Book Image

Practical Data Analysis Using Jupyter Notebook

By : Marc Wintjen
Book Image

Practical Data Analysis Using Jupyter Notebook

By: Marc Wintjen

Overview of this book

Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.
Table of Contents (18 chapters)
1
Section 1: Data Analysis Essentials
7
Section 2: Solutions for Data Discovery
12
Section 3: Working with Unstructured Big Data
16
Works Cited

Foundations of join relationships

For anyone familiar with SQL, the concept of joining data together is well understood. The ability to join one or more tables together for the purpose of analytics has remained relevant throughout my 20+ year career of working with data and I hope it continues to be relevant.

In prior chapters, we introduced the concept of data models and the need for primary and foreign key fields to define relationships. We will now elaborate on these concepts by explaining joins and the different types of joins that exist in SQL and DataFrames.

Joining, in SQL, simply means merging two or more tables into a single dataset. The resulting size and shape of that single dataset will vary depending on the type of join that is used. Some key concepts you want to remember any time you are creating a join between datasets will be that the common unique key should always be used. Ideally, the key field functions as both the primary and foreign key but...