├── .gitignore ├── LICENSE ├── README.md ├── article.md └── demo ├── poetry.lock ├── pydantic-2021.ipynb ├── pydantic.ipynb ├── pydantic_more_examples.ipynb └── pyproject.toml /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | BSD 3-Clause License 2 | 3 | Copyright (c) 2020, Hultnér Technologies 4 | All rights reserved. 5 | 6 | Redistribution and use in source and binary forms, with or without 7 | modification, are permitted provided that the following conditions are met: 8 | 9 | 1. Redistributions of source code must retain the above copyright notice, this 10 | list of conditions and the following disclaimer. 11 | 12 | 2. Redistributions in binary form must reproduce the above copyright notice, 13 | this list of conditions and the following disclaimer in the documentation 14 | and/or other materials provided with the distribution. 15 | 16 | 3. Neither the name of the copyright holder nor the names of its 17 | contributors may be used to endorse or promote products derived from 18 | this software without specific prior written permission. 19 | 20 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 21 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 22 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 23 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 24 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 25 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 26 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 27 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 28 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 29 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 30 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

2 | Hultnér Technologies 3 |

4 |

5 | Hultnér Technologies AB | @ahultner | Blog | Slides | Nordisk Python Community | GitHub 6 |

7 |

8 | 9 | # ⠠⠵ Introduction to pydantic 10 | **Introduction to the Python Pydantic library.** 11 | Take your data classes to the next level! Showcasing run time type checking, data serialization and deserialization, custom validators and of course data class integration. 12 | Make it easy to go from standard data classes to pydantic models. 13 | 14 | ## ⠠⠵ Quick reference 15 | 16 | ```python 17 | # Minimal usage example 18 | from pydantic import BaseModel, validator, root_validator 19 | from enum import Enum 20 | 21 | class Topping(str, Enum): 22 | mozzarella = 'mozzarella' 23 | tomato_sauce = 'tomato sauce' 24 | prosciutto = 'prosciutto' 25 | basil = 'basil' 26 | rucola = 'rucola' 27 | 28 | 29 | class Pizza(BaseModel): 30 | style: str 31 | toppings: Tuple[Topping, ...] 32 | 33 | 34 | class BakedPizza(Pizza): 35 | # For simplicity in the example we use int for temperature 36 | oven_temperature: int 37 | 38 | # A validator looking at a single property 39 | @validator('style') 40 | def check_style(cls, style): 41 | house_styles = ("Napoli", "Roman", "Italian") 42 | if style not in house_styles: 43 | raise ValueError(f"We only cook the following styles: {house_styles}, given: {style}") 44 | return style 45 | 46 | # Root validators check the entire model 47 | @root_validator 48 | def check_temp(cls, values): 49 | style, temp = values.get("style"), values.get("oven_temperature") 50 | 51 | if style != "Napoli": 52 | # We don't have any special rules yet for the other styles 53 | return values 54 | 55 | if 350 <= temp <= 400: 56 | # Target temperature 350 - 400°C, ideally around 375°C 57 | return values 58 | 59 | raise ValueError(f"Napoli pizzas require a oven_temperature in the range of 350 - 400°C, given: {temp}°C") 60 | 61 | 62 | ``` 63 | 64 | ## Talks 65 | ### ⠠⠵ Python Pizza, 2020 April 25 (Remote) 66 | **Give your data classes super powers with pydantic**, _25 April 2020, 14:12 UTC_ 67 | The speaker schedule for the next Python Pizza conference is up, and I'm one of the speakers. The conference will be held remotely, [tickets](https://ti.to/acpyss/remote-python-pizza-2020-1) are €30 and all the proceeds will go to [Doctors Without Borders]( 68 | https://www.msf.org/)! 69 | 70 | 71 | If you or your company can't afford the ticket there's also a [financial aid program](https://docs.google.com/forms/d/e/1FAIpQLSeEzqiE9bTCiM2dOQ9Numku2xJHPJKbRj9cqMGqxSD3KVlxOA/viewform). 72 | 73 | 74 | 75 | [![Speaker at Python Pizza 2020, Alexander Hultnér talks about pydantic](https://i.ytimg.com/vi/LzNBfPVtrPk/maxresdefault.jpg)](https://www.youtube.com/watch?v=LzNBfPVtrPk) 76 | 77 | - [Slides](http://slides.com/hultner/python-pizza-2020/#/) 78 | - [Conference site](https://remote.python.pizza) 79 | - [Jupyter Lab Notebook](demo/pydantic.ipynb) 80 | - [Announcement video](https://www.youtube.com/watch?v=LzNBfPVtrPk) 81 | 82 | ## ⠠⠵ FAQ, pydantic 83 | **Is the pydantic type-checker strict?** 84 | No, pydantic currently favours parsing and will coerce the type if possible. A [strict-mode](https://github.com/samuelcolvin/pydantic/issues/1098) is being worked on. 85 | 86 | **Can pydantics runtime type-checker be used on functions?** 87 | [Yes](https://pydantic-docs.helpmanual.io/usage/validation_decorator/), through the @validate_arguments decorator. But the feature is at the time of writing still in beta (2020-04-25). 88 | 89 | **Do settings managment support `.env`?** 90 | [Yes](https://pydantic-docs.helpmanual.io/usage/settings/) it does! 91 | 92 | **I have a question not covered here, where can I ask it?** 93 | I'm [@ahultner on twitter](https://twitter.com/ahultner), otherwise you can also email me (address in slides). 94 | 95 | ## ⠠⠵ Links 96 | - [Pydantic docs](https://pydantic-docs.helpmanual.io) 97 | - [Pydantic Github](https://github.com/samuelcolvin/pydantic/) 98 | - [FastAPI](https://fastapi.tiangolo.com) 99 | - [Dataclasses](https://docs.python.org/3/library/dataclasses.html) 100 | - [NamedTuple](https://docs.python.org/3/library/typing.html#typing.NamedTuple) 101 | -------------------------------------------------------------------------------- /article.md: -------------------------------------------------------------------------------- 1 | 2 | . 3 | ..: 4 | Hultnér 5 | Technologies 6 | 7 | @ahultner | https://hultner.se/ 8 | 9 | 10 | # Give your dataclasses super powers with pydantic 11 | 12 | ## Index 13 | - Quick refresher on python data classes 14 | - Pydantic introduction 15 | - Prior art 16 | - Minimal example from dataclass 17 | - Runtime type-checking 18 | - JSON (de)serialisation 19 | - JSONSchema 20 | - Validators 21 | - [Custom model validators](https://pydantic-docs.helpmanual.io/usage/validators/) 22 | - [Validation decorator for functions](https://pydantic-docs.helpmanual.io/usage/validation_decorator/), via `@validate_arguments`. Still in beta, API may change. 23 | - FastAPI framework 24 | - OpenAPI Specifications 25 | - Autogenerated tests 26 | - Cool features worth mentioning 27 | - Future 28 | - Conclusion 29 | 30 | 31 | Let's start with a quick `@dataclass`-refresher. 32 | 33 | Well use pizza-based examples in the spirit of [python.pizza](https://remote.python.pizza) 🐍🍕 34 | 35 | 36 | ```python 37 | from dataclasses import dataclass 38 | from typing import Tuple 39 | ``` 40 | 41 | 42 | ```python 43 | @dataclass 44 | class Pizza: 45 | style: str 46 | toppings: Tuple[str, ...] 47 | 48 | ``` 49 | 50 | 51 | ```python 52 | Pizza(1, ("cheese", "ham")) 53 | ``` 54 | 55 | 56 | 57 | 58 | Pizza(style=1, toppings=('cheese', 'ham')) 59 | 60 | 61 | 62 | Now we may want to constrain the toppings to ones we actually offer. 63 | Our pizzeria doesn't offer pineapple 🚫🍍 as a valid topping, hate it 😡 or love it 💕 64 | 65 | 66 | ```python 67 | from enum import Enum 68 | ``` 69 | 70 | 71 | ```python 72 | class Topping(str, Enum): 73 | mozzarella = 'mozzarella' 74 | tomato_sauce = 'tomato sauce' 75 | prosciutto = 'prosciutto' 76 | basil = 'basil' 77 | rucola = 'rucola' 78 | 79 | 80 | @dataclass 81 | class Pizza: 82 | style: str 83 | toppings: Tuple[Topping, ...] 84 | ``` 85 | 86 | Let's see what happens if we try to create a pizza with pineapple 🍍 topping. 87 | 88 | 89 | ```python 90 | Pizza(2, ("pineapple", 24)) 91 | ``` 92 | 93 | 94 | 95 | 96 | Pizza(style=2, toppings=('pineapple', 24)) 97 | 98 | 99 | 100 | With dataclasses the types aren't enforced, this can ofcourse be implemented but in this case we'll lean on the shoulders of a giant, pydantic 🧹🐍🧐 101 | 102 | 103 | ```python 104 | from pydantic.dataclasses import dataclass 105 | 106 | 107 | @dataclass 108 | class Pizza: 109 | style: str 110 | toppings: Tuple[Topping, ...] 111 | ``` 112 | 113 | As you can see the only thing changed in this example is that we import the dataclass decorator from pydantic. 114 | 115 | 116 | ```python 117 | from pydantic import ValidationError 118 | try: 119 | Pizza(2, ("pineapple", 24)) 120 | except ValidationError as err: 121 | print(err) 122 | ``` 123 | 124 | 2 validation errors for Pizza 125 | toppings -> 0 126 | value is not a valid enumeration member; permitted: 'mozzarella', 'tomato sauce', 'prosciutto', 'basil', 'rucola' (type=type_error.enum; enum_values=[, , , , ]) 127 | toppings -> 1 128 | value is not a valid enumeration member; permitted: 'mozzarella', 'tomato sauce', 'prosciutto', 'basil', 'rucola' (type=type_error.enum; enum_values=[, , , , ]) 129 | 130 | 131 | And with that simple chage we can see that our new instance of an invalid pizza actually raises errors 🚫🚨 132 | 133 | Additionally these errors are very readable! 134 | 135 | So let's try to create a valid pizza 🍕✅ 136 | 137 | 138 | ```python 139 | Pizza("Napoli", (Topping.tomato_sauce, Topping.prosciutto, Topping.mozzarella, Topping.basil)) 140 | ``` 141 | 142 | 143 | 144 | 145 | Pizza(style='Napoli', toppings=(, , , )) 146 | 147 | 148 | 149 | So what about JSON? 🧑‍💻 150 | The dataclass dropin replacement decorator from pydantic is great for compability but by using `pydantic.BaseModel` we can get even more out of pydantic. One of those things is (de)serialisation, pydantic have native support JSON encoding and decoding. 151 | 152 | 153 | ```python 154 | from pydantic import BaseModel 155 | 156 | 157 | 158 | class Pizza(BaseModel): 159 | style: str 160 | toppings: Tuple[Topping, ...] 161 | ``` 162 | 163 | *Disclaimer: Pydantic is primarly a parsing library and does validation as a means to an end, so make sure it makes sense for you.* 164 | 165 | When using the BaseModel the default behaviour requires to specify the init arguments using their keywords like below 166 | 167 | 168 | ```python 169 | Pizza(style="Napoli", toppings=(Topping.tomato_sauce, Topping.prosciutto, Topping.mozzarella, Topping.basil)) 170 | ``` 171 | 172 | 173 | 174 | 175 | Pizza(style='Napoli', toppings=(, , , )) 176 | 177 | 178 | 179 | We can now easily encode this object as `JSON`, there's also [built-in support](https://pydantic-docs.helpmanual.io/usage/exporting_models/) for dict, pickle, immutable `copy()`. Pydantic will also (de)serialise subclasses. 180 | 181 | 182 | ```python 183 | _.json() 184 | ``` 185 | 186 | 187 | 188 | 189 | '{"style": "Napoli", "toppings": ["tomato sauce", "prosciutto", "mozzarella", "basil"]}' 190 | 191 | 192 | 193 | And we can also reconstruct our original object using the `parse_raw`-method. 