Mastering Pandas DataFrame Kurtosis

Introduction

In this lab, we will learn how to use the kurtosis() method of the Pandas DataFrame to calculate the kurtosis of a dataset. Kurtosis is a statistical measure that describes the shape of a distribution, specifically, how peaked or flat it is compared to a normal distribution. The kurtosis() method returns the unbiased kurtosis over the requested axis of the DataFrame.

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Skills Graph

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Import the necessary libraries

First, we need to import the pandas library, which provides the DataFrame class.

import pandas as pd

Create a DataFrame

Next, we will create a DataFrame object using the pd.DataFrame() function. This function takes a dictionary as input, where the keys are column names and the values are lists representing the data in each column. For example:

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

Calculate the kurtosis

Now, we can calculate the kurtosis using the kurtosis() method of the DataFrame. This method takes several optional parameters, such as axis, skipna, level, and numeric_only, which allow you to control the computation. For now, we will leave these parameters at their default values.

result = df.kurtosis()

Print the result

Finally, we can print the result to see the calculated kurtosis values.

print(result)

Summary

In this lab, we learned how to use the kurtosis() method of the Pandas DataFrame to calculate the kurtosis of a dataset. We imported the necessary libraries, created a DataFrame, calculated the kurtosis, and printed out the result. The kurtosis value can provide insights into the shape and distribution of the data. Remember to explore the various parameters of the kurtosis() method to further customize the calculation according to your needs.

Pandas DataFrame Kurtosis Method