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Python Across Disciplines
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Table of Contents

1.1.   Introduction 1.2.   About the Author & Contact Info 1.3.   Book Conventions 1.4.   What (Who) is a Programmer? 1.5.   Programming Across Disciplines 1.6.   Foundational Computing Concepts 1.7.   About Python 1.8.   First Steps 1.8.1 Computer Setup 1.8.2 Python print() Function 1.8.3 Comments
2.1. About Data 2.2. Data Types 2.3. Variables 2.4. User Input 2.5. Data Structures (DS)         2.5.1. DS Concepts         2.5.2. Lists         2.5.3. Dictionaries         2.5.4. Others 2.6. Files         2.6.1. Files & File Systems         2.6.2. Python File Object         2.6.3. Data Files 2.7. Databases
3.1. About Processing 3.2. Decisions         3.2.1 Decision Concepts         3.2.2 Conditions & Booleans         3.2.3 if Statements         3.2.4 if-else Statements         3.2.5 if-elif-else Statements         3.2.6 In-Line if Statements 3.3. Repetition (a.k.a. Loops)         3.3.1  Repetition Concepts         3.3.2  while Loops         3.3.3  for Loops         3.3.4  Nested Loops         3.3.5  Validating User Input 3.4. Functions         3.4.1  Function Concepts         3.4.2  Built-In Functions         3.4.3  Programmer Defined Functions 3.5. Libraries         3.5.1  Library Concepts         3.5.2  Standard Library         3.5.3  External Libraries 3.6. Processing Case Studies         3.6.1  Case Studies         3.6.2  Parsing Data
4.1. About Output 4.2. Advanced Printing 4.3. Data Visualization   4.4  Sound
  4.5  Graphics
  4.6  Video
  4.7  Web Output
  4.8  PDFs & Documents
  4.9  Dashboards
  4.10  Animation & Games
  4.11  Text to Speech

5.1 About Disciplines 5.2 Accounting 5.3 Architecture 5.4 Art 5.5 Artificial Intelligence (AI) 5.6 Autonomous Vehicles 5.7 Bioinformatics 5.8 Biology 5.9 Bitcoin 5.10 Blockchain 5.11 Business 5.12 Business Analytics 5.13 Chemistry 5.14 Communication 5.15 Computational Photography 5.16 Computer Science 5.17 Creative Writing 5.18 Cryptocurrency 5.19 Cultural Studies 5.20 Data Analytics 5.21 Data Engineering 5.22 Data Science 5.23 Data Visualization 5.24 Drone Piloting 5.25 Economics 5.26 Education 5.27 Engineering 5.28 English 5.29 Entrepreneurship 5.30 Environmental Studies 5.31 Exercise Science 5.32 Film 5.33 Finance 5.34 Gaming 5.35 Gender Studies 5.36 Genetics 5.37 Geography 5.38 Geology 5.39 Geospatial Analysis ☯ 5.40 History 5.41 Humanities 5.42 Information Systems 5.43 Languages 5.44 Law 5.45 Linguistics 5.46 Literature 5.47 Machine Learning 5.48 Management 5.49 Marketing 5.50 Mathematics 5.51 Medicine 5.52 Military 5.53 Model Railroading 5.54 Music 5.55 Natural Language Processing (NLP) 5.56 Network Analysis 5.57 Neural Networks 5.58 Neurology 5.59 Nursing 5.60 Pharmacology 5.61 Philosophy 5.62 Physiology 5.63 Politics 5.64 Psychiatry 5.65 Psychology 5.66 Real Estate 5.67 Recreation 5.68 Remote Control (RC) Vehicles 5.69 Rhetoric 5.70 Science 5.71 Sociology 5.72 Sports 5.73 Stock Trading 5.74 Text Mining 5.75 Weather 5.76 Writing
6.1. Databases         6.1.1 Overview of Databases         6.1.2 SQLite Databases         6.1.3 Querying a SQLite Database         6.1.4 CRUD Operations with SQLite         6.1.5 Connecting to Other Databases
Built-In Functions Conceptss Data Types Date & Time Format Codes Dictionary Methods Escape Sequences File Access Modes File Object Methods Python Keywords List Methods Operators Set Methods String Methods Tuple Methods Glossary Index Appendices   Software Install & Setup
  Coding Tools:
  A.  Python ↗   B.  Google CoLaboratory ↗   C.  Visual Studio Code ↗   D.  PyCharm IDE ↗   E.  Git ↗   F.  GitHub ↗
  Database Tools:
  G.  SQLite Database ↗   H.  MySQL Database ↗


