# Pandas DataFrame.corr() Function

Python Pandas `DataFrame.corr()` function finds the correlation between the columns of the data frame.

## Syntax of `pandas.DataFrame.corr()`:

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

### Parameters

`method` It is the method of correlation. It can be `pearson`, `kendall` and `spearman`. `pearson` is the default.
`min_periods` This parameter specifies the minimum number of observations required per pair of columns to have a valid result. It is only available for `pearson` and `spearman` correlation currently.

### Return

It returns the Dataframe with the computed correlation between columns.

## Example Codes: `DataFrame.corr()` Method to Find Correlation Matrix Using Pearson Method

``````import pandas as pd

dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print("The Original Data frame is: \n")
print(dataframe)

dataframe1 = dataframe.corr()
print("The Correlation Matrix is: \n")
print(dataframe1)
``````

Output:

``````The Original Data frame is:

Attendance    Name  Obtained Marks
0          60  Olivia              90
1         100    John              75
2          80   Laura              82
3          78     Ben              64
4          95   Kevin              45
The Correlation Matrix is:

Attendance  Obtained Marks
Attendance         1.00000        -0.61515
Obtained Marks    -0.61515         1.00000
``````

The function has returned the correlation matrix. It has ignored the non-numeric column. It has computed the correlation using the `Pearson` method and one pair of values of columns (min_position= 1).

## Example Codes: `DataFrame.corr()` Method to Find Correlation Matrix Using the `kendall` Method

To find the correlation using Kendall method, we will call the `corr()` function for using `method= "kendall"`.

``````import pandas as pd
dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print("The Original Data frame is: \n")
print(dataframe)

dataframe1 = dataframe.corr(method= "kendall")
print("The Correlation Matrix is: \n")
print(dataframe1)
``````

Output:

``````The Original Data frame is:

Attendance    Name  Obtained Marks
0          60  Olivia              90
1         100    John              75
2          80   Laura              82
3          78     Ben              64
4          95   Kevin              45
The Correlation Matrix is:

Attendance  Obtained Marks
Attendance             1.0            -0.4
Obtained Marks        -0.4             1.0
``````

The function has returned the correlation matrix. It has computed the correlation using the Kendall method and one pair of values of columns (`min_position= 1`).

## Example Codes: `DataFrame.corr()` Method to Find Correlation Matrix Using Spearman Method With More Column Value Pairs

Now we will set the value of `min_periods` to `2` using the `spearman` method. The parameter `min_periods` is only available for the `pearson` and `spearman` methods.

``````import pandas as pd
dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print("The Original Data frame is: \n")
print(dataframe)

dataframe1 = dataframe.corr(method= "spearman", min_periods = 2)
print("The Correlation Matrix is: \n")
print(dataframe1)
``````

Output:

``````The Original Data frame is:

Attendance    Name  Obtained Marks
0          60  Olivia              90
1         100    John              75
2          80   Laura              82
3          78     Ben              64
4          95   Kevin              45
The Correlation Matrix is:

Attendance  Obtained Marks
Attendance             1.0            -0.5
Obtained Marks        -0.5             1.0
``````

Now the function has computed correlation using 2 pairs of values of columns.

## Related Article - Pandas DataFrame

• Pandas DataFrame DataFrame.max() Function
• Pandas DataFrame DataFrame.plot.hist() Function