Collections of pydantic models

Overview

pydantic-collections

Build Status Coverage Status

The pydantic-collections package provides BaseCollectionModel class that allows you to manipulate collections of pydantic models (and any other types supported by pydantic).

Requirements

  • Python >= 3.7
  • pydantic >= 1.8.2

Installation

pip install pydantic-collections

Usage

Basic usage

from datetime import datetime

from pydantic import BaseModel
from pydantic_collections import BaseCollectionModel


class User(BaseModel):
    id: int
    name: str
    birth_date: datetime


class UserCollection(BaseCollectionModel[User]):
    pass


 user_data = [
        {'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'},
        {'id': 2, 'name': 'Balaganov', 'birth_date': '2020-04-01T12:59:59'},
    ]

users = UserCollection(user_data)
print(users)
#> UserCollection([User(id=1, name='Bender', birth_date=datetime.datetime(2010, 4, 1, 12, 59, 59)), User(id=2, name='Balaganov', birth_date=datetime.datetime(2020, 4, 1, 12, 59, 59))])
print(users.dict())
#> [{'id': 1, 'name': 'Bender', 'birth_date': datetime.datetime(2010, 4, 1, 12, 59, 59)}, {'id': 2, 'name': 'Balaganov', 'birth_date': datetime.datetime(2020, 4, 1, 12, 59, 59)}]
print(users.json())
#> [{"id": 1, "name": "Bender", "birth_date": "2010-04-01T12:59:59"}, {"id": 2, "name": "Balaganov", "birth_date": "2020-04-01T12:59:59"}]

Strict assignment validation

By default BaseCollectionModel has a strict assignment check

...
users = UserCollection()
users.append(User(id=1, name='Bender', birth_date=datetime.utcnow()))  # OK
users.append({'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'})
#> pydantic.error_wrappers.ValidationError: 1 validation error for UserCollection
#> __root__ -> 2
#>  instance of User expected (type=type_error.arbitrary_type; expected_arbitrary_type=User)

This behavior can be changed via Model Config

...
class UserCollection(BaseCollectionModel[User]):
    class Config:
        validate_assignment_strict = False
        
users = UserCollection()
users.append({'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'})  # OK
assert users[0].__class__ is User
assert users[0].id == 1

Using as a model field

BaseCollectionModel is a subclass of BaseModel, so you can use it as a model field

...
class UserContainer(BaseModel):
    users: UserCollection = []
        
data = {
    'users': [
        {'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'},
        {'id': 2, 'name': 'Balaganov', 'birth_date': '2020-04-01T12:59:59'},
    ]
}

container = UserContainer(**data)
container.users.append(User(...))
...
You might also like...
vartests is a Python library to perform some statistic tests to evaluate Value at Risk (VaR) Models

vartests is a Python library to perform some statistic tests to evaluate Value at Risk (VaR) Models, such as: T-test: verify if mean of distribution i

A model checker for verifying properties in epistemic models

Epistemic Model Checker This is a model checker for verifying properties in epistemic models. The goal of the model checker is to check for Pluralisti

Fit models to your data in Python with Sherpa.

Table of Contents Sherpa License How To Install Sherpa Using Anaconda Using pip Building from source History Release History Sherpa Sherpa is a modeli

 pydantic-i18n is an extension to support an i18n for the pydantic error messages.
pydantic-i18n is an extension to support an i18n for the pydantic error messages.

pydantic-i18n is an extension to support an i18n for the pydantic error messages

Python collections that are backended by sqlite3 DB and are compatible with the built-in collections

sqlitecollections Python collections that are backended by sqlite3 DB and are compatible with the built-in collections Installation $ pip install git+

Seamlessly integrate pydantic models in your Sphinx documentation.
Seamlessly integrate pydantic models in your Sphinx documentation.

Seamlessly integrate pydantic models in your Sphinx documentation.

🪄 Auto-generate Streamlit UI from Pydantic Models and Dataclasses.
🪄 Auto-generate Streamlit UI from Pydantic Models and Dataclasses.

Streamlit Pydantic Auto-generate Streamlit UI elements from Pydantic models. Getting Started • Documentation • Support • Report a Bug • Contribution •

Hyperlinks for pydantic models

Hyperlinks for pydantic models In a typical web application relationships between resources are modeled by primary and foreign keys in a database (int

Pydantic models for pywttr and aiopywttr.

Pydantic models for pywttr and aiopywttr.