194 | 195 | 196 | ```python 197 | Pizza.parse_raw('{"style": "Napoli", "toppings": ["tomato sauce", "prosciutto", "mozzarella", "basil"]}') 198 | ``` 199 | 200 | 201 | 202 | 203 | Pizza(style='Napoli', toppings=(, , , )) 204 | 205 | 206 | 207 | Errors raises a validation error, these can also be represented as JSON. 208 | 209 | 210 | ```python 211 | try: 212 | Pizza(style="Napoli", toppings=(2,)) 213 | except ValidationError as err: 214 | print(err.json()) 215 | ``` 216 | 217 | [ 218 | { 219 | "loc": [ 220 | "toppings", 221 | 0 222 | ], 223 | "msg": "value is not a valid enumeration member; permitted: 'mozzarella', 'tomato sauce', 'prosciutto', 'basil', 'rucola'", 224 | "type": "type_error.enum", 225 | "ctx": { 226 | "enum_values": [ 227 | "mozzarella", 228 | "tomato sauce", 229 | "prosciutto", 230 | "basil", 231 | "rucola" 232 | ] 233 | } 234 | } 235 | ] 236 | 237 | 238 | We can also export a JSONSchema directly from our model, this is very useful for instance if we want to use your model to feed a Swagger/OpenAPI-spec. 📜✅ 239 | 240 | ⚠ *Caution: Pydantic uses the latest draft 7 of JSONSchema, this will be used in the comming OpenAPI 3.1 spec but the current 3.0.x spec uses draft 4. I spoke with Samuel Colvin, the creator of pydantic about this and his recommendation is to write a `schema_extra`function to use the older JSONSchema version if you want strict compability. The FastAPI framework doesn't do this and is slightly incompatible with the current OpenAPI-spec* 241 | 242 | 243 | ```python 244 | Pizza.schema() 245 | ``` 246 | 247 | 248 | 249 | 250 | {'title': 'Pizza', 251 | 'type': 'object', 252 | 'properties': {'style': {'title': 'Style', 'type': 'string'}, 253 | 'toppings': {'title': 'Toppings', 254 | 'type': 'array', 255 | 'items': {'enum': ['mozzarella', 256 | 'tomato sauce', 257 | 'prosciutto', 258 | 'basil', 259 | 'rucola'], 260 | 'type': 'string'}}}, 261 | 'required': ['style', 'toppings']} 262 | 263 | 264 | 265 | That was the basics using the built-in validators, but what if you want to implement your own business rules in a custom validator, we're going to look at this next. 266 | 267 | We now want to add a new property for `oven_temperature`, but in our case we also want to ensure that we are close to the ideal of roughly 375°C for Neapolitan pizzas, which is our imaginary restaurants house style. 268 | 269 | 270 | ```python 271 | from pydantic import validator, root_validator 272 | class BakedPizza(Pizza): 273 | # For simplicity in the example we use int for temperature 274 | oven_temperature: int 275 | 276 | # A validator looking at a single property 277 | @validator('style') 278 | def check_style(cls, style): 279 | house_styles = ("Napoli", "Roman", "Italian") 280 | if style not in house_styles: 281 | raise ValueError(f"We only cook the following styles: {house_styles}, given: {style}") 282 | return style 283 | 284 | # Root validators check the entire model 285 | @root_validator 286 | def check_temp(cls, values): 287 | style, temp = values.get("style"), values.get("oven_temperature") 288 | 289 | if style != "Napoli": 290 | # We don't have any special rules yet for the other styles 291 | return values 292 | 293 | if 350 <= temp <= 400: 294 | # Target temperature 350 - 400°C, ideally around 375°C 295 | return values 296 | 297 | raise ValueError(f"Napoli pizzas require a oven_temperature in the range of 350 - 400°C, given: {temp}°C") 298 | 299 | ``` 300 | 301 | Now let's see if we create some invalid pizzas ⚠️🚨 302 | 303 | 304 | ```python 305 | try: 306 | BakedPizza(style="Panpizza", toppings=["tomato sauce"], oven_temperature=250 ) 307 | except ValidationError as err: 308 | print(err) 309 | ``` 310 | 311 | 1 validation error for BakedPizza 312 | style 313 | We only cook the following styles: ('Napoli', 'Roman', 'Italian'), given: Panpizza (type=value_error) 314 | 315 | 316 | 317 | ```python 318 | try: 319 | BakedPizza(style="Napoli", toppings=["tomato sauce"], oven_temperature=300 ) 320 | except ValidationError as err: 321 | print(err) 322 | ``` 323 | 324 | 1 validation error for BakedPizza 325 | __root__ 326 | Napoli pizzas require a oven_temperature in the range of 350 - 400°C, given: 300°C (type=value_error) 327 | 328 | 329 | Now let's create a pizza 🍕 allowed by our rules! ✨ 330 | 331 | 332 | ```python 333 | BakedPizza(style="Napoli", toppings=["tomato sauce"], oven_temperature=350) 334 | ``` 335 | 336 | 337 | 338 | 339 | BakedPizza(style='Napoli', toppings=(,), oven_temperature=350) 340 | 341 | 342 | 343 | Gosh these runtime type checkers are rather useful, but what about **functions**? 344 | 345 | Pydantic got you covered with `@validate_arguments`. *Still in beta, API may change, release 2020-04-18 in version 1.5* 346 | 347 | 348 | ```python 349 | from pydantic import validate_arguments 350 | 351 | # Validator on function 352 | # Ensure that we use a valid pizza when making orders 353 | @validate_arguments 354 | def make_order(pizza: Pizza): 355 | ... 356 | 357 | ``` 358 | 359 | 360 | ```python 361 | try: 362 | make_order({ 363 | "style":"Napoli", 364 | "toppings":("tomato sauce", "mozzarella", "prosciutto", "pineapple") 365 | }) 366 | except ValidationError as err: 367 | print(err) 368 | ``` 369 | 370 | 1 validation error for MakeOrder 371 | pizza -> toppings -> 3 372 | value is not a valid enumeration member; permitted: 'mozzarella', 'tomato sauce', 'prosciutto', 'basil', 'rucola' (type=type_error.enum; enum_values=[, , , , ]) 373 | 374 | 375 | ## FastAPI 376 | FastAPI is a lean microframework similar to Flask which utilizes pydantic models heavily, it will also automatically generate OpenAPI-specifications from your application based on your models. 377 | 378 | This gives you framework agnostic models while still being able to leverage tight integration with a modern and easy to use framework. If you're going to start a new API-project i highly recommend trying FastAPI. 379 | 380 | 381 | ```python 382 | from fastapi import FastAPI 383 | from pydantic import BaseModel 384 | 385 | app = FastAPI() 386 | 387 | def make_order(pizza: Pizza): 388 | # Business logic for making an order 389 | pass 390 | 391 | def dispatch_order(pizza: BakedPizza): 392 | # Hand over pizza to delivery company 393 | pass 394 | 395 | # Deliver a baked pizza 396 | @app.post("/delivery/pizza") 397 | async def deliver_pizza_order(pizza: BakedPizza): 398 | dispatch = dispatch_order(pizza) 399 | return dispatch 400 | 401 | @app.post("/order/pizza") 402 | async def order_pizza(pizza: Pizza): 403 | order = make_order(pizza) 404 | return order 405 | 406 | ``` 407 | 408 | This is everything we need to create a small API around our models. 409 | 410 | --- 411 | 412 | That's it, a quick introduction to pydantic! 413 | 414 | But this is just the tip of the iceberg 🗻 and I want to give you a hint about what more can be done. 415 | I'm not going to go into detail in any of this but feel free to ask me about it in the chat, on Twitter/LinkedIn or via email. 💬📨 416 | 417 | ## Cool features worth mentioning 418 | 419 | - Post **1.0**, reached this milestone about a year ago 420 | - Support for [standard library types](https://pydantic-docs.helpmanual.io/usage/types/#pydantic-types) 421 | - Offer useful extra types for every day use 422 | - Email 423 | - HttpUrl (and more, stricturl for custom validation) 424 | - PostgresDsn 425 | - IPvAnyAddress (as well as IPv4Address and IPv6Address from ipaddress) 426 | - PositiveInt 427 | - PaymentCardNumber, PaymentCardBrand.[amex, mastercard, visa, other], checks luhn, str of digits and BIN-based lenght. 428 | - [Constrained types](https://pydantic-docs.helpmanual.io/usage/types/#constrained-types) (e.g. conlist, conint, etc.) 429 | - and more… 430 | - Supports [custom datatypes](https://pydantic-docs.helpmanual.io/usage/types/#custom-data-types) 431 | - [Settings management](https://pydantic-docs.helpmanual.io/usage/settings/) 432 | - Typed configuration management 433 | - Automatically reads from environment variables 434 | - Dotenv (`.env`) support via defacto standard [python-dotenv](https://pypi.org/project/python-dotenv/). 435 | - ORM-mode 436 | - Recursive models 437 | - Works with mypy out of the box, [mypy plugin](https://pydantic-docs.helpmanual.io/mypy_plugin/) further improves experience. 438 | - [Postponed annotations, self-referencing models](https://pydantic-docs.helpmanual.io/usage/postponed_annotations/), [PEP-563](https://www.python.org/dev/peps/pep-0563/)-style. 439 | - python-devtools intergration 440 | - PyCharm plugin 441 | - [Fast](https://pydantic-docs.helpmanual.io/benchmarks/) compared to popular alternatives! 442 | But always make your own benchmarks for your own usecase if performance is important for you. 443 | 444 | ## Future 445 | - A strict mode is being worked on, in the future this will enable us to choose between Strict and Coercion on a model level instead of relying on the Strict* types. 446 | - The project is very active and a lot of improvements are constantly being made to the library. 447 | 448 | 449 | ## Conclusion 450 | Pure python syntax 451 | Better validation 452 | Very useful JSON-tools for API's 453 | Easy to migrate from dataclasses 454 | Lots of useful features 455 | Try it out! 456 | 457 | ## Want to hear more from me? 458 | I'm making a course on property based testing in python using Hypothesis. 459 | [Sign up here](https://forms.gle/yRWapypPXdPFSLME7) 460 | -------------------------------------------------------------------------------- /demo/poetry.lock: -------------------------------------------------------------------------------- 1 | [[package]] 2 | name = "anyio" 3 | version = "2.2.0" 4 | description = "High level compatibility layer for multiple asynchronous event loop implementations" 5 | category = "main" 6 | optional = false 7 | python-versions = ">=3.6.2" 8 | 9 | [package.dependencies] 10 | idna = ">=2.8" 11 | sniffio = ">=1.1" 12 | 13 | [package.extras] 14 | curio = ["curio (>=1.4)"] 15 | doc = ["sphinx-rtd-theme", "sphinx-autodoc-typehints (>=1.2.0)"] 16 | test = ["coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "pytest (>=6.0)", "trustme", "uvloop (<0.15)", "uvloop (>=0.15)"] 17 | trio = ["trio (>=0.16)"] 18 | 19 | [[package]] 20 | name = "appnope" 21 | version = "0.1.2" 22 | description = "Disable App Nap on macOS >= 10.9" 23 | category = "main" 24 | optional = false 25 | python-versions = "*" 26 | 27 | [[package]] 28 | name = "argon2-cffi" 29 | version = "20.1.0" 30 | description = "The secure Argon2 password hashing algorithm." 