You're reading from Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801071970

Length 620 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Data Science

Author (1):

Nathan George

View More author details

Table of Contents (30) Chapters

Preface

1. Part I - An Introduction and the Basics

2. Introduction to Data Science FREE CHAPTER

3. Getting Started with Python

4. Part II - Dealing with Data

5. SQL and Built-in File Handling Modules in Python

6. Loading and Wrangling Data with Pandas and NumPy

7. Exploratory Data Analysis and Visualization

8. Data Wrangling Documents and Spreadsheets

9. Web Scraping

10. Part III - Statistics for Data Science

11. Probability, Distributions, and Sampling

12. Statistical Testing for Data Science

13. Part IV - Machine Learning

14. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction

15. Machine Learning for Classification

16. Evaluating Machine Learning Classification Models and Sampling for Classification

17. Machine Learning with Regression

18. Optimizing Models and Using AutoML

19. Tree-Based Machine Learning Models

20. Support Vector Machine (SVM) Machine Learning Models

21. Part V - Text Analysis and Reporting

22. Clustering with Machine Learning

23. Working with Text

24. Part VI - Wrapping Up

25. Data Storytelling and Automated Reporting/Dashboarding

26. Ethics and Privacy

27. Staying Up to Date and the Future of Data Science

28. Other Books You May Enjoy

29. Index

Data wrangling and analyzing iTunes data

The terms "data wrangling" and "data munging" have become common phrases in data science, and generally mean to clean and prepare data for downstream uses such as analytics and modeling. Let's dive into data wrangling with the chinook iTunes dataset.

Loading and saving data with Pandas

In this first example, we're working for Apple in the iTunes analytics department. Our first task is to find any useful information from a set of music sales data that could improve the iTunes business. We'll be using the chinook dataset again, which is a sampling of iTunes data that we used in Chapter 3, SQL and Built-in File Handling Modules in Python.

The first step to wrangling data is, of course, loading it. Pandas provides several functions to load data for a variety of file types. A co-worker in the iTunes department gave us CSV, Excel, and SQLite database files that we need to load into Python for analysis...