Python
Python is a high-level, general-purpose, dynamically typed programming language with readable syntax and automatic memory management.
The Quick Python Book, Fourth Edition
Python is a high-level general-purpose programming language with an interpreted execution model and dynamic features.
- How it is used
- Uses Python to write portable programs quickly, from file-processing scripts to web and data applications.
- How deep it goes
- Develops syntax, types, program structure, libraries, and error handling through examples; gives only a brief account of interpreter internals.
- Examples
- Demonstrates an indented factorial loop, a file-based word counter, and type mismatches reported by mypy.
- What you can do after
- Build it from scratch: write and run small Python programs using built-in types, control flow, files, and modules.
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Fast Python: High performance techniques for large datasets
- How it is used
- Uses Python as the working language for data-processing tasks whose speed and storage demands require measured optimization.
- How deep it goes
- Works through profiling, object costs, execution constraints, and performance comparisons rather than introductory language syntax.
- Examples
- Tests weather-station distance calculations, object sizes, and native Python versus Cython runtimes.
- What you can do after
- Diagnose/debug with profiling, then select and measure a Python optimization suited to the bottleneck and available hardware.
Read how Fast Python: High performance techniques for large datasets explains Python →
Road to Kubernetes
Python is a general-purpose, high-level programming language known for readable syntax and broad application support.
- How it is used
- Uses Python as a recurring web-application deployment case alongside JavaScript/Node.js.
- How deep it goes
- Works through application dependencies, Ubuntu setup, and runtime selection rather than comprehensive Python programming.
- Examples
- Uses a FastAPI application, Ubuntu setup commands, and a version-tagged Python container image.
- What you can do after
- Operate/configure a Python web app on Ubuntu with a virtual environment, or select and run a version-tagged Docker image.
A Pythonic Adventure: From Python basics to a working web app
Python 3 is the third major version of the Python programming language.
- How it is used
- Introduces programming through a practical coffee-shop application and immediate interaction with a computer.
- How deep it goes
- Builds a first runnable program with print(), input(), variables, file naming, and editor execution, without advancing beyond basic interaction.
- Examples
- Uses a coffee-shop greeting, customer prompts, and a receipt listing a name, drink, flavor, and topping.
- What you can do after
- Build a small Python application that collects order details and prints a personalized receipt.
Read how A Pythonic Adventure: From Python basics to a working web app explains Python →
Get Programming: Learn to code with Python
A general-purpose, interpreted programming language with dynamic typing and significant standard-library support.
- How it is used
- Uses Python to teach programming basics before moving to interactive programs and structured-data tasks.
- How deep it goes
- Defines the interpreter and introduces objects and variables, then reaches aliasing and copying mutable objects.
- Examples
- Uses everyday object-naming analogies and dictionary exercises to make variable assignment and mutation concrete.
- What you can do after
- Build it from scratch by writing Python programs with variables, decisions, repetition, and reusable operations.
Read how Get Programming: Learn to code with Python explains Python →
Statistics Every Programmer Needs
Python is a general-purpose programming language known for readable syntax and broad use in software development, data analysis, and scientific computing.
- How it is used
- Deploys Python as the analytical engine for preparing data, fitting models, simulating uncertainty, visualizing results, and supporting decisions.
- How deep it goes
- Demonstrates implementation through annotated examples and library workflows, but stops short of teaching Python syntax, installation, or general-purpose programming.
- Examples
- Uses pandas, NumPy, SciPy, Matplotlib, seaborn, scikit-learn, and statsmodels across regression, simulation, Markov chains, and data analysis.
- What you can do after
- Apply via Python libraries to implement quantitative methods, inspect results, and adapt reusable analytical code to real-world problems.
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Practices of the Python Pro
Python is a general-purpose, high-level programming language known for readable syntax and a broad software ecosystem.
- How it is used
- Uses Python as a vehicle for learning software design, from utility scripts to complex software projects.
- How deep it goes
- Explains readable syntax, enterprise reach, REPL practice, design trade-offs, and dynamic typing, while deferring detailed language features.
- Examples
- Demonstrates Python through a REPL dictionary transformation, sorted state capitals, functions, modules, and refactored constants.
