A living learning lab for building Python intuition, one small system at a time.

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📊 Journey Dashboard

Current checkpoint: Python foundations + mental-bridge applications
Overall: █████████████░░░░░░░ 63%

Plain-text view

Python foundations    [██████████] 100%
Pythonic thinking     [██████████] 100%
Object-oriented code  [█████████░]  90%
Module library        [░░░░░░░░░░]   0%
Intermediate Python   [░░░░░░░░░░]   0%
Small applications    [██████████] 100%
Backend engineering   [██░░░░░░░░]  17%
ML fundamentals       [░░░░░░░░░░]   0%
LLM / RAG / agents    [░░░░░░░░░░]   0%
Production AI         [░░░░░░░░░░]   0%

Edit journey.json, push, and the dashboard, map, and book below rebuild automatically. See How to update.

🗺️ Learning Map

flowchart LR
    t0["🧱 Python foundations<br/>100%"]
    t1["🐍 Pythonic thinking<br/>100%"]
    t2["🧩 Object-oriented code<br/>90%"]
    t3["📚 Module library<br/>0%"]
    t4["🛠️ Intermediate Python<br/>0%"]
    t5["🚀 Small applications<br/>100%"]
    t6["🏗️ Backend engineering<br/>17%"]
    t7["📈 ML fundamentals<br/>0%"]
    t8["🤖 LLM / RAG / agents<br/>0%"]
    t9["🛡️ Production AI<br/>0%"]
    t0 --> t1 --> t2 --> t3 --> t4 --> t5 --> t6 --> t7 --> t8 --> t9
    classDef done fill:#bbf7d0,stroke:#166534,color:#052e16;
    classDef current fill:#fde68a,stroke:#92400e,color:#451a03,stroke-width:3px;
    classDef future fill:#e0f2fe,stroke:#075985,color:#082f49;
    class t0,t1,t5 done;
    class t2,t6 current;
    class t3,t4,t7,t8,t9 future;

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🟢 complete · 🟡 in progress · 🔵 upcoming

📖 The Book: Chapters, Topics, Subtopics

Every chapter lists what was covered, and where that idea shows up later in AI or backend engineering.

Part 1 · 🧱 Python foundations — 100%

Chapter 1: Data, Logic & Functions  ·  7/7 topics

Lessons 1 to 7: the raw material every program is made of.

	Topic	Source	Subtopics covered	Future reference (AI / backend)
✅	Strings	1Strings.py	slicing, f-strings, methods	Prompt templates and text cleaning for LLM inputs
✅	Numeric data	2NumericData.py	int/float, operators, conversion	Token counts, scores, and metrics
✅	Lists, sets, tuples	3List_Set_Tuple.py	mutability, membership, ordering	Batches of documents, permission sets, immutable configs
✅	Dictionaries	4Dictonaries.py	lookup, nesting, iteration	JSON payloads and API records
✅	Conditionals & booleans	5Conditional_Boolean.py	if/elif/else, truthiness	Request routing and guardrail checks
✅	Loops & iterators	6Loosps_Iterator.py	for/while, iter/next	Streaming tokens and retry loops
✅	Functions	7Function.py	args/kwargs, return values, scope	Service handlers and agent tool functions

Part 2 · 🐍 Pythonic thinking — 100%

Chapter 2: Idiomatic Python  ·  2/2 topics

Writing less code that says more.

	Topic	Source	Subtopics covered	Future reference (AI / backend)
✅	Comprehensions	9Comprehension.py	list, dict, set	Fast data shaping in ETL and feature prep
✅	Python mental models	Python_Mental_Models.md	syntax to systems	Seeing backend and AI patterns in plain Python

Part 3 · 🧩 Object-oriented code — 90%

Chapter 3: Classes & Abstraction  ·  4/5 topics

Bundling data and behavior.

	Topic	Source	Subtopics covered	Future reference (AI / backend)
✅	Classes & instances	10OOP.py	init, class variables	Models, clients, and service objects
✅	Class & static methods	10OOP.py	@classmethod, @staticmethod	Alternate constructors, e.g. Model.from_config()
✅	Inheritance	10OOP.py	super(), overriding	Base provider classes for LLM vendors
✅	Special methods & properties	10OOP.py	repr, @property	Validated fields, as in Pydantic
🟡	Composition	10OOP.py	has-a vs is-a	Agents composed of tools, memory, and a model

Part 4 · 📚 Module library — 0%

Chapter 4: Module Library  ·  0/0 topics

Everything in 8Module_Library/, discovered automatically.

	Topic	Source	Subtopics covered	Future reference (AI / backend)

Chapter 5: Still to explore  ·  0/1 topics

Planned stdlib topics.

