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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

Analyzing two variables using a bar chart

The bar chart can be used for both univariate and bivariate analysis. For bivariate analysis, the x axis typically represents the categories in our dataset, while the y axis represents a numerical variable. This means the bar chart is usually used for categorical-numerical analysis. The numerical variable is typically aggregated using functions such as the sum, median, mean, and so on. A 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 for bivariate analysis in seaborn. seaborn has a barplot method that is used for this. seaborn also has a countplot method that plots a bar chart; however, the countplot method only plots the count of each category. Therefore, it is used for univariate analysis. The barplot method, on the other hand, plots a numerical variable against each category.

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