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Exploratory Data Analysis with Python Cookbook

You're reading from   Exploratory Data Analysis with Python Cookbook Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781803231105
Length 382 pages
Edition 1st Edition
Languages
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Author (1):
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Ayodele Oluleye Ayodele Oluleye
Author Profile Icon Ayodele Oluleye
Ayodele Oluleye
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Generating Summary Statistics 2. Chapter 2: Preparing Data for EDA FREE CHAPTER 3. Chapter 3: Visualizing Data in Python 4. Chapter 4: Performing Univariate Analysis in Python 5. Chapter 5: Performing Bivariate Analysis in Python 6. Chapter 6: Performing Multivariate Analysis in Python 7. Chapter 7: Analyzing Time Series Data in Python 8. Chapter 8: Analysing Text Data in Python 9. Chapter 9: Dealing with Outliers and Missing Values 10. Chapter 10: Performing Automated Exploratory Data Analysis in Python 11. Index 12. Other Books You May Enjoy

Generating pairplots on two variables

The pairplot provides a visual representation of the distribution of single variables and the relationship between two variables. The pairplot isn’t just one plot but a group of subplots that display single variables (numerical or categorical) and two variables (numerical-numerical and categorical-numerical). A pairplot combines charts such as histograms, density plots, and scatter plots to represent the variables in the dataset. It provides a simple way to view the distribution and relationship across multiple variables in our dataset.

In this recipe, we will explore how to create pairplots in seaborn. The pairplot method in seaborn can be used for this.

Getting ready

We will work with the Palmer Archipelago (Antarctica) penguin data from Kaggle in this recipe. You can retrieve all the files from the GitHub repository.

How to do it…

We will learn how to create a pairplot using the seaborn library:

  1. Import the...
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