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

Grouping Datasets

During data analysis, it is often essential to cluster or group data together based on certain criteria. For example, an e-commerce store might want to group all the sales that were done during the Christmas period or the orders that were received on Black Friday. These grouping concepts occur in several parts of data analysis. In this chapter, we will cover the fundamentals of grouping techniques and how doing this can improve data analysis. We will discuss different groupby() mechanics that will accumulate our dataset into various classes that we can perform aggregation on. We will also figure out how to dissect this categorical data with visualization by utilizing pivot tables and cross-tabulations.

In this chapter, we will cover the following topics:

  • Understanding groupby()
  • Groupby mechanics
  • Data aggregation
  • Pivot tables and cross-tabulations
...