Python data structures and algorithms : improve the performance and speed of your applications

Python data structures and algorithms improve the performance and speed of your applications

Implement classic and functional data structures and algorithms using Python About This Book A step by step guide, which will provide you with a thorough discussion on the analysis and design of fundamental Python data structures. Get a better understanding of advanced Python concepts such as big-o...

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Detalles Bibliográficos
Otros Autores: Baka, Benjamin, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham : Packt 2017.
Edición:1st edition
Materias:
Python (Computer program language)
Data structures (Computer science)
Computer algorithms.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630212606719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Credits
  • About the Author
  • About the Reviewer
  • www.PacktPub.com
  • Customer Feedback
  • Table of Contents
  • Preface
  • Chapter 1: Python Objects, Types, and Expressions
  • Understanding data structures and algorithms
  • Python for data
  • The Python environment
  • Variables and expressions
  • Variable scope
  • Flow control and iteration
  • Overview of data types and objects
  • Strings
  • Lists
  • Functions as first class objects
  • Higher order functions
  • Recursive functions
  • Generators and co-routines
  • Classes and object programming
  • Special methods
  • Inheritance
  • Data encapsulation and properties
  • Summary
  • Chapter 2: Python Data Types and Structures
  • Operations and expressions
  • Boolean operations
  • Comparison and Arithmetic operators
  • Membership, identity, and logical operations
  • Built-in data types
  • None type
  • Numeric Types
  • Representation error
  • Sequences
  • Tuples
  • Dictionaries
  • Sorting dictionaries
  • Dictionaries for text analysis
  • Sets
  • Immutable sets
  • Modules for data structures and algorithms
  • Collections
  • Deques
  • ChainMaps
  • Counter objects
  • Ordered dictionaries
  • defaultdict
  • Named tuples
  • Arrays
  • Summary
  • Chapter 3: Principles of Algorithm Design
  • Algorithm design paradigms
  • Recursion and backtracking
  • Backtracking
  • Divide and conquer - long multiplication
  • Can we do better? A recursive approach
  • Runtime analysis
  • Asymptotic analysis
  • Big O notation
  • Composing complexity classes
  • Omega notation (Ω)
  • Theta notation (ϴ)
  • Amortized analysis
  • Summary
  • Chapter 4: Lists and Pointer Structures
  • Arrays
  • Pointer structures
  • Nodes
  • Finding endpoints
  • Node
  • Other node types
  • Singly linked lists
  • Singly linked list class
  • Append operation
  • A faster append operation
  • Getting the size of the list.
  • Improving list traversal
  • Deleting nodes
  • List search
  • Clearing a list
  • Doubly linked lists
  • A doubly linked list node
  • Doubly linked list
  • Append operation
  • Delete operation
  • List search
  • Circular lists
  • Appending elements
  • Deleting an element
  • Iterating through a circular list
  • Summary
  • Chapter 5: Stacks and Queues
  • Stacks
  • Stack implementation
  • Push operation
  • Pop operation
  • Peek
  • Bracket-matching application
  • Queues
  • List-based queue
  • Enqueue operation
  • Dequeue operation
  • Stack-based queue
  • Enqueue operation
  • Dequeue operation
  • Node-based queue
  • Queue class
  • Enqueue operation
  • Dequeue operation
  • Application of queues
  • Media player queue
  • Summary
  • Chapter 6: Trees
  • Terminology
  • Tree nodes
  • Binary trees
  • Binary search trees
  • Binary search tree implementation
  • Binary search tree operations
  • Finding the minimum and maximum nodes
  • Inserting nodes
  • Deleting nodes
  • Searching the tree
  • Tree traversal
  • Depth-first traversal
  • In-order traversal and infix notation
  • Pre-order traversal and prefix notation
  • Post-order traversal and postfix notation.
  • Breadth-first traversal
  • Benefits of a binary search tree
  • Expression trees
  • Parsing a reverse Polish expression
  • Balancing trees
  • Heaps
  • Summary
  • Chapter 7: Hashing and Symbol Tables
  • Hashing
  • Perfect hashing functions
  • Hash table
  • Putting elements
  • Getting elements
  • Testing the hash table
  • Using [] with the hash table
  • Non-string keys
  • Growing a hash table
  • Open addressing
  • Chaining
  • Symbol tables
  • Summary
  • Chapter 8: Graphs and Other Algorithms
  • Graphs
  • Directed and undirected graphs
  • Weighted graphs
  • Graph representation
  • Adjacency list
  • Adjacency matrix
  • Graph traversal
  • Breadth-first search
  • Depth-first search
  • Other useful graph methods.
  • Priority queues and heaps
  • Inserting
  • Pop
  • Testing the heap
  • Selection algorithms
  • Summary
  • Chapter 9: Searching
  • Linear Search
  • Unordered linear search
  • Ordered linear search
  • Binary search
  • Interpolation search
  • Choosing a search algorithm
  • Summary
  • Chapter 10: Sorting
  • Sorting algorithms
  • Bubble sort
  • Insertion sort
  • Selection sort
  • Quick sort
  • List partitioning
  • Pivot selection
  • Implementation
  • Heap sort
  • Summary
  • Chapter 11: Selection Algorithms
  • Selection by sorting
  • Randomized selection
  • Quick select
  • Partition step
  • Deterministic selection
  • Pivot selection
  • Median of medians
  • Partitioning step
  • Summary
  • Chapter 12: Design Techniques and Strategies
  • Classification of algorithms
  • Classification by implementation
  • Recursion
  • Logical
  • Serial or parallel
  • Deterministic versus nondeterministic algorithms
  • Classification by complexity
  • Complexity curves
  • Classification by design
  • Divide and conquer
  • Dynamic programming
  • Greedy algorithms
  • Technical implementation
  • Dynamic programming
  • Memoization
  • Tabulation
  • The Fibonacci series
  • The Memoization technique
  • The tabulation technique
  • Divide and conquer
  • Divide
  • Conquer
  • Merge
  • Merge sort
  • Greedy algorithms
  • Coin-counting problem
  • Dijkstra's shortest path algorithm
  • Complexity classes
  • P versus NP
  • NP-Hard
  • NP-Complete
  • Summary
  • Chapter 13: Implementations, Applications, and Tools
  • Tools of the trade
  • Data preprocessing
  • Why process raw data?
  • Missing data
  • Feature scaling
  • Min-max scalar
  • Standard scalar
  • Binarizing data
  • Machine learning
  • Types of machine learning
  • Hello classifier
  • A supervised learning example
  • Gathering data
  • Bag of words
  • Prediction
  • An unsupervised learning example
  • K-means algorithm
  • Prediction.
  • Data visualization
  • Bar chart
  • Multiple bar charts
  • Box plot
  • Pie chart
  • Bubble chart
  • Summary
  • Index.

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