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

Summary

The use of data science techniques with text spans a few different areas, and the broad field of working with language and text with computers is called NLP. We saw first how we can clean text data using Python and the spaCy package by removing things like punctuation, stop words, and numbers. Lowercasing can be used to condense the same words (regardless of capitalization) into the same count for word frequency analysis. We can also use stemming or lemmatizing to reduce words to a stem or root, which further groups similar words for measuring word frequencies. The spaCy package makes cleaning and lemmatizing easy, and this can be done in a few lines of code.

We then saw how basic analytics, such as word frequency plots, POS tags, and word collocations, can be performed to get an understanding of the text. Zipf's law can be used to analyze text as well, to understand a text's characteristic shape parameter from the Zipfian distribution. Although wordclouds...