Python Across Disciplines
by John Gordon © 2023
Table of Contents
1.1.   Introduction 1.2.   About the Author & Contact Info 1.3.   Book Conventions 1.4.   What (Who) is a Programmer? 1.5.   Programming Across Disciplines 1.6.   Foundational Computing Concepts 1.7.   About Python 1.8.   First Steps         1.8.1. Computer Setup         1.8.2. Python print() Function         1.8.3. Comments
2.1. About Data 2.2. Data Types         2.2.1. Variables         2.2.2. Strings         2.2.3. Integers         2.2.4. Floating Points         2.2.5. Booleans 2.3. User Input 2.5. Data Structures (DS)         2.5.1. Introduction to DS         2.5.2. Lists         2.5.3. Dictionaries         2.5.4. Others     2.6. Files         2.6.1. Files & File Systems         2.6.2. Python File Object         2.6.3. Data Files 2.7. Databases
3.1. About Processing     3.2. Decisions         3.2.1 Decision Concepts         3.2.2 Conditions & Booleans         3.2.3 if Statements         3.2.4 if-else Statements         3.2.5 if-elif-else Statements         3.2.6 In-Line if Statements     3.3. Repetition (a.k.a. Loops)         3.3.1  Repetition Concepts         3.3.2  while Loops         3.3.3  for Loops         3.3.4  Nested Loops         3.3.5  Validating User Input     3.4. Functions         3.4.1  Function Concepts         3.4.2  Built-In Functions         3.4.3  Programmer Defined Functions     3.5. Libraries         3.5.1  Library Concepts         3.5.2  Standard Library         3.5.3  External Libraries     3.6. Processing Case Studies         3.6.1  Case Studies         3.6.2  Parsing Data             3.6.3  eCommerce Simulation         3.6.4  Datasets         3.6.5  Web Scraping             3.6.6  Named Entity Reconition         3.6.7  Automation
4.1. About Output 4.2. Advanced Printing 4.3. Data Visualization     4.4  Sound
    4.5  Graphics
    4.6  Video
    4.7  Web Output
    4.8  PDFs & Documents
    4.9  Dashboards
    4.10  Animation & Games
    4.11  Text to Speech

5.1 Disciplinarity 5.2 Accounting 5.3 Architecture 5.4 Art 5.5 Artificial Intelligence (AI) 5.6 Autonomous Vehicles 5.7 Bioinformatics 5.8 Biology 5.9 Bitcoin 5.10 Blockchain 5.11 Business 5.12 Business Analytics 5.13 Chemistry 5.14 Communication 5.15 Computational Photography 5.16 Computer Science 5.17 Creative Writing 5.18 Cryptocurrency 5.19 Cultural Studies 5.20 Data Analytics 5.21 Data Engineering 5.22 Data Science 5.23 Data Visualization 5.24 Drone Piloting 5.25 Economics 5.26 Education 5.27 Engineering 5.28 English 5.29 Entrepreneurship 5.30 Environmental Studies 5.31 Exercise Science 5.32 Film 5.33 Finance 5.34 Gaming 5.35 Gender Studies 5.36 Genetics 5.37 Geography 5.38 Geology 5.39 Geospatial Analysis       5.39.1 Geospatial Concepts       5.39.2 Geospatial in the Humanities       5.39.3 Geospatial Project Ideas       5.39.4 Geospatial Data Sources 5.40 History 5.41 Humanities 5.42 Information Systems 5.43 Languages 5.44 Law 5.45 Linguistics 5.46 Literature 5.47 Machine Learning 5.48 Management 5.49 Marketing 5.50 Mathematics 5.51 Medicine 5.52 Military 5.53 Model Railroading 5.54 Music 5.55 Natural Language Processing 5.56 Network Analysis 5.57 Neural Networks 5.58 Neurology 5.59 Nursing 5.60 Pharmacology 5.61 Philosophy 5.62 Physiology 5.63 Politics 5.64 Psychiatry 5.65 Psychology 5.66 Real Estate 5.67 Recreation 5.68 Remote Control (RC) Vehicles 5.69 Rhetoric 5.70 Science 5.71 Sociology 5.72 Sports 5.73 Stock Trading 5.74 Text Mining 5.75 Weather 5.76 Writing
6.0 Chapter Overview     6.1. Databases
        6.1.1 Overview of Databases         6.1.2 SQLite Databases         6.1.3 Querying a SQLite Database         6.1.4 CRUD Operations w/SQLite         6.1.5 Connecting to Other Databases     6.2. Object Oriented Programming
            6.2.1. Introduction to OOP             6.2.2. Unified Modeling Language             6.2.3. Classes & Objects             6.2.4. Abstraction             6.2.5. Encapsulation             6.2.6. Inheritance             6.2.7. Polymorphism             6.2.8. Exception Handling             6.2.9. Codealong: Employees             6.2.10. Codealong: Cars     6.3. Graphical User Interfaces
            6.3.1. Introduction to GUIs
            6.3.2. Tkinter
            6.3.3. Codealong: Login Form
            6.3.4. Codealong: Data Entry Form
    6.4. Algorithms & Data Structures
            6.4.1. Introduction to Algorithms
            6.4.2. Searching
            6.4.3. Sorting
            6.4.4. Metrics
            6.4.5. Nodes
            6.4.6. Linked Lists 1
            6.4.7. Linked Lists 2
            6.4.8. Stacks
            6.4.9. Queues
            6.4.10. Heaps
            6.4.11. Trees
            6.4.12. Graphs
            6.4.13. Maps
            6.4.14. Hashing
            6.4.15. Cryptography
    6.5. Additional Advanced Topics
            6.5.1. APIs
            6.5.2. Automation
            6.5.3. Recursion
            6.5.4. More About Datasets
            6.5.5. Large Language Models
            6.5.6. Text Analysis
            6.5.7. Web Scraping
            6.5.8. Data Science
            6.5.9. Machline Learning
            6.5.10. Natural Lang Processing
            6.5.11. Artificial Intelligence
            6.5.12. Cython
            6.5.13. PyPy
            6.5.14. Multi-Threading
            6.5.15. Parallel Processing

