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# NumPy | cov method

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schedule Mar 10, 2022
Last updated
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Numpy's cov(~) method computes the covariance given two arrays.

# Parameters

1. x | array-like

Each row represents a separate column data (i.e. a variable). See the example below for clarification.

2. y | array-like | optional

Instead of specifying your dataset as a new row in x, you can just specify it as y.

3. rowvar | boolean | optional

If True, then each row in x represents a variable. If False, then each column in x represents a variable. By default, rowvar=True.

4. bias | boolean | optional

Whether to compute the biased estimate of the covariance, or the unbiased one. By default, bias=False (sample covariance).

5. ddof | int | optional

The delta degree of freedom. This can be used to modify the denominator in the front:

$$\frac{1}{N\color{blue}{-ddof}}\sum_{i=0}^{N}\left(x_i-\bar{x}^2\right)$$

By default, ddof=0.

6. fweights | array-like | optional

The number of times each observation should be repeated. See example below for clarification.

7. aweights | array-like | optional

A 1D array of weights placed on each observation. The higher the weight, the more important the observation is.

# Return value

If one variable is given, then the a scalar is returned. Otherwise, a Numpy array is returned.

# Examples

## Computing sample covariance

 np.cov([2,3,5,6], [3,5,8,12]) array([[ 3.33333333, 7. ], [ 7. , 15.33333333]]) 

This is exactly the same as specifying a 2D array:

 np.cov([[2,3,5,6], [3,5,8,12]]) array([[ 3.33333333, 7. ], [ 7. , 15.33333333]]) 

## Computing population covariance

 np.cov([2,3,5,6], [3,5,8,12], ddof=0) array([[ 2.5 , 5.25], [ 5.25, 11.5 ]]) 

You could also set bias=True:

 np.cov([2,3,5,6], [3,5,8,12], bias=True) array([[ 2.5 , 5.25], [ 5.25, 11.5 ]]) 

## Repeating values using fweights

 np.cov([2,3,5,6], [3,5,8,12], fweights=[2,1,1,1]) array([[ 3.3 , 6.85], [ 6.85, 14.7 ]]) 

This is exactly the same as the following:

 np.cov([2,2,3,5,6], [3,3,5,8,12]) array([[ 3.3 , 6.85], [ 6.85, 14.7 ]]) 

That is, the first value of each array was repeated twice, as specified by fweights.

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