31 | category = "main" 32 | optional = false 33 | python-versions = "*" 34 | 35 | [package.dependencies] 36 | cffi = ">=1.0.0" 37 | six = "*" 38 | 39 | [package.extras] 40 | dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest", "sphinx", "wheel", "pre-commit"] 41 | docs = ["sphinx"] 42 | tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"] 43 | 44 | [[package]] 45 | name = "async-generator" 46 | version = "1.10" 47 | description = "Async generators and context managers for Python 3.5+" 48 | category = "main" 49 | optional = false 50 | python-versions = ">=3.5" 51 | 52 | [[package]] 53 | name = "attrs" 54 | version = "20.3.0" 55 | description = "Classes Without Boilerplate" 56 | category = "main" 57 | optional = false 58 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 59 | 60 | [package.extras] 61 | dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "furo", "sphinx", "pre-commit"] 62 | docs = ["furo", "sphinx", "zope.interface"] 63 | tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface"] 64 | tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six"] 65 | 66 | [[package]] 67 | name = "babel" 68 | version = "2.9.0" 69 | description = "Internationalization utilities" 70 | category = "main" 71 | optional = false 72 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 73 | 74 | [package.dependencies] 75 | pytz = ">=2015.7" 76 | 77 | [[package]] 78 | name = "backcall" 79 | version = "0.2.0" 80 | description = "Specifications for callback functions passed in to an API" 81 | category = "main" 82 | optional = false 83 | python-versions = "*" 84 | 85 | [[package]] 86 | name = "bleach" 87 | version = "3.3.0" 88 | description = "An easy safelist-based HTML-sanitizing tool." 89 | category = "main" 90 | optional = false 91 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" 92 | 93 | [package.dependencies] 94 | packaging = "*" 95 | six = ">=1.9.0" 96 | webencodings = "*" 97 | 98 | [[package]] 99 | name = "blessings" 100 | version = "1.7" 101 | description = "A thin, practical wrapper around terminal coloring, styling, and positioning" 102 | category = "main" 103 | optional = false 104 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 105 | 106 | [package.dependencies] 107 | six = "*" 108 | 109 | [[package]] 110 | name = "bpython" 111 | version = "0.21" 112 | description = "Fancy Interface to the Python Interpreter" 113 | category = "main" 114 | optional = false 115 | python-versions = ">=3.6" 116 | 117 | [package.dependencies] 118 | curtsies = ">=0.3.5" 119 | cwcwidth = "*" 120 | greenlet = "*" 121 | pygments = "*" 122 | pyxdg = "*" 123 | requests = "*" 124 | 125 | [package.extras] 126 | jedi = ["jedi (>=0.16)"] 127 | urwid = ["urwid"] 128 | watch = ["watchdog"] 129 | 130 | [[package]] 131 | name = "certifi" 132 | version = "2020.12.5" 133 | description = "Python package for providing Mozilla's CA Bundle." 134 | category = "main" 135 | optional = false 136 | python-versions = "*" 137 | 138 | [[package]] 139 | name = "cffi" 140 | version = "1.14.5" 141 | description = "Foreign Function Interface for Python calling C code." 142 | category = "main" 143 | optional = false 144 | python-versions = "*" 145 | 146 | [package.dependencies] 147 | pycparser = "*" 148 | 149 | [[package]] 150 | name = "chardet" 151 | version = "4.0.0" 152 | description = "Universal encoding detector for Python 2 and 3" 153 | category = "main" 154 | optional = false 155 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" 156 | 157 | [[package]] 158 | name = "click" 159 | version = "7.1.2" 160 | description = "Composable command line interface toolkit" 161 | category = "main" 162 | optional = false 163 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" 164 | 165 | [[package]] 166 | name = "colorama" 167 | version = "0.4.4" 168 | description = "Cross-platform colored terminal text." 169 | category = "main" 170 | optional = false 171 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" 172 | 173 | [[package]] 174 | name = "curtsies" 175 | version = "0.3.5" 176 | description = "Curses-like terminal wrapper, with colored strings!" 177 | category = "main" 178 | optional = false 179 | python-versions = ">=3.6" 180 | 181 | [package.dependencies] 182 | blessings = ">=1.5" 183 | cwcwidth = "*" 184 | 185 | [[package]] 186 | name = "cwcwidth" 187 | version = "0.1.4" 188 | description = "Python bindings for wc(s)width" 189 | category = "main" 190 | optional = false 191 | python-versions = "*" 192 | 193 | [[package]] 194 | name = "decorator" 195 | version = "4.4.2" 196 | description = "Decorators for Humans" 197 | category = "main" 198 | optional = false 199 | python-versions = ">=2.6, !=3.0.*, !=3.1.*" 200 | 201 | [[package]] 202 | name = "defusedxml" 203 | version = "0.7.1" 204 | description = "XML bomb protection for Python stdlib modules" 205 | category = "main" 206 | optional = false 207 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" 208 | 209 | [[package]] 210 | name = "entrypoints" 211 | version = "0.3" 212 | description = "Discover and load entry points from installed packages." 213 | category = "main" 214 | optional = false 215 | python-versions = ">=2.7" 216 | 217 | [[package]] 218 | name = "fastapi" 219 | version = "0.63.0" 220 | description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production" 221 | category = "main" 222 | optional = false 223 | python-versions = ">=3.6" 224 | 225 | [package.dependencies] 226 | pydantic = ">=1.0.0,<2.0.0" 227 | starlette = "0.13.6" 228 | 229 | [package.extras] 230 | all = ["requests (>=2.24.0,<3.0.0)", "aiofiles (>=0.5.0,<0.6.0)", "jinja2 (>=2.11.2,<3.0.0)", "python-multipart (>=0.0.5,<0.0.6)", "itsdangerous (>=1.1.0,<2.0.0)", "pyyaml (>=5.3.1,<6.0.0)", "graphene (>=2.1.8,<3.0.0)", "ujson (>=3.0.0,<4.0.0)", "orjson (>=3.2.1,<4.0.0)", "email_validator (>=1.1.1,<2.0.0)", "uvicorn[standard] (>=0.12.0,<0.14.0)", "async_exit_stack (>=1.0.1,<2.0.0)", "async_generator (>=1.10,<2.0.0)"] 231 | dev = ["python-jose[cryptography] (>=3.1.0,<4.0.0)", "passlib[bcrypt] (>=1.7.2,<2.0.0)", "autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "uvicorn[standard] (>=0.12.0,<0.14.0)", "graphene (>=2.1.8,<3.0.0)"] 232 | doc = ["mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=6.1.4,<7.0.0)", "markdown-include (>=0.5.1,<0.6.0)", "mkdocs-markdownextradata-plugin (>=0.1.7,<0.2.0)", "typer-cli (>=0.0.9,<0.0.10)", "pyyaml (>=5.3.1,<6.0.0)"] 233 | test = ["pytest (==5.4.3)", "pytest-cov (==2.10.0)", "pytest-asyncio (>=0.14.0,<0.15.0)", "mypy (==0.790)", "flake8 (>=3.8.3,<4.0.0)", "black (==20.8b1)", "isort (>=5.0.6,<6.0.0)", "requests (>=2.24.0,<3.0.0)", "httpx (>=0.14.0,<0.15.0)", "email_validator (>=1.1.1,<2.0.0)", "sqlalchemy (>=1.3.18,<2.0.0)", "peewee (>=3.13.3,<4.0.0)", "databases[sqlite] (>=0.3.2,<0.4.0)", "orjson (>=3.2.1,<4.0.0)", "async_exit_stack (>=1.0.1,<2.0.0)", "async_generator (>=1.10,<2.0.0)", "python-multipart (>=0.0.5,<0.0.6)", "aiofiles (>=0.5.0,<0.6.0)", "flask (>=1.1.2,<2.0.0)"] 234 | 235 | [[package]] 236 | name = "greenlet" 237 | version = "1.0.0" 238 | description = "Lightweight in-process concurrent programming" 239 | category = "main" 240 | optional = false 241 | python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" 242 | 243 | [package.extras] 244 | docs = ["sphinx"] 245 | 246 | [[package]] 247 | name = "h11" 248 | version = "0.12.0" 249 | description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1" 250 | category = "main" 251 | optional = false 252 | python-versions = ">=3.6" 253 | 254 | [[package]] 255 | name = "idna" 256 | version = "2.10" 257 | description = "Internationalized Domain Names in Applications (IDNA)" 258 | category = "main" 259 | optional = false 260 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" 261 | 262 | [[package]] 263 | name = "ipykernel" 264 | version = "5.5.0" 265 | description = "IPython Kernel for Jupyter" 266 | category = "main" 267 | optional = false 268 | python-versions = ">=3.5" 269 | 270 | [package.dependencies] 271 | appnope = {version = "*", markers = "platform_system == \"Darwin\""} 272 | ipython = ">=5.0.0" 273 | jupyter-client = "*" 274 | tornado = ">=4.2" 275 | traitlets = ">=4.1.0" 276 | 277 | [package.extras] 278 | test = ["pytest (!=5.3.4)", "pytest-cov", "flaky", "nose", "jedi (<=0.17.2)"] 279 | 280 | [[package]] 281 | name = "ipython" 282 | version = "7.21.0" 283 | description = "IPython: Productive Interactive Computing" 284 | category = "main" 285 | optional = false 286 | python-versions = ">=3.7" 287 | 288 | [package.dependencies] 289 | appnope = {version = "*", markers = "sys_platform == \"darwin\""} 290 | backcall = "*" 291 | colorama = {version = "*", markers = "sys_platform == \"win32\""} 292 | decorator = "*" 293 | jedi = ">=0.16" 294 | pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""} 295 | pickleshare = "*" 296 | prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0" 297 | pygments = "*" 298 | traitlets = ">=4.2" 299 | 300 | [package.extras] 301 | all = ["Sphinx (>=1.3)", "ipykernel", "ipyparallel", "ipywidgets", "nbconvert", "nbformat", "nose (>=0.10.1)", "notebook", "numpy (>=1.14)", "pygments", "qtconsole", "requests", "testpath"] 302 | doc = ["Sphinx (>=1.3)"] 303 | kernel = ["ipykernel"] 304 | nbconvert = ["nbconvert"] 305 | nbformat = ["nbformat"] 306 | notebook = ["notebook", "ipywidgets"] 307 | parallel = ["ipyparallel"] 308 | qtconsole = ["qtconsole"] 309 | test = ["nose (>=0.10.1)", "requests", "testpath", "pygments", "nbformat", "ipykernel", "numpy (>=1.14)"] 310 | 311 | [[package]] 312 | name = "ipython-genutils" 313 | version = "0.2.0" 314 | description = "Vestigial utilities from IPython" 315 | category = "main" 316 | optional = false 317 | python-versions = "*" 318 | 319 | [[package]] 320 | name = "jedi" 321 | version = "0.18.0" 322 | description = "An autocompletion tool for Python that can be used for text editors." 323 | category = "main" 324 | optional = false 325 | python-versions = ">=3.6" 326 | 327 | [package.dependencies] 328 | parso = ">=0.8.0,<0.9.0" 329 | 330 | [package.extras] 331 | qa = ["flake8 (==3.8.3)", "mypy (==0.782)"] 332 | testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<6.0.0)"] 333 | 334 | [[package]] 335 | name = "jinja2" 336 | version = "2.11.3" 337 | description = "A very fast and expressive template engine." 338 | category = "main" 339 | optional = false 340 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" 341 | 342 | [package.dependencies] 343 | MarkupSafe = ">=0.23" 344 | 345 | [package.extras] 346 | i18n = ["Babel (>=0.8)"] 347 | 348 | [[package]] 349 | name = "json5" 350 | version = "0.9.5" 351 | description = "A Python implementation of the JSON5 data format." 352 | category = "main" 353 | optional = false 354 | python-versions = "*" 355 | 356 | [package.extras] 357 | dev = ["hypothesis"] 358 | 359 | [[package]] 360 | name = "jsonschema" 361 | version = "3.2.0" 362 | description = "An implementation of JSON Schema validation for Python" 363 | category = "main" 364 | optional = false 365 | python-versions = "*" 366 | 367 | [package.dependencies] 368 | attrs = ">=17.4.0" 369 | pyrsistent = ">=0.14.0" 370 | six = ">=1.11.0" 371 | 372 | [package.extras] 373 | format = ["idna", "jsonpointer (>1.13)", "rfc3987", "strict-rfc3339", "webcolors"] 374 | format_nongpl = ["idna", "jsonpointer (>1.13)", "webcolors", "rfc3986-validator (>0.1.0)", "rfc3339-validator"] 375 | 376 | [[package]] 377 | name = "jupyter-client" 378 | version = "6.1.12" 379 | description = "Jupyter protocol implementation and client libraries" 380 | category = "main" 381 | optional = false 382 | python-versions = ">=3.5" 383 | 384 | [package.dependencies] 385 | jupyter-core = ">=4.6.0" 386 | python-dateutil = ">=2.1" 387 | pyzmq = ">=13" 388 | tornado = ">=4.1" 389 | traitlets = "*" 390 | 391 | [package.extras] 392 | doc = ["sphinx (>=1.3.6)", "sphinx-rtd-theme", "sphinxcontrib-github-alt"] 393 | test = ["async-generator", "ipykernel", "ipython", "mock", "pytest-asyncio", "pytest-timeout", "pytest", "jedi (<0.18)"] 394 | 395 | [[package]] 396 | name = "jupyter-core" 397 | version = "4.7.1" 398 | description = "Jupyter core package. A base package on which Jupyter projects rely." 399 | category = "main" 400 | optional = false 401 | python-versions = ">=3.6" 402 | 403 | [package.dependencies] 404 | pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\""} 405 | traitlets = "*" 406 | 407 | [[package]] 408 | name = "jupyter-packaging" 409 | version = "0.7.12" 410 | description = "Jupyter Packaging Utilities" 411 | category = "main" 412 | optional = false 413 | python-versions = ">=3.6" 414 | 415 | [package.dependencies] 416 | packaging = "*" 417 | 418 | [package.extras] 419 | test = ["pytest"] 420 | 421 | [[package]] 422 | name = "jupyter-server" 423 | version = "1.5.1" 424 | description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." 