How to compute covariance in pandas?
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How to compute covariance in pandas?

How to compute covariance in pandas?

This recipe helps you compute covariance in pandas

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Recipe Objective

Compute the pairwise covariance among the series of a DataFrame can be carried out by cov() function. For similar series, it becomes basically variance.

So this recipe is a short example on How to compute covariance in pandas. Let's get started.

Step 1 - Import the library

import pandas as pd

Let's pause and look at these imports. Pandas is generally used for performing mathematical operation and preferably over arrays.

Step 2 - Setup the Data

df = pd.DataFrame({"A":[0, 1, 2, 3, 5, 9], "B":[11, 5, 8, 6, 7, 8], "C":[2, 5, 10, 11, 9, 8]})

Here we have setup a random dataset with some random values in it.

Step 3 - Finding covariance

print(df.cov())

Here we are applied cov() to find out the covariance among the series.

Step 4 - Let's look at our dataset now

Once we run the above code snippet, we will see:

Scroll down to the ipython file to look at the results.

We can see the how the covarianve being calculated among all the 3 series.

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