Pydantic Series

Welcome to the Pydantic Series repository!
This playlist is designed to take you from the basics of Python data validation to building production-ready models using Pydantic.

You’ll learn everything from simple model creation to advanced features like validators, computed fields, nested models, and environment-based settings.

Each video includes hands-on examples to help you use Pydantic in real-world applications.

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🐍 Install Python Using Miniconda / Miniforge

To ensure your Pydantic projects stay clean and maintainable, it is recommended to use conda environments.

Follow the steps below to install Miniforge and set up your environment.

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🔗 Download Miniforge for macOS (ARM64)

Download from the official repository:
https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh

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💻 Install Miniforge

Run:

chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh
sh ~/Downloads/Miniforge3-MacOSX-arm64.sh
source ~/miniforge3/bin/activate

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🧱 Create a project-specific conda environment

conda create --prefix ./env python=3.13
conda activate ./env

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📦 Install packages from requirements.txt

pip install -r requirements.txt

Your Pydantic environment is ready! ⚡ Start validating and structuring your Python data like a pro.

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📺 Playlist Breakdown

1. Basics of Pydantic — Write Your First Pydantic Models

• Creating your first BaseModel
• Type-driven validation
• Serialization & deserialization basics

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2. Mixing Typing Module with Pydantic

• Using List, Dict, Optional, Union, etc.
• Benefits of Python type hints in data validation

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3. How to Use Field() for Validation

• Adding constraints: min_length, gt, lt, pattern, etc.
• Adding metadata: titles, descriptions, examples

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4. Using @field_validator & @model_validator

• Field-level custom validation
• Model-level pre/post validation
• Cleaner, reusable validation logic

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5. Using @computed_field in Pydantic

• Creating dynamic fields from existing data
• Practical examples: full names, totals, formatting

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6. Handling ValidationError

• Understanding error structures
• Returning readable error messages
• Debugging invalid payloads effectively

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7. Working with Nested Models

• Embedding models inside each other
• Validating complex JSON structures
• Best practices for hierarchical data

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8. Using model_dump() & model_dump_json()

• Converting models to dicts & JSON
• Including/excluding fields
• Handling field aliases

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9. Using default_factory

• Creating dynamic default values
• Avoiding mutable default issues

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10. Using Annotated in Pydantic

• Cleaner constraints using Annotated
• Combining typing + validation metadata

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11. Using Pydantic-Settings

• Managing application settings
• Loading environment variables & .env
• Structuring configs for real applications

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📄 requirements.txt

pydantic
pydantic-settings
python-dotenv
notebook

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🤝 Contributing

Got ideas, suggestions, or improvements? Feel free to open an issue or submit a pull request.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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📬 Stay Connected

• YouTube Channel
• LinkedIn

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Thank you for checking out the Pydantic Series! Happy validating and modeling your data with Pydantic 🚀