	Topic	Source	Subtopics covered	Future reference (AI / backend)
⬜	logging & typing	—	—	Observability and typed API contracts

Part 5 · 🛠️ Intermediate Python — 0%

Chapter 6: Real-world Plumbing  ·  0/0 topics

Everything in intermediate/, discovered automatically.

	Topic	Source	Subtopics covered	Future reference (AI / backend)

Chapter 7: Still to explore  ·  0/2 topics

Planned next steps.

	Topic	Source	Subtopics covered	Future reference (AI / backend)
⬜	Validation	—	—	Structured outputs from models
⬜	Async basics	—	—	Concurrent model and tool calls

Part 6 · 🚀 Small applications — 100%

Chapter 8: Mental-Bridge Applications  ·  10/10 topics

Ten scenarios that connect syntax to systems.

	Topic	Source	Subtopics covered	Future reference (AI / backend)
✅	01 Zip & dictionary mapping	01_zip_dictionary_mapping.py	zip, filtering	Authorization and tool access
✅	02 Set permissions	02_set_permissions.py	set ops	Permission checks
✅	03 Filter users	03_filter_users.py	predicates	Query-like selection / metadata retrieval
✅	04 Transform API data	04_transform_api_data.py	map	API response shaping
✅	05 Group users by role	05_group_users_by_role.py	grouping	Aggregation and reporting
✅	06 JSON data processing	06_json_data_processing.py	json	External data boundaries
✅	07 Function as data	07_function_as_data.py	callbacks	Pluggable model/tool strategies
✅	08 Decorator logging	08_decorator_logging.py	decorators	Tracing and observability
✅	09 Generator data	09_generator_large_data.py	yield	Streaming and memory efficiency
✅	10 Error-handling pipeline	10_error_handling_pipeline.py	try/except	Resilient processing

Part 7 · 🏗️ Backend engineering — 17%

Chapter 9: Services & Data  ·  0/3 topics

Turning scripts into services.

	Topic	Source	Subtopics covered	Future reference (AI / backend)
🟡	Patterns connected	—	handlers, pipelines	FastAPI services
⬜	REST with FastAPI	—	—	Serving models
⬜	Databases	—	—	Vector stores and metadata

Part 8 · 📈 ML fundamentals — 0%

Chapter 10: Learning from Data  ·  0/2 topics

The next frontier.

	Topic	Source	Subtopics covered	Future reference (AI / backend)
⬜	NumPy & pandas	—	—	Embeddings and datasets
⬜	Model basics	—	—	Evaluation mindset

Part 9 · 🤖 LLM / RAG / agents — 0%

Chapter 11: AI Systems  ·  0/3 topics

Prompts, retrieval, tools.

	Topic	Source	Subtopics covered	Future reference (AI / backend)
⬜	LLM APIs & structured output	—	—	—
⬜	RAG	—	—	—
⬜	Agents & tool use	—	—	—

Part 10 · 🛡️ Production AI — 0%

Chapter 12: Shipping Safely  ·  0/3 topics

Testing, observability, deployment.

	Topic	Source	Subtopics covered	Future reference (AI / backend)
⬜	Testing	—	—	—
⬜	Observability	—	—	—
⬜	Deployment	—	—	—

🧪 Projects and the Concepts They Use

No projects yet. Add one to journey.json under projects and this table fills itself in.

🧠 The Core Mental Model

flowchart TD
    A[Lists + dictionaries + sets] --> B[Records, mappings, membership]
    B --> C[Filtering, grouping, validation]
    C --> D[Functions, callbacks, pipelines]
    D --> E[Modules, decorators, generators, errors]
    E --> F[Backend services and data flows]
    F --> G[Prompts, tools, retrieval, model responses]
    G --> H[Observable production AI]

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Python idea	Becomes
Dictionary mapping	Authorization
Filter	Metadata retrieval
Generator	Streaming
Decorator	Logging / tracing
Exception handler	Resilient processing
Function passed as data	Model or tool strategy

🧭 Repository Guide

• Python mental models: the ideas behind the progression
• Mental model map: syntax to systems
• Core lessons: 1Strings.py to 10OOP.py, plus 8Module_Library/ and intermediate/
• Application exercises: scenario-driven practice modules

python Python_Application/01_zip_dictionary_mapping.py

🔄 How to update

1. Change a topic's status in journey.json to done, wip, or todo (wip counts as half).
2. Add subtopics and the future reference as you learn them.
3. Add a project under projects:

   {"name": "Tool router", "link": "https://github.com/you/tool-router", "status": "building",
    "concepts": ["Inheritance", "10 Error-handling pipeline"]}

   concepts must match topic names; each resolves to its chapter automatically.
4. Run python scripts/build_readme.py locally, or just push: the GitHub Action does it for you.

⭐ North Star

Learn a concept. Use it in a realistic problem.
Recognize the larger system it belongs to. Build the next layer.