- What you can do after
- Evaluate/understand Python as a language for prototyping, interactive experiments, maintainable systems, and enterprise software.
Coding with AI
Python is a general-purpose, dynamically typed programming language commonly used for application development, automation, and data science.
- How it is used
- Uses Python to build small applications while introducing AI-assisted code completion and chat.
- How deep it goes
- Works through environments, file reading, text cleaning, generated alternatives, and revisions without presenting a broad language theory.
- Examples
- Demonstrates Python through a text-file word-counting application and project environments created from terminal commands.
- What you can do after
- Build it from scratch as a small Python application with AI-generated code and project-isolated dependencies.
Build a Robo-Advisor with Python (From Scratch)
A general-purpose, high-level programming language with dynamic typing and extensive support for scripting, data analysis, and application development.
- How it is used
- Automates robo-advising tasks such as asset allocation, tax-loss optimization, and Monte Carlo financial planning.
- How deep it goes
- Introduces Python’s flexible style and major libraries, then applies them to financial modeling rather than language internals.
- Examples
- Uses Social Security claiming break-even analysis and portfolio optimization, including tax-aware rebalancing and withdrawal strategies.
- What you can do after
- Build it from scratch: implement automated wealth-management analyses using Python libraries and stated financial assumptions.
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Build Financial Software with Generative AI (From Scratch)
Python is a general-purpose, dynamically typed programming language with a concise syntax and an extensive standard library.
- How it is used
- Positions Python as the primary language for modernizing an ACH processing application.
- How deep it goes
- Works through input, numeric conversion, formatting, functions, type hints, and return values, while deferring deeper backend and language topics.
- Examples
- Uses an interactive current-balance formatter, progressively revised into a documented, typed function that returns a string.
- What you can do after
- Practice/perform a small interactive balance-formatting program using input, float conversion, f-strings, functions, and type hints.
Read how Build Financial Software with Generative AI (From Scratch) explains Python →
Python How-To
Python is a high-level programming language with dynamic semantics and extensive support for readable application code.
- How it is used
- Uses Python to solve practical programming tasks before applying domain-specific frameworks and libraries.
- How deep it goes
- Explains practical language choices, membership syntax, dynamic typing, and optional hints while prioritizing core skills over domain-specific tools.
- Examples
- Demonstrates `in` with strings and lists, and shows an integer variable becoming a string after reassignment.
- What you can do after
- Practice/perform it by writing readable Python solutions and selecting other languages when mobile or low-level requirements warrant them.
The Well-Grounded Python Developer: How the pros use Python and Flask
Python 3 is the third major version line of the Python programming language.
- How it is used
- Build maintainable applications by combining Python with modules, conventions, community knowledge, and third-party packages.
- How deep it goes
- Explains Python's development role, paradigms, version choice, and maintainability concerns without teaching syntax or implementation internals.
- Examples
- Uses reusable modules, class-based applications, first-class functions, event-driven programs, and Python 3.10.3 as concrete reference points.
- What you can do after
- Evaluate/understand Python's role, select Python 3 for new projects, and recognize when another language may better fit a domain.
Read how The Well-Grounded Python Developer: How the pros use Python and Flask explains Python →
Learn AI-Assisted Python Programming, Second Edition
Python is a high-level, general-purpose programming language designed for readable syntax and broad application development.
- How it is used
- Uses Python as the readable programming language for AI-assisted creation of practical software.
- How deep it goes
- Introduces syntax, execution, errors, and interactive experimentation, while deferring detailed code reading and low-level implementation.
- Examples
- Demonstrates number ranges, addition, square areas, syntax errors, and calculations in the Python REPL.
- What you can do after
- Apply it via Visual Studio Code and the Python REPL to run small programs, inspect results, and check AI-generated code.
Read how Learn AI-Assisted Python Programming, Second Edition explains Python →
Grokking Statistics
Python is a general-purpose, high-level programming language with dynamic typing and extensive support for scientific and data-oriented computing.
- How it is used
- Supports statistical analysis, data handling, visualization, and machine-learning workflows.
