Pandas DataFrame Kurt Method

Introduction

The DataFrame.kurt() method in Python pandas is used to calculate the kurtosis of a DataFrame. Kurtosis is a statistical measure that describes the shape of a distribution. It measures whether the data is heavy-tailed or light-tailed compared to a normal distribution. A positive kurtosis value indicates a heavy-tailed distribution, while a negative kurtosis value indicates a light-tailed distribution.

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Skills Graph

Import the necessary libraries

To use the DataFrame.kurt() method, we first need to import the pandas library.

import pandas as pd

Create a DataFrame

We will create a DataFrame that contains some numerical data.

df = pd.DataFrame({"A": [55, 60, 74, 50], "B": [30, 55, 40, 47], "C": [12, 55, 44, 66]})

Apply the DataFrame.kurt() method

To calculate the kurtosis of the DataFrame, we can use the DataFrame.kurt() method. By default, the method calculates the kurtosis for each column.

kurtosis = df.kurt()

Print the result

Finally, we can print the kurtosis values for each column.

print(kurtosis)

Summary

In this lab, we learned how to use the DataFrame.kurt() method in Python pandas to calculate the kurtosis of a DataFrame. By following the steps, we were able to import the necessary libraries, create a DataFrame, apply the method, and print the result. The kurtosis values provide insights into the shape of the data distribution and can be used for statistical analysis.