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Pandas DataFrame corr() Method - GeeksforGeeks

Last Updated : 11 Jul, 2025

Pandas dataframe.corr() is used to find the pairwise correlation of all columns in the Pandas Dataframe in Python. Any NaN values are automatically excluded. To ignore any non-numeric values, use the parameter numeric_only = True. In this article, we will learn about DataFrame.corr() method in Python.

Syntax: DataFrame.corr(self, method='pearson', min_periods=1, numeric_only = False) 

Parameters: 

• method: 
  • pearson: standard correlation coefficient 
  • kendall: Kendall Tau correlation coefficient 
  • spearman: Spearman rank correlation
• min_periods: Minimum number of observations required per pair of columns to have a valid result. Currently only available for pearson and spearman correlation
• numeric_only: Whether only the numeric values are to be operated upon or not. It is set to False by default.

Returns: count :y : DataFrame

A good correlation depends on the use, but it is safe to say you have at least 0.6 (or -0.6) to call it a good correlation. A simple example to show how correlation work in Python.

import pandas as pd

df = {
    "Array_1": [30, 70, 100],
    "Array_2": [65.1, 49.50, 30.7]
}

data = pd.DataFrame(df)

print(data.corr())

Output

          Array_1   Array_2
Array_1  1.000000 -0.990773
Array_2 -0.990773  1.000000

Printing the first 10 rows of the Dataframe.

Note: The correlation of a variable with itself is 1. For a link to the CSV file Used in Code, click here.

# importing pandas as pd
import pandas as pd

# Making data frame from the csv file
df = pd.read_csv("nba.csv")

# Printing the first 10 rows of the data frame for visualization
df[:10]

Output

Here, we are using corr() function to find the correlation among the columns in the Dataframe using 'Pearson' method. We are only having four numeric columns in the Dataframe. The output Dataframe can be interpreted as for any cell, row variable correlation with the column variable is the value of the cell. As mentioned earlier, the correlation of a variable with itself is 1. For that reason, all the diagonal values are 1.00.

# To find the correlation among
# the columns using pearson method
df.corr(method='pearson')

Output

Use Pandas df.corr() function to find the correlation among the columns in the Dataframe using 'kendall' method. The output Dataframe can be interpreted as for any cell, row variable correlation with the column variable is the value of the cell. As mentioned earlier, the correlation of a variable with itself is 1. For that reason, all the diagonal values are 1.00.

# importing pandas as pd
import pandas as pd

# Making data frame from the csv file
df = pd.read_csv("nba.csv")

# To find the correlation among
# the columns using kendall method
df.corr(method='kendall')

Output

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