EMNLP 2021 Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections

Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections Ruiqi Zhong, Kristy Lee*, Zheng Zhang*, Dan Klein EMN

PyTorch implementation of
PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper.

deep-linear-shapes PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper. If you find this code useful i

flask extension for integration with the awesome pydantic package

Flask-Pydantic Flask extension for integration of the awesome pydantic package with Flask. Installation python3 -m pip install Flask-Pydantic Basics v

flask extension for integration with the awesome pydantic package

Flask-Pydantic Flask extension for integration of the awesome pydantic package with Flask. Installation python3 -m pip install Flask-Pydantic Basics v

A curated list of awesome things related to Pydantic! 🌪️

Awesome Pydantic A curated list of awesome things related to Pydantic. These packages have not been vetted or approved by the pydantic team. Feel free

Pydantic model support for Django ORM

Pydantic model support for Django ORM

flask extension for integration with the awesome pydantic package

flask extension for integration with the awesome pydantic package

Flask Sugar is a web framework for building APIs with Flask, Pydantic and Python 3.6+ type hints.
Flask Sugar is a web framework for building APIs with Flask, Pydantic and Python 3.6+ type hints.

Flask Sugar is a web framework for building APIs with Flask, Pydantic and Python 3.6+ type hints. check parameters and generate API documents automatically. Flask Sugar是一个基于flask,pyddantic,类型注解的API框架, 可以检查参数并自动生成API文档

Pydantic-ish YAML configuration management.
Pydantic-ish YAML configuration management.

Pydantic-ish YAML configuration management.

(A)sync client for sms.ru with pydantic responses

🚧 aioSMSru Send SMS Check SMS status Get SMS cost Get balance Get limit Get free limit Get my senders Check login/password Add to stoplist Remove fro

Comments
  • Bug dict() method: ignore or raised exception when using dict function attribute (ex. include, exclude, etc.)

    Bug dict() method: ignore or raised exception when using dict function attribute (ex. include, exclude, etc.)

    Hi there, I tried to use the method dict but i got an error: KeyError(__root__) Here an example:

    1. Model structure:
    
    from datetime import datetime, time
    from typing import Optional, Union
    from pydantic import Field, validator, BaseModel
    from pydantic_collections import BaseCollectionModel
    
    class OpeningTime(BaseModel):
        weekday: int = Field(..., alias="weekday")
        day: Optional[str] = Field(alias="day")  # NB: keep it after number_weekday attribute
        from_time: Optional[time] = Field(alias="fromTime")
        to_time: Optional[time] = Field(alias="toTime")
    
        @validator("day", pre=True)
        def generate_weekday(cls, weekday: str, values) -> str:
            if weekday is None or len(weekday) == 0:
                return WEEKDAYS[str(values["weekday"])]
            return weekday
    
    
    
    class OpeningTimes(BaseCollectionModel[OpeningTime]):
        pass
    
    
    class PaymentMethod(BaseModel):
        type: str = Field(..., alias="type")
        card_type: str = Field(..., alias="cardType")
    
    
    class PaymentMethods(BaseCollectionModel[PaymentMethod]):
        pass
    
    
    class FuelType(BaseModel):
        type: str = Field(..., alias="Fuel")
    
    
    class FuelTypes(BaseCollectionModel[FuelType]):
        pass
    
    
    class AdditionalInfoStation(BaseModel):
        opening_times: Optional[OpeningTimes] = Field(alias="openingTimes")
        car_wash_opening_times: Optional[OpeningTimes] = Field(alias="openingTimesCarWash")
        payment_methods: PaymentMethods = Field(..., alias="paymentMethods")
        fuel_types: FuelTypes = Field(..., alias="fuelTypes")
    
    
    class Example(BaseModel):
        hash_key: int = Field(..., alias="hashKey")
        range_key: str = Field(..., alias="rangeKey")
        location_id: str = Field(..., alias="locationId")
        name: str = Field(..., alias="name")
        street: str = Field(..., alias="street")
        address_number: str = Field(..., alias="addressNumber")
        zip_code: int = Field(..., alias="zipCode")
        city: str = Field(..., alias="city")
        region: str = Field(..., alias="region")
        country: str = Field(..., alias="country")
        additional_info: Union[AdditionalInfoStation] = Field(..., alias="additionalInfo")
    
    
    class ExampleList(BaseCollectionModel[EniGeoPoint]):
        pass
    
    1. Imagine that there is an ExampleList populated object and needed filters field during apply of dict method:
    example_list: ExampleList = ExampleList.parse_obj([{......}])
    
    #This istruction raised exception
    example_list.dict(by_alias=True, inlcude={"hash_key", "range_key"})
    
    1. The last istruction raise an error: Message: KeyError('__root__')

    My env is:

    • pydantic==1.9.1
    • pydantic-collections==0.2.0
    • python version 3.9.7

    If you need more info please contact me.

    opened by aferrari94 6
Releases(v0.4.0)
Owner
Roman Snegirev
Roman Snegirev
Python package for processing UC module spectral data.

