Book Image

Hands-On Exploratory Data Analysis with Python

By : Suresh Kumar Mukhiya, Usman Ahmed
Book Image

Hands-On Exploratory Data Analysis with Python

By: Suresh Kumar Mukhiya, Usman Ahmed

Overview of this book

Exploratory 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.
Table of Contents (17 chapters)
1
Section 1: The Fundamentals of EDA
6
Section 2: Descriptive Statistics
11
Section 3: Model Development and Evaluation

Pie chart

This is one of the more interesting types of data visualization graphs. We say interesting not because it has a higher preference or higher illustrative capacity, but because it is one of the most argued-about types of visualization in research.

A paper by Ian Spence in 2005, No Humble Pie: The Origins and Usage of a Statistical Chart, argues that the pie chart fails to appeal to most experts. Despite similar studies, people have still chosen to use pie charts. There are several arguments given by communities for not adhering to the pie chart. One of the arguments is that human beings are naturally poor at distinguishing differences in slices of a circle at a glance. Another argument is that people tend to overestimate the size of obtuse angles. Similarly, people seem to underestimate the size of acute angles.

Having looked at the criticism, let's also have some...