# How to calculate correlation and covariance using pandas in python

In this tutorial, you will learn how to write a program to calculate correlation and covariance using pandas in python. We can do easily by using inbuilt functions like corr() an cov().

corr():

Syntax : DataFrame.corr(method=’pearson’, min_periods=1)

Parameters : method : {‘pearson’, ‘kendall’, ‘spearman’}

pearson : standard correlation coefficient
kendall : Kendall Tau correlation coefficient
spearman : Spearman rank correlation
min_periods : int, optional

Minimum number of observations required per pair of columns to have a valid result. Currently only available for pearson and spearman correlation

cov():

Syntax: DataFrame.cov(min_periods=None)

min_periods : int, optional

Minimum number of observations required per pair of columns to have a valid result.

#python program to calculate correlation and covariance

` ` ``` import pandas as pd data = pd.DataFrame({ 'name':['ravi','david','raju','david','kumar','teju'],'experience':[1,2,3,4,5,2], 'salary':[15000,20000,30000,45389,50000,20000], 'join_year' :[2017,2017,2018,2018,2019,2018] }) print(data.corr()) print(data.cov()) ``` ` ` ` `

Output:

```            experience  join_year    salary
experience    1.000000   0.872402  0.983243
join_year     0.872402   1.000000  0.821615
salary        0.983243   0.821615  1.000000
experience    join_year        salary
experience      2.166667     0.966667  2.109077e+04
join_year       0.966667     0.566667  9.012967e+03
salary      21090.766667  9012.966667  2.123592e+08```