Numpy is the best python library for array creation and manipulation. There are many functions in it that allow you to do complex mathematical operations of the array in an efficient way. * Numpy cov* is one of them. In this entire tutorial, you will know how to implement the NumPy cov function through easy steps.

## Syntax of NumPy cov

Before going to the demonstration part let’s know the syntax of the NumPy cov.

`numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None, *, dtype=None). `

Below is the explanation of each argument.

* m:* It is a 1d or 2d input array.

* y: *An additional set of variables and observations. y has the same form as that of m.

* rowvar: *A boolean value. If rowvar is True (default), then each row represents a variable, with observations in the columns.

* bias:* It is a boolean value. If it is true then normalization is done by N and if it is false then normalization is done by N – 1.

* ddof:* If not None the default value implied by bias is overridden.

* fweights:* 1-D array of integer frequency weights; the number of times each observation vector should be repeated.

* aweights: *1-D array of observation vector weights. These relative weights are typically large for observations considered “important” and smaller for observations considered less “important”.

* dtype:* The type of the output array.

## Steps to Implement Numpy cov Method

In this section, you will know all the steps to implement* numy.cov()* function in python. Just follow the given steps for more understanding.

### Step 1: Import all the required libraries

The first step is to import all the necessary libraries for implementing* cov() *function. I am using the NumPy package only so let’s import it using the import statement.

`import numpy as np`

### Step 2: Create a sample NumPy array

Now for the demonstration purpose only I am creating a dummy NumPy array for implementing the NumPy cov() method. But you can choose your own dataset. You can create a NumPy array using* numpy.array() *method. In our example, I am creating both 1d and 2d Numpy arrays. Let’s create it.

**Sample 1D Numpy array**

`numpy_1d_array = np.array([1,5,2])`

**Sample 2D Numpy array**

`numpy_2d_array = np.array([[1,5,2],[4,9,6],[10,23,34]])`

### Step 3: Implement Numpy cov on sample array

After the creation of the NumPy array, let’s apply the * numpy.cov() *method on them. Execute the complete code for each 1D AND 2D NumPy array and see the output.

**1D Numpy array**

```
import numpy as np
numpy_1d_array = np.array([1,5,2])
print(np.cov(numpy_1d_array))
```

**Output**

**2D Numpy array**

```
import numpy as np
numpy_2d_array = np.array([[1,5,2],[4,9,6],[10,23,34]])
print(np.cov(numpy_2d_array))
```

**Output**

## Conclusion

Covariance allows you to find the strength of correlation between two variables or more sets of variables. If the value is negative then We say variables are negatively correlated, positive then positively correlated, and if zero then variables are uncorrelated. These are steps to implement* numpy.cov()* function in python. I hope you have liked this tutorial. If you have any queries then you can contact us for more help.

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