covariance matrix``print``(``"\nCovariance matrix of the said arrays:\n"``, ``      ``np.cov(array1, array2))`

Output

```[1 2]
[1 2]

Covariance matrix of the said arrays:
[[0.5 0.5]
[0.5 0.5]]
```

Example 4:

## Python

 `import` `numpy as np ``   ` `x ``=` `[``1.23``, ``2.12``, ``3.34``, ``4.5``] ``y ``=` `[``2.56``, ``2.89``, ``3.76``, ``3.95``] ``   ` `# find out covariance with respect ``# rows ``cov_mat ``=` `np.stack((x, y), axis ``=` `1``)  ``   ` `print``(``"shape of matrix x and y:"``, ``      ``np.shape(cov_mat)) `` ` `print``(``"shape of covariance matrix:"``,``      ``np.shape(np.cov(cov_mat))) `` ` `print``(np.cov(cov_mat))`

Output

```shape of matrix x and y: (4, 2)
shape of covariance matrix: (4, 4)
[[ 0.88445  0.51205  0.2793  -0.36575]
[ 0.51205  0.29645  0.1617  -0.21175]
[ 0.2793   0.1617   0.0882  -0.1155 ]
[-0.36575 -0.21175 -0.1155   0.15125]]
```

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