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

Ayodele Oluleye

€26.98 ~~€29.99~~

4.8 (5 Ratings)

eBook Jun 2023 382 pages 1st Edition

Ayodele Oluleye

€26.98 ~~€29.99~~

4.8 (5 Ratings)

eBook Jun 2023 382 pages 1st Edition

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Key benefits

Gain practical experience in conducting EDA on a single variable of interest in Python
Learn the different techniques for analyzing and exploring tabular, time series, and textual data in Python
Get well versed in data visualization using leading Python libraries like Matplotlib and seaborn

Description

In today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data. This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights. Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries. By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.

Who is this book for?

Whether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights. It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning experience.

What you will learn

Perform EDA with leading python data visualization libraries
Execute univariate, bivariate and multivariate analysis on tabular data
Uncover patterns and relationships within time series data
Identify hidden patterns within textual data
Learn different techniques to prepare data for analysis
Overcome challenge of outliers and missing values during data analysis
Leverage automated EDA for fast and efficient analysis

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

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Total € 113.97

Ram Seshadri Sep 05, 2023

EDA is a very difficult step in machine learning process. Many newbies find this the most challenging part of their ML journey. This book makes that somewhat easy by providing step by step instructions on how to perform various steps in EDA by taking different kinds of data and performing EDA on them. Here are the highlights that I found useful:1. Performing univariate analysis is the first step when performing EDA. This book provides 6 charts to use to analyze data in this section.2. In bivariate and multivariate analysis there are over 15 methods discussed.3. Next there are sections on analyzing time series data and how to analyze Text variables for NLP use cases. These are not usually discussed in many EDA books.4. Finally the book handles missing values and outliers along with an overview of auto EDA tools.In summary I found the book comprehensive and a quick way to improve your EDA skills. There are over 50 recipes discussed and I think you will find many of them useful. All in all I highly recommend this book.

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Om S Aug 11, 2023

Navigating through the pages of the "Exploratory Data Analysis with Python Cookbook" feels like following a guiding beacon into the realm of unraveling hidden insights within data. This resourceful book introduces Python's capabilities in single-variable EDA, providing techniques to analyze tabular, time series, and textual data with tools like Matplotlib and Seaborn. From crafting visual narratives to adeptly handling outliers and missing values, it serves as a comprehensive companion for data analysts, scientists, and curious learners alike. Its pragmatic approach makes it an invaluable asset for those new to the field as well as seasoned practitioners.

Dr. Chu Meh Chu Oct 13, 2023

I have been looking for an introductory book on exploratory data analysis for a while. This book, more than answered and satisfied my curiosity. It is a truly a cookbook and will give the reader clear solutions to their data driven problems. I found the organization of the book very easy to follow which correspondingly boosted my confidence in my ability to work on EDA (Exploratory data analysis). I highly recommend this book for its level of detail, readability, and understanding.

Mojeed Abisiga Jul 20, 2023

This book excels in every category, ranking in the top 2 to 3% of all the data analytics books I've ever read or come across. And yes my favorite part of the book was where he used pyLDAvis module to plot topics and top words of a reviews data, it reminded me of an interesting project I worked on that I used those same set of visualizations.

Taylor B. Oct 30, 2023

I’m starting to learn python and have taken a couple machine learning classes. I bought this book to brush up on my knowledge a bit more. It is easy to follow, and you can easily recreate a lot of the recipes in this book. The lessons build on one another, so that you learn how to do combinations of things. I wish there were some more advanced examples or assignments. Like, this is how to do joining. Now here’s another more extreme version of it, or something like that. I feel that the single examples don’t provide a strong knowledge of the concepts. But if you are just learning these for the first time, it’s a great book.

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

What do you get with eBook?