- How deep it goes
- Introduces practical calculations, libraries, and projects while stopping short of detailed language internals.
- Examples
- Uses employee polling, retail spending, educational testing, and house-price prediction as applied data examples.
- What you can do after
- Apply it via NumPy, pandas, and Matplotlib to perform practical statistical and data-analysis tasks.
Python Workout: 50 ten-minute exercises
Python 3 is the major version of the Python programming language in which input returns text entered by the user.
- How it is used
- Uses Python for input-driven programs, numeric processing, accumulation, reporting, and broader problem solving.
- How deep it goes
- Demonstrates conversions, loops, accumulation, comparisons, and formatting through exercises, while stopping short of language internals.
- Examples
- Uses a run-timing average, a number-guessing interaction, and contrasting assignment forms inside input loops.
- What you can do after
- Apply Python to break problems down, choose constructs, compare trade-offs, and write readable, Pythonic solutions.
Read how Python Workout: 50 ten-minute exercises explains Python →
Build Python Web Apps with Streamlit
Python is a general-purpose, high-level programming language widely used for software development and artificial intelligence.
- How it is used
- Builds Streamlit interfaces and data-oriented applications without requiring separate HTML, CSS, and JavaScript development.
- How deep it goes
- Demonstrates files, imports, variables, conversion, context managers, widgets, and reruns through an incremental interface, not language internals.
- Examples
- Uses a Streamlit unit converter with radio buttons, text input, dropdowns, columns, a sidebar, and a conversion button.
- What you can do after
- Build a small browser-based interface, run it with Streamlit, and connect user controls to Python variables.
Read how Build Python Web Apps with Streamlit explains Python →
Investing for Programmers
Python is a general-purpose programming language with a large ecosystem for data analysis and scientific computing.
- How it is used
- Integrates LLM prompting into financial research workflows, notebooks, and AI-agent development.
- How deep it goes
- Demonstrates provider calls, prompt parameters, model selection, and local execution without detailing Python language fundamentals.
- Examples
- Uses OpenAI, Gemini, Anthropic, Mistral, and Hugging Face prompt-submission examples.
- What you can do after
- Build Python scripts that submit prompts, configure generation, process responses, or invoke locally downloaded models.
Python Workout, Second Edition
Python is a general-purpose, high-level programming language known for readable syntax and broad application support.
- How it is used
- Supports readable problem solving, automation, data work, web development, testing, operations, research, and security.
- How deep it goes
- Develops fluency through exercises in core structures and program organization, while leaving continuing libraries, techniques, and language nuances for further practice.
- Examples
- Uses exercises with core data structures, recurring-task automation, solution comparison, and open-source contribution as practice contexts.
- What you can do after
- Apply Python to break down unfamiliar problems, select suitable constructs, write readable code, and automate recurring tasks.
Data Without Labels
Python is a general-purpose, high-level programming language widely used for software development, data analysis, and machine learning.
- How it is used
- Uses Python to build reproducible data-analysis and unsupervised machine-learning workflows around concrete business problems.
- How deep it goes
- Walks through data preparation, text processing, sentiment scoring, visualization, and interpretation, without developing Python language internals.
- Examples
- Demonstrates Python through telecom and product-review analysis using pandas, NLTK, TextBlob, matplotlib, and scikit-learn.
- What you can do after
- Build reproducible Python workflows that load, clean, analyze, visualize, and score review data for machine-learning use cases.
Refactoring to Rust
Python is a high-level general-purpose programming language commonly implemented with automatic memory management and garbage collection.
- How it is used
- Uses Python for rapid prototyping, research, machine learning, data mining, and production applications.
- How deep it goes
- Explains syntax, interpretation, typing, allocation, native integration, and one measured performance comparison, but defers full runtime internals.
- Examples
- Uses CSV column aggregation, Python extension modules, and computationally expensive asynchronous applications as concrete examples.
- What you can do after
- Evaluate Python bottlenecks with benchmarking and profiling, then apply Rust integration or extensions to performance-critical paths.
Build Applications with Local AI Models on a Mac
A high-level general-purpose programming language known for readable syntax and a broad software ecosystem.
