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

Loading and Wrangling Data with Pandas and NumPy

Data sources come in many formats: plain text files, CSVs, SQL databases, Excel files, and many more. We saw how to deal with some of these data sources in the last chapter, but there is one library in Python that takes the cake when it comes to data preparation: pandas. The pandas library is a core tool for a data scientist, and we will learn how to use it effectively in this chapter. We will learn about:

Loading data from and saving data to several different data source types
Some basic exploratory data analysis (EDA) and plotting with pandas
Preparing and cleaning data for later use, including the imputation of missing data (filling in missing values) and outlier detection
Essential data wrangling tools such as filtering, groupby, and replace

Overall, this chapter will be another foundational chapter in your data science journey, giving you the tools necessary to get started working with data...