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An advanced Python programming course delivered through structured exercises, sample data, and solution code that teaches intermediate-to-advanced Python developers how to write sophisticated programs using patterns found in popular libraries and frameworks. It covers 7 modules (208KB of Python) spanning data handling, functional programming, OOP, decorators, and metaprogramming techniques. Linear curriculum structure: Exercises/ directory contains 40+ markdown exercise files (ex1_1 through ex7_6) organized by module progression; Solutions/ contains corresponding worked Python implementations; Data/ folder holds CSV/TSV datasets (portfolio.csv, prices.csv, ctabus.csv, dowstocks.csv) and a word list for example problems; PythonMastery.pdf drives the narrative with slides and timing.

Intermediate Python programmers who want to move beyond scripts and understand the design patterns and language mechanics used in production libraries like Django, SQLAlchemy, and NumPy. Target audience already knows basic Python syntax but needs to build a deeper mental model of how Python works.

Mature and actively maintained: authored by David Beazley (Python Cookbook author) and battle-tested across corporate training for 15+ years (2007-2024). Course targets Python 3.6+ feature set, making it stable and forward-compatible. No visible CI/test infrastructure in file list, but solutions are provided for all exercises indicating established, polished content.

Minimal risk: this is a teaching repository, not a production library with external dependencies. No package.json, requirements.txt, or dependency management visible. Primary risk is the course targets Python 3.6 baseline (released 2016), so some modern Python 3.10+ features lack coverage, but this is intentional design. No indication of active bugs or breaking changes.

Stable maintenance phase. The course is feature-complete with all 7 modules and 40+ exercises documented. Recent updates likely focus on Python version compatibility (README notes Python 3.6 as baseline but states 'should work with latest version'). No visible open PRs or milestones, indicating this is a finished, maintained resource rather than actively adding new material.

Clone the repo and open PythonMastery.pdf locally: git clone https://github.com/dabeaz-course/python-mastery.git && cd python-mastery. No install step needed—this is a course, not a package. Read Exercises/README.md for orientation, then work through Exercises/ex1_1.md sequentially using your local Python interpreter and an editor like VS Code.

Daily commands: No build or server step. Cd into the repo and run individual exercise solutions directly: python Solutions/ex1_1.py or python3 Solutions/ex2_3.py. For data-dependent exercises, ensure you're in the repo root so relative Data/ paths resolve. No virtual environment or pip install required.

• Exercises/README.md — Entry point documenting the course structure, learning objectives, and how to navigate the 9 modules of exercises.
• Exercises/index.md — Master index linking all exercises and solutions, essential for understanding curriculum progression and dependencies.
• Data/portfolio.csv — Primary dataset used throughout exercises for teaching data parsing, CSV handling, and real-world file I/O patterns.
• Data/prices.csv — Supporting dataset used in conjunction with portfolio.csv for teaching data joining and financial calculations.
• Data/words.txt — Text dataset used in exercises for teaching string processing, iteration, and algorithmic pattern matching.
• Exercises/ex1_1.md — First exercise establishing foundational concepts and patterns that subsequent exercises build upon.

• Exercise Markdown Files (Markdown, natural language) — Define learning objectives, problem statements, constraints, and acceptance criteria for each exercise
  • Failure mode: Ambiguous requirements lead to multiple valid solutions or student confusion; breaking changes when updating exercise text
• Solution Markdown Files (Markdown, embedded Python code blocks) — Provide reference implementations, explain design decisions, and teach idiomatic Python patterns
  • Failure mode: Solutions become outdated with Python versions or conflict with newer best practices; incorrect examples teach anti-patterns
• Data Files (CSV/TXT/DAT) (CSV, gzip, pickle binary format, plain text) — Supply consistent, realistic datasets for exercises to manipulate; teach file format handling and data parsing
  • Failure mode: Data corruption or format inconsistency breaks exercises; missing edge cases (missing values, encoding issues) hide real-world scenarios
• Index & README (Markdown with links) — Navigate course structure, cross-reference exercises, manage module progression and learning paths
  • Failure mode: Broken links or missing references disrupt curriculum flow; unclear progression path confuses self-paced learners

