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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

Doing Automated EDA using AutoViz

AutoViz is an Automated EDA library used for the automatic visualization of datasets. Unlike the previous libraries, it is built on top of the matplotlib library. It provides a wide array of visuals to summarize and analyze datasets to provide quick insights. The library does most of the heavy lifting and requires minimal user input.

The reports generated by the AutoViz library typically provide the following:

  • Data cleaning suggestions: They provide insights into missing values, unique values, and outliers. They also provide suggestions on how to handle outliers, irrelevant columns, rare categories, columns with constant values, and more. This can be useful for data cleaning.
  • Univariate analysis: They use histograms, density plots, and violin plots to provide insights into the distribution of the data, outliers, and more.
  • Bivariate analysis: They use scatterplots, heatmaps, and pair plots to provide insights into the relationship...
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