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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

Performing univariate analysis using a histogram

When visualizing one numeric variable in our dataset, there are various options to consider, and the histogram is one of them. A histogram is a bar graph-like representation that provides insights into our dataset’s underlying frequency distribution, usually a continuous dataset. The x-axis of a histogram represents continuous values that have been split into bins or intervals while the y-axis represents the number or percentage of occurrences for each bin.

With the histogram, we can quickly identify outliers, data spread, skewness, and more.

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

Getting ready

In this chapter, we will work with two datasets: the Amsterdam House Prices Data and the Palmer Archipelago (Antarctica) Penguins data, both from Kaggle.

Create a folder for this chapter and create a new Python script or Jupyter Notebook file...

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