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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  »  Chapter 5 : Disciplines : Computer Science

Computer Science

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Overview

Python programming is fundamentally intertwined with the field of computer science, serving as a key tool in diverse areas such as software development, data analysis, artificial intelligence (AI), and web development. Python's simplicity and readability make it a popular first language for computer science students, often used to teach fundamental concepts like control structures, data types, and algorithms. Its versatile nature allows it to be used for creating both small scripts and large, complex systems, making it valuable in software engineering. In the realm of AI and machine learning, Python's extensive libraries, including TensorFlow, Keras, and PyTorch, have made it a go-to language. Its capability to handle data manipulation and analysis tasks efficiently with libraries like pandas and NumPy, along with data visualization tools like matplotlib and seaborn, have made it a preferred choice in data science. In web development, frameworks like Django and Flask enable rapid development of web applications. Python's use also extends to areas like network programming, cybersecurity, and cloud computing. Overall, Python is a vital language in computer science, its utility spanning the breadth of the field and contributing significantly to advancements in computer science research and applications.

Python in Computer Science



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Cascade Street Publishing, LLC