Hands-On Exploratory Data Analysis with Python: Perform EDA techniques to understand, summarize, and investigate your data

by Suresh Kumar Mukhiya, Usman Ahmed

Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas

Key FeaturesUnderstand the fundamental concepts of exploratory data analysis using PythonFind missing values in your data and identify the correlation between different variablesPractice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python packageBook DescriptionExploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization.

You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence.

By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes.

What you will learnImport, clean, and explore data to perform preliminary analysis using powerful Python packagesIdentify and transform erroneous data using different data wrangling techniquesExplore the use of multiple regression to describe non-linear relationshipsDiscover hypothesis testing and explore techniques of time-series analysisUnderstand and interpret results obtained from graphical analysisBuild, train, and optimize predictive models to estimate resultsPerform complex EDA techniques on open source datasetsWho this book is forThis EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.

Table of ContentsExploratory Data Analysis FundamentalsVisual Aids for EDAEDA with Personal EmailData TransformationDescriptive StatisticsGrouping DatasetCorrelationTime Series AnalysisHypothesis Testing and RegressionModel Development and EvaluationEDA on Wine Quality Data AnalysisAppendix

666 pages, Kindle Edition

Published March 27, 2020

Reviews

[Amelie]

I attempted to read Python for Data Analysis by Wes Mckinney but I found that I am learning so much functions in Python without any hands-on experience: Which means I'll most probably just forget everything I've studied. I decided that perhaps it was not yet time for me to read the book.

So, I picked this book because I wanted something that will give me a guided hands-on experience on EDA.

I am not a master at, but still definitely familiar with Pandas, Seaborn, Object-oriented programming and Numpy, but I still found myself googling so much stuff just to understand the code in this book.

[Manish Choudhary]

a good reference for beginners who just started their journey in data science.

[Johnny · Rating 5 out of 5]

Probably the best book I read on the topic of Exploratory Data Analysis with Python. It not only has the practical parts you can use right away, it also covers the theory behind.