Built-In Functions ↗ Concepts: Alphabetical ↗ Concepts: By Chapter ↗ Data Types ↗ Date & Time Format Codes ↗ Dictionary Methods ↗ Escape Sequences ↗ File Access Modes ↗ File Object Methods ↗ Python Keywords ↗ List Methods ↗ Operators ↗ Set Methods ↗ String Formatting Types ↗ String Methods ↗ Tuple Methods ↗
  Software Install & Setup

  Coding Tools:
  A.  Python ↗   B.  Google CoLaboratory ↗   C.  Visual Studio Code ↗   D.  PyCharm IDE ↗   E.  Git ↗   F.  GitHub ↗
  Database Tools:
  G.  SQLite Database ↗   H.  MySQL Database ↗



Table of Contents  »  Reference Materials : Set Methods

Set Methods

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Python sets are collections of unique elements, and their methods provide powerful ways to handle and manipulate these collections. Key methods include add(element) for adding a single element, and update([elements]) for adding multiple elements. To remove elements, remove(element) deletes an element and raises an error if it doesn't exist, whereas discard(element) deletes an element without raising an error. The pop() method removes and returns an arbitrary set element. Sets can be combined using union() or the | operator, and their intersection is found using intersection() or the & operator. Similarly, difference() or - and symmetric_difference() or ^ are used to find unique elements in sets. Sets also have methods like issubset() and issuperset() to compare sets. These methods make Python sets a robust and versatile tool for handling unique collections, enabling efficient operations for mathematical set-like computations and more.


Set Method Description
add() Adds an element to the specified set. 
set_var.add(var)
clear() Removes all elements from the specified set. 
set_var.clear()
copy() Returns a copy of the specified set. 
set_var.copy()
difference() Returns a set containing the difference between two or more specified sets. 
set_var1.difference(set_var2)
difference_update() Removes the items from one specified set that are also included in another specified set. 
set_var1.difference_update(set_var2)
discard() Removes the specified item from the specified set. 
set_var.discard(value)
intersection() Returns a set that is the intersection of two or more specified sets. 
set_var1.intersection(set_var_2, set_var_3, ... set_var_n)
intersection_update() Removes items from a specified set that are not in another specified set. 
set_var.intersection_update(set_var_2, set_var_3, ... set_var_n)
isdisjoint() Returns True or False whether two specified sets have an intersection or not. 
set_var1.isdisjoint(set_var2)
issubset() Returns True or False whether a specified set contains another specified set. 
set_var1.issubset(set_var2)
issuperset() Returns True or False whether a specified set contains another specified set. 
set_var1.issuperset(set_var2)
pop() Removes an element from the specified set. 
set_var.pop()
remove() Removes a specified element from a specified set. 
set_var.remove(element)
symmetric_difference() Returns a set of values that are the symmetric difference between two specified sets. 
set_var1.symmetric_difference(set_var2)
symmetric_difference_update() Inserts the symmetric differences from a specified set into another specified set. 
set_var1.symmetric_difference_update(set_var2)
union() Returns a set containing the union of one or more specified sets. 
set_var1.union(set_var_2, set_var_3, ... set_var_n)
update() Updates the specified set with another specified set, or any other specified iterable. 
set_var1.update(set_var2)







© 2023 John Gordon
Cascade Street Publishing, LLC