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

Reading and writing data with Excel files

Excel files seem to be ubiquitous in organizations and knowing how to work with them will help us extract more data for analysis. There are a few packages in Python for working with Excel files:

pandas
openpyxl (for .xlsx files, also used by pandas)
xlrd and xlwt (for .xls files)
and more

For most Excel data wrangling situations, pandas does everything we need.

Using pandas for wrangling Excel files

We need to ensure that pandas is installed with conda or pip, and also install openpyxl for reading Excel files: conda install -c conda-forge openpyxl -y. Then we can open Excel files with the pd.read_excel() function.

We will analyze some Excel files from the Midcontinent Independent System Operator (MISO) organization, which provides electricity to large parts of the US and Canada. In this section, imagine you are working for Dynasty Power Inc. as a junior power analyst analyzing energy markets...