425 | category = "main" 426 | optional = false 427 | python-versions = ">=3.6" 428 | 429 | [package.dependencies] 430 | anyio = ">=2.0.2" 431 | argon2-cffi = "*" 432 | ipython-genutils = "*" 433 | jinja2 = "*" 434 | jupyter-client = ">=6.1.1" 435 | jupyter-core = ">=4.4.0" 436 | nbconvert = "*" 437 | nbformat = "*" 438 | prometheus-client = "*" 439 | pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\""} 440 | pyzmq = ">=17" 441 | Send2Trash = "*" 442 | terminado = ">=0.8.3" 443 | tornado = ">=6.1.0" 444 | traitlets = ">=4.2.1" 445 | 446 | [package.extras] 447 | test = ["coverage", "requests", "pytest", "pytest-cov", "pytest-tornasync", "pytest-console-scripts", "ipykernel"] 448 | 449 | [[package]] 450 | name = "jupyterlab" 451 | version = "3.0.12" 452 | description = "The JupyterLab server extension." 453 | category = "main" 454 | optional = false 455 | python-versions = ">=3.6" 456 | 457 | [package.dependencies] 458 | ipython = "*" 459 | jinja2 = ">=2.10" 460 | jupyter-core = "*" 461 | jupyter-packaging = ">=0.7.3,<0.8.0" 462 | jupyter-server = ">=1.4,<2.0" 463 | jupyterlab-server = ">=2.3,<3.0" 464 | nbclassic = ">=0.2,<1.0" 465 | packaging = "*" 466 | tornado = ">=6.1.0" 467 | 468 | [package.extras] 469 | docs = ["jsx-lexer", "recommonmark", "sphinx", "sphinx-rtd-theme", "sphinx-copybutton"] 470 | test = ["pytest (>=6.0)", "pytest-cov", "pytest-console-scripts", "pytest-check-links", "jupyterlab-server[test] (>=2.0,<3.0)", "requests", "wheel", "virtualenv", "nose-exclude"] 471 | 472 | [[package]] 473 | name = "jupyterlab-pygments" 474 | version = "0.1.2" 475 | description = "Pygments theme using JupyterLab CSS variables" 476 | category = "main" 477 | optional = false 478 | python-versions = "*" 479 | 480 | [package.dependencies] 481 | pygments = ">=2.4.1,<3" 482 | 483 | [[package]] 484 | name = "jupyterlab-server" 485 | version = "2.3.0" 486 | description = "JupyterLab Server" 487 | category = "main" 488 | optional = false 489 | python-versions = ">=3.6" 490 | 491 | [package.dependencies] 492 | babel = "*" 493 | jinja2 = ">=2.10" 494 | json5 = "*" 495 | jsonschema = ">=3.0.1" 496 | jupyter-server = ">=1.4,<2.0" 497 | packaging = "*" 498 | requests = "*" 499 | 500 | [package.extras] 501 | test = ["codecov", "ipykernel", "pytest (>=5.3.2)", "pytest-cov", "jupyter-server", "pytest-console-scripts", "strict-rfc3339", "wheel"] 502 | 503 | [[package]] 504 | name = "markupsafe" 505 | version = "1.1.1" 506 | description = "Safely add untrusted strings to HTML/XML markup." 507 | category = "main" 508 | optional = false 509 | python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*" 510 | 511 | [[package]] 512 | name = "mistune" 513 | version = "0.8.4" 514 | description = "The fastest markdown parser in pure Python" 515 | category = "main" 516 | optional = false 517 | python-versions = "*" 518 | 519 | [[package]] 520 | name = "mypy" 521 | version = "0.812" 522 | description = "Optional static typing for Python" 523 | category = "main" 524 | optional = false 525 | python-versions = ">=3.5" 526 | 527 | [package.dependencies] 528 | mypy-extensions = ">=0.4.3,<0.5.0" 529 | typed-ast = ">=1.4.0,<1.5.0" 530 | typing-extensions = ">=3.7.4" 531 | 532 | [package.extras] 533 | dmypy = ["psutil (>=4.0)"] 534 | 535 | [[package]] 536 | name = "mypy-extensions" 537 | version = "0.4.3" 538 | description = "Experimental type system extensions for programs checked with the mypy typechecker." 539 | category = "main" 540 | optional = false 541 | python-versions = "*" 542 | 543 | [[package]] 544 | name = "nbclassic" 545 | version = "0.2.6" 546 | description = "Jupyter Notebook as a Jupyter Server Extension." 547 | category = "main" 548 | optional = false 549 | python-versions = ">=3.6" 550 | 551 | [package.dependencies] 552 | jupyter-server = ">=1.1,<2.0" 553 | notebook = "<7" 554 | 555 | [package.extras] 556 | test = ["pytest", "pytest-tornasync", "pytest-console-scripts"] 557 | 558 | [[package]] 559 | name = "nbclient" 560 | version = "0.5.3" 561 | description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor." 562 | category = "main" 563 | optional = false 564 | python-versions = ">=3.6.1" 565 | 566 | [package.dependencies] 567 | async-generator = "*" 568 | jupyter-client = ">=6.1.5" 569 | nbformat = ">=5.0" 570 | nest-asyncio = "*" 571 | traitlets = ">=4.2" 572 | 573 | [package.extras] 574 | dev = ["codecov", "coverage", "ipython", "ipykernel", "ipywidgets", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "check-manifest", "flake8", "mypy", "tox", "bumpversion", "xmltodict", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "black"] 575 | sphinx = ["Sphinx (>=1.7)", "sphinx-book-theme", "mock", "moto", "myst-parser"] 576 | test = ["codecov", "coverage", "ipython", "ipykernel", "ipywidgets", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "check-manifest", "flake8", "mypy", "tox", "bumpversion", "xmltodict", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "black"] 577 | 578 | [[package]] 579 | name = "nbconvert" 580 | version = "6.0.7" 581 | description = "Converting Jupyter Notebooks" 582 | category = "main" 583 | optional = false 584 | python-versions = ">=3.6" 585 | 586 | [package.dependencies] 587 | bleach = "*" 588 | defusedxml = "*" 589 | entrypoints = ">=0.2.2" 590 | jinja2 = ">=2.4" 591 | jupyter-core = "*" 592 | jupyterlab-pygments = "*" 593 | mistune = ">=0.8.1,<2" 594 | nbclient = ">=0.5.0,<0.6.0" 595 | nbformat = ">=4.4" 596 | pandocfilters = ">=1.4.1" 597 | pygments = ">=2.4.1" 598 | testpath = "*" 599 | traitlets = ">=4.2" 600 | 601 | [package.extras] 602 | all = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (==0.2.2)", "tornado (>=4.0)", "sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"] 603 | docs = ["sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"] 604 | serve = ["tornado (>=4.0)"] 605 | test = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", 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wcwidth = [ 1656 | {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"}, 1657 | {file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"}, 1658 | ] 1659 | webencodings = [ 1660 | {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"}, 1661 | {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"}, 1662 | ] 1663 | -------------------------------------------------------------------------------- /demo/pydantic-2021.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | " .\n", 13 | " ..:\n", 14 | " Hultnér\n", 15 | " Technologies\n", 16 | "\n", 17 | "@ahultner | https://hultner.se/\n" 18 | ] 19 | } 20 | ], 21 | "source": [ 22 | "def s(x): return \" \"*(10+x)\n", 23 | "print(s(5)+\".\\n\"+s(4)+\"..:\\n\"+s(2)+\"Hultnér\\n\"+s(0)+\"Technologies\\n\\n@ahultner | https://hultner.se/\")" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "# Intro to pydantic\n", 31 | "### Run-Time Type Checking For Your Dataclasses\n", 32 | "\n", 33 | "## Index\n", 34 | "- Quick refresher on python data classes\n", 35 | "- Pydantic introduction\n", 36 | " - Prior art\n", 37 | " - Minimal example from dataclass\n", 38 | " - Runtime type-checking\n", 39 | " - JSON (de)serialisation\n", 40 | " - JSONSchema\n", 41 | " - Validators\n", 42 | " - [Custom model validators](https://pydantic-docs.helpmanual.io/usage/validators/)\n", 43 | " - [Validation decorator for functions](https://pydantic-docs.helpmanual.io/usage/validation_decorator/), via `@validate_arguments`. Still in beta, API may change.\n", 44 | " - FastAPI framework\n", 45 | " - OpenAPI Specifications\n", 46 | " - Autogenerated tests\n", 47 | "- Cool features worth mentioning\n", 48 | "- Future\n", 49 | "- Conclusion\n", 50 | " " 51 | ] 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "metadata": {}, 56 | "source": [ 57 | "Let's start with a quick `@dataclass`-refresher.\n", 58 | "\n", 59 | "I love waffles, don't everyone?\n", 60 | "For our examples we'll use our imaginary café, \"The Waffle Bistro\" 🧇 🌟" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 164, 66 | "metadata": {}, 67 | "outputs": [], 68 | "source": [ 69 | "from dataclasses import dataclass\n", 70 | "from typing import Tuple" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 165, 76 | "metadata": {}, 77 | "outputs": [], 78 | "source": [ 79 | "@dataclass\n", 80 | "class Waffle:\n", 81 | " style: str\n", 82 | " toppings: Tuple[str, ...]\n", 83 | " " 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 166, 89 | "metadata": {}, 90 | "outputs": [ 91 | { 92 | "data": { 93 | "text/plain": [ 94 | "Waffle(style='Swedish', toppings=('chocolate sauce', 'ham'))" 95 | ] 96 | }, 97 | "execution_count": 166, 98 | "metadata": {}, 99 | "output_type": "execute_result" 100 | } 101 | ], 102 | "source": [ 103 | "Waffle(\"Swedish\", (\"chocolate sauce\", \"ham\"))" 104 | ] 105 | }, 106 | { 107 | "cell_type": "markdown", 108 | "metadata": {}, 109 | "source": [ 110 | "Now we may want to constrain the toppings to ones we actually offer\n", 111 | "🥛 🍓 🟠 🍫\n", 112 | "\n", 113 | "We offer a couple of cream based toppings, and a couple of _\"dessert sauces\"_" 114 | ] 115 | }, 116 | { 117 | "cell_type": "code", 118 | "execution_count": 167, 119 | "metadata": {}, 120 | "outputs": [], 121 | "source": [ 122 | "from typing import Union\n", 123 | "from enum import Enum" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": 168, 129 | "metadata": {}, 130 | "outputs": [], 131 | "source": [ 132 | "class Cream(str, Enum):\n", 133 | " whipped_cream = \"whipped cream\"\n", 134 | " ice_cream = \"icecream\"\n", 135 | "\n", 136 | "class DessertSauce(str, Enum):\n", 137 | " cloudberry_jam = \"cloudberry jam\"\n", 138 | " raspberry_jam = \"raspberry jam\"\n", 139 | " choclate_sauce = \"chocolate sauce\"\n", 140 | "\n", 141 | "Topping = Union[DessertSauce, Cream]\n", 142 | "\n", 143 | "class WaffleStyle(str, Enum):\n", 144 | " swedish = \"Swedish\"\n", 145 | " belgian = \"Belgian\"\n", 146 | "\n", 147 | "\n", 148 | "@dataclass\n", 149 | "class Waffle:\n", 150 | " style: WaffleStyle\n", 151 | " toppings: Tuple[Topping, ...]" 152 | ] 153 | }, 154 | { 155 | "cell_type": "markdown", 156 | "metadata": {}, 157 | "source": [ 158 | "Let's see what happens if we try to create a waffle with ham topping." 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 100, 164 | "metadata": {}, 165 | "outputs": [ 166 | { 167 | "data": { 168 | "text/plain": [ 169 | "Waffle(style='Swedish', toppings=('chocolate sauce', 'ham'))" 170 | ] 171 | }, 172 | "execution_count": 100, 173 | "metadata": {}, 174 | "output_type": "execute_result" 175 | } 176 | ], 177 | "source": [ 178 | "Waffle(\"Swedish\", (\"chocolate sauce\", \"ham\"))" 179 | ] 180 | }, 181 | { 182 | "cell_type": "markdown", 183 | "metadata": {}, 184 | "source": [ 185 | "With dataclasses the types aren't enforced, this can ofcourse be implemented but in this case we'll lean on the shoulders of a giant, pydantic 🧹🐍🧐" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": 101, 191 | "metadata": {}, 192 | "outputs": [], 193 | "source": [ 194 | "from pydantic.dataclasses import dataclass\n", 195 | "\n", 196 | "@dataclass\n", 197 | "class Waffle:\n", 198 | " style: WaffleStyle\n", 199 | " toppings: Tuple[Topping, ...]" 