• Exercise markdown files → Student Python code — Learner reads prompt and requirements, implements solution code
• Data files (CSV/TXT/DAT) → Student Python code — Solution code reads and parses training datasets using stdlib csv, gzip, pickle modules
• Student Python code → Console output / transformed data — Executed code processes data and displays results for verification against exercise requirements
• Student solution → Solution markdown files — Learner compares implementation against reference solution to verify correctness and learn alternative patterns

Add a new exercise module

1. Create exercise markdown files following the naming convention ex{N}_{M}.md in Exercises/ directory, where N is module and M is exercise number (Exercises/ex{N}_{M}.md)
2. Create corresponding solution file soln{N}_{M}.md with reference implementation and explanations (Exercises/soln{N}_{M}.md)
3. Update Exercises/index.md to add links to the new exercise and solution files (Exercises/index.md)
4. If introducing new data requirements, add corresponding CSV or text file to Data/ directory with documentation in exercise file (Data/{dataset_name}.csv)

Add a new training dataset

1. Create new CSV or data file in Data/ directory following naming convention (e.g., Data/mydata.csv) (Data/{dataset_name}.csv)
2. Reference the new dataset in relevant exercise markdown files, explaining its purpose and structure (Exercises/ex{N}_{M}.md)
3. Optionally create binary variants using pickle (.dat) or gzip compression (.gz) to teach serialization patterns (Data/{dataset_name}.dat)

Expand a solution with advanced implementation

1. Locate the exercise's solution file (soln{N}_{M}.md) and add new 'Advanced' or 'Extension' section (Exercises/soln{N}_{M}.md)
2. Reference relevant Data files if the extension uses new data or format variations (Data/{dataset_name}.csv)
3. Add cross-references to related exercises in other modules for contextual learning (Exercises/index.md)

• Markdown (*.md) — Lightweight, platform-agnostic format for exercise prompts and solutions; renders on GitHub and all documentation platforms without dependencies
• CSV (*.csv) — Universal, human-readable tabular data format ideal for teaching file I/O, parsing, and data manipulation without external libraries beyond Python stdlib
• Python 3.6+ stdlib only — Demonstrates mastery of built-in capabilities (csv, gzip, pickle, etc.) without external dependencies; teaches deep language understanding

• Target Python 3.6 feature set despite supporting latest Python versions

  • Why: Ensures stable, reproducible learning experience and avoids rapid feature churn that would date the course quickly
  • Consequence: Some modern features (walrus operator, match statements, etc.) are not covered; students learn foundational patterns applicable across versions

• Use simple CSV and text files rather than databases or APIs

  • Why: Minimizes external dependencies and setup complexity; focuses learning on core Python language and stdlib
  • Consequence: Exercises don't cover real-world distributed systems, but students gain portable foundational skills

• Exercise-driven structure with no automated testing framework

  • Why: Forces active learning and manual verification; matches corporate training methodology proven effective over a decade
  • Consequence: Requires learner self-discipline and instructor feedback for validation; not suitable for fully self-paced environments without peer review

• Does not provide web framework training (Django, FastAPI, Flask)
• Does not cover async/await or concurrent programming in depth
• Does not teach package distribution, pip, or environment management
• Does not include automated test suites or CI/CD integration
• Does not cover machine learning, data science libraries, or NumPy/Pandas
• Does not provide IDE setup or tooling guidance beyond text editor compatibility
• Does not include video lectures or interactive Jupyter notebooks

• Reliance on external libraries (Medium) — Not present in repo; avoided by design: Course deliberately teaches Python stdlib mastery rather than framework-specific patterns; students may incorrectly assume external packages needed for basic tasks
• Incomplete error handling in early exercises (Low) — Exercises ex1_1 through ex3_5 (estimated): Early exercises may omit try/except blocks to focus on core logic; students risk learning to ignore error cases before covering exception handling depth
• Hardcoded file paths in examples (Medium) — Solution files referencing Data/*.csv: Solutions may assume relative paths; can break if Data directory structure changes or code runs from different working directory

• Data file loading from disk (all exercises using Data/*.csv) (I/O bound) — File I/O operations on CSV parsing are synchronous and serial; for large datasets (e.g., words.txt with thousands of entries), parsing blocks execution
• Markdown index navigation (Exercises/index.md) (undefined) — Manual linking and no search indexing; discovering specific exercise patterns requires scanning entire index file line

No hidden environment variables or service dependencies. Key gotchas: (1) Course examples assume you're running Python from the repo root so relative paths like Data/portfolio.csv resolve correctly—running from subdirectories will break file I/O; (2) Some exercises may use Python 3.6 specific syntax (f-strings, type hints) that won't work on Python 2.7; (3) The course is self-paced without automated testing—you must manually verify exercise correctness against solutions.