- How it is used
- Uses Python to move from terminal-based model chats to a local AI application with a web interface.
- How deep it goes
- Walks through environment setup and a first script, then introduces coding concepts gradually rather than requiring Python mastery upfront.
- Examples
- Uses a first Python script and a Streamlit chatbot with streaming responses and conversation history.
- What you can do after
- Build it from scratch as Python scripts and a Streamlit chatbot that interacts with local AI models.
Read how Build Applications with Local AI Models on a Mac explains Python →
DuckDB in Action
Python is a high-level general-purpose programming language with a dynamic runtime and extensive support for data processing.
- How it is used
- Integrates DuckDB into Python workflows for file ingestion, relational querying, data interchange, and pipeline transformations.
- How deep it goes
- Explains Python ingestion and queryable relations with runnable operations, while deferring SQLAlchemy and broader persistence patterns.
- Examples
- Uses a remote Populations CSV, httpfs, DuckDBPyRelation inspection, SQL execution, counting, and relation-to-table conversion.
- What you can do after
- Apply it via DuckDB's Python API to ingest files, query Python-accessible data, transform results, and feed data pipelines.
The Complete Obsolete Guide to Generative AI
Python is a general-purpose programming language commonly used for scripting, automation, and software development.
- How it is used
- Turns Playground prompt experiments into portable API code that can be copied, run, and adapted.
- How deep it goes
- Demonstrates generated API code and targeted learning prompts, while deferring Python syntax and API argument details.
- Examples
- Uses generated Completion API code, list iteration questions, and MoviePy-based 4K-to-1080p video conversion.
- What you can do after
- Apply it via the OpenAI API by copying configured Playground code and adapting it for a task.
Read how The Complete Obsolete Guide to Generative AI explains Python →
Everyday Data Visualization
Python is a general-purpose programming language known for readable syntax and a broad ecosystem of libraries.
- How it is used
- Positions Python as an approachable language for data visualization practitioners who prefer it over JavaScript or R.
- How deep it goes
- Demonstrates library selection and a short chart example, but stops short of teaching Python syntax or detailed implementation.
- Examples
- Uses fruit data and an approximately 18-line Matplotlib bar-chart example.
- What you can do after
- Apply it via Matplotlib to create a simple bar chart from supplied data.
Build Your Own Robot: Using Python, CRICKIT, and Raspberry Pi
Python is a high-level, general-purpose programming language commonly used for automation, data processing, and application development.
- How it is used
- Powers incremental robot projects by connecting Linux software libraries with sensors, motors, cameras, and user interfaces.
- How deep it goes
- Descends from stack placement and environment setup to hardware interfaces, reusable motor functions, and a Python shell.
- Examples
- Uses LEDs, touch sensors, motor movement, robot shells, computer vision, QR-code detection, and network control as practical applications.
- What you can do after
- Build it from scratch by configuring Python, installing libraries, and writing applications that operate robot hardware and interfaces.
Read how Build Your Own Robot: Using Python, CRICKIT, and Raspberry Pi explains Python →
Data Storytelling with Altair and AI
Python is a general-purpose, high-level programming language widely used for data analysis and scientific computing.
- How it is used
- Uses Python for data analysis, visualization, storytelling, and reporting across the data science project life cycle.
- How deep it goes
- Explains Python through visualization workflows and reporting integration, stopping short of general language internals or syntax instruction.
- Examples
- Demonstrates square-number line charts through contrasting Matplotlib and Altair implementations.
- What you can do after
- Apply Python to build visualizations and reports without repeatedly transferring analyzed data into external tools.
Read how Data Storytelling with Altair and AI explains Python →
Related concepts
- programming languagekind of · A programming language is a formal language with defined syntax and semantics for expressing computations and instructions that computers can execute or interpret.
- Matplotlibuses · Matplotlib is an open-source Python library for creating static, animated, and interactive data visualizations.
- Streamlitused by · Streamlit is an open-source Python framework for building interactive web applications, particularly for data and machine-learning applications.
- .NETalternative to · .NET is Microsoft's cross-platform development platform, providing runtimes, libraries, and tools for building and running applications.
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