200 | ] 201 | }, 202 | { 203 | "cell_type": "markdown", 204 | "metadata": {}, 205 | "source": [ 206 | "As you can see the only thing changed in this example is that we import the dataclass decorator from pydantic." 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": 102, 212 | "metadata": {}, 213 | "outputs": [ 214 | { 215 | "name": "stdout", 216 | "output_type": "stream", 217 | "text": [ 218 | "2 validation errors for Waffle\n", 219 | "toppings -> 1\n", 220 | " value is not a valid enumeration member; permitted: 'cloudberry jam', 'raspberry jam', 'chocolate sauce' (type=type_error.enum; enum_values=[, , ])\n", 221 | "toppings -> 1\n", 222 | " value is not a valid enumeration member; permitted: 'whipped cream', 'icecream' (type=type_error.enum; enum_values=[, ])\n" 223 | ] 224 | } 225 | ], 226 | "source": [ 227 | "from pydantic import ValidationError\n", 228 | "try:\n", 229 | " Waffle(\"Swedish\", (\"chocolate sauce\", \"ham\"))\n", 230 | "except ValidationError as err:\n", 231 | " print(err)" 232 | ] 233 | }, 234 | { 235 | "cell_type": "markdown", 236 | "metadata": {}, 237 | "source": [ 238 | "With that simple change we can see that our new instance of an unsupported waffle actually raises errors 🚫🚨\n", 239 | "\n", 240 | "These errors are very readable!" 241 | ] 242 | }, 243 | { 244 | "cell_type": "markdown", 245 | "metadata": {}, 246 | "source": [ 247 | "So let's try to create a valid waffle ✅" 248 | ] 249 | }, 250 | { 251 | "cell_type": "code", 252 | "execution_count": 103, 253 | "metadata": {}, 254 | "outputs": [ 255 | { 256 | "data": { 257 | "text/plain": [ 258 | "Waffle(style=, toppings=(, ))" 259 | ] 260 | }, 261 | "execution_count": 103, 262 | "metadata": {}, 263 | "output_type": "execute_result" 264 | } 265 | ], 266 | "source": [ 267 | "Waffle(\"Swedish\", (Cream.whipped_cream, \"cloudberry jam\"))" 268 | ] 269 | }, 270 | { 271 | "cell_type": "markdown", 272 | "metadata": {}, 273 | "source": [ 274 | "See how the cloudberry jam was automatically parsed as a Dessert Sauce. This is a feature of pydantic, more strictness can be achieved with the strict types and a fully [strict mode](https://github.com/samuelcolvin/pydantic/issues/1098) is being worked on." 275 | ] 276 | }, 277 | { 278 | "cell_type": "markdown", 279 | "metadata": {}, 280 | "source": [ 281 | "So what about JSON? 🧑‍💻 \n", 282 | "The dataclass dropin replacement decorator from pydantic is great for compability but by using `pydantic.BaseModel` we can get even more out of pydantic. One of those things is (de)serialisation, pydantic have native support JSON encoding and decoding." 283 | ] 284 | }, 285 | { 286 | "cell_type": "code", 287 | "execution_count": 169, 288 | "metadata": {}, 289 | "outputs": [], 290 | "source": [ 291 | "from pydantic import BaseModel\n", 292 | "\n", 293 | "class Waffle(BaseModel):\n", 294 | " style: WaffleStyle\n", 295 | " toppings: Tuple[Topping, ...]" 296 | ] 297 | }, 298 | { 299 | "cell_type": "markdown", 300 | "metadata": {}, 301 | "source": [ 302 | "*Disclaimer: Pydantic is primarly a parsing library and does validation as a means to an end, so make sure it makes sense for you.*" 303 | ] 304 | }, 305 | { 306 | "cell_type": "markdown", 307 | "metadata": {}, 308 | "source": [ 309 | "When using the BaseModel the default behaviour requires to specify the init arguments using their keywords like below" 310 | ] 311 | }, 312 | { 313 | "cell_type": "code", 314 | "execution_count": 105, 315 | "metadata": {}, 316 | "outputs": [ 317 | { 318 | "data": { 319 | "text/plain": [ 320 | "Waffle(style=, toppings=(, ))" 321 | ] 322 | }, 323 | "execution_count": 105, 324 | "metadata": {}, 325 | "output_type": "execute_result" 326 | } 327 | ], 328 | "source": [ 329 | "Waffle(style=\"Swedish\", toppings=(Cream.whipped_cream, \"cloudberry jam\"))" 330 | ] 331 | }, 332 | { 333 | "cell_type": "markdown", 334 | "metadata": {}, 335 | "source": [ 336 | "We can now easily encode this object as `JSON`, there's also [built-in support](https://pydantic-docs.helpmanual.io/usage/exporting_models/) for dict, pickle, immutable `copy()`. Pydantic will also (de)serialise subclasses." 337 | ] 338 | }, 339 | { 340 | "cell_type": "code", 341 | "execution_count": 106, 342 | "metadata": {}, 343 | "outputs": [ 344 | { 345 | "data": { 346 | "text/plain": [ 347 | "'{\"style\": \"Swedish\", \"toppings\": [\"whipped cream\", \"cloudberry jam\"]}'" 348 | ] 349 | }, 350 | "execution_count": 106, 351 | "metadata": {}, 352 | "output_type": "execute_result" 353 | } 354 | ], 355 | "source": [ 356 | "_.json()" 357 | ] 358 | }, 359 | { 360 | "cell_type": "markdown", 361 | "metadata": {}, 362 | "source": [ 363 | "And we can also reconstruct our original object using the `parse_raw`-method." 364 | ] 365 | }, 366 | { 367 | "cell_type": "code", 368 | "execution_count": 107, 369 | "metadata": {}, 370 | "outputs": [ 371 | { 372 | "data": { 373 | "text/plain": [ 374 | "Waffle(style=, toppings=(, ))" 375 | ] 376 | }, 377 | "execution_count": 107, 378 | "metadata": {}, 379 | "output_type": "execute_result" 380 | } 381 | ], 382 | "source": [ 383 | "Waffle.parse_raw('{\"style\": \"Swedish\", \"toppings\": [\"whipped cream\", \"cloudberry jam\"]}')" 384 | ] 385 | }, 386 | { 387 | "cell_type": "markdown", 388 | "metadata": {}, 389 | "source": [ 390 | "Errors raises a validation error, these can also be represented as JSON." 391 | ] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "execution_count": 124, 396 | "metadata": {}, 397 | "outputs": [ 398 | { 399 | "name": "stdout", 400 | "output_type": "stream", 401 | "text": [ 402 | "[\n", 403 | " {\n", 404 | " \"loc\": [\n", 405 | " \"style\"\n", 406 | " ],\n", 407 | " \"msg\": \"value is not a valid enumeration member; permitted: 'Swedish', 'Belgian'\",\n", 408 | " \"type\": \"type_error.enum\",\n", 409 | " \"ctx\": {\n", 410 | " \"enum_values\": [\n", 411 | " \"Swedish\",\n", 412 | " \"Belgian\"\n", 413 | " ]\n", 414 | " }\n", 415 | " }\n", 416 | "]\n" 417 | ] 418 | } 419 | ], 420 | "source": [ 421 | "try:\n", 422 | " Waffle(style=42, toppings=(Cream.whipped_cream, \"cloudberry jam\"))\n", 423 | "except ValidationError as err:\n", 424 | " print(err.json())" 425 | ] 426 | }, 427 | { 428 | "cell_type": "markdown", 429 | "metadata": {}, 430 | "source": [ 431 | "We can also export a JSONSchema directly from our model, this is very useful for instance if we want to use your model to feed a Swagger/OpenAPI-spec. 📜✅\n", 432 | "\n", 433 | "⚠ *Caution: Pydantic uses draft 7 of JSONSchema, this is used in the just released OpenAPI 3.1 spec. \n", 434 | "The still common 3.0.x spec uses draft 4. \n", 435 | "I spoke with Samuel Colvin, the creator of pydantic about this and his recommendation is to write a `schema_extra`function to use the older JSONSchema version if you want strict compability. The FastAPI framework doesn't do this and is slightly incompatible with the older OpenAPI-spec*" 436 | ] 437 | }, 438 | { 439 | "cell_type": "code", 440 | "execution_count": 125, 441 | "metadata": {}, 442 | "outputs": [ 443 | { 444 | "data": { 445 | "text/plain": [ 446 | "{'title': 'Waffle',\n", 447 | " 'type': 'object',\n", 448 | " 'properties': {'style': {'$ref': '#/definitions/WaffleStyle'},\n", 449 | " 'toppings': {'title': 'Toppings',\n", 450 | " 'type': 'array',\n", 451 | " 'items': {'anyOf': [{'$ref': '#/definitions/DessertSauce'},\n", 452 | " {'$ref': '#/definitions/Cream'}]}}},\n", 453 | " 'required': ['style', 'toppings'],\n", 454 | " 'definitions': {'WaffleStyle': {'title': 'WaffleStyle',\n", 455 | " 'description': 'An enumeration.',\n", 456 | " 'enum': ['Swedish', 'Belgian'],\n", 457 | " 'type': 'string'},\n", 458 | " 'DessertSauce': {'title': 'DessertSauce',\n", 459 | " 'description': 'An enumeration.',\n", 460 | " 'enum': ['cloudberry jam', 'raspberry jam', 'chocolate sauce'],\n", 461 | " 'type': 'string'},\n", 462 | " 'Cream': {'title': 'Cream',\n", 463 | " 'description': 'An enumeration.',\n", 464 | " 'enum': ['whipped cream', 'icecream'],\n", 465 | " 'type': 'string'}}}" 466 | ] 467 | }, 468 | "execution_count": 125, 469 | "metadata": {}, 470 | "output_type": "execute_result" 471 | } 472 | ], 473 | "source": [ 474 | "Waffle.schema()" 475 | ] 476 | }, 477 | { 478 | "cell_type": "markdown", 479 | "metadata": {}, 480 | "source": [ 481 | "That was the basics using the built-in validators, but what if you want to implement your own business rules in a custom validator, we're going to look at this next.\n", 482 | "\n", 483 | "We now want to add some custom busniess logic specific for \"The Waffle Bistro\".\n", 484 | "In this case we want to only allow:\n", 485 | "- Either icecream or whipped cream 🍦 ⊕🥛\n", 486 | "- Jam for Swedish waffles 🇸🇪 🟠 🔴\n", 487 | "- Choclate for Belgian waffles 🇧🇪 🍫" 488 | ] 489 | }, 490 | { 491 | "cell_type": "code", 492 | "execution_count": 172, 493 | "metadata": {}, 494 | "outputs": [], 495 | "source": [ 496 | "from pydantic import validator, root_validator\n", 497 | "\n", 498 | "swedish_toppings = (\n", 499 | " DessertSauce.raspberry_jam, DessertSauce.cloudberry_jam,\n", 500 | ")\n", 501 | "\n", 502 | "belgian_toppings = (DessertSauce.choclate_sauce,)\n", 503 | "\n", 504 | "class WaffleOrder(Waffle):\n", 505 | "\n", 506 | " \n", 507 | " # Root validators check the entire model\n", 508 | " @root_validator(pre=False)\n", 509 | " def check_style_topping(cls, values):\n", 510 | " style, toppings = values.get(\"style\"), values.get(\"toppings\")\n", 511 | " # Check swedish style\n", 512 | " if (\n", 513 | " style == WaffleStyle.swedish and \n", 514 | " all(t in swedish_toppings for t in toppings if type(t) is DessertSauce)\n", 515 | " ):\n", 516 | " return values\n", 517 | " \n", 518 | " # Check belgian style\n", 519 | " if (\n", 520 | " style == WaffleStyle.belgian and \n", 521 | " all(t in belgian_toppings for t in toppings if type(t) is DessertSauce)\n", 522 | " ):\n", 523 | " return values\n", 524 | " \n", 525 | " # Doesn't match any of our allowed styles\n", 526 | " raise ValueError(f\"The Waffle Bistro doesn't sell this waffle.