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

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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

We learned some useful tools for extracting and wrangling data from some common data sources here: documents (MS Word and PDF files) and spreadsheets (MS Excel files). The textract package proved useful for extracting data from .docx files, and works for .doc and many other files as well, including reading scanned PDFs with OCR. We also learned that several other packages can be used to read text-encoded PDFs: pdfminer.six, tika, pymupdf, and pypdf2 (among others). Recall that tika will give us metadata from PDFs, but also requires Java to be properly installed on our system. Once we loaded text from documents, we saw how we can perform some basic analysis on them with n-grams and frequency plots in order to see a summary of the content of the documents.

The other major file type we examined was Excel spreadsheets. We saw how pandas works well for simpler tasks, such as reading and writing simple Excel spreadsheets. For more complex tasks, we should use another package...