Book Image

Learn Python Programming, 3rd edition - Third Edition

By : Fabrizio Romano, Heinrich Kruger
5 (1)
Book Image

Learn Python Programming, 3rd edition - Third Edition

5 (1)
By: Fabrizio Romano, Heinrich Kruger

Overview of this book

Learn Python Programming, Third Edition is both a theoretical and practical introduction to Python, an extremely flexible and powerful programming language that can be applied to many disciplines. This book will make learning Python easy and give you a thorough understanding of the language. You'll learn how to write programs, build modern APIs, and work with data by using renowned Python data science libraries. This revised edition covers the latest updates on API management, packaging applications, and testing. There is also broader coverage of context managers and an updated data science chapter. The book empowers you to take ownership of writing your software and become independent in fetching the resources you need. You will have a clear idea of where to go and how to build on what you have learned from the book. Through examples, the book explores a wide range of applications and concludes by building real-world Python projects based on the concepts you have learned.
Table of Contents (18 chapters)
16
Other Books You May Enjoy
17
Index

Where do we go from here?

Data science is indeed a fascinating subject. As we said in the introduction, those who want to delve into its meanders need to be well trained in mathematics and statistics. Working with data that has been interpolated incorrectly renders any result about it useless. The same goes for data that has been extrapolated incorrectly or sampled with the wrong frequency. To give you an example, imagine a population of individuals that are aligned in a queue. If for some reason, the gender of that population alternated between male and female, the queue would look something like this: F-M-F-M-F-M-F-M-F...

If you sampled it taking only the even elements, you would draw the conclusion that the population was made up only of males, while sampling the odd ones would tell you exactly the opposite.

Of course, this was just a silly example, but it's very easy to make mistakes in this field, especially when dealing with big datasets where sampling is mandatory...