UC Module Python Package How To Install clone repo. cd UC-module pip install . How to Use uc.module.UC(measurment=str, dark=str, reference=str, heade

Nicolai Haaber Junge 1 Oct 20, 2021
Fit models to your data in Python with Sherpa.

Table of Contents Sherpa License How To Install Sherpa Using Anaconda Using pip Building from source History Release History Sherpa Sherpa is a modeli

134 Jan 07, 2023
A Python and R autograding solution

Otter-Grader Otter Grader is a light-weight, modular open-source autograder developed by the Data Science Education Program at UC Berkeley. It is desi

Infrastructure Team 93 Jan 03, 2023
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Jan 09, 2023
Incubator for useful bioinformatics code, primarily in Python and R

Collection of useful code related to biological analysis. Much of this is discussed with examples at Blue collar bioinformatics. All code, images and

Brad Chapman 560 Jan 03, 2023
The OHSDI OMOP Common Data Model allows for the systematic analysis of healthcare observational databases.

The OHSDI OMOP Common Data Model allows for the systematic analysis of healthcare observational databases.

Bell Eapen 14 Jan 02, 2023
Spectacular AI SDK fuses data from cameras and IMU sensors and outputs an accurate 6-degree-of-freedom pose of a device.

Spectacular AI SDK examples Spectacular AI SDK fuses data from cameras and IMU sensors (accelerometer and gyroscope) and outputs an accurate 6-degree-

Spectacular AI 94 Jan 04, 2023
Tools for the analysis, simulation, and presentation of Lorentz TEM data.

ltempy ltempy is a set of tools for Lorentz TEM data analysis, simulation, and presentation. Features Single Image Transport of Intensity Equation (SI

McMorran Lab 1 Dec 26, 2022
A script to "SHUA" H1-2 map of Mercenaries mode of Hearthstone

lushi_script Introduction This script is to "SHUA" H1-2 map of Mercenaries mode of Hearthstone Installation Make sure you installed python=3.6. To in

210 Jan 02, 2023
Hue Editor: Open source SQL Query Assistant for Databases/Warehouses

Hue Editor: Open source SQL Query Assistant for Databases/Warehouses

Cloudera 759 Jan 07, 2023
Basis Set Format Converter

Basis Set Format Converter Repository for the online tool that allows you to enter a basis set in the form of text input for a variety of Quantum Chem

Manas Sharma 3 Jun 27, 2022
Parses data out of your Google Takeout (History, Activity, Youtube, Locations, etc...)

google_takeout_parser parses both the Historical HTML and new JSON format for Google Takeouts caches individual takeout results behind cachew merge mu

Sean Breckenridge 27 Dec 28, 2022
Yet Another Workflow Parser for SecurityHub

YAWPS Yet Another Workflow Parser for SecurityHub "Screaming pepper" by Rum Bucolic Ape is licensed with CC BY-ND 2.0. To view a copy of this license,

myoung34 8 Dec 22, 2022
Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

Amundsen 3.7k Jan 03, 2023
Calculate multilateral price indices in Python (with Pandas and PySpark).

IndexNumCalc Calculate multilateral price indices using the GEKS-T (CCDI), Time Product Dummy (TPD), Time Dummy Hedonic (TDH), Geary-Khamis (GK) metho

Dr. Usman Kayani 3 Apr 27, 2022
Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks

The following Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks (MOFs). The training set is extracted from the Cambridge S

1 Jan 09, 2022
Stock Analysis dashboard Using Streamlit and Python

StDashApp Stock Analysis Dashboard Using Streamlit and Python If you found the content useful and want to support my work, you can buy me a coffee! Th

StreamAlpha 27 Dec 09, 2022
PyChemia, Python Framework for Materials Discovery and Design

PyChemia, Python Framework for Materials Discovery and Design PyChemia is an open-source Python Library for materials structural search. The purpose o

Materials Discovery Group 61 Oct 02, 2022
Binance Kline Data With Python

Binance Kline Data by seunghan(gingerthorp) reference https://github.com/binance/binance-public-data/ All intervals are supported: 1m, 3m, 5m, 15m, 30

shquant 5 Jul 13, 2022
MidTerm Project for the Data Analysis FT Bootcamp, Adam Tycner and Florent ZAHOUI

MidTerm Project for the Data Analysis FT Bootcamp, Adam Tycner and Florent ZAHOUI Hallo

Florent Zahoui 1 Feb 07, 2022