Pandas DataFrame corr() Method

Last Updated : 17 Feb, 2026

DataFrame.corr() method in Pandas is used to calculate the correlation between numeric columns in a DataFrame. Correlation shows how strongly two columns are related. The result is returned as a new DataFrame called a correlation matrix, where each value ranges from -1 to 1.

  • 1: perfect positive correlation
  • -1: perfect negative correlation
  • 0: no correlation

Non-numeric columns are ignored automatically.

Example: This code creates a simple DataFrame and finds the correlation between its columns.

Python 
import pandas as pd

d = {"A": [10, 20, 30], "B": [15, 25, 35]}
df = pd.DataFrame(d)

r = df.corr()
print(r)

Output
     A    B
A  1.0  1.0
B  1.0  1.0

Explanation:

  • df.corr() calculates correlation between all numeric columns.
  • Column B = Column A + 5, which is a perfect linear relationship and hence the correlation = 1.

Syntax

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

Parameters:

  • method: Correlation method (pearson, spearman, kendall), we get pearson correlation by default.
  • min_periods: Minimum required matching values
  • numeric_only: Includes only numeric columns if True

Examples

Example 1: This code finds correlation between height and weight columns with strong positive correlation.

Python 
import pandas as pd
d = {"Height": [150, 160, 170, 180], "Weight": [50, 65, 68, 80]}
df = pd.DataFrame(d)
print(df.corr())

Output
          Height    Weight
Height  1.000000  0.973035
Weight  0.973035  1.000000

Explanation:

  • Correlation is 0.97, which is close to +1 meaning Height and Weight increase together and and have a strong positive relationship, but not perfectly proportional.
  • Pearson correlation uses covariance of actual values and since most Height and Weight values increase together, covariance is high positive, giving a value close to +1.

Example 2: This code shows negative correlation between two columns using Kendall rank method.

Python 
import pandas as pd
d = {"StudyHours": [2, 4, 6, 8], "StressLevel": [8, 7, 5, 6]}
df = pd.DataFrame(d)
print(df.corr(method="kendall"))

Output

StudyHours StressLevel
StudyHours 1.000000 -0.666667
StressLevel -0.666667 1.000000

Explanation:

  • Correlation is -0.6, which shows a moderate negative relationship, meaning as StudyHours increase, StressLevel generally decreases, but not always.
  • Kendall correlation counts concordant and discordant rank pairs and since more pairs have opposite order than same order, result is negative, but not all pairs are opposite, so it is -0.6 instead of -1.

Example 3: This code calculates correlation using sperman rank-based method.

Python 
import pandas as pd
d = {"MathMarks": [95, 70, 85, 60, 80], "SportsScore": [60, 75, 65, 80, 70]}
df = pd.DataFrame(d)
print(df.corr(method="spearman"))

Output
             MathMarks  SportsScore
MathMarks          1.0         -1.0
SportsScore       -1.0          1.0

Explanation:

  • The correlation is -1.0, which shows a perfect negative relationship between the two columns.
  • Spearman correlation uses rank differences and here the rank order is exactly reversed, so rank difference is maximum opposite, resulting in perfect negative correlation (-1).
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