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Learn Python Programming

Learn Python Programming

By : Fabrizio Romano, Fabrizio Romano, Heinrich Kruger, Heinrich Kruger
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Learn Python Programming

Learn Python Programming

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

Overview of this book

Learn Python Programming, Fourth Edition, provides a comprehensive, up-to-date introduction to Python programming, covering fundamental concepts and practical applications. This edition has been meticulously updated to include the latest features from Python versions 3.9 to 3.12, new chapters on type hinting and CLI applications, and updated examples reflecting modern Python web development practices. This Python book empowers you to take ownership of writing your software and become independent in fetching the resources you need. By the end of this book, 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. This Python book offers a clear and practical guide to mastering Python and applying it effectively in various domains, such as data science, web development, and automation.
Table of Contents (20 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
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1
A Gentle Introduction to Python
A Gentle Introduction to Python
A brief introduction to programming
Enter the Python
About Python
What are the drawbacks?
Who is using Python today?
Setting up the environment
How to run a Python program
How is Python code organized?
Python’s execution model
Guidelines for writing good code
Python culture
A note on IDEs
A word about AI
Summary
2
Built-In Data Types
Built-In Data Types
Everything is an object
Mutability
Numbers
Immutable sequences
Mutable sequences
Set types
Mapping types: dictionaries
Data types
Final considerations
Summary
3
Conditionals and Iteration
Conditionals and Iteration
Conditional programming
Looping
Assignment expressions
Putting all this together
A quick peek at the itertools module
Summary
4
Functions, the Building Blocks of Code
Functions, the Building Blocks of Code
Why use functions?
Scopes and name resolution
Input parameters
Return values
A few useful tips
Recursive functions
Anonymous functions
Function attributes
Built-in functions
Documenting your code
Importing objects
One final example
Summary
5
Comprehensions and Generators
Comprehensions and Generators
The map, zip, and filter functions
Comprehensions
Generators
Some performance considerations
Do not overdo comprehensions and generators
Name localization
Generation behavior in built-ins
One last example
Summary
6
OOP, Decorators, and Iterators
OOP, Decorators, and Iterators
Decorators
OOP
Writing a custom iterator
Summary
7
Exceptions and Context Managers
Exceptions and Context Managers
Exceptions
Context managers
Summary
8
Files and Data Persistence
Files and Data Persistence
Working with files and directories
Data interchange formats
I/O, streams, and requests
Persisting data on disk
Configuration files
Summary
9
Cryptography and Tokens
Cryptography and Tokens
The need for cryptography
Hashlib
HMAC
Secrets
JSON Web Tokens
Useful references
Summary
10
Testing
Testing
Testing your application
Test-driven development
Summary
11
Debugging and Profiling
Debugging and Profiling
Debugging techniques
Troubleshooting guidelines
Profiling Python
Summary
12
Introduction to Type Hinting
Introduction to Type Hinting
Python approach to types
History of type hinting
Benefits of type hinting
Type annotations
The Mypy static type checker
Some useful resources
Summary
13
Data Science in Brief
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Data Science in Brief
IPython and Jupyter Notebook
Dealing with data
Where do we go from here?
Summary
14
Introduction to API Development
Introduction to API Development
The Hypertext Transfer Protocol
APIs – An introduction
The railway API
Where do we go from here?
Summary
15
CLI Applications
CLI Applications
Command-line arguments
Building a CLI client for the railway API
Other resources and tools
Summary
16
Packaging Python Applications
Packaging Python Applications
The Python Package Index
Packaging with Setuptools
Building and publishing packages
Advice for starting new projects
Other files
Alternative tools
Further reading
Summary
17
Programming Challenges
Programming Challenges
Advent of Code
Final considerations
Other programming challenge websites
Summary
18
Other Books You May Enjoy
Other Books You May Enjoy
19
Index
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 have a solid foundation 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 is easy to make mistakes in this field, especially when dealing with big datasets where sampling is mandatory...

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