Book Image

Getting Started with Python

By : Fabrizio Romano, Benjamin Baka, Dusty Phillips
Book Image

Getting Started with Python

By: Fabrizio Romano, Benjamin Baka, Dusty Phillips

Overview of this book

This Learning Path helps you get comfortable with the world of Python. It starts with a thorough and practical introduction to Python. You’ll quickly start writing programs, building websites, and working with data by harnessing Python's renowned data science libraries. With the power of linked lists, binary searches, and sorting algorithms, you'll easily create complex data structures, such as graphs, stacks, and queues. After understanding cooperative inheritance, you'll expertly raise, handle, and manipulate exceptions. You will effortlessly integrate the object-oriented and not-so-object-oriented aspects of Python, and create maintainable applications using higher level design patterns. Once you’ve covered core topics, you’ll understand the joy of unit testing and just how easy it is to create unit tests. By the end of this Learning Path, you will have built components that are easy to understand, debug, and can be used across different applications. This Learning Path includes content from the following Packt products: • Learn Python Programming - Second Edition by Fabrizio Romano • Python Data Structures and Algorithms by Benjamin Baka • Python 3 Object-Oriented Programming by Dusty Phillips

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Getting Started with Python
Fabrizio Romano | Benjamin Baka | Dusty Phillips
Feb, 2019 | 12 hours 2 minutes
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Table of Contents (31 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
About Packt
About Packt
Contributors
Contributors
Preface
Preface
Free Chapter
1
A Gentle Introduction to Python
A Gentle Introduction to Python
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 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
Enums
Final considerations
Summary
3
Iterating and Making Decisions
Iterating and Making Decisions
Conditional programming
Looping
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
One final example
Documenting your code
Importing objects
Summary
5
Files and Data Persistence
Files and Data Persistence
Working with files and directories
Data interchange formats
IO, streams, and requests
Persisting data on disk
Summary
6
Principles of Algorithm Design
Principles of Algorithm Design
Algorithm design paradigms
Recursion and backtracking
Runtime analysis
Amortized analysis
Summary
7
Lists and Pointer Structures
Lists and Pointer Structures
Arrays
Pointer structures
Nodes
Finding endpoints
Singly linked lists
A faster append operation
Getting the size of the list
Improving list traversal
Deleting nodes
Clearing a list
Doubly linked lists
Circular lists
Summary
8
Stacks and Queues
Stacks and Queues
Stacks
Queues
Summary
9
Trees
Trees
Terminology
Tree nodes
Binary trees
Summary
10
Hashing and Symbol Tables
Hashing and Symbol Tables
Hashing
Hash table
Summary
11
Graphs and Other Algorithms
Graphs and Other Algorithms
Graphs
Directed and undirected graphs
Weighted graphs
Graph representation
Graph traversal
Other useful graph methods
Priority queues and heaps
Selection algorithms
Summary
12
Searching
Searching
Linear Search
Binary search
Interpolation search
Summary
13
Sorting
Sorting
Sorting algorithms
Bubble sort
Insertion sort
Selection sort
Quick sort
Summary
14
Selection Algorithms
Selection Algorithms
Selection by sorting
Randomized selection
Deterministic selection
Summary
15
Object-Oriented Design
Object-Oriented Design
Introducing object-oriented
Objects and classes
Specifying attributes and behaviors
Hiding details and creating the public interface
Composition
Inheritance
Case study
Exercises
Summary
16
Objects in Python
Objects in Python
Creating Python classes
Modules and packages
Organizing module content
Who can access my data?
Third-party libraries
Case study
Exercises
Summary
17
When Objects Are Alike
When Objects Are Alike
Basic inheritance
Multiple inheritance
Polymorphism
Abstract base classes
Case study
Exercises
Summary
18
Expecting the Unexpected
Expecting the Unexpected
Raising exceptions
Case study
Exercises
Summary
19
When to Use Object-Oriented Programming
When to Use Object-Oriented Programming
Treat objects as objects
Adding behaviors to class data with properties
Manager objects
Case study
Exercises
Summary
20
Python Object-Oriented Shortcuts
Python Object-Oriented Shortcuts
Python built-in functions
An alternative to method overloading
Functions are objects too
Case study
Exercises
Summary
21
The Iterator Pattern
The Iterator Pattern
Design patterns in brief
Iterators
Comprehensions
Generators
Coroutines
Case study
Exercises
Summary
22
Python Design Patterns I
Python Design Patterns I
The decorator pattern
The observer pattern
The strategy pattern
The state pattern
The singleton pattern
The template pattern
Exercises
Summary
23
Python Design Patterns II
Python Design Patterns II
The adapter pattern
The facade pattern
The flyweight pattern
The command pattern
The abstract factory pattern
The composite pattern
Exercises
Summary
24
Testing Object-Oriented Programs
Testing Object-Oriented Programs
Why test?
Unit testing
Testing with pytest
Imitating expensive objects
How much testing is enough?
Case study
Exercises
Summary
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Index
Index

Graph representation

Graphs can be represented in two main forms. One way is to use an adjacency matrix and the other is to use an adjacency list.

We shall be working with the following figure to develop both types of representation for graphs:

Adjacency list

A simple list can be used to present a graph. The indices of the list will represent the nodes or vertices in the graph. At each index, the adjacent nodes to that vertex can be stored:

The numbers in the box represent the vertices. Index 0 represents vertex A, with its adjacent nodes being B and C.

 

Using a list for the representation is quite restrictive because we lack the ability to directly use the vertex labels. A dictionary is therefore more suited. To represent the graph in the diagram, we can use the following statements:

    graph = dict() 
    graph['A'] = ['B', 'C'] 
    graph['B'] = ['E','A'] 
    graph['C'] = ['A', 'B', 'E','F'] 
    graph['E'] = ['B', 'C'] 
    graph['F'] = ['C']

Now we easy establish that vertex A has the adjacent...

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