\")\n", 527 | " \n", 528 | " \n", 529 | " # A validator looking at a single property\n", 530 | " @validator('toppings')\n", 531 | " def check_cream(cls, toppings):\n", 532 | " creams = [t for t in toppings if type(t) is Cream]\n", 533 | " if len(creams) > 1:\n", 534 | " raise ValueError(f\"We only allow for one cream topping, given: {creams}\")\n", 535 | " return toppings\n" 536 | ] 537 | }, 538 | { 539 | "cell_type": "markdown", 540 | "metadata": {}, 541 | "source": [ 542 | "Now let's see if we create some invalid waffles 🧇 ⚠️🚨" 543 | ] 544 | }, 545 | { 546 | "cell_type": "code", 547 | "execution_count": 173, 548 | "metadata": {}, 549 | "outputs": [ 550 | { 551 | "name": "stdout", 552 | "output_type": "stream", 553 | "text": [ 554 | "2 validation errors for WaffleOrder\n", 555 | "toppings\n", 556 | " We only allow for one cream topping, given: [, ] (type=value_error)\n", 557 | "__root__\n", 558 | " 'NoneType' object is not iterable (type=type_error)\n" 559 | ] 560 | } 561 | ], 562 | "source": [ 563 | "try: \n", 564 | " WaffleOrder(style=\"Swedish\", toppings=[\"icecream\", \"whipped cream\", \"cloudberry jam\"])\n", 565 | "except ValidationError as err:\n", 566 | " print(err)" 567 | ] 568 | }, 569 | { 570 | "cell_type": "code", 571 | "execution_count": 174, 572 | "metadata": {}, 573 | "outputs": [ 574 | { 575 | "name": "stdout", 576 | "output_type": "stream", 577 | "text": [ 578 | "1 validation error for WaffleOrder\n", 579 | "__root__\n", 580 | " The Waffle Bistro doesn't sell this waffle. (type=value_error)\n" 581 | ] 582 | } 583 | ], 584 | "source": [ 585 | "try: \n", 586 | " WaffleOrder(style=\"Swedish\", toppings=[\"icecream\", \"cloudberry jam\", \"chocolate sauce\"])\n", 587 | "except ValidationError as err:\n", 588 | " print(err)" 589 | ] 590 | }, 591 | { 592 | "cell_type": "markdown", 593 | "metadata": {}, 594 | "source": [ 595 | "Now let's create a waffle 🧇 allowed by our rules! ✨" 596 | ] 597 | }, 598 | { 599 | "cell_type": "code", 600 | "execution_count": 156, 601 | "metadata": {}, 602 | "outputs": [ 603 | { 604 | "data": { 605 | "text/plain": [ 606 | "WaffleOrder(style=, toppings=(, ))" 607 | ] 608 | }, 609 | "execution_count": 156, 610 | "metadata": {}, 611 | "output_type": "execute_result" 612 | } 613 | ], 614 | "source": [ 615 | "WaffleOrder(style=\"Belgian\", toppings=[\"whipped cream\", \"chocolate sauce\"])" 616 | ] 617 | }, 618 | { 619 | "cell_type": "markdown", 620 | "metadata": {}, 621 | "source": [ 622 | "Gosh these runtime type checkers are rather useful, but what about **functions**? \n", 623 | "\n", 624 | "Pydantic got you covered with `@validate_arguments`. *Still in beta, API may change, release 2020-04-18 in version 1.5*" 625 | ] 626 | }, 627 | { 628 | "cell_type": "code", 629 | "execution_count": 175, 630 | "metadata": {}, 631 | "outputs": [], 632 | "source": [ 633 | "from pydantic import validate_arguments\n", 634 | "\n", 635 | "# Validator on function\n", 636 | "# Ensure valid waffles when making orders\n", 637 | "@validate_arguments\n", 638 | "def make_order(waffle: WaffleOrder):\n", 639 | " ...\n", 640 | " " 641 | ] 642 | }, 643 | { 644 | "cell_type": "code", 645 | "execution_count": 161, 646 | "metadata": {}, 647 | "outputs": [ 648 | { 649 | "name": "stdout", 650 | "output_type": "stream", 651 | "text": [ 652 | "2 validation errors for MakeOrder\n", 653 | "waffle -> style\n", 654 | " value is not a valid enumeration member; permitted: 'Swedish', 'Belgian' (type=type_error.enum; enum_values=[, ])\n", 655 | "waffle -> __root__\n", 656 | " The Waffle Bistro doesn't sell this waffle. (type=value_error)\n" 657 | ] 658 | } 659 | ], 660 | "source": [ 661 | "try:\n", 662 | " make_order({\n", 663 | " \"style\":\"Breakfast\",\n", 664 | " \"toppings\":(\"whipped cream\", \"raspberry jam\")\n", 665 | " })\n", 666 | "except ValidationError as err:\n", 667 | " print(err)" 668 | ] 669 | }, 670 | { 671 | "cell_type": "code", 672 | "execution_count": null, 673 | "metadata": {}, 674 | "outputs": [], 675 | "source": [] 676 | }, 677 | { 678 | "cell_type": "markdown", 679 | "metadata": {}, 680 | "source": [ 681 | "## FastAPI\n", 682 | "FastAPI is a lean microframework similar to Flask which utilizes pydantic models heavily, it will also automatically generate OpenAPI-specifications from your application based on your models.\n", 683 | "\n", 684 | "This gives you framework agnostic models while still being able to leverage tight integration with a modern and easy to use framework. If you're going to start a new API-project i highly recommend trying FastAPI." 685 | ] 686 | }, 687 | { 688 | "cell_type": "code", 689 | "execution_count": 176, 690 | "metadata": {}, 691 | "outputs": [], 692 | "source": [ 693 | "from fastapi import FastAPI\n", 694 | "from pydantic import BaseModel\n", 695 | "\n", 696 | "app = FastAPI()\n", 697 | "\n", 698 | "def make_order(waffle: WaffleOrder):\n", 699 | " # Business logic for making an order\n", 700 | " pass\n", 701 | "\n", 702 | "def dispatch_order(waffle: WaffleOrder):\n", 703 | " # Hand over waffle customer\n", 704 | " pass\n", 705 | "\n", 706 | "# Deliver a waffle\n", 707 | "@app.post(\"/delivery/waffle\")\n", 708 | "async def deliver_waffle_order(waffle: WaffleOrder):\n", 709 | " dispatch = dispatch_order(waffle)\n", 710 | " return dispatch\n", 711 | "\n", 712 | "@app.post(\"/order/waffle\")\n", 713 | "async def order_waffle(waffle: WaffleOrder):\n", 714 | " order = make_order(waffle)\n", 715 | " return order\n" 716 | ] 717 | }, 718 | { 719 | "cell_type": "markdown", 720 | "metadata": {}, 721 | "source": [ 722 | "This is everything we need to create a small API around our models." 723 | ] 724 | }, 725 | { 726 | "cell_type": "markdown", 727 | "metadata": {}, 728 | "source": [ 729 | "---\n", 730 | "\n", 731 | "That's it, a quick introduction to pydantic! \n", 732 | "\n", 733 | "But this is just the tip of the iceberg 🗻 and I want to give you a hint about what more can be done. \n", 734 | "I'm not going to go into detail in any of this but feel free to ask me about it in the chat, on Twitter/LinkedIn or via email. 💬📨" 735 | ] 736 | }, 737 | { 738 | "cell_type": "markdown", 739 | "metadata": {}, 740 | "source": [ 741 | "## Cool features worth mentioning\n", 742 | "\n", 743 | "- Post **1.0**, reached this milestone about a year ago\n", 744 | "- Support for [standard library types](https://pydantic-docs.helpmanual.io/usage/types/#pydantic-types)\n", 745 | "- Offer useful extra types for every day use\n", 746 | " - Email\n", 747 | " - HttpUrl (and more, stricturl for custom validation)\n", 748 | " - PostgresDsn\n", 749 | " - IPvAnyAddress (as well as IPv4Address and IPv6Address from ipaddress)\n", 750 | " - PositiveInt\n", 751 | " - PaymentCardNumber, PaymentCardBrand.[amex, mastercard, visa, other], checks luhn, str of digits and BIN-based lenght.\n", 752 | " - [Constrained types](https://pydantic-docs.helpmanual.io/usage/types/#constrained-types) (e.g. conlist, conint, etc.)\n", 753 | " - and more…\n", 754 | "- Supports [custom datatypes](https://pydantic-docs.helpmanual.io/usage/types/#custom-data-types)\n", 755 | "- [Settings management](https://pydantic-docs.helpmanual.io/usage/settings/)\n", 756 | " - Typed configuration management\n", 757 | " - Automatically reads from environment variables\n", 758 | " - Dotenv (`.env`) support via defacto standard [python-dotenv](https://pypi.org/project/python-dotenv/).\n", 759 | "- ORM-mode\n", 760 | "- Recursive models\n", 761 | "- Works with mypy out of the box, [mypy plugin](https://pydantic-docs.helpmanual.io/mypy_plugin/) further improves experience.\n", 762 | "- [Postponed annotations, self-referencing models](https://pydantic-docs.helpmanual.io/usage/postponed_annotations/), [PEP-563](https://www.python.org/dev/peps/pep-0563/)-style.\n", 763 | "- python-devtools intergration\n", 764 | "- PyCharm plugin\n", 765 | "- [Fast](https://pydantic-docs.helpmanual.io/benchmarks/) compared to popular alternatives! \n", 766 | " But always make your own benchmarks for your own usecase if performance is important for you.\n", 767 | "\n", 768 | "## Future\n", 769 | "- A strict mode is being worked on, in the future this will enable us to choose between Strict and Coercion on a model level instead of relying on the Strict* types.\n", 770 | "- The project is very active and a lot of improvements are constantly being made to the library.\n", 771 | " " 772 | ] 773 | }, 774 | { 775 | "cell_type": "markdown", 776 | "metadata": {}, 777 | "source": [ 778 | "## Conclusion\n", 779 | "Pure python syntax\n", 780 | "Better validation\n", 781 | "Very useful JSON-tools for API's\n", 782 | "Easy to migrate from dataclasses\n", 783 | "Lots of useful features\n", 784 | "Try it out!" 785 | ] 786 | }, 787 | { 788 | "cell_type": "markdown", 789 | "metadata": {}, 790 | "source": [ 791 | "## Want to hear more from me? \n", 792 | "I'm making a course on property based testing in python using Hypothesis. \n", 793 | "[Sign up here](https://forms.gle/yRWapypPXdPFSLME7)" 794 | ] 795 | }, 796 | { 797 | "cell_type": "code", 798 | "execution_count": null, 799 | "metadata": {}, 800 | "outputs": [], 801 | "source": [] 802 | } 803 | ], 804 | "metadata": { 805 | "kernelspec": { 806 | "display_name": "Python 3", 807 | "language": "python", 808 | "name": "python3" 809 | }, 810 | "language_info": { 811 | "codemirror_mode": { 812 | "name": "ipython", 813 | "version": 3 814 | }, 815 | "file_extension": ".py", 816 | "mimetype": "text/x-python", 817 | "name": "python", 818 | "nbconvert_exporter": "python", 819 | "pygments_lexer": "ipython3", 820 | "version": "3.9.1" 821 | } 822 | }, 823 | "nbformat": 4, 824 | "nbformat_minor": 4 825 | } 826 | -------------------------------------------------------------------------------- /demo/pydantic.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | " .\n", 13 | " ..:\n", 14 | " Hultnér\n", 15 | " Technologies\n", 16 | "\n", 17 | "@ahultner | https://hultner.se/\n" 18 | ] 19 | } 20 | ], 21 | "source": [ 22 | "def s(x): return \" \"*(10+x)\n", 23 | "print(s(5)+\".\\n\"+s(4)+\"..:\\n\"+s(2)+\"Hultnér\\n\"+s(0)+\"Technologies\\n\\n@ahultner | https://hultner.se/\")" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "# Give your dataclasses super powers with pydantic\n", 31 | "\n", 32 | "## Index\n", 33 | "- Quick refresher on python data classes\n", 34 | "- Pydantic introduction\n", 35 | " - Prior art\n", 36 | " - Minimal example from dataclass\n", 37 | " - Runtime type-checking\n", 38 | " - JSON (de)serialisation\n", 39 | " - JSONSchema\n", 40 | " - Validators\n", 41 | " - [Custom model validators](https://pydantic-docs.helpmanual.io/usage/validators/)\n", 42 | " - [Validation decorator for functions](https://pydantic-docs.helpmanual.io/usage/validation_decorator/), via `@validate_arguments`. Still in beta, API may change.\n", 43 | " - FastAPI framework\n", 44 | " - OpenAPI Specifications\n", 45 | " - Autogenerated tests\n", 46 | "- Cool features worth mentioning\n", 47 | "- Future\n", 48 | "- Conclusion\n", 49 | " " 50 | ] 51 | }, 52 | { 53 | "cell_type": "markdown", 54 | "metadata": {}, 55 | "source": [ 56 | "Let's start with a quick `@dataclass`-refresher.\n", 57 | "\n", 58 | "Well use pizza-based examples in the spirit of [python.pizza](https://remote.python.pizza) 🐍🍕" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": 2, 64 | "metadata": {}, 65 | "outputs": [], 66 | "source": [ 67 | "from dataclasses import dataclass\n", 68 | "from typing import Tuple" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 3, 74 | "metadata": {}, 75 | "outputs": [], 76 | "source": [ 77 | "@dataclass\n", 78 | "class Pizza:\n", 79 | " style: str\n", 80 | " toppings: Tuple[str, ...]\n", 81 | " " 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 4, 87 | "metadata": {}, 88 | "outputs": [ 89 | { 90 | "data": { 91 | "text/plain": [ 92 | "Pizza(style=1, toppings=('cheese', 'ham'))" 93 | ] 94 | }, 95 | "execution_count": 4, 96 | "metadata": {}, 97 | "output_type": "execute_result" 98 | } 99 | ], 100 | "source": [ 101 | "Pizza(1, (\"cheese\", \"ham\"))" 102 | ] 103 | }, 104 | { 105 | "cell_type": "markdown", 106 | "metadata": {}, 107 | "source": [ 108 | "Now we may want to constrain the toppings to ones we actually offer.