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

Automated reporting and dashboarding

When dealing with data that is updated periodically and frequently, it's helpful to use automation tools to generate data reports. This saves us the trouble of constantly re-running a repetitive analysis. There are some different ways to deal with sharing data: reports and dashboarding.

Automated reporting options

Reports will usually consist of something like a PDF or an other document (such as MS Word) or a spreadsheet like MS Excel. We already saw how we can do some work with Excel using pandas, but for more powerful control of Excel, we can use other Python packages as well:

xlsxwriter (easily generates charts; this works well with pandas' ExcelWriter)
openpyxl (also allows for charts)

There are also other Excel Python packages. The site http://www.python-excel.org/ is one place with a list of many of these packages. The win32com package can also be used to read and write Excel files on Windows, but...