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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 bar chart

Like histograms, bar charts consist of rectangular bars. However, while the histogram analyzes numerical data, the bar chart analyzes categories. The x-axis typically represents the categories in our dataset while the y-axis represents the count of the categories or their occurrences by percentage. In some cases, the y-axis can also be the sum or average of a numerical column within our dataset. The bar chart provides quick insights especially when we need to quickly compare categories within our dataset.

In this recipe, we will explore how to create bar charts in seaborn. The countplot method in seaborn can be used for this. seaborn also has a barplot method. While countplot plots the count of each category, barplot plots a numeric variable against each category. This makes countplot better suited for univariate analysis, while the barplot method is better suited for bivariate analysis.

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