Book Image

Learning Python

By : Fabrizio Romano
Book Image

Learning Python

By: Fabrizio Romano

Overview of this book

Learning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application. The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned.

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Learning Python
Fabrizio Romano
Dec, 2015 | 442 pages
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Table of Contents (20 chapters)
Learning Python
Learning Python
Credits
Credits
About the Author
About the Author
Acknowledgements
Acknowledgements
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Introduction and First Steps – Take a Deep Breath
Introduction and First Steps – Take a Deep Breath
A proper introduction
Enter the Python
About Python
What are the drawbacks?
Who is using Python today?
Setting up the environment
Installing Python
How you can run a Python program
How is Python code organized
Python's execution model
Guidelines on how to write good code
The Python culture
A note on the IDEs
Summary
2
Built-in Data Types
Built-in Data Types
Everything is an object
Mutable or immutable? That is the question
Numbers
Immutable sequences
Mutable sequences
Set types
Mapping types – dictionaries
The collections module
Final considerations
Summary
3
Iterating and Making Decisions
Iterating and Making Decisions
Conditional programming
Looping
Putting this all 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
One final example
Documenting your code
Importing objects
Summary
5
Saving Time and Memory
Saving Time and Memory
map, zip, and filter
Comprehensions
Generators
Some performance considerations
Don't overdo comprehensions and generators
Name localization
Generation behavior in built-ins
One last example
Summary
6
Advanced Concepts – OOP, Decorators, and Iterators
Advanced Concepts – OOP, Decorators, and Iterators
Decorators
Object-oriented programming
Writing a custom iterator
Summary
7
Testing, Profiling, and Dealing with Exceptions
Testing, Profiling, and Dealing with Exceptions
Testing your application
Test-driven development
Exceptions
Profiling Python
Summary
8
The Edges – GUIs and Scripts
The Edges – GUIs and Scripts
First approach – scripting
Second approach – a GUI application
Where do we go from here?
Summary
9
Data Science
Data Science
IPython and Jupyter notebook
Dealing with data
Where do we go from here?
Summary
10
Web Development Done Right
Web Development Done Right
What is the Web?
How does the Web work?
The Django web framework
A regex website
The future of web development
Summary
11
Debugging and Troubleshooting
Debugging and Troubleshooting
Debugging techniques
Troubleshooting guidelines
Summary
12
Summing Up – A Complete Example
Summing Up – A Complete Example
The challenge
Our implementation
Implementing the Django interface
Implementing the Falcon API
Where do you go from here?
Summary
A word of farewell
Index
Index

Chapter 9. Data Science

 

"If we have data, let's look at data. If all we have are opinions, let's go with mine."

 
 --Jim Barksdale, former Netscape CEO

Data science is a very broad term, and can assume several different meanings according to context, understanding, tools, and so on. There are countless books about this subject, which is not suitable for the faint-hearted.

In order to do proper data science, you need to know mathematics and statistics at the very least. Then, you may want to dig into other subjects such as pattern recognition and machine learning and, of course, there is a plethora of languages and tools you can choose from.

Unless I transform into The Amazing Fabrizio in the next few minutes, I won't be able to talk about everything; I won't even get close to it. Therefore, in order to render this chapter meaningful, we're going to work on a cool project together.

About 3 years ago, I was working for a top-tier social media company in London. I stayed there for 2 years, and...

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