\n", 109 | "Our pizzeria doesn't offer pineapple 🚫🍍 as a valid topping, hate it 😡 or love it 💕" 110 | ] 111 | }, 112 | { 113 | "cell_type": "code", 114 | "execution_count": 5, 115 | "metadata": {}, 116 | "outputs": [], 117 | "source": [ 118 | "from enum import Enum" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": 6, 124 | "metadata": {}, 125 | "outputs": [], 126 | "source": [ 127 | "class Topping(str, Enum):\n", 128 | " mozzarella = 'mozzarella'\n", 129 | " tomato_sauce = 'tomato sauce'\n", 130 | " prosciutto = 'prosciutto'\n", 131 | " basil = 'basil'\n", 132 | " rucola = 'rucola'\n", 133 | " \n", 134 | "\n", 135 | "@dataclass\n", 136 | "class Pizza:\n", 137 | " style: str\n", 138 | " toppings: Tuple[Topping, ...]" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "Let's see what happens if we try to create a pizza with pineapple 🍍 topping." 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": 7, 151 | "metadata": {}, 152 | "outputs": [ 153 | { 154 | "data": { 155 | "text/plain": [ 156 | "Pizza(style=2, toppings=('pineapple', 24))" 157 | ] 158 | }, 159 | "execution_count": 7, 160 | "metadata": {}, 161 | "output_type": "execute_result" 162 | } 163 | ], 164 | "source": [ 165 | "Pizza(2, (\"pineapple\", 24))" 166 | ] 167 | }, 168 | { 169 | "cell_type": "markdown", 170 | "metadata": {}, 171 | "source": [ 172 | "With dataclasses the types aren't enforced, this can ofcourse be implemented but in this case we'll lean on the shoulders of a giant, pydantic 🧹🐍🧐" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": 8, 178 | "metadata": {}, 179 | "outputs": [], 180 | "source": [ 181 | "from pydantic.dataclasses import dataclass\n", 182 | "\n", 183 | " \n", 184 | "@dataclass\n", 185 | "class Pizza:\n", 186 | " style: str\n", 187 | " toppings: Tuple[Topping, ...]" 188 | ] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "As you can see the only thing changed in this example is that we import the dataclass decorator from pydantic." 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 9, 200 | "metadata": {}, 201 | "outputs": [ 202 | { 203 | "name": "stdout", 204 | "output_type": "stream", 205 | "text": [ 206 | "2 validation errors for Pizza\n", 207 | "toppings -> 0\n", 208 | " value is not a valid enumeration member; permitted: 'mozzarella', 'tomato sauce', 'prosciutto', 'basil', 'rucola' (type=type_error.enum; enum_values=[, , , , ])\n", 209 | "toppings -> 1\n", 210 | " value is not a valid enumeration member; permitted: 'mozzarella', 'tomato sauce', 'prosciutto', 'basil', 'rucola' (type=type_error.enum; enum_values=[, , , , ])\n" 211 | ] 212 | } 213 | ], 214 | "source": [ 215 | "from pydantic import ValidationError\n", 216 | "try:\n", 217 | " Pizza(2, (\"pineapple\", 24))\n", 218 | "except ValidationError as err:\n", 219 | " print(err)" 220 | ] 221 | }, 222 | { 223 | "cell_type": "markdown", 224 | "metadata": {}, 225 | "source": [ 226 | "And with that simple chage we can see that our new instance of an invalid pizza actually raises errors 🚫🚨\n", 227 | "\n", 228 | "Additionally these errors are very readable!" 229 | ] 230 | }, 231 | { 232 | "cell_type": "markdown", 233 | "metadata": {}, 234 | "source": [ 235 | "So let's try to create a valid pizza 🍕✅" 236 | ] 237 | }, 238 | { 239 | "cell_type": "code", 240 | "execution_count": 10, 241 | "metadata": {}, 242 | "outputs": [ 243 | { 244 | "data": { 245 | "text/plain": [ 246 | "Pizza(style='Napoli', toppings=(, , , ))" 247 | ] 248 | }, 249 | "execution_count": 10, 250 | "metadata": {}, 251 | "output_type": "execute_result" 252 | } 253 | ], 254 | "source": [ 255 | "Pizza(\"Napoli\", (Topping.tomato_sauce, Topping.prosciutto, Topping.mozzarella, Topping.basil))" 256 | ] 257 | }, 258 | { 259 | "cell_type": "markdown", 260 | "metadata": {}, 261 | "source": [ 262 | "So what about JSON? 🧑‍💻 \n", 263 | "The dataclass dropin replacement decorator from pydantic is great for compability but by using `pydantic.BaseModel` we can get even more out of pydantic. One of those things is (de)serialisation, pydantic have native support JSON encoding and decoding." 264 | ] 265 | }, 266 | { 267 | "cell_type": "code", 268 | "execution_count": 11, 269 | "metadata": {}, 270 | "outputs": [], 271 | "source": [ 272 | "from pydantic import BaseModel\n", 273 | "\n", 274 | "\n", 275 | "\n", 276 | "class Pizza(BaseModel):\n", 277 | " style: str\n", 278 | " toppings: Tuple[Topping, ...]" 279 | ] 280 | }, 281 | { 282 | "cell_type": "markdown", 283 | "metadata": {}, 284 | "source": [ 285 | "*Disclaimer: Pydantic is primarly a parsing library and does validation as a means to an end, so make sure it makes sense for you.*" 286 | ] 287 | }, 288 | { 289 | "cell_type": "markdown", 290 | "metadata": {}, 291 | "source": [ 292 | "When using the BaseModel the default behaviour requires to specify the init arguments using their keywords like below" 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": 12, 298 | "metadata": {}, 299 | "outputs": [ 300 | { 301 | "data": { 302 | "text/plain": [ 303 | "Pizza(style='Napoli', toppings=(, , , ))" 304 | ] 305 | }, 306 | "execution_count": 12, 307 | "metadata": {}, 308 | "output_type": "execute_result" 309 | } 310 | ], 311 | "source": [ 312 | "Pizza(style=\"Napoli\", toppings=(Topping.tomato_sauce, Topping.prosciutto, Topping.mozzarella, Topping.basil))" 313 | ] 314 | }, 315 | { 316 | "cell_type": "markdown", 317 | "metadata": {}, 318 | "source": [ 319 | "We can now easily encode this object as `JSON`, there's also [built-in support](https://pydantic-docs.helpmanual.io/usage/exporting_models/) for dict, pickle, immutable `copy()`. Pydantic will also (de)serialise subclasses." 320 | ] 321 | }, 322 | { 323 | "cell_type": "code", 324 | "execution_count": 13, 325 | "metadata": {}, 326 | "outputs": [ 327 | { 328 | "data": { 329 | "text/plain": [ 330 | "'{\"style\": \"Napoli\", \"toppings\": [\"tomato sauce\", \"prosciutto\", \"mozzarella\", \"basil\"]}'" 331 | ] 332 | }, 333 | "execution_count": 13, 334 | "metadata": {}, 335 | "output_type": "execute_result" 336 | } 337 | ], 338 | "source": [ 339 | "_.json()" 340 | ] 341 | }, 342 | { 343 | "cell_type": "markdown", 344 | "metadata": {}, 345 | "source": [ 346 | "And we can also reconstruct our original object using the `parse_raw`-method." 347 | ] 348 | }, 349 | { 350 | "cell_type": "code", 351 | "execution_count": 14, 352 | "metadata": {}, 353 | "outputs": [ 354 | { 355 | "data": { 356 | "text/plain": [ 357 | "Pizza(style='Napoli', toppings=(, , , ))" 358 | ] 359 | }, 360 | "execution_count": 14, 361 | "metadata": {}, 362 | "output_type": "execute_result" 363 | } 364 | ], 365 | "source": [ 366 | "Pizza.parse_raw('{\"style\": \"Napoli\", \"toppings\": [\"tomato sauce\", \"prosciutto\", \"mozzarella\", \"basil\"]}')" 367 | ] 368 | }, 369 | { 370 | "cell_type": "markdown", 371 | "metadata": {}, 372 | "source": [ 373 | "Errors raises a validation error, these can also be represented as JSON." 374 | ] 375 | }, 376 | { 377 | "cell_type": "code", 378 | "execution_count": 15, 379 | "metadata": {}, 380 | "outputs": [ 381 | { 382 | "name": "stdout", 383 | "output_type": "stream", 384 | "text": [ 385 | "[\n", 386 | " {\n", 387 | " \"loc\": [\n", 388 | " \"toppings\",\n", 389 | " 0\n", 390 | " ],\n", 391 | " \"msg\": \"value is not a valid enumeration member; permitted: 'mozzarella', 'tomato sauce', 'prosciutto', 'basil', 'rucola'\",\n", 392 | " \"type\": \"type_error.enum\",\n", 393 | " \"ctx\": {\n", 394 | " \"enum_values\": [\n", 395 | " \"mozzarella\",\n", 396 | " \"tomato sauce\",\n", 397 | " \"prosciutto\",\n", 398 | " \"basil\",\n", 399 | " \"rucola\"\n", 400 | " ]\n", 401 | " }\n", 402 | " }\n", 403 | "]\n" 404 | ] 405 | } 406 | ], 407 | "source": [ 408 | "try:\n", 409 | " Pizza(style=\"Napoli\", toppings=(2,))\n", 410 | "except ValidationError as err:\n", 411 | " print(err.json())" 412 | ] 413 | }, 414 | { 415 | "cell_type": "markdown", 416 | "metadata": {}, 417 | "source": [ 418 | "We can also export a JSONSchema directly from our model, this is very useful for instance if we want to use your model to feed a Swagger/OpenAPI-spec. 📜✅\n", 419 | "\n", 420 | "⚠ *Caution: Pydantic uses the latest draft 7 of JSONSchema, this will be used in the comming OpenAPI 3.1 spec but the current 3.0.x spec uses draft 4. I spoke with Samuel Colvin, the creator of pydantic about this and his recommendation is to write a `schema_extra`function to use the older JSONSchema version if you want strict compability. The FastAPI framework doesn't do this and is slightly incompatible with the current OpenAPI-spec*" 421 | ] 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": 16, 426 | "metadata": {}, 427 | "outputs": [ 428 | { 429 | "data": { 430 | "text/plain": [ 431 | "{'title': 'Pizza',\n", 432 | " 'type': 'object',\n", 433 | " 'properties': {'style': {'title': 'Style', 'type': 'string'},\n", 434 | " 'toppings': {'type': 'array', 'items': {'$ref': '#/definitions/Topping'}}},\n", 435 | " 'required': ['style', 'toppings'],\n", 436 | " 'definitions': {'Topping': {'title': 'Topping',\n", 437 | " 'description': 'An enumeration.',\n", 438 | " 'enum': ['mozzarella', 'tomato sauce', 'prosciutto', 'basil', 'rucola'],\n", 439 | " 'type': 'string'}}}" 440 | ] 441 | }, 442 | "execution_count": 16, 443 | "metadata": {}, 444 | "output_type": "execute_result" 445 | } 446 | ], 447 | "source": [ 448 | "Pizza.schema()" 449 | ] 450 | }, 451 | { 452 | "cell_type": "markdown", 453 | "metadata": {}, 454 | "source": [ 455 | "That was the basics using the built-in validators, but what if you want to implement your own business rules in a custom validator, we're going to look at this next.\n", 456 | "\n", 457 | "We now want to add a new property for `oven_temperature`, but in our case we also want to ensure that we are close to the ideal of roughly 375°C for Neapolitan pizzas, which is our imaginary restaurants house style." 458 | ] 459 | }, 460 | { 461 | "cell_type": "code", 462 | "execution_count": 17, 463 | "metadata": {}, 464 | "outputs": [], 465 | "source": [ 466 | "from pydantic import validator, root_validator\n", 467 | "class BakedPizza(Pizza):\n", 468 | " # For simplicity in the example we use int for temperature\n", 469 | " oven_temperature: int\n", 470 | " \n", 471 | " # A validator looking at a single property\n", 472 | " @validator('style')\n", 473 | " def check_style(cls, style):\n", 474 | " house_styles = (\"Napoli\", \"Roman\", \"Italian\")\n", 475 | " if style not in house_styles:\n", 476 | " raise ValueError(f\"We only cook the following styles: {house_styles}, given: {style}\")\n", 477 | " return style\n", 478 | " \n", 479 | " # Root validators check the entire model\n", 480 | " @root_validator\n", 481 | " def check_temp(cls, values):\n", 482 | " style, temp = values.get(\"style\"), values.get(\"oven_temperature\")\n", 483 | " \n", 484 | " if style != \"Napoli\":\n", 485 | " # We don't have any special rules yet for the other styles\n", 486 | " return values\n", 487 | "\n", 488 | " if 350 <= temp <= 400: \n", 489 | " # Target temperature 350 - 400°C, ideally around 375°C\n", 490 | " return values\n", 491 | "\n", 492 | " raise ValueError(f\"Napoli pizzas require a oven_temperature in the range of 350 - 400°C, given: {temp}°C\")\n" 493 | ] 494 | }, 495 | { 496 | "cell_type": "markdown", 497 | "metadata": {}, 498 | "source": [ 499 | "Now let's see if we create some invalid pizzas ⚠️🚨" 500 | ] 501 | }, 502 | { 503 | "cell_type": "code", 504 | "execution_count": 18, 505 | "metadata": {}, 506 | "outputs": [ 507 | { 508 | "name": "stdout", 509 | "output_type": "stream", 510 | "text": [ 511 | "1 validation error for BakedPizza\n", 512 | "style\n", 513 | " We only cook the following styles: ('Napoli', 'Roman', 'Italian'), given: Panpizza (type=value_error)\n" 514 | ] 515 | } 516 | ], 517 | "source": [ 518 | "try: \n", 519 | " BakedPizza(style=\"Panpizza\", toppings=[\"tomato sauce\"], oven_temperature=250 )\n", 520 | "except ValidationError as err:\n", 521 | " print(err)" 522 | ] 523 | }, 524 | { 525 | "cell_type": "code", 526 | "execution_count": 19, 527 | "metadata": {}, 528 | "outputs": [ 529 | { 530 | "name": "stdout", 531 | "output_type": "stream", 532 | "text": [ 533 | "1 validation error for BakedPizza\n", 534 | "__root__\n", 535 | " Napoli pizzas require a oven_temperature in the range of 350 - 400°C, given: 300°C (type=value_error)\n" 536 | ] 537 | } 538 | ], 539 | "source": [ 540 | "try: \n", 541 | " BakedPizza(style=\"Napoli\", toppings=[\"tomato sauce\"], oven_temperature=300 )\n", 542 | "except ValidationError as err:\n", 543 | " print(err)" 544 | ] 545 | }, 546 | { 547 | "cell_type": "markdown", 548 | "metadata": {}, 549 | "source": [ 550 | "Now let's create a pizza 🍕 allowed by our rules! ✨" 551 | ] 552 | }, 553 | { 554 | "cell_type": "code", 555 | "execution_count": 20, 556 | "metadata": {}, 557 | "outputs": [ 558 | { 559 | "data": { 560 | "text/plain": [ 561 | "BakedPizza(style='Napoli', toppings=(,), oven_temperature=350)" 562 | ] 563 | }, 564 | "execution_count": 20, 565 | "metadata": {}, 566 | "output_type": "execute_result" 567 | } 568 | ], 569 | "source": [ 570 | "BakedPizza(style=\"Napoli\", toppings=[\"tomato sauce\"], oven_temperature=350)" 571 | ] 572 | }, 573 | { 574 | "cell_type": "markdown", 575 | "metadata": {}, 576 | "source": [ 577 | "Gosh these runtime type checkers are rather useful, but what about **functions**? \n", 578 | "\n", 579 | "Pydantic got you covered with `@validate_arguments`. *Still in beta, API may change, release 2020-04-18 in version 1.5*" 580 | ] 581 | }, 582 | { 583 | "cell_type": "code", 584 | "execution_count": 21, 585 | "metadata": {}, 586 | "outputs": [], 587 | "source": [ 588 | "from pydantic import validate_arguments\n", 589 | "\n", 590 | "# Validator on function\n", 591 | "# Ensure that we use a valid pizza when making orders\n", 592 | "@validate_arguments\n", 593 | "def make_order(pizza: Pizza):\n", 594 | " ...\n", 595 | " " 596 | ] 597 | }, 598 | { 599 | "cell_type": "code", 600 | "execution_count": 22, 601 | "metadata": {}, 602 | "outputs": [ 603 | { 604 | "name": "stdout", 605 | "output_type": "stream", 606 | "text": [ 607 | "1 validation error for MakeOrder\n", 608 | "pizza -> toppings -> 3\n", 609 | " value is not a valid enumeration member; permitted: 'mozzarella', 'tomato sauce', 'prosciutto', 'basil', 'rucola' (type=type_error.enum; enum_values=[, , , , ])\n" 610 | ] 611 | } 612 | ], 613 | "source": [ 614 | "try:\n", 615 | " make_order({\n", 616 | " \"style\":\"Napoli\",\n", 617 | " \"toppings\":(\"tomato sauce\", \"mozzarella\", \"prosciutto\", \"pineapple\")\n", 618 | " })\n", 619 | "except ValidationError as err:\n", 620 | " print(err)" 621 | ] 622 | }, 623 | { 624 | "cell_type": "markdown", 625 | "metadata": {}, 626 | "source": [ 627 | "## FastAPI\n", 628 | "FastAPI is a lean microframework similar to Flask which utilizes pydantic models heavily, it will also automatically generate OpenAPI-specifications from your application based on your models.\n", 629 | "\n", 630 | "This gives you framework agnostic models while still being able to leverage tight integration with a modern and easy to use framework. If you're going to start a new API-project i highly recommend trying FastAPI." 631 | ] 632 | }, 633 | { 634 | "cell_type": "code", 635 | "execution_count": 23, 636 | "metadata": {}, 637 | "outputs": [], 638 | "source": [ 639 | "from fastapi import FastAPI\n", 640 | "from pydantic import BaseModel\n", 641 | "\n", 642 | "app = FastAPI()\n", 643 | "\n", 644 | "def make_order(pizza: Pizza):\n", 645 | " # Business logic for making an order\n", 646 | " pass\n", 647 | "\n", 648 | "def dispatch_order(pizza: BakedPizza):\n", 649 | " # Hand over pizza to delivery company\n", 650 | " pass\n", 651 | "\n", 652 | "# Deliver a baked pizza\n", 653 | "@app.post(\"/delivery/pizza\")\n", 654 | "async def deliver_pizza_order(pizza: BakedPizza):\n", 655 | " dispatch = dispatch_order(pizza)\n", 656 | " return dispatch\n", 657 | "\n", 658 | "@app.post(\"/order/pizza\")\n", 659 | "async def order_pizza(pizza: Pizza):\n", 660 | " order = make_order(pizza)\n", 661 | " return order\n" 662 | ] 663 | }, 664 | { 665 | "cell_type": "markdown", 666 | "metadata": {}, 667 | "source": [ 668 | "This is everything we need to create a small API around our models." 669 | ] 670 | }, 671 | { 672 | "cell_type": "markdown", 673 | "metadata": {}, 674 | "source": [ 675 | "---\n", 676 | "\n", 677 | "That's it, a quick introduction to pydantic! \n", 678 | "\n", 679 | "But this is just the tip of the iceberg 🗻 and I want to give you a hint about what more can be done. \n", 680 | "I'm not going to go into detail in any of this but feel free to ask me about it in the chat, on Twitter/LinkedIn or via email. 💬📨" 681 | ] 682 | }, 683 | { 684 | "cell_type": "markdown", 685 | "metadata": {}, 686 | "source": [ 687 | "## Cool features worth mentioning\n", 688 | "\n", 689 | "- Post **1.0**, reached this milestone about a year ago\n", 690 | "- Support for [standard library types](https://pydantic-docs.helpmanual.io/usage/types/#pydantic-types)\n", 691 | "- Offer useful extra types for every day use\n", 692 | " - Email\n", 693 | " - HttpUrl (and more, stricturl for custom validation)\n", 694 | " - PostgresDsn\n", 695 | " - IPvAnyAddress (as well as IPv4Address and IPv6Address from ipaddress)\n", 696 | " - PositiveInt\n", 697 | " - PaymentCardNumber, PaymentCardBrand.[amex, mastercard, visa, other], checks luhn, str of digits and BIN-based lenght.\n", 698 | " - [Constrained types](https://pydantic-docs.helpmanual.io/usage/types/#constrained-types) (e.g. conlist, conint, etc.)\n", 699 | " - and more…\n", 700 | "- Supports [custom datatypes](https://pydantic-docs.helpmanual.io/usage/types/#custom-data-types)\n", 701 | "- [Settings management](https://pydantic-docs.helpmanual.io/usage/settings/)\n", 702 | " - Typed configuration management\n", 703 | " - Automatically reads from environment variables\n", 704 | " - Dotenv (`.env`) support via defacto standard [python-dotenv](https://pypi.org/project/python-dotenv/).\n", 705 | "- ORM-mode\n", 706 | "- Recursive models\n", 707 | "- Works with mypy out of the box, [mypy plugin](https://pydantic-docs.helpmanual.io/mypy_plugin/) further improves experience.\n", 708 | "- [Postponed annotations, self-referencing models](https://pydantic-docs.helpmanual.io/usage/postponed_annotations/), [PEP-563](https://www.python.org/dev/peps/pep-0563/)-style.\n", 709 | "- python-devtools intergration\n", 710 | "- PyCharm plugin\n", 711 | "- [Fast](https://pydantic-docs.helpmanual.io/benchmarks/) compared to popular alternatives! \n", 712 | " But always make your own benchmarks for your own usecase if performance is important for you.\n", 713 | "\n", 714 | "## Future\n", 715 | "- A strict mode is being worked on, in the future this will enable us to choose between Strict and Coercion on a model level instead of relying on the Strict* types.\n", 716 | "- The project is very active and a lot of improvements are constantly being made to the library.\n", 717 | " " 718 | ] 719 | }, 720 | { 721 | "cell_type": "markdown", 722 | "metadata": {}, 723 | "source": [ 724 | "## Conclusion\n", 725 | "Pure python syntax\n", 726 | "Better validation\n", 727 | "Very useful JSON-tools for API's\n", 728 | "Easy to migrate from dataclasses\n", 729 | "Lots of useful features\n", 730 | "Try it out!" 731 | ] 732 | }, 733 | { 734 | "cell_type": "markdown", 735 | "metadata": {}, 736 | "source": [ 737 | "## Want to hear more from me? \n", 738 | "I'm making a course on property based testing in python using Hypothesis. \n", 739 | "[Sign up here](https://forms.gle/yRWapypPXdPFSLME7)" 740 | ] 741 | }, 742 | { 743 | "cell_type": "code", 744 | "execution_count": null, 745 | "metadata": {}, 746 | "outputs": [], 747 | "source": [] 748 | } 749 | ], 750 | "metadata": { 751 | "kernelspec": { 752 | "display_name": "Python 3", 753 | "language": "python", 754 | "name": "python3" 755 | }, 756 | "language_info": { 757 | "codemirror_mode": { 758 | "name": "ipython", 759 | "version": 3 760 | }, 761 | "file_extension": ".py", 762 | "mimetype": "text/x-python", 763 | "name": "python", 764 | "nbconvert_exporter": "python", 765 | "pygments_lexer": "ipython3", 766 | "version": "3.9.1" 767 | } 768 | }, 769 | "nbformat": 4, 770 | "nbformat_minor": 4 771 | } 772 | -------------------------------------------------------------------------------- /demo/pydantic_more_examples.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 6, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "\"Foo\" object has no field \"patched_attribute\"\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "from pydantic import BaseModel\n", 18 | "from pydantic.dataclasses import dataclass\n", 19 | "\n", 20 | "# Based on questions from Gergo Jedlicska\n", 21 | "class Foo(BaseModel):\n", 22 | " bar: str\n", 23 | "\n", 24 | "\n", 25 | "@dataclass\n", 26 | "class DataFoo():\n", 27 | " bar: str\n", 28 | "\n", 29 | "\n", 30 | "# pydantic dataclass is just a thin wrapper around builtin dataclass,\n", 31 | "# so this is completely fine\n", 32 | "data_foo = DataFoo(bar=\"bar\")\n", 33 | "\n", 34 | "data_foo.patched_attribute = 123\n", 35 | "\n", 36 | "# but with the pydantic base model, the object behaves like a class with __slot__ implementation\n", 37 | "try:\n", 38 | " foo = Foo(bar=\"bar\")\n", 39 | "\n", 40 | " # this is fine, I want the class to stay mutable\n", 41 | " foo.bar = \"a higher bar\"\n", 42 | "\n", 43 | " # this is not ok. It patches the attribute at runtime\n", 44 | " foo.patched_attribute = 123\n", 45 | "except ValueError as err:\n", 46 | " print(err)" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": null, 52 | "metadata": {}, 53 | "outputs": [], 54 | "source": [] 55 | } 56 | ], 57 | "metadata": { 58 | "kernelspec": { 59 | "display_name": "Python 3", 60 | "language": "python", 61 | "name": "python3" 62 | }, 63 | "language_info": { 64 | "codemirror_mode": { 65 | "name": "ipython", 66 | "version": 3 67 | }, 68 | "file_extension": ".py", 69 | "mimetype": "text/x-python", 70 | "name": "python", 71 | "nbconvert_exporter": "python", 72 | "pygments_lexer": "ipython3", 73 | "version": "3.8.0" 74 | } 75 | }, 76 | "nbformat": 4, 77 | "nbformat_minor": 4 78 | } 79 | -------------------------------------------------------------------------------- /demo/pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "pydantic-intro-demo" 3 | version = "0.1.0" 4 | description = "Introduction to pydantic, code demo" 5 | authors = ["Alexander Hultnér "] 6 | license = "BSD-3-Clause" 7 | 8 | [tool.poetry.dependencies] 9 | python = "^3.8" 10 | pydantic = "^1.8.1" 11 | jupyterlab = "^3.0.12" 12 | bpython = "^0.21" 13 | fastapi = "^0.63.0" 14 | uvicorn = "^0.13.4" 15 | mypy = "^0.812" 16 | python-devtools = "^2" 17 | 18 | [tool.poetry.dev-dependencies] 19 | 20 | [build-system] 21 | requires = ["poetry>=0.12"] 22 | build-backend = "poetry.masonry.api" 23 | --------------------------------------------------------------------------------