Pandas – Using DataFrame.kurt() method

Updated: February 20, 2024 By: Guest Contributor Post a comment


Pandas is a prevalent library in Python for data manipulation and analysis. It offers various functions and methods that allow for easy data processing and statistical analysis. One such method is DataFrame.kurt(), which is used to calculate the kurtosis of the data present in a DataFrame. Kurtosis is a statistical measure that describes the shape of a distribution’s tails in relation to a normal distribution. In this tutorial, we will explore how to use the DataFrame.kurt() method in Pandas with multiple code examples, ranging from basic to advanced usage.

Understanding Kurtosis

Before diving into code examples, it’s essential to have a fundamental understanding of kurtosis. Kurtosis is a measure of the “tailedness” of the probability distribution of a real-valued random variable. It’s a way to quantify whether the data distribution is heavy-tailed (more outliers) or light-tailed (fewer outliers) compared to a normal distribution. The kurtosis of a normal distribution is 3. Distributions with kurtosis greater than 3 are considered leptokurtic, indicating heavy tails, while those with kurtosis less than 3 are platykurtic, indicating light tails.

Basic Usage of DataFrame.kurt()

To begin, let’s start with a simple example of calculating the kurtosis of a single column in a DataFrame.

import pandas as pd
import numpy as np


# Creating a random DataFrame
data = np.random.normal(0, 1, 1000)
df = pd.DataFrame(data, columns=["A"])

# Calculating kurtosis
df_kurtosis = df['A'].kurt()
print("Kurtosis of column A:", df_kurtosis)


Kurtosis of column A: 0.1035974455747466

This code snippet calculates the kurtosis of column ‘A’ in the DataFrame. Since the data was generated using a normal distribution, the kurtosis will be close to 0, indicating a normal distribution shape.

Calculating Kurtosis for Multiple Columns

Next, let’s calculate the kurtosis for multiple columns in a DataFrame.

import pandas as pd
import numpy as np

# Creating a DataFrame with multiple columns
data = np.random.normal(0, 1, (1000, 3))
df = pd.DataFrame(data, columns=['A', 'B', 'C'])

# Calculating kurtosis for each column
df_kurtosis = df.kurt()


A   -0.075793
B    0.209380
C   -0.199788
dtype: float64

Here, we create a DataFrame ‘df’ with three columns (‘A’, ‘B’, ‘C’) and 1000 rows. The df.kurt() method calculates the kurtosis for each column and returns a Series object with the kurtosis values for each column.

Advanced Usage of DataFrame.kurt()

For more advanced data analysis, you might want to calculate the kurtosis for grouped data. Let’s look at how to accomplish this.

import pandas as pd
import numpy as np


# Creating a DataFrame with an additional "group" column
data = np.random.normal(0, 1, (1000, 4))
groups = np.random.choice(['Group1', 'Group2'], 1000)
df = pd.DataFrame(data, columns=['A', 'B', 'C', 'D'])
df['Group'] = groups

# Calculating kurtosis for each column, grouped by 'Group'
grouped_kurtosis = df.groupby('Group').apply(lambda x: x.kurt())


               A         B         C         D
Group1 -0.298885  0.621000  0.022590 -0.072273
Group2 -0.347733 -0.170688 -0.081289  0.213774

In this example, .apply(lambda x: x.kurt()) applies the kurt() function to each group of the DataFrame grouped by ‘Group’, calculating the kurtosis for each column within each group

Handling Missing Values

It’s not uncommon to encounter missing values in a dataset. Let’s explore how DataFrame.kurt() handles missing values.

import pandas as pd
import numpy as np


# Creating a DataFrame with missing values
data = np.random.normal(0, 1, (1000, 3))
data[::10] = np.nan # Introducing NaN values at regular intervals
df = pd.DataFrame(data, columns=['A', 'B', 'C'])

# Calculating kurtosis with missing values
kurtosis_with_nan = df.kurt()


A   -0.048824
B    0.265698
C   -0.230321
dtype: float64

The df.kurt() method automatically handles missing values by excluding them from the calculation. This behavior ensures that the kurtosis is calculated only on the available data, providing accurate statistical measures even in the presence of NaN values.


The DataFrame.kurt() method in Pandas is a robust tool for calculating the kurtosis of dataset distributions. Through the examples provided, from basic to advanced, we’ve seen how it can be applied to understand the shape of data distributions better. Whether analyzing single columns, multiple columns, or grouped data, DataFrame.kurt() provides insights into the distribution’s tail heaviness, offering a valuable perspective on data analysis projects.