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  - ANTLR
    
    ANTLR
    
    Parser and ANTLR4
    
    From parser to interpreter
    
    Parser, intepreter and compiler
  - Airflow + PySpark
  - Parallel and distributed computing
    
    Parallel and distributed computing
    
    Intro
    
    Communication between processes
    
    Dask, Modin and Celery libraries
  - Data versioning
  - Data Science - focus Pharma
  - Technical debt and architectural smell
  - Django
    
    Django
    
    Introduction to Django
    
    Django models
    
    Django authentication and model form
  - Docker
    
    Docker
    
    Intro Docker
    
    Advanced concepts
  - FastAPI
    
    FastAPI
    
    Intro
    
    First API with TDD approach
    
    SQLModel
    
    Back-end with FastAPI and SQLModel
    
    Async, OAuth2, profiling and dockerization
  - Flask
    
    Flask
    
    Intro to Flask
    
    Advanced Jinja and forms
    
    Flask with database
    
    Review
    
    App evolution
    
    Blueprints, refactoring and automated testing
    
    Authentication
    
    Profiles, roles and blog
    
    Heroku and docker - deploy
    
    Rest API
  - Generative AI
    
    Generative AI
    
    Discovering large language model
    
    Building an intelligent agent with LangChain, Gradio, and OpenAI
  - Genropy
    
    Genropy
    
    Introduction, demo, and first steps
    
    Bag, Structure, ORM, and project start
    
    UI resources, lookup tables, and formula columns
    
    Other UI resources, data import, hierarchical tables,printing, users, multi-tenancy
    
    Users, Staff, and Donors in a multi-tenant perspective
  - Exception handling
  - Bear and hunters game
    
    Bear and hunters game
    
    Part 1
    
    Part 2
  - Git basics
  - GitHub Actions
  - GPT Detection system
  - GraphQL and Strawberry
  - Introduction to Machine Learning
    
    Introduction to Machine Learning
    
    Introduction to Machine Learning and Scikit-Learn
    
    Scikit-Learn pipeline, metrics, and linear regression
    
    Scikit-Learn - unsupervised learning
  - Introduction to Deep Learning
    
    Introduction to Deep Learning
    
    Introduction to Deep Learning and TensorFlow
    
    Neural Networks for images
    
    Recurrent Neural Networks
  - Introduction to Kubernetes
    
    Introduction to Kubernetes
    
    Part 1
    
    Part 2
  - Introduction to Databricks
  - Learning Python through games
    
    Learning Python through games
    
    Python basics
    
    PyGameZero
    
    Creating games
    
    Review and creation of complex games
  - Text manipulation
    
    Text manipulation
    
    Introduction to regular expressions
    
    Regex library in Python
    
    Text analysis pipeline and Spacy
    
    A text analysis and classification project with ML
  - Micropython
    
    Micropython
    
    Introduction to the makers world
    
    IoT e cloud
  - MkDocs
    
    MkDocs
    
    Main elements of mkdocs
    
    Extra, plugins and site deployment
  - Modern data engineering
    
    Modern data engineering
    
    State of the art and Apache Airflow
    
    Project
  - Modern python development
    
    Modern python development
    
    Tools, libraries, best practices for Python development
    
    Poetry, PDM, Ruff, and other useful tools
  - MLOps
    
    MLOps
    
    Introduction to MLOps
    
    MLOps on AWS
  - ML Pipeline
    
    ML Pipeline
    
    Introduction to Pytorch
    
    Introduction to Hydra
  - NLP - topic modeling
  - NVIDIA Triton
  - Open Source
    
    Open Source
    
    First interview - Marcelo Trylesinski
    
    Second interview - Patrick Arminio
    
    Third interview - Juliana Nicacio
    
    Fourth interview - Giovanni Barillari
  - Pandas basics
    
    Pandas basics
    
    Introduction and csv module
    
    Operations with dataframes
    
    Series and exercises
    
    Live coding and in-depth discussions
    
    In-depth, indexing, filters, multiIndexing
  - Data analysis learning path
    
    Data analysis learning path
    
    Pandas - data manipulation
    
    Tools for data visualization
    
    Tools for data engineering
    
    Tools for production deployment
  - Polars
    
    Polars
    
    Introduction to Polars
    
    Data wrangling with Polars
    
    Discover functime
  - Pygame
    
    Pygame
    
    Introduction to pygame
    
    Sprites, collisions, and simulations
    
    Animations, camera, and gravity
  - Python basics
    
    Python basics
    
    Basic concepts and primitive types
    
    Flow control, iterations, and functions
    
    Basic OOP
    
    Advanced OOP
  - Reinforcement learning - DIAMBRA
    
    Reinforcement learning - DIAMBRA
    
    Theoretical concepts and environment setup
    
    Practical session and coding
  - Robotic Process Automation
  - Squirrel Telegram Bot
  - Telegram Bot
  - Tensorflow serving
  - Testing and documenting data
  - Testing with python
    
    Testing with python
    
    Testing - Intro
    
    Testing - Pytest and unittest
    
    Testing - Doctest and behave
  - Twitter sentiment analysis
  - Web scraping
    
    Web scraping
    
    Beautiful Soup and Requests
    
    Selenium and Scrapy
  - XGBoost
Learning
Learning
- Better code
  Better code
  - Bandit
  - Black
  - Flake8
  - Mypy
  - Pydantic
  - Ruff
- Create a package
  Create a package
- Data analysis
  Data analysis
  - DuckDB
  - Numpy
  - Pandas
  - Polars
- Document your code
  Document your code
- Linux basics
  Linux basics
- Manage dependencies
  Manage dependencies
  - Anaconda
  - PDM
  - Pipx
  - Poetry
  - Poetry avanzato
  - Pyenv
  - Virtualenv
- Extra material
- MkDocs tutorial
  MkDocs tutorial
  - Setup
  - Document your code
  - Extra functionalities
  - Deploy
    Deploy
    
    Deployment
    
    Github pages
  - Extensions
  - MkDocs vs Sphinx
- Pre-commit
- Project structure
  Project structure
  - Bear
- Python basics
  Python basics
  - Perchè Python
- Advanced Python
  Advanced Python
- Manage different python versions
  Manage different python versions
  - ASDF
  - REPL
- Roadmap
  Roadmap
- Manage configurations
  Manage configurations
- Tech independence
  Tech independence
- Testing
  Testing
  - Tutorial
    
    Tutorial
    
    Introduction
    
    What a Test Looks Like
    
    Types
    
    Good practices for writing tests
    
    Unittest
    
    Using pytest
    
    Doctest
    
    BDD tests with Behave
  - Resources
    
    Resources
    
    Bibliography
    
    Software
    
    Code snippets
    
    Reccommended talks Reccommended talks
    On this page
    
    Write better tests
    
    Write better Python code
- VS Code
Blog
Blog
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- Speaker guide

On this page

Write better tests
Write better Python code

Recommended Talks¶

Estimated time to read: 1 minute

A selection of lectures recommended for consumption by any real Pythonista.

Write better tests¶

Pro tips for writing great unit tests (Raymond Hettinger, 2022)

Write better Python code¶

Transforming Code into Beautiful, Idiomatic Python (Raymond Hettinger, 2013)
Beyond PEP 8 – Best practices for beautiful intelligible code (Raymond Hettinger, 2015)

February 25, 2024

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