# Pandas DataFrame corr() Method

Correlation is the measure of the linear relationship between the two variables. In this tutorial, we'll learn the python pandas `DataFrame.corr()` method. This method computes the pairwise correlation of columns, excluding NA/null values. It returns correlation matrix DataFrame.

The below shows the syntax of the `DataFrame.corr()` function.

### Syntax

``DataFrame.corr(method='pearson', min_periods=1)``

### Parameters

method{‘pearson’, ‘kendall’, ‘spearman’} or callable

Method of correlation:

• pearson : standard correlation coefficient

• kendall : Kendall Tau correlation coefficient

• spearman : Spearman rank correlation

• callable: callable with input two 1d ndarrays and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior.

min_periods: int, optional. A minimum number of observations required per pair of columns to have a valid result. Currently only available for Pearson and Spearman correlation.

The output of the methods is between 1 and -1.

• 1 indicates a strong positive relationship.
• -1 indicates a strong negative relationship.
• A result of zero indicates no relationship at all.

## Example 1: Find correlation among the columns of DataFrame using the `DataFrame.corr()` Method

The below example shows how to find the correlation between the columns of the dataframe using the `pearson` method.

``````import pandas as pd
chart = {'Name':['Chetan','yashas','yuvraj'],'Age':  [20,25,30],'Height': [155,160,175],'Weight': [55,60,75]}
df = pd.DataFrame(chart)
print(df)
print(df.corr(method='pearson'))``````

Once we run the program we will get the following output. In the output, we can see the positive correlation between the columns.

Name Age Height Weight
0 Chetan 20 155 55
1 yashas 25 160 60
2 yuvraj 30 175 75
Age Height Weight
Age 1.000000 0.960769 0.960769
Height 0.960769 1.000000 1.000000
Weight 0.960769 1.000000 1.000000

## Example 2: Find correlation among the columns of DataFrame using the `DataFrame.corr()` Method

The below example shows how to find the correlation between the columns of the dataframe using the `kendall` method.

``````import pandas as pd
chart = {'Name':['Chetan','yashas','yuvraj'],'Age':  [20,25,30],'Height': [155,160,175],'Weight': [55,60,75]}
df = pd.DataFrame(chart)
print(df)
print(df.corr(method='kendall'))``````

Once we run the program we will get the following output.

Name Age Height Weight
0 Chetan 20 155 55
1 yashas 25 160 60
2 yuvraj 30 175 75
Age Height Weight
Age 1.0 1.0 1.0
Height 1.0 1.0 1.0
Weight 1.0 1.0 1.0

### Conclusion

In this tutorial, we learned the python pandas DataFrame.corr() method. We find the correlation between the DataFrame columns using the Pearson, kendall, spearman methods.