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
ETL flow framework based on Yaml configs in Python

ETL framework based on Yaml configs in Python A light framework for creating data streams. Setting up streams through configuration in the Yaml file.

Павел Максимов 18 Jul 06, 2022
An ETL Pipeline of a large data set from a fictitious music streaming service named Sparkify.

An ETL Pipeline of a large data set from a fictitious music streaming service named Sparkify. The ETL process flows from AWS's S3 into staging tables in AWS Redshift.

1 Feb 11, 2022
Kennedy Institute of Rheumatology University of Oxford Project November 2019

TradingBot6M Kennedy Institute of Rheumatology University of Oxford Project November 2019 Run Change api.txt to binance api key: https://www.binance.c

Kannan SAR 2 Nov 16, 2021
Recommendations from Cramer: On the show Mad-Money (CNBC) Jim Cramer picks stocks which he recommends to buy. We will use this data to build a portfolio

Backtesting the "Cramer Effect" & Recommendations from Cramer Recommendations from Cramer: On the show Mad-Money (CNBC) Jim Cramer picks stocks which

Gábor Vecsei 12 Aug 30, 2022
Gaussian processes in TensorFlow

Website | Documentation (release) | Documentation (develop) | Glossary Table of Contents What does GPflow do? Installation Getting Started with GPflow

GPflow 1.7k Jan 06, 2023
Streamz helps you build pipelines to manage continuous streams of data

Streamz helps you build pipelines to manage continuous streams of data. It is simple to use in simple cases, but also supports complex pipelines that involve branching, joining, flow control, feedbac

Python Streamz 1.1k Dec 28, 2022
Data and code accompanying the paper Politics and Virality in the Time of Twitter

Politics and Virality in the Time of Twitter Data and code accompanying the paper Politics and Virality in the Time of Twitter. In specific: the code

Cardiff NLP 3 Jul 02, 2022
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

PyMC 7.2k Dec 30, 2022
Port of dplyr and other related R packages in python, using pipda.

Unlike other similar packages in python that just mimic the piping syntax, datar follows the API designs from the original packages as much as possible, and is tested thoroughly with the cases from t

179 Dec 21, 2022
CINECA molecular dynamics tutorial set

High Performance Molecular Dynamics Logging into CINECA's computer systems To logon to the M100 system use the following command from an SSH client ss

J. W. Dell 0 Mar 13, 2022
A simple and efficient tool to parallelize Pandas operations on all available CPUs

Pandaral·lel Without parallelization With parallelization Installation $ pip install pandarallel [--upgrade] [--user] Requirements On Windows, Pandara

Manu NALEPA 2.8k Dec 31, 2022
Using Python to scrape some basic player information from www.premierleague.com and then use Pandas to analyse said data.

PremiershipPlayerAnalysis Using Python to scrape some basic player information from www.premierleague.com and then use Pandas to analyse said data. No

5 Sep 06, 2021
A Python Tools to imaging the shallow seismic structure

ShallowSeismicImaging Tools to imaging the shallow seismic structure, above 10 km, based on the ZH ratio measured from the ambient seismic noise, and

Xiao Xiao 9 Aug 09, 2022
COVID-19 deaths statistics around the world

COVID-19-Deaths-Dataset COVID-19 deaths statistics around the world This is a daily updated dataset of COVID-19 deaths around the world. The dataset c

Nisa Efendioğlu 4 Jul 10, 2022
nrgpy is the Python package for processing NRG Data Files

nrgpy nrgpy is the Python package for processing NRG Data Files Website and source: https://github.com/nrgpy/nrgpy Documentation: https://nrgpy.github

NRG Tech Services 23 Dec 08, 2022
The Spark Challenge Student Check-In/Out Tracking Script

The Spark Challenge Student Check-In/Out Tracking Script This Python Script uses the Student ID Database to match the entries with the ID Card Swipe a

1 Dec 09, 2021
Active Learning demo using two small datasets

ActiveLearningDemo How to run step one put the dataset folder and use command below to split the dataset to the required structure run utils.py For ea

3 Nov 10, 2021
fds is a tool for Data Scientists made by DAGsHub to version control data and code at once.

Fast Data Science, AKA fds, is a CLI for Data Scientists to version control data and code at once, by conveniently wrapping git and dvc

DAGsHub 359 Dec 22, 2022
Option Pricing Calculator using the Binomial Pricing Method (No Libraries Required)

Binomial Option Pricing Calculator Option Pricing Calculator using the Binomial Pricing Method (No Libraries Required) Background A derivative is a fi

sammuhrai 1 Nov 29, 2021
Renato 214 Jan 02, 2023