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# How to Calculate Correlation Between Two Columns in Pandas?

• Last Updated : 30 Nov, 2021

In this article, we will discuss how to calculate the correlation between two columns in pandas

Correlation is used to summarize the strength and direction of the linear association between two quantitative variables. It is denoted by r and values between -1 and +1. A positive value for r indicates a positive association, and a negative value for r indicates a negative association.

By using corr() function we can get the correlation between two columns in the dataframe.

Syntax:

dataframe[‘first_column’].corr(dataframe[‘second_column’])

where,

• dataframe is the input dataframe
• first_column is correlated with second_column of the dataframe

## Python3

 `# import pandas module` `import` `pandas as pd`   `# create dataframe with 3 columns` `data ``=` `pd.DataFrame({` `    ``"column1"``: [``12``, ``23``, ``45``, ``67``],` `    ``"column2"``: [``67``, ``54``, ``32``, ``1``],` `    ``"column3"``: [``34``, ``23``, ``56``, ``23``]` `}` `)` `# display dataframe` `print``(data)`   `# correlation between column 1 and column2` `print``(data[``'column1'``].corr(data[``'column2'``]))`   `# correlation between column 2 and column3` `print``(data[``'column2'``].corr(data[``'column3'``]))`   `# correlation between column 1 and column3` `print``(data[``'column1'``].corr(data[``'column3'``]))`

Output:

``` column1  column2  column3
0       12       67       34
1       23       54       23
2       45       32       56
3       67        1       23
-0.9970476685163736
0.07346999975265099
0.0```

It is also possible to get element-wise correlation for numeric valued columns using just corr() function.

Syntax:

`dataset.corr()`

## Python3

 `# import pandas module` `import` `pandas as pd`   `# create dataframe with 3 columns` `data ``=` `pd.DataFrame({` `    ``"column1"``: [``12``, ``23``, ``45``, ``67``],` `    ``"column2"``: [``67``, ``54``, ``32``, ``1``],` `    ``"column3"``: [``34``, ``23``, ``56``, ``23``]` `}` `)` `# get correlation between element wise` `print``(data.corr())`

Output:

```          column1   column2  column3
column1  1.000000 -0.997048  0.00000
column2 -0.997048  1.000000  0.07347
column3  0.000000  0.073470  1.00000```

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