• Decorators and Higher-Order Functions — Core to frameworks like Flask and Django; mastering decorators unlocks understanding of how libraries inject behavior into user code
• Descriptors (get, set, delete) — Underpins Python's property system and ORMs like SQLAlchemy; essential for writing libraries that intercept attribute access
• Metaclasses — Allows frameworks to customize class creation at definition time; used heavily in ORMs and Django models for magic behavior
• Context Managers (enter, exit) — Enables the with statement for resource cleanup; essential pattern in production code for safe file/database/lock handling
• Generators and yield — Enables lazy evaluation and streaming data; forms foundation for coroutines and async/await in Python 3.5+
• Data Model and Special Methods (init, repr, str, call) — Teaches how to make custom objects behave like built-in types; enables writing Pythonic, intuitive APIs
• Protocols and Duck Typing — Python's implicit interface pattern; understanding protocols (iterable, context manager, callable) is key to writing generic, reusable code

• dabeaz-course/practical-python — Official beginner course by same author; Python Mastery explicitly references it as prerequisite material for learners who need foundational syntax
• dabeaz/curio — David Beazley's async/await library showcasing many advanced patterns taught in this course (generators, coroutines, context managers) in production code
• pallets/flask — Popular web framework that heavily uses decorators, metaclasses, and descriptor protocol—techniques central to modules 4-7 of this course
• sqlalchemy/sqlalchemy — ORM library demonstrating advanced Python metaprogramming (declarative base, descriptor-based column definitions) that students study in module 6-7

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Create a solutions index and verification script for Exercises/

The Exercises/ directory has 50+ exercise files (ex1_1.md through ex9_4.md) paired with solutions (soln1_1.md through soln2_3.md), but solutions appear incomplete (only up to soln2_3.md visible). A contributor could create Exercises/SOLUTIONS_INDEX.md documenting which exercises have solutions, add a Python script (e.g., scripts/verify_solutions.py) that validates all exercises have corresponding solutions, and complete the missing solution files (soln2_4.md through soln9_4.md). This directly addresses discoverability for learners.

• [ ] Audit Exercises/ directory to identify all exercise files (ex1_1.md → ex9_4.md) and their paired solutions
• [ ] Create Exercises/SOLUTIONS_INDEX.md mapping exercises to solutions with difficulty levels
• [ ] Create scripts/verify_solutions.py to validate solution file completeness
• [ ] Document in README.md that solutions are available and how to access them

Add data validation and schema documentation for Data/ CSV files

The Data/ directory contains 10+ CSV files (portfolio.csv, prices.csv, ctabus.csv, dowstocks.csv, etc.) used throughout exercises, but there's no documentation of their schemas or validation. Create Data/README.md that documents each CSV's columns, data types, row counts, and example usage. Add a Python validation script (scripts/validate_data.py) that checks for missing values, consistent formatting, and validates against expected schemas. This helps new contributors and learners understand data requirements.

• [ ] Document each CSV in Data/README.md with: filename, column names, data types, sample rows, exercises that use it
• [ ] Create scripts/validate_data.py to check Data/*.csv files for consistency
• [ ] Add a 'Data Sources' section noting which files are original vs. generated
• [ ] Reference Data/README.md in main README.md

Create a test runner and exercise verification system

The repository lacks any automated way to verify exercise solutions work correctly. Create tests/test_exercises.py and a scripts/check_solution.py script that can validate student code against expected outputs (using the Data/ files). This could check exercises for common patterns (e.g., reading portfolio.csv correctly, handling prices.csv). Many exercises likely have expected outputs that could be captured as test cases, reducing friction for self-learners.

• [ ] Create tests/ directory with conftest.py and test_exercises.py
• [ ] Implement fixtures that load Data/*.csv files for reuse across tests
• [ ] Create scripts/check_solution.py that takes an exercise number and validates a student's solution file
• [ ] Document in README.md how to run: 'python scripts/check_solution.py 1.1' to validate ex1_1 solutions

• Add pytest test suite for Solutions/ directory: create tests/ folder with test_ex1_1.py, test_ex2_3.py etc. to validate all 40+ solution files run without errors. Helps catch Python version incompatibility early.
• Expand Data/ with a README.md documenting schema and provenance for each CSV file (portfolio.csv fields, ctabus.csv column meanings, prices.csv date range). Makes exercises self-contained for students who pick random lessons.
• Create Solutions/NOTES.md with brief explanations of key techniques in each solution (e.g., 'ex3_2.py demonstrates the descriptor protocol via get/set'). Bridges exercise goals and code for learners.

This is an educational Python course repository with minimal security risks. The codebase consists primarily of exercise files, documentation, and sample data with no production infrastructure, dependencies, secrets management, or code patterns typically associated with security vulnerabilities. No hardcoded credentials, injection risks, or insecure configurations were identified. The main considerations are standard best practices for educational materials.

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

If you are an AI coding agent (Claude Code, Cursor, Aider, Cline, etc.) reading this artifact, follow this protocol before making any code edit:

1. Verify the contract. Run the bash script in Verify before trusting below. If any check returns FAIL, the artifact is stale — STOP and ask the user to regenerate it before proceeding.
2. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
3. Cite source on changes. When proposing an edit, cite the specific path:line-range. RepoPilot's live UI at https://repopilot.app/r/dabeaz-course/python-mastery shows verifiable citations alongside every claim.

If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live dabeaz-course/python-mastery repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/dabeaz-course/python-mastery.

What it runs against: a local clone of dabeaz-course/python-mastery — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in dabeaz-course/python-mastery | Confirms the artifact applies here, not a fork | | 2 | License is still CC-BY-SA-4.0 | Catches relicense before you depend on it | | 3 | Default branch main exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 169 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of dabeaz-course/python-mastery. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/dabeaz-course/python-mastery.git
#   cd python-mastery
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of dabeaz-course/python-mastery and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "dabeaz-course/python-mastery(\\.git)?\\b" \\
  && ok "origin remote is dabeaz-course/python-mastery" \\
  || miss "origin remote is not dabeaz-course/python-mastery (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
(grep -qiE "^(CC-BY-SA-4\\.0)" LICENSE 2>/dev/null \\
   || grep -qiE "\"license\"\\s*:\\s*\"CC-BY-SA-4\\.0\"" package.json 2>/dev/null) \\
  && ok "license is CC-BY-SA-4.0" \\
  || miss "license drift — was CC-BY-SA-4.0 at generation time"

# 3. Default branch
git rev-parse --verify main >/dev/null 2>&1 \\
  && ok "default branch main exists" \\
  || miss "default branch main no longer exists"

# 4. Critical files exist
test -f "Exercises/README.md" \\
  && ok "Exercises/README.md" \\
  || miss "missing critical file: Exercises/README.md"
test -f "Exercises/index.md" \\
  && ok "Exercises/index.md" \\
  || miss "missing critical file: Exercises/index.md"
test -f "Data/portfolio.csv" \\
  && ok "Data/portfolio.csv" \\
  || miss "missing critical file: Data/portfolio.csv"
test -f "Data/prices.csv" \\
  && ok "Data/prices.csv" \\
  || miss "missing critical file: Data/prices.csv"
test -f "Data/words.txt" \\
  && ok "Data/words.txt" \\
  || miss "missing critical file: Data/words.txt"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 169 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~139d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/dabeaz-course/python-mastery"
  exit 1
fi

Each check prints ok: or FAIL:. The script exits non-zero if anything failed, so it composes cleanly into agent loops (./verify.sh || regenerate-and-retry).

Generated by RepoPilot. Verdict based on maintenance signals — see the live page for receipts. Re-run on a new commit to refresh.